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1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -1,3 +1,774 @@
 ---
-license: mit
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:6190
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: nomic-ai/modernbert-embed-base
+widget:
+- source_sentence: What is the duration of the period mentioned in the text?
+  sentences:
+  - . The only excep Ɵon to the requirement that the plainƟff must be a lending i nsƟtuƟon
+    in order to invoke the provisions of the Act is contained in SecƟon 25, in terms
+    of which a person who inter alia knowingly draws a cheque which is subsequently
+    dishonoured by the bank for want of funds is guilty of an offence under the Act,
+    and proceedings can be insƟtuted against such person in the Magistrate’s
+  - '? The 1st question of law is formulated on the basis that , the 1st Defendant
+    is the licensee of the 2nd Defendant and therefore, the 1st Defendant cannot claim
+    prescriptive title to the subject matter'
+  - .50,000/ - (that is , a period of 36 months) but such “Facility” is subject to
+    review on 30 /09/2000”, (that is, a period of about only 5 months from the date
+    of P4)
+- source_sentence: What is the purpose of the disposition of the property by Lanka
+    Tractors Limited as mentioned in the text?
+  sentences:
+  - . (3) is whether the said disposition of the property by Lanka Tractors Limited
+    was done with the sole object of defrauding its creditors. Section 348 of the
+    Companies Act which describes about Fraudulent reference would be relevant in
+    this regard
+  - . In the arbitration process, the Government is not involved; the court system
+    is not involved (except as provided for in the Act); the parties do not have to
+    rely on any Government institution for resolution of their dispute. Process of
+    conducting the arbitration, venue, time, mode of adducing evidence are all decided
+    by agreement of parties
+  - . This is broadly similar to the provision in the summary procedure on liquid
+    claims. The amendment in clause 8 of the Bill, repeals the defini Ɵon of the term
+    ‘debt’ in sec Ɵon 30. The subs Ɵtuted defini Ɵon excludes the words referred to
+    above which limit its applicability to money owed under a promise or agreement
+    which is in wri Ɵng
+- source_sentence: What is one of the topics covered in the training program?
+  sentences:
+  - . The resul Ɵng posiƟon is that the court would not have any wri Ʃen evidence
+    of the commitment on the part of the debtor when it issues decree nisi in the
+    first instance
+  - '? Before this C ourt, there is no dispute on the manner in which the appellant
+    obtained the title of the land in question'
+  - . Detail reporting procedures to government of Sri Lanka’s contact points. - 4
+    Weeks Phase 3 Training of Port Facility Security Officers SATHSINDU/BAGNOLD undertakes
+    to design a training program and conducted aid program for up to ten persons.
+    • Understanding the reasons for the ISPS code • ISPS Code content and requirements.
+    • Understanding the ISPS Code
+- source_sentence: What type of action was taken by the Divisional Secretary?
+  sentences:
+  - .2020 was also sent by the Divisional Secretary of Th amankaduwa imposing similar
+    restrictions as by the Polonnaruwa Pradeshiya Sabha
+  - . When Seylan Bank published the resolution of its board of directors which exercised
+    its powers of Parate Execution in the newspaper on 10th March 2006-, HNB had made
+    the application dated 21st March [SC Appeal No. 85A /2009 ] Page 6 of 25 2006
+    to the District Court of Colombo in terms of Sections 260, 261, 348, 359 and 352
+    of the Companies Act No
+  - . Having regard to the above -mentioned stipulated circumstances , I consider
+    the facts put forward for the appellant , seeking a reduction of sentence. The
+    offence was committed in 2004. The appellant had been in remand custody for more
+    than three years and the appell ant did not have any previous convictions
+- source_sentence: What is described in Section 25 of the Arbitration Act?
+  sentences:
+  - . But where a matter is within the plenary jurisdiction of the Court if no objection
+    is taken, the Court will then have jurisdiction to proceed on with the matter
+    and make a valid order.” 14 31. Further , in the case of Don Tilakaratne v
+  - '. (3) The provision of subsections (1) and (2) shall apply only to the extent
+    agreed to by the parties. (4) The arbitral tribunal shall decide according to
+    considerations of general justice and fairness or trade usages only if the parties
+    have expressly authorised it to do so. Section 25 of the Arbitration Act describes
+    the form and content of the arbitral award as follows: 25'
+  - '. 9 and 10 based on the objection taken to them by the Counsel for HNB, despite
+    the fact that they did not arise from the pleadings, and were altogether inconsistent
+    with them, answered the afore-stated question of law (in respect of which this
+    Court had granted Leave to Appeal in that case) in the affirmative and in favour
+    of HNB, and stated as follows: “In conclusion, it needs to be emphasised'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: Fine-tuned with [QuicKB](https://github.com/ALucek/QuicKB)
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5741279069767442
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7616279069767442
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8197674418604651
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8851744186046512
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5741279069767442
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.25387596899224807
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.163953488372093
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.0885174418604651
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5741279069767442
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7616279069767442
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8197674418604651
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8851744186046512
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7308126785084815
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6812459625322997
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6852483059452662
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5741279069767442
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7630813953488372
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8212209302325582
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.875
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5741279069767442
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2543604651162791
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16424418604651161
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.0875
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5741279069767442
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7630813953488372
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8212209302325582
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.875
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.726227401269234
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6782132475083055
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6827936993080407
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5552325581395349
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7281976744186046
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7921511627906976
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8619186046511628
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5552325581395349
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.24273255813953487
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.15843023255813954
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08619186046511627
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5552325581395349
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7281976744186046
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.7921511627906976
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8619186046511628
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7077790398550751
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6585646225544481
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6630890497309057
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.49709302325581395
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.6758720930232558
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7354651162790697
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8241279069767442
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.49709302325581395
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.22529069767441862
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.14709302325581394
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08241279069767442
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.49709302325581395
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.6758720930232558
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.7354651162790697
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8241279069767442
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6567813216281579
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6037779162052417
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6090388181529673
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.39680232558139533
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.5581395348837209
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.622093023255814
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7252906976744186
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.39680232558139533
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.18604651162790695
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.12441860465116278
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07252906976744186
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.39680232558139533
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.5581395348837209
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.622093023255814
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7252906976744186
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.5513541983050395
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.497020348837209
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.5050183064129367
+      name: Cosine Map@100
 ---
+
+# Fine-tuned with [QuicKB](https://github.com/ALucek/QuicKB)
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) <!-- at revision d556a88e332558790b210f7bdbe87da2fa94a8d8 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: ModernBertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'What is described in Section 25 of the Arbitration Act?',
+    '. (3) The provision of subsections (1) and (2) shall apply only to the extent agreed to by the parties. (4) The arbitral tribunal shall decide according to considerations of general justice and fairness or trade usages only if the parties have expressly authorised it to do so. Section 25 of the Arbitration Act describes the form and content of the arbitral award as follows: 25',
+    '. 9 and 10 based on the objection taken to them by the Counsel for HNB, despite the fact that they did not arise from the pleadings, and were altogether inconsistent with them, answered the afore-stated question of law (in respect of which this Court had granted Leave to Appeal in that case) in the affirmative and in favour of HNB, and stated as follows: “In conclusion, it needs to be emphasised',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | dim_768    | dim_512    | dim_256    | dim_128    | dim_64     |
+|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|
+| cosine_accuracy@1   | 0.5741     | 0.5741     | 0.5552     | 0.4971     | 0.3968     |
+| cosine_accuracy@3   | 0.7616     | 0.7631     | 0.7282     | 0.6759     | 0.5581     |
+| cosine_accuracy@5   | 0.8198     | 0.8212     | 0.7922     | 0.7355     | 0.6221     |
+| cosine_accuracy@10  | 0.8852     | 0.875      | 0.8619     | 0.8241     | 0.7253     |
+| cosine_precision@1  | 0.5741     | 0.5741     | 0.5552     | 0.4971     | 0.3968     |
+| cosine_precision@3  | 0.2539     | 0.2544     | 0.2427     | 0.2253     | 0.186      |
+| cosine_precision@5  | 0.164      | 0.1642     | 0.1584     | 0.1471     | 0.1244     |
+| cosine_precision@10 | 0.0885     | 0.0875     | 0.0862     | 0.0824     | 0.0725     |
+| cosine_recall@1     | 0.5741     | 0.5741     | 0.5552     | 0.4971     | 0.3968     |
+| cosine_recall@3     | 0.7616     | 0.7631     | 0.7282     | 0.6759     | 0.5581     |
+| cosine_recall@5     | 0.8198     | 0.8212     | 0.7922     | 0.7355     | 0.6221     |
+| cosine_recall@10    | 0.8852     | 0.875      | 0.8619     | 0.8241     | 0.7253     |
+| **cosine_ndcg@10**  | **0.7308** | **0.7262** | **0.7078** | **0.6568** | **0.5514** |
+| cosine_mrr@10       | 0.6812     | 0.6782     | 0.6586     | 0.6038     | 0.497      |
+| cosine_map@100      | 0.6852     | 0.6828     | 0.6631     | 0.609      | 0.505      |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 6,190 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                           |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 15.11 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 69.53 tokens</li><li>max: 214 tokens</li></ul> |
+* Samples:
+  | anchor                                                                     | positive                                                                                                                                                                                                                                                                                                                                                                                       |
+  |:---------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>How must the District Court exercise its discretion?</code>          | <code>imposition of ‘ a’ term; (5) It is not mandatory to impose security, as evinced by the use of the conjunction “or”; (6) In imposing terms, the District Court must be mindful of the objectives of the Act, and its discretion must be exercised judicially</code>                                                                                                                       |
+  | <code>What is the source of the observation made by Christian Appu?</code> | <code>. Christian Appu , (1895) 1 NLR 288 observed that , “possession is "disturbed" either by an action intended to remove the possessor from the land, or by acts which prevent the possessor from enjoying the free and full use of 12 the land of which he is in the course of acquiring the dominion, and which convert his continuous user into a disconnected and divided user ”</code> |
+  | <code>What must the defendant do regarding the plaintiff's claim?</code>   | <code>. The Court of Appeal in Ramanayake v Sampath Bank Ltd and Others [(1993) 1 Sri LR 145 at page 153] has held that, “The defendant has to deal with the plaintiff’s claim on its merits; it is not competent for the defendant to merely set out technical objections. It is also incumbent on the defendant to reveal his defence, if he has any</code>                                  |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 16
+- `gradient_accumulation_steps`: 8
+- `learning_rate`: 2e-05
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `tf32`: True
+- `load_best_model_at_end`: True
+- `optim`: adamw_torch_fused
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 8
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: True
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch   | Step   | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
+|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
+| 0.1034  | 5      | 29.8712       | -                      | -                      | -                      | -                      | -                     |
+| 0.2067  | 10     | 26.1323       | -                      | -                      | -                      | -                      | -                     |
+| 0.3101  | 15     | 17.8585       | -                      | -                      | -                      | -                      | -                     |
+| 0.4134  | 20     | 14.0232       | -                      | -                      | -                      | -                      | -                     |
+| 0.5168  | 25     | 11.6897       | -                      | -                      | -                      | -                      | -                     |
+| 0.6202  | 30     | 10.8431       | -                      | -                      | -                      | -                      | -                     |
+| 0.7235  | 35     | 9.264         | -                      | -                      | -                      | -                      | -                     |
+| 0.8269  | 40     | 11.2186       | -                      | -                      | -                      | -                      | -                     |
+| 0.9302  | 45     | 9.9143        | -                      | -                      | -                      | -                      | -                     |
+| 1.0     | 49     | -             | 0.7134                 | 0.7110                 | 0.6902                 | 0.6341                 | 0.5282                |
+| 1.0207  | 50     | 7.2581        | -                      | -                      | -                      | -                      | -                     |
+| 1.1240  | 55     | 6.066         | -                      | -                      | -                      | -                      | -                     |
+| 1.2274  | 60     | 6.3626        | -                      | -                      | -                      | -                      | -                     |
+| 1.3307  | 65     | 6.8135        | -                      | -                      | -                      | -                      | -                     |
+| 1.4341  | 70     | 5.5556        | -                      | -                      | -                      | -                      | -                     |
+| 1.5375  | 75     | 6.0144        | -                      | -                      | -                      | -                      | -                     |
+| 1.6408  | 80     | 6.1965        | -                      | -                      | -                      | -                      | -                     |
+| 1.7442  | 85     | 5.596         | -                      | -                      | -                      | -                      | -                     |
+| 1.8475  | 90     | 6.631         | -                      | -                      | -                      | -                      | -                     |
+| 1.9509  | 95     | 6.3319        | -                      | -                      | -                      | -                      | -                     |
+| **2.0** | **98** | **-**         | **0.7331**             | **0.7304**             | **0.7074**             | **0.6569**             | **0.5477**            |
+| 2.0413  | 100    | 4.7382        | -                      | -                      | -                      | -                      | -                     |
+| 2.1447  | 105    | 4.1516        | -                      | -                      | -                      | -                      | -                     |
+| 2.2481  | 110    | 4.3517        | -                      | -                      | -                      | -                      | -                     |
+| 2.3514  | 115    | 3.7044        | -                      | -                      | -                      | -                      | -                     |
+| 2.4548  | 120    | 4.1593        | -                      | -                      | -                      | -                      | -                     |
+| 2.5581  | 125    | 4.8081        | -                      | -                      | -                      | -                      | -                     |
+| 2.6615  | 130    | 3.908         | -                      | -                      | -                      | -                      | -                     |
+| 2.7649  | 135    | 3.7684        | -                      | -                      | -                      | -                      | -                     |
+| 2.8682  | 140    | 3.8927        | -                      | -                      | -                      | -                      | -                     |
+| 2.9509  | 144    | -             | 0.7308                 | 0.7262                 | 0.7078                 | 0.6568                 | 0.5514                |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.13.3
+- Sentence Transformers: 3.4.0
+- Transformers: 4.48.1
+- PyTorch: 2.6.0+cu126
+- Accelerate: 1.3.0
+- Datasets: 3.2.0
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning},
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

checkpoint-144/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

checkpoint-144/README.md

@@ -0,0 +1,773 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:6190
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: nomic-ai/modernbert-embed-base
+widget:
+- source_sentence: What is the duration of the period mentioned in the text?
+  sentences:
+  - . The only excep Ɵon to the requirement that the plainƟff must be a lending i nsƟtuƟon
+    in order to invoke the provisions of the Act is contained in SecƟon 25, in terms
+    of which a person who inter alia knowingly draws a cheque which is subsequently
+    dishonoured by the bank for want of funds is guilty of an offence under the Act,
+    and proceedings can be insƟtuted against such person in the Magistrate’s
+  - '? The 1st question of law is formulated on the basis that , the 1st Defendant
+    is the licensee of the 2nd Defendant and therefore, the 1st Defendant cannot claim
+    prescriptive title to the subject matter'
+  - .50,000/ - (that is , a period of 36 months) but such “Facility” is subject to
+    review on 30 /09/2000”, (that is, a period of about only 5 months from the date
+    of P4)
+- source_sentence: What is the purpose of the disposition of the property by Lanka
+    Tractors Limited as mentioned in the text?
+  sentences:
+  - . (3) is whether the said disposition of the property by Lanka Tractors Limited
+    was done with the sole object of defrauding its creditors. Section 348 of the
+    Companies Act which describes about Fraudulent reference would be relevant in
+    this regard
+  - . In the arbitration process, the Government is not involved; the court system
+    is not involved (except as provided for in the Act); the parties do not have to
+    rely on any Government institution for resolution of their dispute. Process of
+    conducting the arbitration, venue, time, mode of adducing evidence are all decided
+    by agreement of parties
+  - . This is broadly similar to the provision in the summary procedure on liquid
+    claims. The amendment in clause 8 of the Bill, repeals the defini Ɵon of the term
+    ‘debt’ in sec Ɵon 30. The subs Ɵtuted defini Ɵon excludes the words referred to
+    above which limit its applicability to money owed under a promise or agreement
+    which is in wri Ɵng
+- source_sentence: What is one of the topics covered in the training program?
+  sentences:
+  - . The resul Ɵng posiƟon is that the court would not have any wri Ʃen evidence
+    of the commitment on the part of the debtor when it issues decree nisi in the
+    first instance
+  - '? Before this C ourt, there is no dispute on the manner in which the appellant
+    obtained the title of the land in question'
+  - . Detail reporting procedures to government of Sri Lanka’s contact points. - 4
+    Weeks Phase 3 Training of Port Facility Security Officers SATHSINDU/BAGNOLD undertakes
+    to design a training program and conducted aid program for up to ten persons.
+    • Understanding the reasons for the ISPS code • ISPS Code content and requirements.
+    • Understanding the ISPS Code
+- source_sentence: What type of action was taken by the Divisional Secretary?
+  sentences:
+  - .2020 was also sent by the Divisional Secretary of Th amankaduwa imposing similar
+    restrictions as by the Polonnaruwa Pradeshiya Sabha
+  - . When Seylan Bank published the resolution of its board of directors which exercised
+    its powers of Parate Execution in the newspaper on 10th March 2006-, HNB had made
+    the application dated 21st March [SC Appeal No. 85A /2009 ] Page 6 of 25 2006
+    to the District Court of Colombo in terms of Sections 260, 261, 348, 359 and 352
+    of the Companies Act No
+  - . Having regard to the above -mentioned stipulated circumstances , I consider
+    the facts put forward for the appellant , seeking a reduction of sentence. The
+    offence was committed in 2004. The appellant had been in remand custody for more
+    than three years and the appell ant did not have any previous convictions
+- source_sentence: What is described in Section 25 of the Arbitration Act?
+  sentences:
+  - . But where a matter is within the plenary jurisdiction of the Court if no objection
+    is taken, the Court will then have jurisdiction to proceed on with the matter
+    and make a valid order.” 14 31. Further , in the case of Don Tilakaratne v
+  - '. (3) The provision of subsections (1) and (2) shall apply only to the extent
+    agreed to by the parties. (4) The arbitral tribunal shall decide according to
+    considerations of general justice and fairness or trade usages only if the parties
+    have expressly authorised it to do so. Section 25 of the Arbitration Act describes
+    the form and content of the arbitral award as follows: 25'
+  - '. 9 and 10 based on the objection taken to them by the Counsel for HNB, despite
+    the fact that they did not arise from the pleadings, and were altogether inconsistent
+    with them, answered the afore-stated question of law (in respect of which this
+    Court had granted Leave to Appeal in that case) in the affirmative and in favour
+    of HNB, and stated as follows: “In conclusion, it needs to be emphasised'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: Fine-tuned with [QuicKB](https://github.com/ALucek/QuicKB)
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5741279069767442
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7616279069767442
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8197674418604651
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8851744186046512
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5741279069767442
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.25387596899224807
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.163953488372093
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.0885174418604651
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5741279069767442
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7616279069767442
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8197674418604651
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8851744186046512
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7308126785084815
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6812459625322997
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6852483059452662
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5741279069767442
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7630813953488372
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8212209302325582
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.875
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5741279069767442
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2543604651162791
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16424418604651161
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.0875
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5741279069767442
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7630813953488372
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8212209302325582
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.875
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.726227401269234
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6782132475083055
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6827936993080407
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5552325581395349
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7281976744186046
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7921511627906976
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8619186046511628
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5552325581395349
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.24273255813953487
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.15843023255813954
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08619186046511627
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5552325581395349
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7281976744186046
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.7921511627906976
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8619186046511628
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7077790398550751
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6585646225544481
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6630890497309057
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.49709302325581395
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.6758720930232558
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7354651162790697
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8241279069767442
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.49709302325581395
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.22529069767441862
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.14709302325581394
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08241279069767442
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.49709302325581395
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.6758720930232558
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.7354651162790697
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8241279069767442
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6567813216281579
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6037779162052417
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6090388181529673
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.39680232558139533
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.5581395348837209
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.622093023255814
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7252906976744186
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.39680232558139533
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.18604651162790695
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.12441860465116278
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07252906976744186
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.39680232558139533
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.5581395348837209
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.622093023255814
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7252906976744186
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.5513541983050395
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.497020348837209
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.5050183064129367
+      name: Cosine Map@100
+---
+
+# Fine-tuned with [QuicKB](https://github.com/ALucek/QuicKB)
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) <!-- at revision d556a88e332558790b210f7bdbe87da2fa94a8d8 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: ModernBertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'What is described in Section 25 of the Arbitration Act?',
+    '. (3) The provision of subsections (1) and (2) shall apply only to the extent agreed to by the parties. (4) The arbitral tribunal shall decide according to considerations of general justice and fairness or trade usages only if the parties have expressly authorised it to do so. Section 25 of the Arbitration Act describes the form and content of the arbitral award as follows: 25',
+    '. 9 and 10 based on the objection taken to them by the Counsel for HNB, despite the fact that they did not arise from the pleadings, and were altogether inconsistent with them, answered the afore-stated question of law (in respect of which this Court had granted Leave to Appeal in that case) in the affirmative and in favour of HNB, and stated as follows: “In conclusion, it needs to be emphasised',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | dim_768    | dim_512    | dim_256    | dim_128    | dim_64     |
+|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|
+| cosine_accuracy@1   | 0.5741     | 0.5741     | 0.5552     | 0.4971     | 0.3968     |
+| cosine_accuracy@3   | 0.7616     | 0.7631     | 0.7282     | 0.6759     | 0.5581     |
+| cosine_accuracy@5   | 0.8198     | 0.8212     | 0.7922     | 0.7355     | 0.6221     |
+| cosine_accuracy@10  | 0.8852     | 0.875      | 0.8619     | 0.8241     | 0.7253     |
+| cosine_precision@1  | 0.5741     | 0.5741     | 0.5552     | 0.4971     | 0.3968     |
+| cosine_precision@3  | 0.2539     | 0.2544     | 0.2427     | 0.2253     | 0.186      |
+| cosine_precision@5  | 0.164      | 0.1642     | 0.1584     | 0.1471     | 0.1244     |
+| cosine_precision@10 | 0.0885     | 0.0875     | 0.0862     | 0.0824     | 0.0725     |
+| cosine_recall@1     | 0.5741     | 0.5741     | 0.5552     | 0.4971     | 0.3968     |
+| cosine_recall@3     | 0.7616     | 0.7631     | 0.7282     | 0.6759     | 0.5581     |
+| cosine_recall@5     | 0.8198     | 0.8212     | 0.7922     | 0.7355     | 0.6221     |
+| cosine_recall@10    | 0.8852     | 0.875      | 0.8619     | 0.8241     | 0.7253     |
+| **cosine_ndcg@10**  | **0.7308** | **0.7262** | **0.7078** | **0.6568** | **0.5514** |
+| cosine_mrr@10       | 0.6812     | 0.6782     | 0.6586     | 0.6038     | 0.497      |
+| cosine_map@100      | 0.6852     | 0.6828     | 0.6631     | 0.609      | 0.505      |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 6,190 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                           |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 15.11 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 69.53 tokens</li><li>max: 214 tokens</li></ul> |
+* Samples:
+  | anchor                                                                     | positive                                                                                                                                                                                                                                                                                                                                                                                       |
+  |:---------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>How must the District Court exercise its discretion?</code>          | <code>imposition of ‘ a’ term; (5) It is not mandatory to impose security, as evinced by the use of the conjunction “or”; (6) In imposing terms, the District Court must be mindful of the objectives of the Act, and its discretion must be exercised judicially</code>                                                                                                                       |
+  | <code>What is the source of the observation made by Christian Appu?</code> | <code>. Christian Appu , (1895) 1 NLR 288 observed that , “possession is "disturbed" either by an action intended to remove the possessor from the land, or by acts which prevent the possessor from enjoying the free and full use of 12 the land of which he is in the course of acquiring the dominion, and which convert his continuous user into a disconnected and divided user ”</code> |
+  | <code>What must the defendant do regarding the plaintiff's claim?</code>   | <code>. The Court of Appeal in Ramanayake v Sampath Bank Ltd and Others [(1993) 1 Sri LR 145 at page 153] has held that, “The defendant has to deal with the plaintiff’s claim on its merits; it is not competent for the defendant to merely set out technical objections. It is also incumbent on the defendant to reveal his defence, if he has any</code>                                  |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 16
+- `gradient_accumulation_steps`: 8
+- `learning_rate`: 2e-05
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `tf32`: True
+- `load_best_model_at_end`: True
+- `optim`: adamw_torch_fused
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 8
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: True
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
+|:------:|:----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
+| 0.1034 | 5    | 29.8712       | -                      | -                      | -                      | -                      | -                     |
+| 0.2067 | 10   | 26.1323       | -                      | -                      | -                      | -                      | -                     |
+| 0.3101 | 15   | 17.8585       | -                      | -                      | -                      | -                      | -                     |
+| 0.4134 | 20   | 14.0232       | -                      | -                      | -                      | -                      | -                     |
+| 0.5168 | 25   | 11.6897       | -                      | -                      | -                      | -                      | -                     |
+| 0.6202 | 30   | 10.8431       | -                      | -                      | -                      | -                      | -                     |
+| 0.7235 | 35   | 9.264         | -                      | -                      | -                      | -                      | -                     |
+| 0.8269 | 40   | 11.2186       | -                      | -                      | -                      | -                      | -                     |
+| 0.9302 | 45   | 9.9143        | -                      | -                      | -                      | -                      | -                     |
+| 1.0    | 49   | -             | 0.7134                 | 0.7110                 | 0.6902                 | 0.6341                 | 0.5282                |
+| 1.0207 | 50   | 7.2581        | -                      | -                      | -                      | -                      | -                     |
+| 1.1240 | 55   | 6.066         | -                      | -                      | -                      | -                      | -                     |
+| 1.2274 | 60   | 6.3626        | -                      | -                      | -                      | -                      | -                     |
+| 1.3307 | 65   | 6.8135        | -                      | -                      | -                      | -                      | -                     |
+| 1.4341 | 70   | 5.5556        | -                      | -                      | -                      | -                      | -                     |
+| 1.5375 | 75   | 6.0144        | -                      | -                      | -                      | -                      | -                     |
+| 1.6408 | 80   | 6.1965        | -                      | -                      | -                      | -                      | -                     |
+| 1.7442 | 85   | 5.596         | -                      | -                      | -                      | -                      | -                     |
+| 1.8475 | 90   | 6.631         | -                      | -                      | -                      | -                      | -                     |
+| 1.9509 | 95   | 6.3319        | -                      | -                      | -                      | -                      | -                     |
+| 2.0    | 98   | -             | 0.7331                 | 0.7304                 | 0.7074                 | 0.6569                 | 0.5477                |
+| 2.0413 | 100  | 4.7382        | -                      | -                      | -                      | -                      | -                     |
+| 2.1447 | 105  | 4.1516        | -                      | -                      | -                      | -                      | -                     |
+| 2.2481 | 110  | 4.3517        | -                      | -                      | -                      | -                      | -                     |
+| 2.3514 | 115  | 3.7044        | -                      | -                      | -                      | -                      | -                     |
+| 2.4548 | 120  | 4.1593        | -                      | -                      | -                      | -                      | -                     |
+| 2.5581 | 125  | 4.8081        | -                      | -                      | -                      | -                      | -                     |
+| 2.6615 | 130  | 3.908         | -                      | -                      | -                      | -                      | -                     |
+| 2.7649 | 135  | 3.7684        | -                      | -                      | -                      | -                      | -                     |
+| 2.8682 | 140  | 3.8927        | -                      | -                      | -                      | -                      | -                     |
+| 2.9509 | 144  | -             | 0.7308                 | 0.7262                 | 0.7078                 | 0.6568                 | 0.5514                |
+
+
+### Framework Versions
+- Python: 3.13.3
+- Sentence Transformers: 3.4.0
+- Transformers: 4.48.1
+- PyTorch: 2.6.0+cu126
+- Accelerate: 1.3.0
+- Datasets: 3.2.0
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning},
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

checkpoint-144/config.json

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+{
+  "_name_or_path": "nomic-ai/modernbert-embed-base",
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
+  "reference_compile": false,
+  "repad_logits_with_grad": false,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.1",
+  "vocab_size": 50368
+}

checkpoint-144/config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.4.0",
+    "transformers": "4.48.1",
+    "pytorch": "2.6.0+cu126"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

checkpoint-144/model.safetensors

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checkpoint-144/modules.json

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+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

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checkpoint-144/rng_state.pth

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checkpoint-144/sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

checkpoint-144/special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoint-144/tokenizer_config.json

@@ -0,0 +1,945 @@
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+}

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+size 5624

checkpoint-98/1_Pooling/config.json

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+  "word_embedding_dimension": 768,
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+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

checkpoint-98/README.md

@@ -0,0 +1,763 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:6190
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: nomic-ai/modernbert-embed-base
+widget:
+- source_sentence: What is the duration of the period mentioned in the text?
+  sentences:
+  - . The only excep Ɵon to the requirement that the plainƟff must be a lending i nsƟtuƟon
+    in order to invoke the provisions of the Act is contained in SecƟon 25, in terms
+    of which a person who inter alia knowingly draws a cheque which is subsequently
+    dishonoured by the bank for want of funds is guilty of an offence under the Act,
+    and proceedings can be insƟtuted against such person in the Magistrate’s
+  - '? The 1st question of law is formulated on the basis that , the 1st Defendant
+    is the licensee of the 2nd Defendant and therefore, the 1st Defendant cannot claim
+    prescriptive title to the subject matter'
+  - .50,000/ - (that is , a period of 36 months) but such “Facility” is subject to
+    review on 30 /09/2000”, (that is, a period of about only 5 months from the date
+    of P4)
+- source_sentence: What is the purpose of the disposition of the property by Lanka
+    Tractors Limited as mentioned in the text?
+  sentences:
+  - . (3) is whether the said disposition of the property by Lanka Tractors Limited
+    was done with the sole object of defrauding its creditors. Section 348 of the
+    Companies Act which describes about Fraudulent reference would be relevant in
+    this regard
+  - . In the arbitration process, the Government is not involved; the court system
+    is not involved (except as provided for in the Act); the parties do not have to
+    rely on any Government institution for resolution of their dispute. Process of
+    conducting the arbitration, venue, time, mode of adducing evidence are all decided
+    by agreement of parties
+  - . This is broadly similar to the provision in the summary procedure on liquid
+    claims. The amendment in clause 8 of the Bill, repeals the defini Ɵon of the term
+    ‘debt’ in sec Ɵon 30. The subs Ɵtuted defini Ɵon excludes the words referred to
+    above which limit its applicability to money owed under a promise or agreement
+    which is in wri Ɵng
+- source_sentence: What is one of the topics covered in the training program?
+  sentences:
+  - . The resul Ɵng posiƟon is that the court would not have any wri Ʃen evidence
+    of the commitment on the part of the debtor when it issues decree nisi in the
+    first instance
+  - '? Before this C ourt, there is no dispute on the manner in which the appellant
+    obtained the title of the land in question'
+  - . Detail reporting procedures to government of Sri Lanka’s contact points. - 4
+    Weeks Phase 3 Training of Port Facility Security Officers SATHSINDU/BAGNOLD undertakes
+    to design a training program and conducted aid program for up to ten persons.
+    • Understanding the reasons for the ISPS code • ISPS Code content and requirements.
+    • Understanding the ISPS Code
+- source_sentence: What type of action was taken by the Divisional Secretary?
+  sentences:
+  - .2020 was also sent by the Divisional Secretary of Th amankaduwa imposing similar
+    restrictions as by the Polonnaruwa Pradeshiya Sabha
+  - . When Seylan Bank published the resolution of its board of directors which exercised
+    its powers of Parate Execution in the newspaper on 10th March 2006-, HNB had made
+    the application dated 21st March [SC Appeal No. 85A /2009 ] Page 6 of 25 2006
+    to the District Court of Colombo in terms of Sections 260, 261, 348, 359 and 352
+    of the Companies Act No
+  - . Having regard to the above -mentioned stipulated circumstances , I consider
+    the facts put forward for the appellant , seeking a reduction of sentence. The
+    offence was committed in 2004. The appellant had been in remand custody for more
+    than three years and the appell ant did not have any previous convictions
+- source_sentence: What is described in Section 25 of the Arbitration Act?
+  sentences:
+  - . But where a matter is within the plenary jurisdiction of the Court if no objection
+    is taken, the Court will then have jurisdiction to proceed on with the matter
+    and make a valid order.” 14 31. Further , in the case of Don Tilakaratne v
+  - '. (3) The provision of subsections (1) and (2) shall apply only to the extent
+    agreed to by the parties. (4) The arbitral tribunal shall decide according to
+    considerations of general justice and fairness or trade usages only if the parties
+    have expressly authorised it to do so. Section 25 of the Arbitration Act describes
+    the form and content of the arbitral award as follows: 25'
+  - '. 9 and 10 based on the objection taken to them by the Counsel for HNB, despite
+    the fact that they did not arise from the pleadings, and were altogether inconsistent
+    with them, answered the afore-stated question of law (in respect of which this
+    Court had granted Leave to Appeal in that case) in the affirmative and in favour
+    of HNB, and stated as follows: “In conclusion, it needs to be emphasised'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: Fine-tuned with [QuicKB](https://github.com/ALucek/QuicKB)
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5784883720930233
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7601744186046512
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8197674418604651
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8880813953488372
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5784883720930233
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.253391472868217
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.163953488372093
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08880813953488371
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5784883720930233
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7601744186046512
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8197674418604651
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8880813953488372
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.733110755438693
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6835271317829454
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6874162730700494
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5770348837209303
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7616279069767442
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8197674418604651
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8822674418604651
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5770348837209303
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.25387596899224807
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.163953488372093
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.0882267441860465
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5770348837209303
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7616279069767442
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8197674418604651
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8822674418604651
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7303694393079266
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6816127953119231
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.685728403908298
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5552325581395349
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7354651162790697
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7950581395348837
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8604651162790697
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5552325581395349
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.24515503875968994
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.15901162790697673
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08604651162790695
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5552325581395349
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7354651162790697
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.7950581395348837
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8604651162790697
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7073914263638542
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6584688768918417
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6631022921972395
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.498546511627907
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.6700581395348837
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7369186046511628
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8226744186046512
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.498546511627907
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.22335271317829455
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.14738372093023255
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08226744186046511
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.498546511627907
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.6700581395348837
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.7369186046511628
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8226744186046512
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6569451636973174
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6045300387596899
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6099407824679366
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.39680232558139533
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.5523255813953488
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.626453488372093
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7209302325581395
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.39680232558139533
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.18410852713178294
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.12529069767441858
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07209302325581395
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.39680232558139533
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.5523255813953488
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.626453488372093
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7209302325581395
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.5476937013127858
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.4936317598744924
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.5014802548028123
+      name: Cosine Map@100
+---
+
+# Fine-tuned with [QuicKB](https://github.com/ALucek/QuicKB)
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) <!-- at revision d556a88e332558790b210f7bdbe87da2fa94a8d8 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: ModernBertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'What is described in Section 25 of the Arbitration Act?',
+    '. (3) The provision of subsections (1) and (2) shall apply only to the extent agreed to by the parties. (4) The arbitral tribunal shall decide according to considerations of general justice and fairness or trade usages only if the parties have expressly authorised it to do so. Section 25 of the Arbitration Act describes the form and content of the arbitral award as follows: 25',
+    '. 9 and 10 based on the objection taken to them by the Counsel for HNB, despite the fact that they did not arise from the pleadings, and were altogether inconsistent with them, answered the afore-stated question of law (in respect of which this Court had granted Leave to Appeal in that case) in the affirmative and in favour of HNB, and stated as follows: “In conclusion, it needs to be emphasised',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | dim_768    | dim_512    | dim_256    | dim_128    | dim_64     |
+|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|
+| cosine_accuracy@1   | 0.5785     | 0.577      | 0.5552     | 0.4985     | 0.3968     |
+| cosine_accuracy@3   | 0.7602     | 0.7616     | 0.7355     | 0.6701     | 0.5523     |
+| cosine_accuracy@5   | 0.8198     | 0.8198     | 0.7951     | 0.7369     | 0.6265     |
+| cosine_accuracy@10  | 0.8881     | 0.8823     | 0.8605     | 0.8227     | 0.7209     |
+| cosine_precision@1  | 0.5785     | 0.577      | 0.5552     | 0.4985     | 0.3968     |
+| cosine_precision@3  | 0.2534     | 0.2539     | 0.2452     | 0.2234     | 0.1841     |
+| cosine_precision@5  | 0.164      | 0.164      | 0.159      | 0.1474     | 0.1253     |
+| cosine_precision@10 | 0.0888     | 0.0882     | 0.086      | 0.0823     | 0.0721     |
+| cosine_recall@1     | 0.5785     | 0.577      | 0.5552     | 0.4985     | 0.3968     |
+| cosine_recall@3     | 0.7602     | 0.7616     | 0.7355     | 0.6701     | 0.5523     |
+| cosine_recall@5     | 0.8198     | 0.8198     | 0.7951     | 0.7369     | 0.6265     |
+| cosine_recall@10    | 0.8881     | 0.8823     | 0.8605     | 0.8227     | 0.7209     |
+| **cosine_ndcg@10**  | **0.7331** | **0.7304** | **0.7074** | **0.6569** | **0.5477** |
+| cosine_mrr@10       | 0.6835     | 0.6816     | 0.6585     | 0.6045     | 0.4936     |
+| cosine_map@100      | 0.6874     | 0.6857     | 0.6631     | 0.6099     | 0.5015     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 6,190 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                           |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 15.11 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 69.53 tokens</li><li>max: 214 tokens</li></ul> |
+* Samples:
+  | anchor                                                                     | positive                                                                                                                                                                                                                                                                                                                                                                                       |
+  |:---------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>How must the District Court exercise its discretion?</code>          | <code>imposition of ‘ a’ term; (5) It is not mandatory to impose security, as evinced by the use of the conjunction “or”; (6) In imposing terms, the District Court must be mindful of the objectives of the Act, and its discretion must be exercised judicially</code>                                                                                                                       |
+  | <code>What is the source of the observation made by Christian Appu?</code> | <code>. Christian Appu , (1895) 1 NLR 288 observed that , “possession is "disturbed" either by an action intended to remove the possessor from the land, or by acts which prevent the possessor from enjoying the free and full use of 12 the land of which he is in the course of acquiring the dominion, and which convert his continuous user into a disconnected and divided user ”</code> |
+  | <code>What must the defendant do regarding the plaintiff's claim?</code>   | <code>. The Court of Appeal in Ramanayake v Sampath Bank Ltd and Others [(1993) 1 Sri LR 145 at page 153] has held that, “The defendant has to deal with the plaintiff’s claim on its merits; it is not competent for the defendant to merely set out technical objections. It is also incumbent on the defendant to reveal his defence, if he has any</code>                                  |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 16
+- `gradient_accumulation_steps`: 8
+- `learning_rate`: 2e-05
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `tf32`: True
+- `load_best_model_at_end`: True
+- `optim`: adamw_torch_fused
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 8
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: True
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
+|:------:|:----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
+| 0.1034 | 5    | 29.8712       | -                      | -                      | -                      | -                      | -                     |
+| 0.2067 | 10   | 26.1323       | -                      | -                      | -                      | -                      | -                     |
+| 0.3101 | 15   | 17.8585       | -                      | -                      | -                      | -                      | -                     |
+| 0.4134 | 20   | 14.0232       | -                      | -                      | -                      | -                      | -                     |
+| 0.5168 | 25   | 11.6897       | -                      | -                      | -                      | -                      | -                     |
+| 0.6202 | 30   | 10.8431       | -                      | -                      | -                      | -                      | -                     |
+| 0.7235 | 35   | 9.264         | -                      | -                      | -                      | -                      | -                     |
+| 0.8269 | 40   | 11.2186       | -                      | -                      | -                      | -                      | -                     |
+| 0.9302 | 45   | 9.9143        | -                      | -                      | -                      | -                      | -                     |
+| 1.0    | 49   | -             | 0.7134                 | 0.7110                 | 0.6902                 | 0.6341                 | 0.5282                |
+| 1.0207 | 50   | 7.2581        | -                      | -                      | -                      | -                      | -                     |
+| 1.1240 | 55   | 6.066         | -                      | -                      | -                      | -                      | -                     |
+| 1.2274 | 60   | 6.3626        | -                      | -                      | -                      | -                      | -                     |
+| 1.3307 | 65   | 6.8135        | -                      | -                      | -                      | -                      | -                     |
+| 1.4341 | 70   | 5.5556        | -                      | -                      | -                      | -                      | -                     |
+| 1.5375 | 75   | 6.0144        | -                      | -                      | -                      | -                      | -                     |
+| 1.6408 | 80   | 6.1965        | -                      | -                      | -                      | -                      | -                     |
+| 1.7442 | 85   | 5.596         | -                      | -                      | -                      | -                      | -                     |
+| 1.8475 | 90   | 6.631         | -                      | -                      | -                      | -                      | -                     |
+| 1.9509 | 95   | 6.3319        | -                      | -                      | -                      | -                      | -                     |
+| 2.0    | 98   | -             | 0.7331                 | 0.7304                 | 0.7074                 | 0.6569                 | 0.5477                |
+
+
+### Framework Versions
+- Python: 3.13.3
+- Sentence Transformers: 3.4.0
+- Transformers: 4.48.1
+- PyTorch: 2.6.0+cu126
+- Accelerate: 1.3.0
+- Datasets: 3.2.0
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning},
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

checkpoint-98/config.json

@@ -0,0 +1,47 @@
+{
+  "_name_or_path": "nomic-ai/modernbert-embed-base",
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
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+  "classifier_pooling": "mean",
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+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
+  "reference_compile": false,
+  "repad_logits_with_grad": false,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.1",
+  "vocab_size": 50368
+}

checkpoint-98/config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.0",
+    "transformers": "4.48.1",
+    "pytorch": "2.6.0+cu126"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

checkpoint-98/modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

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checkpoint-98/rng_state.pth

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checkpoint-98/sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

checkpoint-98/special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "content": "[SEP]",
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+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoint-98/tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "|||IP_ADDRESS|||",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
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checkpoint-98/trainer_state.json

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eval/Information-Retrieval_evaluation_dim_512_results.csv

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eval/Information-Retrieval_evaluation_dim_64_results.csv

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eval/Information-Retrieval_evaluation_dim_768_results.csv

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metrics_comparison.txt

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+Model Performance Metrics Comparison
+====================================
+
+Baseline Performance
+--------------------
+Metric       │  768  │  512  │  256  │  128  │   64 
+─────────────┼───────┼───────┼───────┼───────┼──────
+ndcg@10      │ 0.548 │ 0.537 │ 0.513 │ 0.469 │ 0.379
+mrr@10       │ 0.503 │ 0.490 │ 0.469 │ 0.423 │ 0.337
+map@100      │ 0.512 │ 0.499 │ 0.478 │ 0.431 │ 0.347
+accuracy@1   │ 0.416 │ 0.403 │ 0.384 │ 0.336 │ 0.262
+accuracy@3   │ 0.561 │ 0.551 │ 0.517 │ 0.471 │ 0.384
+accuracy@5   │ 0.618 │ 0.608 │ 0.576 │ 0.532 │ 0.432
+accuracy@10  │ 0.690 │ 0.686 │ 0.654 │ 0.616 │ 0.515
+precision@1  │ 0.416 │ 0.403 │ 0.384 │ 0.336 │ 0.262
+precision@3  │ 0.187 │ 0.184 │ 0.172 │ 0.157 │ 0.128
+precision@5  │ 0.124 │ 0.122 │ 0.115 │ 0.106 │ 0.086
+precision@10 │ 0.069 │ 0.069 │ 0.065 │ 0.062 │ 0.051
+recall@1     │ 0.416 │ 0.403 │ 0.384 │ 0.336 │ 0.262
+recall@3     │ 0.561 │ 0.551 │ 0.517 │ 0.471 │ 0.384
+recall@5     │ 0.618 │ 0.608 │ 0.576 │ 0.532 │ 0.432
+recall@10    │ 0.690 │ 0.686 │ 0.654 │ 0.616 │ 0.515
+
+Fine-Tuned Performance
+----------------------
+Metric       │  768  │  512  │  256  │  128  │   64 
+─────────────┼───────┼───────┼───────┼───────┼──────
+ndcg@10      │ 0.732 │ 0.730 │ 0.707 │ 0.657 │ 0.548
+mrr@10       │ 0.682 │ 0.681 │ 0.658 │ 0.605 │ 0.494
+map@100      │ 0.686 │ 0.685 │ 0.663 │ 0.610 │ 0.502
+accuracy@1   │ 0.576 │ 0.577 │ 0.555 │ 0.499 │ 0.397
+accuracy@3   │ 0.760 │ 0.760 │ 0.735 │ 0.673 │ 0.552
+accuracy@5   │ 0.820 │ 0.820 │ 0.795 │ 0.737 │ 0.626
+accuracy@10  │ 0.887 │ 0.882 │ 0.860 │ 0.823 │ 0.721
+precision@1  │ 0.576 │ 0.577 │ 0.555 │ 0.499 │ 0.397
+precision@3  │ 0.253 │ 0.253 │ 0.245 │ 0.224 │ 0.184
+precision@5  │ 0.164 │ 0.164 │ 0.159 │ 0.147 │ 0.125
+precision@10 │ 0.089 │ 0.088 │ 0.086 │ 0.082 │ 0.072
+recall@1     │ 0.576 │ 0.577 │ 0.555 │ 0.499 │ 0.397
+recall@3     │ 0.760 │ 0.760 │ 0.735 │ 0.673 │ 0.552
+recall@5     │ 0.820 │ 0.820 │ 0.795 │ 0.737 │ 0.626
+recall@10    │ 0.887 │ 0.882 │ 0.860 │ 0.823 │ 0.721
+
+Absolute Changes (Δ)
+--------------------
+Metric       │  768   │  512   │  256   │  128   │   64  
+─────────────┼────────┼────────┼────────┼────────┼───────
+ndcg@10      │ +0.184 │ +0.193 │ +0.194 │ +0.188 │ +0.168
+mrr@10       │ +0.179 │ +0.191 │ +0.190 │ +0.182 │ +0.156
+map@100      │ +0.174 │ +0.187 │ +0.185 │ +0.179 │ +0.155
+accuracy@1   │ +0.160 │ +0.174 │ +0.172 │ +0.163 │ +0.135
+accuracy@3   │ +0.199 │ +0.209 │ +0.218 │ +0.202 │ +0.169
+accuracy@5   │ +0.202 │ +0.212 │ +0.219 │ +0.205 │ +0.195
+accuracy@10  │ +0.196 │ +0.196 │ +0.206 │ +0.206 │ +0.206
+precision@1  │ +0.160 │ +0.174 │ +0.172 │ +0.163 │ +0.135
+precision@3  │ +0.066 │ +0.070 │ +0.073 │ +0.067 │ +0.056
+precision@5  │ +0.040 │ +0.042 │ +0.044 │ +0.041 │ +0.039
+precision@10 │ +0.020 │ +0.020 │ +0.021 │ +0.021 │ +0.021
+recall@1     │ +0.160 │ +0.174 │ +0.172 │ +0.163 │ +0.135
+recall@3     │ +0.199 │ +0.209 │ +0.218 │ +0.202 │ +0.169
+recall@5     │ +0.202 │ +0.212 │ +0.219 │ +0.205 │ +0.195
+recall@10    │ +0.196 │ +0.196 │ +0.206 │ +0.206 │ +0.206
+
+Percentage Changes
+------------------
+Metric       │  768   │  512   │  256   │  128   │   64  
+─────────────┼────────┼────────┼────────┼────────┼───────
+ndcg@10      │ +33.5% │ +35.9% │ +37.9% │ +40.2% │ +44.4%
+mrr@10       │ +35.5% │ +39.0% │ +40.5% │ +43.1% │ +46.3%
+map@100      │ +34.0% │ +37.5% │ +38.8% │ +41.5% │ +44.6%
+accuracy@1   │ +38.5% │ +43.3% │ +44.7% │ +48.5% │ +51.7%
+accuracy@3   │ +35.5% │ +38.0% │ +42.1% │ +42.9% │ +43.9%
+accuracy@5   │ +32.7% │ +34.9% │ +38.1% │ +38.5% │ +45.1%
+accuracy@10  │ +28.4% │ +28.6% │ +31.6% │ +33.5% │ +40.1%
+precision@1  │ +38.5% │ +43.3% │ +44.7% │ +48.5% │ +51.7%
+precision@3  │ +35.5% │ +38.0% │ +42.1% │ +42.9% │ +43.9%
+precision@5  │ +32.7% │ +34.9% │ +38.1% │ +38.5% │ +45.1%
+precision@10 │ +28.4% │ +28.6% │ +31.6% │ +33.5% │ +40.1%
+recall@1     │ +38.5% │ +43.3% │ +44.7% │ +48.5% │ +51.7%
+recall@3     │ +35.5% │ +38.0% │ +42.1% │ +42.9% │ +43.9%
+recall@5     │ +32.7% │ +34.9% │ +38.1% │ +38.5% │ +45.1%
+recall@10    │ +28.4% │ +28.6% │ +31.6% │ +33.5% │ +40.1%

model.safetensors

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+oid sha256:82a95c0ece1c727d6626a1c60751b2639dd6d1e394fbec8c54720bc393d18192
+size 596070136

modules.json

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+    "name": "0",
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+    "type": "sentence_transformers.models.Transformer"
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+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
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+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
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sentence_bert_config.json

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+  "do_lower_case": false
+}

special_tokens_map.json

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+}

tokenizer_config.json

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