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Browse files- README.md +177 -199
- config.json +1 -1
- config_sentence_transformers.json +2 -2
- model.safetensors +1 -1
- training_args.bin +1 -1
README.md
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- loss:MultipleNegativesRankingLoss
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base_model: intfloat/multilingual-e5-large-instruct
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widget:
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\ can complicate cases since the determination of the plaintiff's awareness and\
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\ behavior prior to the accident can alter the outcome. \n\n### Legal Standard\
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\ for Warnings and Liability \nIn negligence cases, the adequacy of warnings\
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\ provided is crucial. Courts often assess whether the warnings were sufficient\
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\ to inform the individual of the specific hazards present. A simple sign may\
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\ not meet the threshold if it fails to clearly communicate the danger involved,\
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\ especially if the harm is not immediately obvious or if the context (e.g., a\
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\ crowded street) suggests additional risks."
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- source_sentence: "While shopping at a grocery store, a customer tripped over a broken\
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\ tile, fell, and suffered a concussion. A few months after the accident, the\
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\ customer's attorney deposed a store employee. In the deposition, the employee\
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\ testified, \"I'd been telling the store manager for years to get that broken\
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\ tile fixed, but he wouldn't do it. \" The employee died in an automobile accident\
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\ after being deposed. At trial, the deposition should be\nA. admitted, as a dying\
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\ declaration. \nB. admitted, as former testimony. \nC. not admitted, because\
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\ it is hearsay not within any exception. \nD. not admitted, because the employee\
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\ is not available for cross-examination. "
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A.
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E. increased species diversity due to a prolonged ice age followed by a severe
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drought.
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F. decreased species diversity due to a prolonged ice age followed by a severe
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drought.
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G. a great amount of species diversity, or a single species that exhibited a lot
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of diversity.
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H. increased species diversity but with decreased population numbers due to harsh
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climate conditions.
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I. increased species diversity but decreased numbers of hammerstones and flakes,
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indicating less stone tool manufacture.
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J. very little species diversity during this period and very few hominids.'
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sentences:
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datasets:
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- DoDucAnh/
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pipeline_tag: sentence-similarity
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library_name: sentence-transformers
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---
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# SentenceTransformer based on intfloat/multilingual-e5-large-instruct
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-
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) on the [
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## Model Details
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@@ -176,7 +154,7 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [i
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- **Output Dimensionality:** 1024 dimensions
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- **Similarity Function:** Cosine Similarity
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- **Training Dataset:**
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- [
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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@@ -214,9 +192,9 @@ from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("sentence_transformers_model_id")
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# Run inference
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sentences = [
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'
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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### Training Dataset
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####
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* Dataset: [
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* Size: 594,028 training samples
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* Columns: <code>anchor</code> and <code>positive</code>
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* Approximate statistics based on the first 1000 samples:
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| | anchor
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| type | string
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| details | <ul><li>min:
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* Samples:
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| anchor
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| <code>
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| <code>
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| <code>
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
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```json
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{
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### Evaluation Dataset
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####
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* Dataset: [
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* Size:
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* Columns: <code>anchor</code> and <code>positive</code>
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* Approximate statistics based on the first 1000 samples:
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| | anchor | positive |
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#### Non-Default Hyperparameters
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- `eval_strategy`: steps
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- `per_device_train_batch_size`:
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- `per_device_eval_batch_size`:
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- `
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- `
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- `warmup_steps`:
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- `fp16`: True
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- `load_best_model_at_end`: True
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- `do_predict`: False
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- `eval_strategy`: steps
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- `prediction_loss_only`: True
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- `per_device_train_batch_size`:
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- `per_device_eval_batch_size`:
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- `per_gpu_train_batch_size`: None
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- `per_gpu_eval_batch_size`: None
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- `gradient_accumulation_steps`:
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- `eval_accumulation_steps`: None
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- `torch_empty_cache_steps`: None
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- `learning_rate`:
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- `weight_decay`: 0.0
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- `adam_beta1`: 0.9
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- `adam_beta2`: 0.999
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- `adam_epsilon`: 1e-08
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- `max_grad_norm`: 1.0
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- `num_train_epochs`:
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- `max_steps`: -1
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- `lr_scheduler_type`: linear
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- `lr_scheduler_kwargs`: {}
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- `warmup_ratio`: 0.0
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- `warmup_steps`:
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- `log_level`: passive
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- `log_level_replica`: warning
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- `log_on_each_node`: True
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### Training Logs
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| Epoch | Step | Training Loss | Validation Loss |
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|:--------:|:--------:|:-------------:|:---------------:|
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* The bold row denotes the saved checkpoint.
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### Framework Versions
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- Python: 3.
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- Sentence Transformers: 4.1.0
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- Transformers: 4.52.
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- PyTorch: 2.7.0+
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- Accelerate: 1.7.0
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- Datasets: 3.6.0
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- Tokenizers: 0.21.1
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base_model: intfloat/multilingual-e5-large-instruct
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widget:
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\ came upon the scene of a three-car accident. She was so busy gawking at the\
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\ damaged vehicles that she failed to see one of the victims lying on the road\
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\ in front of her car. She hit and ran over the victim, who survived and sued\
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\ the woman's company. The victim offers the testimony of a witness to the incident.\
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\ Referring to the woman, the witness stated, \"The driver of that car ran over\
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\ the victim as he was lying on the ground awaiting an ambulance, and said \x80\
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\x98It is all my fault, I should have been paying more attention to my driving.\
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\ \" Assume for this question that the woman is available to testify. The trial\
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\ judge should rule that the testimony is\nA. admissible as a declaration against\
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\ interest.\nB. admissible as a present sense impression.\nC. admissible as an\
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\ admission.\nD. inadmissible as hearsay not within any recognized exception."
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sentences:
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- A present sense impression is a statement describing or explaining an event or
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condition made while the declarant was perceiving the event or condition, or immediately
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thereafter. This is an exception to the hearsay rule.
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- Corporate managers are professionals within a business environment who handle
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various aspects of management, including planning, organizing, leading, and controlling
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resources. Their roles often draw from established management theories, such as
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those by Henri Fayol, which emphasize functions like forecasting, commanding,
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and coordinating to support organizational success.
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- Ο ποιοτικός έλεγχος είναι μια διαδικασία που εφαρμόζεται στη βιομηχανία και σε
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άλλους τομείς για να διασφαλιστεί ότι τα προϊόντα ή οι υπηρεσίες πληρούν συγκεκριμένες
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προδιαγραφές και πρότυπα ποιότητας.
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A. পিছিয়ে পড়া জনগােষ্ঠীর ক্ষেত্রে
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B. . নারীদের ক্ষেত্রে
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C. প্রতিবন্ধীদের ক্ষেত্রে
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D. সংখ্যালঘুদের ক্ষেত্রে'
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sentences:
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- বিপরীত বৈষম্য সাধারণত পিছিয়ে পড়া জনগোষ্ঠী বা সংখ্যালঘুদের মতো গোষ্ঠীর ক্ষেত্রে
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প্রয়োগ করা হয় যারা ঐতিহাসিকভাবে বৈষম্যের শিকার হয়েছেন।
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- Hummingbirds reproduce by laying eggs, usually in small nests that they build
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on branches. The female is responsible for incubating the eggs and caring for
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the young, which necessitates energy management to ensure survival and growth.
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- In the Mughal Empire, zamindars were initially indigenous local chiefs of towns
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and villages in rural areas. Later, they became landholders who could collect
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taxes from peasants and tenants, transmitting a tenth or eleventh of their produce
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to the imperial treasury. In contrast to the jagirdars, who were given land grants
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as part of their service to the Mughal government, the zamindar tenure was hereditary.
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The zamindars performed the functions of the ancient rajas (kings) or chieftains.
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They were landowners who were expected to pay a fixed tribute to the Mughal emperor.
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due to their ability to threaten governments, in the face of ________ with relocation
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to other territories, which Beck (1998) describes as ''corporate power of _______.
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A. Economic, Commercial competition, Social sanction
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B. Political, Undesirable regulation, Transnational withdrawal
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C. Social, Commercial competition, Social sanction
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D. Social, Undesirable regulation, Transnational withdrawal'
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- L'anémie est une condition caractérisée par une diminution du nombre de globules
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rouges ou de la quantité d'hémoglobine dans le sang, entraînant une réduction
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du transport de l'oxygène.
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- Another critical method for evaluating internal controls is to focus on risk identification
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and the specific potential losses associated with those risks. Organizations often
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start with a thorough risk analysis to understand vulnerabilities, which can then
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inform the development or enhancement of control activities intended to mitigate
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those risks.
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- The concept of 'transnational withdrawal' refers to the phenomenon where companies
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threaten to relocate their operations to countries with more favorable conditions.
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This can include lighter regulations, lower taxes, or more lenient labor standards.
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to keep corporations within their jurisdictions, thereby illustrating the leverage
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that global companies hold.
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- source_sentence: 'Can armed violence perpetrated by non-State actors ever amount
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to an armed attack under Article 51 UN Charter?
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B. The Caroline case serves as precedent that non-State actors can under particular
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circumstances commit an armed attack
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C. There is no precedent in international law for the proposition that non-State
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actors can commit an armed attack
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D. Non-State can both commit an armed attack and possess a right of self-defence
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under international law'
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- In international law, the concept of an armed attack typically refers to the use
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of force by one state against another, which is significant under the UN Charter
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as it may trigger the right of self-defense. This term is often discussed in the
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context of customary international law and the interpretations by bodies like
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the International Court of Justice.
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- '2. **Force and Motion**: According to Newton''s second law, the acceleration
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of an object is directly proportional to the net force acting on it and inversely
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proportional to its mass (F = ma). If an object can accelerate in response to
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a force, this indicates that the force applied contributes to the net work done
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on the object, thereby altering its kinetic energy.'
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- 委託に伴って個人データを提供する場合、委託先は「第三者」に該当しないとみなされることがあります。この場合、原則として本人の同意��不要です。
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- source_sentence: 'A builder had a contract to build a swimming pool for a residential
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customer. That customer''s next door neighbor went to the builder and paid him
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extra to break the contract with the customer and instead to build a swimming
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pool on the neighbor''s premises. The builder commenced building a swimming pool
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for the neighbor and breached his contract with the original customer. The original
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customer sued his neighbor in a tort claim for damages. Does the original customer
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have a valid claim against his neighbor?
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A. Yes, the neighbor committed the tort of interference with contract relations
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by intentionally interfering with an existing contract.
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B. No, people cannot be held in slavery
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C. they have the right to contract with whomever they please.
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D. No, the only remedy for the original customer is to sue the builder for breach
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of contract.
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E. Yes, the neighbor committed the tort of interference with prospective advantage.'
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sentences:
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- Ներքին գործերի նախարար - Պաշտոն, որը պատասխանատու է երկրի ներքին անվտանգության,
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+
հասարակական կարգի և օրենքի պահպանման համար։
|
130 |
+
- A tort is a civil wrong that causes harm or loss to another person, resulting
|
131 |
+
in legal liability for the person who commits the tort. Tort law allows individuals
|
132 |
+
to seek compensation for injuries or damages caused by the wrongful acts of others,
|
133 |
+
distinct from breaches of contract.
|
134 |
+
- Substance use, such as alcohol and tobacco, during pregnancy can lead to various
|
135 |
+
complications including low birth weight, developmental issues, and increased
|
136 |
+
risk of infections, highlighting the importance of cessation and support for affected
|
137 |
+
mothers.
|
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datasets:
|
139 |
+
- DoDucAnh/MNLP_M3_rag_dataset
|
140 |
pipeline_tag: sentence-similarity
|
141 |
library_name: sentence-transformers
|
142 |
---
|
143 |
|
144 |
# SentenceTransformer based on intfloat/multilingual-e5-large-instruct
|
145 |
|
146 |
+
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) on the [mnlp_m3_rag_dataset](https://huggingface.co/datasets/DoDucAnh/MNLP_M3_rag_dataset) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
|
147 |
|
148 |
## Model Details
|
149 |
|
|
|
154 |
- **Output Dimensionality:** 1024 dimensions
|
155 |
- **Similarity Function:** Cosine Similarity
|
156 |
- **Training Dataset:**
|
157 |
+
- [mnlp_m3_rag_dataset](https://huggingface.co/datasets/DoDucAnh/MNLP_M3_rag_dataset)
|
158 |
<!-- - **Language:** Unknown -->
|
159 |
<!-- - **License:** Unknown -->
|
160 |
|
|
|
192 |
model = SentenceTransformer("sentence_transformers_model_id")
|
193 |
# Run inference
|
194 |
sentences = [
|
195 |
+
"A builder had a contract to build a swimming pool for a residential customer. That customer's next door neighbor went to the builder and paid him extra to break the contract with the customer and instead to build a swimming pool on the neighbor's premises. The builder commenced building a swimming pool for the neighbor and breached his contract with the original customer. The original customer sued his neighbor in a tort claim for damages. Does the original customer have a valid claim against his neighbor?\nA. Yes, the neighbor committed the tort of interference with contract relations by intentionally interfering with an existing contract.\nB. No, people cannot be held in slavery\nC. they have the right to contract with whomever they please.\nD. No, the only remedy for the original customer is to sue the builder for breach of contract.\nE. Yes, the neighbor committed the tort of interference with prospective advantage.",
|
196 |
+
'A tort is a civil wrong that causes harm or loss to another person, resulting in legal liability for the person who commits the tort. Tort law allows individuals to seek compensation for injuries or damages caused by the wrongful acts of others, distinct from breaches of contract.',
|
197 |
+
'Substance use, such as alcohol and tobacco, during pregnancy can lead to various complications including low birth weight, developmental issues, and increased risk of infections, highlighting the importance of cessation and support for affected mothers.',
|
198 |
]
|
199 |
embeddings = model.encode(sentences)
|
200 |
print(embeddings.shape)
|
|
|
246 |
|
247 |
### Training Dataset
|
248 |
|
249 |
+
#### mnlp_m3_rag_dataset
|
250 |
|
251 |
+
* Dataset: [mnlp_m3_rag_dataset](https://huggingface.co/datasets/DoDucAnh/MNLP_M3_rag_dataset) at [e16d937](https://huggingface.co/datasets/DoDucAnh/MNLP_M3_rag_dataset/tree/e16d937f0bb9981bb081e8c16a3eda5b3fbbc68a)
|
252 |
* Size: 594,028 training samples
|
253 |
* Columns: <code>anchor</code> and <code>positive</code>
|
254 |
* Approximate statistics based on the first 1000 samples:
|
255 |
+
| | anchor | positive |
|
256 |
+
|:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
|
257 |
+
| type | string | string |
|
258 |
+
| details | <ul><li>min: 21 tokens</li><li>mean: 359.4 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 56.63 tokens</li><li>max: 433 tokens</li></ul> |
|
259 |
* Samples:
|
260 |
+
| anchor | positive |
|
261 |
+
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
262 |
+
| <code>Little Lopsy fluttered into our home and our hearts one Saturday morning this summer. My husband went out to do something, and when he opened the door there was a great flutter on the ground and something came into the living room. It was clear that whatever it was was hurt. I was in a bit of a shock and didn't know what to do next. Fortunately it calmed down and tried to hide itself in a corner. I realized it was a sparrow chick . There are a few sparrow nests under the roof of our apartment, and this little fellow must have fallen out and hurt itself. It was also very young, and obviously far from ready to leave the safety of the nest. I ran to the place and found a box. Having read somewhere that one shouldn't touch a baby bird with one's hands, I picked the chick up with a hand towel and put it in the box. I placed the box outside the front door in the hope that the parents would try to feed it. They never came near it and I brought it inside. I placed the box on a table and it sl...</code> | <code>Having read somewhere that one shouldn't touch a baby bird with one's hands, I picked the chick up with a hand towel and put it in the box.</code> |
|
263 |
+
| <code>A thermal conductor is made of<br>A. types of rubber<br>B. types of wire<br>C. electrodes<br>D. that which conducts</code> | <code>A thermal conductor is a material that allows heat to flow through it easily. Common examples of thermal conductors include metals such as copper and aluminum, known for their high thermal conductivity due to their free-flowing electrons. Heat transfer occurs via conduction when heat energy moves from the hotter part of a conductor to the cooler part, often described by Fourier's Law of heat conduction.</code> |
|
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+
| <code>A good example of increased demand may equal increased production is<br>A. soldiers eat beans, so beans are planted when there is war<br>B. dogs eat kibble, so stores sell it<br>C. cats eat mice, so mice are afraid of cats<br>D. people have babies, so baby clothes are made</code> | <code>Supply is the total amount of a specific good or service that is available to consumers. Supply can relate to the amount available at a specific price or the amount available across a range of prices if displayed on a graph.</code> |
|
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
|
266 |
```json
|
267 |
{
|
|
|
272 |
|
273 |
### Evaluation Dataset
|
274 |
|
275 |
+
#### mnlp_m3_rag_dataset
|
276 |
|
277 |
+
* Dataset: [mnlp_m3_rag_dataset](https://huggingface.co/datasets/DoDucAnh/MNLP_M3_rag_dataset) at [e16d937](https://huggingface.co/datasets/DoDucAnh/MNLP_M3_rag_dataset/tree/e16d937f0bb9981bb081e8c16a3eda5b3fbbc68a)
|
278 |
+
* Size: 5,920 evaluation samples
|
279 |
* Columns: <code>anchor</code> and <code>positive</code>
|
280 |
* Approximate statistics based on the first 1000 samples:
|
281 |
| | anchor | positive |
|
|
|
300 |
#### Non-Default Hyperparameters
|
301 |
|
302 |
- `eval_strategy`: steps
|
303 |
+
- `per_device_train_batch_size`: 16
|
304 |
+
- `per_device_eval_batch_size`: 16
|
305 |
+
- `gradient_accumulation_steps`: 2
|
306 |
+
- `learning_rate`: 2e-05
|
307 |
+
- `warmup_steps`: 5569
|
308 |
- `fp16`: True
|
309 |
- `load_best_model_at_end`: True
|
310 |
|
|
|
315 |
- `do_predict`: False
|
316 |
- `eval_strategy`: steps
|
317 |
- `prediction_loss_only`: True
|
318 |
+
- `per_device_train_batch_size`: 16
|
319 |
+
- `per_device_eval_batch_size`: 16
|
320 |
- `per_gpu_train_batch_size`: None
|
321 |
- `per_gpu_eval_batch_size`: None
|
322 |
+
- `gradient_accumulation_steps`: 2
|
323 |
- `eval_accumulation_steps`: None
|
324 |
- `torch_empty_cache_steps`: None
|
325 |
+
- `learning_rate`: 2e-05
|
326 |
- `weight_decay`: 0.0
|
327 |
- `adam_beta1`: 0.9
|
328 |
- `adam_beta2`: 0.999
|
329 |
- `adam_epsilon`: 1e-08
|
330 |
- `max_grad_norm`: 1.0
|
331 |
+
- `num_train_epochs`: 3
|
332 |
- `max_steps`: -1
|
333 |
- `lr_scheduler_type`: linear
|
334 |
- `lr_scheduler_kwargs`: {}
|
335 |
- `warmup_ratio`: 0.0
|
336 |
+
- `warmup_steps`: 5569
|
337 |
- `log_level`: passive
|
338 |
- `log_level_replica`: warning
|
339 |
- `log_on_each_node`: True
|
|
|
430 |
### Training Logs
|
431 |
| Epoch | Step | Training Loss | Validation Loss |
|
432 |
|:--------:|:--------:|:-------------:|:---------------:|
|
433 |
+
| **0.15** | **2785** | **0.2684** | **0.0411** |
|
434 |
+
| 0.3001 | 5570 | 0.1112 | 0.0541 |
|
435 |
+
| 0.4501 | 8355 | 0.1153 | 0.0633 |
|
436 |
+
| 0.6001 | 11140 | 0.1045 | 0.0582 |
|
437 |
+
| 0.7501 | 13925 | 0.0943 | 0.0606 |
|
438 |
+
| 0.9002 | 16710 | 0.0883 | 0.0563 |
|
439 |
+
| 1.0502 | 19495 | 0.0744 | 0.0505 |
|
440 |
+
| 1.2002 | 22280 | 0.0592 | 0.0523 |
|
441 |
+
| 1.3502 | 25065 | 0.059 | 0.0516 |
|
442 |
+
| 1.5002 | 27850 | 0.0544 | 0.0617 |
|
443 |
+
| 1.6503 | 30635 | 0.0521 | 0.0549 |
|
444 |
+
| 1.8003 | 33420 | 0.0502 | 0.0589 |
|
445 |
+
| 1.9503 | 36205 | 0.0449 | 0.0550 |
|
446 |
+
| 2.1003 | 38990 | 0.0369 | 0.0619 |
|
447 |
+
| 2.2503 | 41775 | 0.0331 | 0.0604 |
|
448 |
+
| 2.4004 | 44560 | 0.0308 | 0.0566 |
|
449 |
+
| 2.5504 | 47345 | 0.0294 | 0.0533 |
|
450 |
+
| 2.7004 | 50130 | 0.0286 | 0.0531 |
|
451 |
+
| 2.8504 | 52915 | 0.0266 | 0.0537 |
|
452 |
+
|
453 |
+
* The bold row denotes the saved checkpoint. The training took 6h52m on a RTX5090
|
454 |
|
455 |
### Framework Versions
|
456 |
+
- Python: 3.12.3
|
457 |
- Sentence Transformers: 4.1.0
|
458 |
+
- Transformers: 4.52.4
|
459 |
+
- PyTorch: 2.7.0+cu128
|
460 |
- Accelerate: 1.7.0
|
461 |
- Datasets: 3.6.0
|
462 |
- Tokenizers: 0.21.1
|
config.json
CHANGED
@@ -20,7 +20,7 @@
|
|
20 |
"pad_token_id": 1,
|
21 |
"position_embedding_type": "absolute",
|
22 |
"torch_dtype": "float32",
|
23 |
-
"transformers_version": "4.52.
|
24 |
"type_vocab_size": 1,
|
25 |
"use_cache": true,
|
26 |
"vocab_size": 250002
|
|
|
20 |
"pad_token_id": 1,
|
21 |
"position_embedding_type": "absolute",
|
22 |
"torch_dtype": "float32",
|
23 |
+
"transformers_version": "4.52.4",
|
24 |
"type_vocab_size": 1,
|
25 |
"use_cache": true,
|
26 |
"vocab_size": 250002
|
config_sentence_transformers.json
CHANGED
@@ -1,8 +1,8 @@
|
|
1 |
{
|
2 |
"__version__": {
|
3 |
"sentence_transformers": "4.1.0",
|
4 |
-
"transformers": "4.52.
|
5 |
-
"pytorch": "2.7.0+
|
6 |
},
|
7 |
"prompts": {},
|
8 |
"default_prompt_name": null,
|
|
|
1 |
{
|
2 |
"__version__": {
|
3 |
"sentence_transformers": "4.1.0",
|
4 |
+
"transformers": "4.52.4",
|
5 |
+
"pytorch": "2.7.0+cu128"
|
6 |
},
|
7 |
"prompts": {},
|
8 |
"default_prompt_name": null,
|
model.safetensors
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 2239607176
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:a2cb5ff432aafe9465077d3be0c2750508fcebf23fcc965e95b4c30a355b20ed
|
3 |
size 2239607176
|
training_args.bin
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 5905
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ba8eb39843c6da8e2a874d163673a0ac9df77631f42e8c92e768d75bead0d471
|
3 |
size 5905
|