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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:78879 |
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- loss:CosineSimilarityLoss |
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base_model: intfloat/multilingual-e5-base |
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widget: |
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- source_sentence: Somatotropin Ab |
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sentences: |
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- Desethylamiodarone |
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- Glucose^7H post XXX challenge |
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- Somatotropin Ab |
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- source_sentence: Erythrocytes.fetal/1000 erythrocytes |
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sentences: |
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- levoFLOXacin |
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- Pathologist interpretation |
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- Pepsinogen I |
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- source_sentence: Aggregazione piastrinica.arachidonato indotta |
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sentences: |
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- Epidermal growth factor |
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- Bilirubin.glucuronidated/Bilirubin.total |
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- Platelet aggregation.arachidonate induced |
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- source_sentence: Parathormoon.intact^5 min na uitsnijding in serum of plasma |
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sentences: |
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- Fatty acids.very long chain |
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- Estradiol^4th specimen post XXX challenge |
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- Parathyrin.intact^5M post excision |
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- source_sentence: Karboksühemoglobiin/hemoglobiin.üld |
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sentences: |
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- Ammonia |
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- Carboxyhemoglobin/Hemoglobin.total |
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- Procainamide+N-acetylprocainamide |
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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-base |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-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. |
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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:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) <!-- at revision d13f1b27baf31030b7fd040960d60d909913633f --> |
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- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
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(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}) |
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(2): 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("iddqd21/fine-tuned-e5-semantic-similarity") |
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# Run inference |
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sentences = [ |
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'Karboksühemoglobiin/hemoglobiin.üld', |
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'Carboxyhemoglobin/Hemoglobin.total', |
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'Procainamide+N-acetylprocainamide', |
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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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### 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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## 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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### 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: 78,879 training samples |
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* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | label | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
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| type | string | string | float | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 11.64 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.26 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.59</li><li>max: 1.0</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | label | |
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|:--------------------------------------------------|:-------------------------------------------------|:-----------------| |
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| <code>Rakud.CD3+HLA-DR+/100 raku kohta</code> | <code>Cells.CD3+HLA-DR+/100 cells</code> | <code>1.0</code> | |
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| <code>Zellen.FMC7/100 Zellen</code> | <code>Cells.FMC7/100 cells</code> | <code>1.0</code> | |
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| <code>Apolipoprotéine AI/apolipoprotéine B</code> | <code>Apolipoprotein A-I/Apolipoprotein B</code> | <code>1.0</code> | |
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* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
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```json |
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{ |
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"loss_fct": "torch.nn.modules.loss.MSELoss" |
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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`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `num_train_epochs`: 10 |
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- `multi_dataset_batch_sampler`: round_robin |
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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`: 16 |
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- `per_device_eval_batch_size`: 16 |
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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`: 5e-05 |
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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 |
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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`: 0.0 |
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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`: batch_sampler |
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- `multi_dataset_batch_sampler`: round_robin |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | |
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|:------:|:-----:|:-------------:| |
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| 0.1014 | 500 | 0.0633 | |
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| 0.2028 | 1000 | 0.0332 | |
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| 0.3043 | 1500 | 0.0296 | |
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| 0.4057 | 2000 | 0.0266 | |
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| 0.5071 | 2500 | 0.024 | |
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| 0.6085 | 3000 | 0.0239 | |
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| 0.7099 | 3500 | 0.0216 | |
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| 0.8114 | 4000 | 0.0205 | |
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| 0.9128 | 4500 | 0.0187 | |
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| 1.0142 | 5000 | 0.0185 | |
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| 1.1156 | 5500 | 0.0149 | |
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| 1.2170 | 6000 | 0.015 | |
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| 1.3185 | 6500 | 0.0142 | |
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| 1.4199 | 7000 | 0.0152 | |
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| 1.5213 | 7500 | 0.0138 | |
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| 1.6227 | 8000 | 0.0131 | |
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| 1.7241 | 8500 | 0.014 | |
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| 1.8256 | 9000 | 0.0133 | |
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| 1.9270 | 9500 | 0.0125 | |
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| 2.0284 | 10000 | 0.0128 | |
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| 2.1298 | 10500 | 0.0093 | |
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| 2.2312 | 11000 | 0.0091 | |
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| 2.3327 | 11500 | 0.0097 | |
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| 2.4341 | 12000 | 0.0096 | |
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| 2.5355 | 12500 | 0.0097 | |
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| 2.6369 | 13000 | 0.0093 | |
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| 2.7383 | 13500 | 0.0099 | |
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| 2.8398 | 14000 | 0.0104 | |
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| 2.9412 | 14500 | 0.009 | |
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| 3.0426 | 15000 | 0.0084 | |
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| 3.1440 | 15500 | 0.0065 | |
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| 3.2454 | 16000 | 0.0062 | |
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| 3.3469 | 16500 | 0.0062 | |
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| 3.4483 | 17000 | 0.0068 | |
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| 3.5497 | 17500 | 0.0076 | |
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| 3.6511 | 18000 | 0.0078 | |
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| 3.7525 | 18500 | 0.0068 | |
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| 3.8540 | 19000 | 0.008 | |
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| 3.9554 | 19500 | 0.0076 | |
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| 4.0568 | 20000 | 0.0057 | |
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| 4.1582 | 20500 | 0.0054 | |
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| 4.2596 | 21000 | 0.0052 | |
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| 4.3611 | 21500 | 0.0052 | |
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| 4.4625 | 22000 | 0.0056 | |
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| 4.5639 | 22500 | 0.0055 | |
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| 4.6653 | 23000 | 0.0057 | |
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| 4.7667 | 23500 | 0.006 | |
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| 4.8682 | 24000 | 0.0054 | |
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| 4.9696 | 24500 | 0.0052 | |
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| 5.0710 | 25000 | 0.0045 | |
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| 5.1724 | 25500 | 0.0039 | |
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| 5.2738 | 26000 | 0.0043 | |
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| 5.3753 | 26500 | 0.004 | |
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| 5.4767 | 27000 | 0.0044 | |
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| 5.5781 | 27500 | 0.0045 | |
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| 5.6795 | 28000 | 0.0039 | |
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| 5.7809 | 28500 | 0.0043 | |
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| 5.8824 | 29000 | 0.0047 | |
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| 5.9838 | 29500 | 0.0049 | |
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| 6.0852 | 30000 | 0.003 | |
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| 6.1866 | 30500 | 0.0034 | |
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| 6.2880 | 31000 | 0.003 | |
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| 6.3895 | 31500 | 0.0031 | |
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| 6.4909 | 32000 | 0.0033 | |
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| 6.5923 | 32500 | 0.0035 | |
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| 6.6937 | 33000 | 0.0037 | |
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| 6.7951 | 33500 | 0.0039 | |
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| 6.8966 | 34000 | 0.004 | |
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| 6.9980 | 34500 | 0.003 | |
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| 7.0994 | 35000 | 0.0024 | |
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| 7.2008 | 35500 | 0.0026 | |
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| 7.3022 | 36000 | 0.0029 | |
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| 7.4037 | 36500 | 0.0029 | |
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| 7.5051 | 37000 | 0.0025 | |
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| 7.6065 | 37500 | 0.0026 | |
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| 7.7079 | 38000 | 0.0032 | |
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| 7.8093 | 38500 | 0.0032 | |
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| 7.9108 | 39000 | 0.0029 | |
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| 8.0122 | 39500 | 0.0028 | |
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| 8.1136 | 40000 | 0.0024 | |
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| 8.2150 | 40500 | 0.0021 | |
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| 8.3164 | 41000 | 0.0022 | |
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| 8.4178 | 41500 | 0.0022 | |
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| 8.5193 | 42000 | 0.0024 | |
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| 8.6207 | 42500 | 0.0025 | |
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| 8.7221 | 43000 | 0.0023 | |
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| 8.8235 | 43500 | 0.0021 | |
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| 8.9249 | 44000 | 0.0026 | |
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| 9.0264 | 44500 | 0.0025 | |
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| 9.1278 | 45000 | 0.0021 | |
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| 9.2292 | 45500 | 0.0017 | |
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| 9.3306 | 46000 | 0.0022 | |
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| 9.4320 | 46500 | 0.002 | |
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| 9.5335 | 47000 | 0.0021 | |
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| 9.6349 | 47500 | 0.0019 | |
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| 9.7363 | 48000 | 0.0021 | |
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| 9.8377 | 48500 | 0.002 | |
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| 9.9391 | 49000 | 0.0021 | |
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### Framework Versions |
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- Python: 3.9.20 |
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- Sentence Transformers: 3.3.1 |
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- Transformers: 4.47.1 |
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- PyTorch: 2.5.1+rocm6.2 |
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- Accelerate: 1.2.1 |
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- Datasets: 3.2.0 |
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- Tokenizers: 0.21.0 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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