Updated README to a bare minimum template

README.md

@@ -2,24 +2,16 @@
 tags:
 - sentence-transformers
 - sentence-similarity
-- feature-extraction
-- generated_from_trainer
-- dataset_size:2438
-- loss:MatryoshkaLoss
 - loss:OnlineContrastiveLoss
 base_model: Alibaba-NLP/gte-modernbert-base
-
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
 metrics:
 - cosine_accuracy
-- cosine_accuracy_threshold
-- cosine_f1
-- cosine_f1_threshold
 - cosine_precision
 - cosine_recall
+- cosine_f1
 - cosine_ap
-- cosine_mcc
 model-index:
 - name: SentenceTransformer based on Alibaba-NLP/gte-modernbert-base
   results:
@@ -27,38 +19,29 @@ model-index:
       type: my-binary-classification
       name: My Binary Classification
     dataset:
-      name: Unknown
+      name: Quora
       type: unknown
     metrics:
     - type: cosine_accuracy
-      value: 0.9159836065573771
+      value:
       name: Cosine Accuracy
-    - type: cosine_accuracy_threshold
-      value: 0.8090976476669312
-      name: Cosine Accuracy Threshold
     - type: cosine_f1
-      value: 0.9216061185468452
+      value:
       name: Cosine F1
-    - type: cosine_f1_threshold
-      value: 0.8090976476669312
-      name: Cosine F1 Threshold
     - type: cosine_precision
-      value: 0.9305019305019305
+      value:
       name: Cosine Precision
     - type: cosine_recall
-      value: 0.9128787878787878
+      value:
       name: Cosine Recall
     - type: cosine_ap
-      value: 0.974188222191262
+      value:
       name: Cosine Ap
-    - type: cosine_mcc
-      value: 0.8312925398469787
-      name: Cosine Mcc
 ---
 
 # SentenceTransformer based on Alibaba-NLP/gte-modernbert-base
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the csv dataset. 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.
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the Quora csv dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity for the purpose of semantic caching.
 
 ## Model Details
 
@@ -69,7 +52,7 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [A
 - **Output Dimensionality:** 768 dimensions
 - **Similarity Function:** Cosine Similarity
 - **Training Dataset:**
-    - csv
+    - Quora csv
 <!-- - **Language:** Unknown -->
 <!-- - **License:** Unknown -->
 
@@ -83,15 +66,13 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [A
 
 ```
 SentenceTransformer(
-  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
+  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
   (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
 )
 ```
 
 ## Usage
 
-### Direct Usage (Sentence Transformers)
-
 First install the Sentence Transformers library:
 
 ```bash
@@ -103,12 +84,12 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("waris-gill/ModernBert-Medical-v1")
+model = SentenceTransformer("redis/langcache-embed-v1")
 # Run inference
 sentences = [
-    'My rheumatologist said \'if a patient has lupus then prednisone doesn\'t work." why is that?',
-    "I have lupus,my rheumatologist told me that prednisone doesn't work in my case. Could you educate me why? What are my chances? ",
-    'Hello doctor, my grandmother has 3rd degree bed sore. What can be done to help?',
+    'Will the value of Indian rupee increase after the ban of 500 and 1000 rupee notes?',
+    'What will be the implications of banning 500 and 1000 rupees currency notes on Indian economy?',
+    "Are Danish Sait's prank calls fake?",
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -117,286 +98,36 @@ print(embeddings.shape)
 # 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
-
-#### My Binary Classification
-
-* Evaluated with <code>scache.train.MyBinaryClassificationEvaluator</code>
-
-| Metric                    | Value      |
-|:--------------------------|:-----------|
-| cosine_accuracy           | 0.916      |
-| cosine_accuracy_threshold | 0.8091     |
-| cosine_f1                 | 0.9216     |
-| cosine_f1_threshold       | 0.8091     |
-| cosine_precision          | 0.9305     |
-| cosine_recall             | 0.9129     |
-| **cosine_ap**             | **0.9742** |
-| cosine_mcc                | 0.8313     |
-
-<!--
-## 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.*
--->
+#### Binary Classification
 
-<!--
-### Recommendations
 
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
+| Metric                    | Value     |
+|:--------------------------|:----------|
+| cosine_accuracy           |     |
+| cosine_f1                 |     |
+| cosine_precision          |    |
+| cosine_recall             |     |
+| **cosine_ap**             |  |
 
-## Training Details
 
 ### Training Dataset
 
 #### csv
 
 * Dataset: csv
-* Size: 2,438 training samples
+* Size:  training samples
 * Columns: <code>question_1</code>, <code>question_2</code>, and <code>label</code>
-* Approximate statistics based on the first 1000 samples:
-
-* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
-  ```json
-  {
-      "loss": "OnlineContrastiveLoss",
-      "matryoshka_dims": [
-          768,
-          512,
-          256,
-          128,
-          64
-      ],
-      "matryoshka_weights": [
-          1,
-          1,
-          1,
-          1,
-          1
-      ],
-      "n_dims_per_step": -1
-  }
-  ```
 
 ### Evaluation Dataset
 
 #### csv
 
 * Dataset: csv
-* Size: 2,438 evaluation samples
-
-
-### Training Hyperparameters
-#### Non-Default Hyperparameters
-
-- `eval_strategy`: steps
-- `per_device_train_batch_size`: 16
-- `per_device_eval_batch_size`: 256
-- `learning_rate`: 6.5383156211679e-05
-- `max_grad_norm`: 0.5
-- `num_train_epochs`: 1
-- `lr_scheduler_type`: constant
-- `load_best_model_at_end`: True
-- `torch_compile`: True
-- `torch_compile_backend`: inductor
-- `batch_sampler`: no_duplicates
-
-#### All Hyperparameters
-<details><summary>Click to expand</summary>
-
-- `overwrite_output_dir`: False
-- `do_predict`: False
-- `eval_strategy`: steps
-- `prediction_loss_only`: True
-- `per_device_train_batch_size`: 16
-- `per_device_eval_batch_size`: 256
-- `per_gpu_train_batch_size`: None
-- `per_gpu_eval_batch_size`: None
-- `gradient_accumulation_steps`: 1
-- `eval_accumulation_steps`: None
-- `torch_empty_cache_steps`: None
-- `learning_rate`: 6.5383156211679e-05
-- `weight_decay`: 0.0
-- `adam_beta1`: 0.9
-- `adam_beta2`: 0.999
-- `adam_epsilon`: 1e-08
-- `max_grad_norm`: 0.5
-- `num_train_epochs`: 1
-- `max_steps`: -1
-- `lr_scheduler_type`: constant
-- `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.0
-- `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`: None
-- `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
-- `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`: True
-- `torch_compile_backend`: inductor
-- `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 | Validation Loss | cosine_ap  |
-|:----------:|:------:|:-------------:|:---------------:|:----------:|
-| 0.0323     | 1      | 4.4977        | -               | -          |
-| 0.0645     | 2      | 4.9952        | -               | -          |
-| 0.0968     | 3      | 2.9984        | -               | -          |
-| 0.1290     | 4      | 4.8052        | -               | -          |
-| 0.1613     | 5      | 4.0031        | -               | -          |
-| 0.1935     | 6      | 3.7682        | -               | -          |
-| 0.2258     | 7      | 4.0361        | -               | -          |
-| 0.2581     | 8      | 3.4003        | -               | -          |
-| 0.2903     | 9      | 1.1674        | -               | -          |
-| **0.3226** | **10** | **2.3826**    | **14.3756**     | **0.9742** |
-| 0.3548     | 11     | 3.8777        | -               | -          |
-| 0.3871     | 12     | 2.6367        | -               | -          |
-| 0.4194     | 13     | 2.5763        | -               | -          |
-| 0.4516     | 14     | 3.5591        | -               | -          |
-| 0.4839     | 15     | 2.3568        | -               | -          |
-| 0.5161     | 16     | 2.9432        | -               | -          |
-| 0.5484     | 17     | 2.746         | -               | -          |
-| 0.5806     | 18     | 3.647         | -               | -          |
-| 0.6129     | 19     | 3.0907        | -               | -          |
-| 0.6452     | 20     | 3.9776        | 12.4766         | 0.9771     |
-| 0.6774     | 21     | 3.4131        | -               | -          |
-| 0.7097     | 22     | 3.0084        | -               | -          |
-| 0.7419     | 23     | 2.7182        | -               | -          |
-| 0.7742     | 24     | 1.5211        | -               | -          |
-| 0.8065     | 25     | 1.8332        | -               | -          |
-| 0.8387     | 26     | 3.4883        | -               | -          |
-| 0.8710     | 27     | 2.0585        | -               | -          |
-| 0.9032     | 28     | 2.775         | -               | -          |
-| 0.9355     | 29     | 2.9137        | -               | -          |
-| 0.9677     | 30     | 2.4238        | 12.4805         | 0.9769     |
-| 1.0        | 31     | 1.2115        | 14.3756         | 0.9742     |
-
-* The bold row denotes the saved checkpoint.
-
-### Framework Versions
-- Python: 3.11.11
-- Sentence Transformers: 3.4.1
-- Transformers: 4.49.0
-- PyTorch: 2.5.1+cu124
-- Accelerate: 1.4.0
-- Datasets: 3.3.2
-- Tokenizers: 0.21.0
+* Size: evaluation samples
+* Columns: <code>question_1</code>, <code>question_2</code>, and <code>label</code>
 
 ## Citation
 
@@ -404,33 +135,14 @@ You can finetune this model on your own dataset.
 
 #### 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",
+@inproceedings{redisetal.,
+    title = "",
+    author = "",
+    month = "",
+    year = "",
+    publisher = "",
+    url = "",
 }
 ```
 
-
-
 <!--
-## 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.*
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