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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:149098 |
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- loss:MultipleNegativesRankingLoss |
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base_model: sentence-transformers/LaBSE |
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widget: |
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- source_sentence: چگونه می توانید واقعاً بدانید که کسی یک جامعه شناسی/روانی است؟ |
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(علاوه بر این که آنها اسکن مغزی دارند) |
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sentences: |
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- تفاوت بین وکیل و وکیل چیست؟ |
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- چگونه می توانم برای آزمون ادبیات انگلیسی خالص UGC آماده شوم؟ |
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- از کجا می دانید کسی روانپزشکی است یا یک جامعه شناسی؟ |
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- source_sentence: ایده شما از ازدواج چیست؟ |
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sentences: |
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- کدام برنامه برای C و C ++ مهمترین است؟ |
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- How will the ban on Rs. 1000 and Rs. 500 notes impact Indian economy? |
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- ایده ازدواج چیست؟ |
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- source_sentence: کدام یک بهترین لپ تاپ برای خرید زیر 30k است؟ |
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sentences: |
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- چگونه قیمت املاک و مستغلات تحت تأثیر تصمیم دولت هند برای از بین بردن 500 و 1000 |
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یادداشت قرار می گیرد؟ |
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- کدام بهترین لپ تاپ برای خرید بالاتر از 25000 پوند و زیر/تا 30000 پوند است؟ |
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- چگونه استرس در ذهن را کاهش می دهیم؟ |
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- source_sentence: چگونه می توانم به طور جامع برای ادبیات انگلیسی خالص UGC آماده شوم؟ |
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sentences: |
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- چگونه می توانم یک حساب پس انداز تعقیب را بصورت آنلاین ببندم؟ |
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- چگونه می توانم برای NET JRF در ادبیات انگلیسی آماده شوم؟ |
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- تفاوت بین گربه و علاقه مندان به GMAT چیست؟ |
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- source_sentence: آیا با دختری که باکره نیست ازدواج خواهید کرد؟ |
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sentences: |
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- زنی با شلوار جین کنار اسبی با زین ایستاده است |
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- آیا تا به حال چیزی ماوراء الطبیعه یا فوق طبیعی را تجربه کرده اید؟ |
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- آیا با کسی که باکره نیست ازدواج می کنید؟ |
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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 sentence-transformers/LaBSE |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). 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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision b7f947194ceae0ddf90bafe213722569e274ad28 --> |
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- **Maximum Sequence Length:** 256 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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(2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) |
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(3): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("codersan/FaLaBSE-v6") |
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# Run inference |
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sentences = [ |
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'آیا با دختری که باکره نیست ازدواج خواهید کرد؟', |
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'آیا با کسی که باکره نیست ازدواج می کنید؟', |
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'زنی با شلوار جین کنار اسبی با زین ایستاده است', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 149,098 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 | positive | |
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|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 5 tokens</li><li>mean: 15.1 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.54 tokens</li><li>max: 57 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
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|:---------------------------------------------------------------------|:-------------------------------------------------------------------| |
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| <code>اگر هند تقسیم نشده بود ، هند امروز چگونه به نظر می رسد؟</code> | <code>اگر پارتیشن اتفاق نیفتاد ، هند امروز چگونه خواهد بود؟</code> | |
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| <code>چگونه می توانم وارد امنیت اینترنت شوم؟</code> | <code>چگونه می توانم شروع به یادگیری امنیت اطلاعات کنم؟</code> | |
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| <code>برخی از بهترین مؤسسات مربیگری GMAT در دهلی/NCR چیست؟</code> | <code>بهترین مؤسسات مربیگری برای GMAT در NCR چیست؟</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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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `per_device_train_batch_size`: 32 |
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- `learning_rate`: 3e-05 |
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- `weight_decay`: 0.15 |
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- `num_train_epochs`: 10 |
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- `warmup_ratio`: 0.15 |
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- `batch_sampler`: no_duplicates |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 8 |
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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`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 3e-05 |
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- `weight_decay`: 0.15 |
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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`: 10 |
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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`: 0.15 |
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- `warmup_steps`: 0 |
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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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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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### Training Logs |
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<details><summary>Click to expand</summary> |
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| Epoch | Step | Training Loss | |
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|:------:|:-----:|:-------------:| |
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| 0.0429 | 100 | 0.1219 | |
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| 0.0858 | 200 | 0.0626 | |
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| 0.1288 | 300 | 0.0489 | |
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| 0.1717 | 400 | 0.0414 | |
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| 0.2146 | 500 | 0.0432 | |
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| 0.2575 | 600 | 0.0419 | |
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| 0.3004 | 700 | 0.0313 | |
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| 0.3433 | 800 | 0.0339 | |
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| 0.3863 | 900 | 0.0317 | |
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| 0.4292 | 1000 | 0.035 | |
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| 0.4721 | 1100 | 0.0378 | |
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| 0.5150 | 1200 | 0.0308 | |
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| 0.5579 | 1300 | 0.0305 | |
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| 0.6009 | 1400 | 0.0312 | |
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| 0.6438 | 1500 | 0.0304 | |
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| 0.6867 | 1600 | 0.0295 | |
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| 0.7296 | 1700 | 0.0301 | |
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| 0.7725 | 1800 | 0.033 | |
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| 0.8155 | 1900 | 0.0263 | |
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| 0.8584 | 2000 | 0.0276 | |
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| 0.9013 | 2100 | 0.0236 | |
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| 0.9442 | 2200 | 0.0276 | |
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| 0.9871 | 2300 | 0.0278 | |
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| 1.0300 | 2400 | 0.0309 | |
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| 1.0730 | 2500 | 0.0269 | |
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| 1.1159 | 2600 | 0.0299 | |
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| 1.1588 | 2700 | 0.0272 | |
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| 1.2017 | 2800 | 0.029 | |
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| 1.2446 | 2900 | 0.0309 | |
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| 1.2876 | 3000 | 0.0247 | |
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| 1.3305 | 3100 | 0.0244 | |
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| 1.3734 | 3200 | 0.0261 | |
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| 1.4163 | 3300 | 0.0254 | |
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| 1.4592 | 3400 | 0.0273 | |
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| 1.5021 | 3500 | 0.0298 | |
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| 1.5451 | 3600 | 0.0225 | |
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| 1.5880 | 3700 | 0.0278 | |
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| 1.6309 | 3800 | 0.027 | |
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| 1.6738 | 3900 | 0.0218 | |
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| 1.7167 | 4000 | 0.0247 | |
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| 1.7597 | 4100 | 0.023 | |
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| 1.8026 | 4200 | 0.0225 | |
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| 1.8455 | 4300 | 0.0191 | |
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| 1.8884 | 4400 | 0.0174 | |
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| 1.9313 | 4500 | 0.0214 | |
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| 1.9742 | 4600 | 0.018 | |
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| 2.0172 | 4700 | 0.0227 | |
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| 2.0601 | 4800 | 0.0222 | |
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| 2.1030 | 4900 | 0.0211 | |
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| 2.1459 | 5000 | 0.0204 | |
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| 2.1888 | 5100 | 0.0215 | |
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| 2.2318 | 5200 | 0.0206 | |
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| 2.2747 | 5300 | 0.0213 | |
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| 2.3176 | 5400 | 0.0168 | |
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| 2.3605 | 5500 | 0.0189 | |
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| 2.4034 | 5600 | 0.0206 | |
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| 2.4464 | 5700 | 0.0194 | |
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| 2.4893 | 5800 | 0.0182 | |
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| 2.5322 | 5900 | 0.017 | |
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| 2.5751 | 6000 | 0.0186 | |
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| 2.6180 | 6100 | 0.017 | |
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| 2.6609 | 6200 | 0.0152 | |
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| 2.7039 | 6300 | 0.0164 | |
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| 2.7468 | 6400 | 0.0142 | |
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| 2.7897 | 6500 | 0.0162 | |
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| 2.8326 | 6600 | 0.0123 | |
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| 2.8755 | 6700 | 0.0162 | |
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| 2.9185 | 6800 | 0.0138 | |
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| 2.9614 | 6900 | 0.0163 | |
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| 3.0043 | 7000 | 0.0138 | |
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| 3.0472 | 7100 | 0.0164 | |
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| 3.0901 | 7200 | 0.016 | |
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| 3.1330 | 7300 | 0.0175 | |
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| 3.1760 | 7400 | 0.0143 | |
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| 3.2189 | 7500 | 0.0142 | |
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| 3.2618 | 7600 | 0.0176 | |
|
|
| 3.3047 | 7700 | 0.0147 | |
|
|
| 3.3476 | 7800 | 0.0164 | |
|
|
| 3.3906 | 7900 | 0.0133 | |
|
|
| 3.4335 | 8000 | 0.0168 | |
|
|
| 3.4764 | 8100 | 0.0166 | |
|
|
| 3.5193 | 8200 | 0.0138 | |
|
|
| 3.5622 | 8300 | 0.0126 | |
|
|
| 3.6052 | 8400 | 0.0145 | |
|
|
| 3.6481 | 8500 | 0.0114 | |
|
|
| 3.6910 | 8600 | 0.0137 | |
|
|
| 3.7339 | 8700 | 0.014 | |
|
|
| 3.7768 | 8800 | 0.0134 | |
|
|
| 3.8197 | 8900 | 0.0108 | |
|
|
| 3.8627 | 9000 | 0.012 | |
|
|
| 3.9056 | 9100 | 0.0102 | |
|
|
| 3.9485 | 9200 | 0.0119 | |
|
|
| 3.9914 | 9300 | 0.0122 | |
|
|
| 4.0343 | 9400 | 0.0116 | |
|
|
| 4.0773 | 9500 | 0.0136 | |
|
|
| 4.1202 | 9600 | 0.0135 | |
|
|
| 4.1631 | 9700 | 0.0108 | |
|
|
| 4.2060 | 9800 | 0.0119 | |
|
|
| 4.2489 | 9900 | 0.0142 | |
|
|
| 4.2918 | 10000 | 0.0111 | |
|
|
| 4.3348 | 10100 | 0.0131 | |
|
|
| 4.3777 | 10200 | 0.0103 | |
|
|
| 4.4206 | 10300 | 0.0124 | |
|
|
| 4.4635 | 10400 | 0.0163 | |
|
|
| 4.5064 | 10500 | 0.0123 | |
|
|
| 4.5494 | 10600 | 0.0112 | |
|
|
| 4.5923 | 10700 | 0.01 | |
|
|
| 4.6352 | 10800 | 0.0096 | |
|
|
| 4.6781 | 10900 | 0.0103 | |
|
|
| 4.7210 | 11000 | 0.0102 | |
|
|
| 4.7639 | 11100 | 0.0092 | |
|
|
| 4.8069 | 11200 | 0.0107 | |
|
|
| 4.8498 | 11300 | 0.0114 | |
|
|
| 4.8927 | 11400 | 0.0091 | |
|
|
| 4.9356 | 11500 | 0.0108 | |
|
|
| 4.9785 | 11600 | 0.0092 | |
|
|
| 5.0215 | 11700 | 0.0086 | |
|
|
| 5.0644 | 11800 | 0.0104 | |
|
|
| 5.1073 | 11900 | 0.0123 | |
|
|
| 5.1502 | 12000 | 0.009 | |
|
|
| 5.1931 | 12100 | 0.0106 | |
|
|
| 5.2361 | 12200 | 0.0114 | |
|
|
| 5.2790 | 12300 | 0.0098 | |
|
|
| 5.3219 | 12400 | 0.0093 | |
|
|
| 5.3648 | 12500 | 0.0092 | |
|
|
| 5.4077 | 12600 | 0.011 | |
|
|
| 5.4506 | 12700 | 0.0113 | |
|
|
| 5.4936 | 12800 | 0.0091 | |
|
|
| 5.5365 | 12900 | 0.0079 | |
|
|
| 5.5794 | 13000 | 0.01 | |
|
|
| 5.6223 | 13100 | 0.0067 | |
|
|
| 5.6652 | 13200 | 0.0081 | |
|
|
| 5.7082 | 13300 | 0.0097 | |
|
|
| 5.7511 | 13400 | 0.0081 | |
|
|
| 5.7940 | 13500 | 0.0094 | |
|
|
| 5.8369 | 13600 | 0.0074 | |
|
|
| 5.8798 | 13700 | 0.0071 | |
|
|
| 5.9227 | 13800 | 0.0074 | |
|
|
| 5.9657 | 13900 | 0.0076 | |
|
|
| 6.0086 | 14000 | 0.0063 | |
|
|
| 6.0515 | 14100 | 0.0083 | |
|
|
| 6.0944 | 14200 | 0.0101 | |
|
|
| 6.1373 | 14300 | 0.0084 | |
|
|
| 6.1803 | 14400 | 0.0074 | |
|
|
| 6.2232 | 14500 | 0.007 | |
|
|
| 6.2661 | 14600 | 0.0078 | |
|
|
| 6.3090 | 14700 | 0.0074 | |
|
|
| 6.3519 | 14800 | 0.0086 | |
|
|
| 6.3948 | 14900 | 0.0069 | |
|
|
| 6.4378 | 15000 | 0.0083 | |
|
|
| 6.4807 | 15100 | 0.0082 | |
|
|
| 6.5236 | 15200 | 0.0066 | |
|
|
| 6.5665 | 15300 | 0.0086 | |
|
|
| 6.6094 | 15400 | 0.0059 | |
|
|
| 6.6524 | 15500 | 0.0052 | |
|
|
| 6.6953 | 15600 | 0.0081 | |
|
|
| 6.7382 | 15700 | 0.0054 | |
|
|
| 6.7811 | 15800 | 0.0063 | |
|
|
| 6.8240 | 15900 | 0.0065 | |
|
|
| 6.8670 | 16000 | 0.0068 | |
|
|
| 6.9099 | 16100 | 0.0047 | |
|
|
| 6.9528 | 16200 | 0.0065 | |
|
|
| 6.9957 | 16300 | 0.0064 | |
|
|
| 7.0386 | 16400 | 0.0051 | |
|
|
| 7.0815 | 16500 | 0.0066 | |
|
|
| 7.1245 | 16600 | 0.0069 | |
|
|
| 7.1674 | 16700 | 0.0074 | |
|
|
| 7.2103 | 16800 | 0.0062 | |
|
|
| 7.2532 | 16900 | 0.0071 | |
|
|
| 7.2961 | 17000 | 0.005 | |
|
|
| 7.3391 | 17100 | 0.008 | |
|
|
| 7.3820 | 17200 | 0.0047 | |
|
|
| 7.4249 | 17300 | 0.0073 | |
|
|
| 7.4678 | 17400 | 0.0078 | |
|
|
| 7.5107 | 17500 | 0.0058 | |
|
|
| 7.5536 | 17600 | 0.0055 | |
|
|
| 7.5966 | 17700 | 0.0049 | |
|
|
| 7.6395 | 17800 | 0.0046 | |
|
|
| 7.6824 | 17900 | 0.0051 | |
|
|
| 7.7253 | 18000 | 0.005 | |
|
|
| 7.7682 | 18100 | 0.0059 | |
|
|
| 7.8112 | 18200 | 0.0056 | |
|
|
| 7.8541 | 18300 | 0.0049 | |
|
|
| 7.8970 | 18400 | 0.0038 | |
|
|
| 7.9399 | 18500 | 0.005 | |
|
|
| 7.9828 | 18600 | 0.005 | |
|
|
| 8.0258 | 18700 | 0.0036 | |
|
|
| 8.0687 | 18800 | 0.0049 | |
|
|
| 8.1116 | 18900 | 0.0067 | |
|
|
| 8.1545 | 19000 | 0.0056 | |
|
|
| 8.1974 | 19100 | 0.0061 | |
|
|
| 8.2403 | 19200 | 0.0054 | |
|
|
| 8.2833 | 19300 | 0.0046 | |
|
|
| 8.3262 | 19400 | 0.0048 | |
|
|
| 8.3691 | 19500 | 0.0052 | |
|
|
| 8.4120 | 19600 | 0.0059 | |
|
|
| 8.4549 | 19700 | 0.0053 | |
|
|
| 8.4979 | 19800 | 0.0049 | |
|
|
| 8.5408 | 19900 | 0.0036 | |
|
|
| 8.5837 | 20000 | 0.0049 | |
|
|
| 8.6266 | 20100 | 0.0033 | |
|
|
| 8.6695 | 20200 | 0.0049 | |
|
|
| 8.7124 | 20300 | 0.0043 | |
|
|
| 8.7554 | 20400 | 0.0039 | |
|
|
| 8.7983 | 20500 | 0.0038 | |
|
|
| 8.8412 | 20600 | 0.0035 | |
|
|
| 8.8841 | 20700 | 0.0041 | |
|
|
| 8.9270 | 20800 | 0.0042 | |
|
|
| 8.9700 | 20900 | 0.0056 | |
|
|
| 9.0129 | 21000 | 0.0031 | |
|
|
| 9.0558 | 21100 | 0.004 | |
|
|
| 9.0987 | 21200 | 0.0043 | |
|
|
| 9.1416 | 21300 | 0.0047 | |
|
|
| 9.1845 | 21400 | 0.0051 | |
|
|
| 9.2275 | 21500 | 0.0032 | |
|
|
| 9.2704 | 21600 | 0.0045 | |
|
|
| 9.3133 | 21700 | 0.0038 | |
|
|
| 9.3562 | 21800 | 0.0045 | |
|
|
| 9.3991 | 21900 | 0.0047 | |
|
|
| 9.4421 | 22000 | 0.0048 | |
|
|
| 9.4850 | 22100 | 0.0042 | |
|
|
| 9.5279 | 22200 | 0.0039 | |
|
|
| 9.5708 | 22300 | 0.0042 | |
|
|
| 9.6137 | 22400 | 0.003 | |
|
|
| 9.6567 | 22500 | 0.0031 | |
|
|
| 9.6996 | 22600 | 0.0042 | |
|
|
| 9.7425 | 22700 | 0.0028 | |
|
|
| 9.7854 | 22800 | 0.0037 | |
|
|
| 9.8283 | 22900 | 0.0035 | |
|
|
| 9.8712 | 23000 | 0.0033 | |
|
|
| 9.9142 | 23100 | 0.0029 | |
|
|
| 9.9571 | 23200 | 0.0048 | |
|
|
| 10.0 | 23300 | 0.0039 | |
|
|
|
|
|
</details> |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.10.12 |
|
|
- Sentence Transformers: 3.3.1 |
|
|
- Transformers: 4.47.0 |
|
|
- PyTorch: 2.5.1+cu121 |
|
|
- Accelerate: 1.2.1 |
|
|
- Datasets: 3.2.0 |
|
|
- Tokenizers: 0.21.0 |
|
|
|
|
|
## 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", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### 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} |
|
|
} |
|
|
``` |
|
|
|
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