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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:1200000 |
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- loss:CosineSimilarityLoss |
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base_model: sentence-transformers/all-mpnet-base-v2 |
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widget: |
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- source_sentence: Mutton, roasted |
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sentences: |
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- Imagine Creamy Butternut Squash Soup |
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- Perrier Water, bottled |
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- Crackers, whole-wheat |
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- source_sentence: Beef Chuck Mock Tender Steak, lean and fat raw |
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sentences: |
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- Lamb, Australian leg roasted, bone-in |
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- Chicken wing, meat and skin, cooked fried flour |
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- Peaches, canned in heavy syrup |
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- source_sentence: Squash, zucchini baby raw |
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sentences: |
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- Dandelion greens, cooked with salt |
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- Beets, pickled canned |
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- Cod, Atlantic canned |
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- source_sentence: Veggie Meatballs |
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sentences: |
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- Salt, iodized |
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- Sweet and Sour Sauce, ready-to-serve |
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- Salt pork, raw |
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- source_sentence: Beef Top Round, lean raw |
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sentences: |
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- Ravioli, meat-filled with tomato or meat sauce canned |
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- Pasta Sauce, spaghetti/marinara ready-to-serve |
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- Luncheon Slices, meatless |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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model-index: |
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- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: validation |
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type: validation |
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metrics: |
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- type: pearson_cosine |
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value: 0.9913128359649296 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.9868170667730207 |
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name: Spearman Cosine |
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new_version: jonny9f/food_embeddings2 |
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--- |
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# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 --> |
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- **Maximum Sequence Length:** 384 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel |
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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("jonny9f/food_embeddings") |
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# Run inference |
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sentences = [ |
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'Beef Top Round, lean raw', |
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'Luncheon Slices, meatless', |
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'Pasta Sauce, spaghetti/marinara ready-to-serve', |
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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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## Evaluation |
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### Metrics |
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#### Semantic Similarity |
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* Dataset: `validation` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.9913 | |
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| **spearman_cosine** | **0.9868** | |
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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: 1,200,000 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: 4 tokens</li><li>mean: 10.2 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.65 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.26</li><li>max: 0.92</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>Beef top round roast, boneless lean select cooked</code> | <code>Blueberries, canned wild in heavy syrup drained</code> | <code>0.21440656185150148</code> | |
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| <code>Nance, frozen unsweetened</code> | <code>Soymilk, unsweetened</code> | <code>0.3654276132583618</code> | |
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| <code>Drops - Lemonade</code> | <code>Pickle relish, sweet</code> | <code>0.30108280181884767</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`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `num_train_epochs`: 1 |
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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`: 32 |
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- `per_device_eval_batch_size`: 32 |
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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`: 1 |
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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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<details><summary>Click to expand</summary> |
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| Epoch | Step | Training Loss | validation_spearman_cosine | |
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|:------:|:-----:|:-------------:|:--------------------------:| |
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| 0.0133 | 500 | 0.0031 | - | |
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| 0.0267 | 1000 | 0.0028 | - | |
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| 0.04 | 1500 | 0.0025 | - | |
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| 0.0533 | 2000 | 0.0024 | - | |
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| 0.0667 | 2500 | 0.0023 | - | |
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| 0.08 | 3000 | 0.0022 | - | |
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| 0.0933 | 3500 | 0.0021 | - | |
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| 0.1067 | 4000 | 0.002 | - | |
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| 0.12 | 4500 | 0.002 | - | |
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| 0.1333 | 5000 | 0.0019 | - | |
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| 0.1467 | 5500 | 0.0018 | - | |
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| 0.16 | 6000 | 0.0018 | - | |
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| 0.1733 | 6500 | 0.0017 | - | |
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| 0.1867 | 7000 | 0.0017 | - | |
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| 0.2 | 7500 | 0.0016 | - | |
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| 0.2133 | 8000 | 0.0016 | - | |
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| 0.2267 | 8500 | 0.0016 | - | |
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| 0.24 | 9000 | 0.0015 | - | |
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| 0.2533 | 9500 | 0.0015 | - | |
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| 0.2667 | 10000 | 0.0015 | - | |
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| 0.28 | 10500 | 0.0015 | - | |
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| 0.2933 | 11000 | 0.0015 | - | |
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| 0.3067 | 11500 | 0.0014 | - | |
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| 0.32 | 12000 | 0.0014 | - | |
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| 0.3333 | 12500 | 0.0013 | - | |
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| 0.3467 | 13000 | 0.0013 | - | |
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| 0.36 | 13500 | 0.0013 | - | |
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| 0.3733 | 14000 | 0.0013 | - | |
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| 0.3867 | 14500 | 0.0012 | - | |
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| 0.4 | 15000 | 0.0012 | - | |
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| 0.4133 | 15500 | 0.0012 | - | |
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| 0.4267 | 16000 | 0.0012 | - | |
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| 0.44 | 16500 | 0.0012 | - | |
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| 0.4533 | 17000 | 0.0012 | - | |
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| 0.4667 | 17500 | 0.0011 | - | |
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| 0.48 | 18000 | 0.0011 | - | |
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| 0.4933 | 18500 | 0.0011 | - | |
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| 0.5067 | 19000 | 0.0011 | - | |
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| 0.52 | 19500 | 0.0011 | - | |
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| 0.5333 | 20000 | 0.0011 | - | |
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| 0.5467 | 20500 | 0.0011 | - | |
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| 0.56 | 21000 | 0.001 | - | |
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| 0.5733 | 21500 | 0.001 | - | |
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| 0.5867 | 22000 | 0.001 | - | |
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| 0.6 | 22500 | 0.001 | - | |
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| 0.6133 | 23000 | 0.001 | - | |
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| 0.6267 | 23500 | 0.001 | - | |
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| 0.64 | 24000 | 0.0009 | - | |
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| 0.6533 | 24500 | 0.0009 | - | |
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| 0.6667 | 25000 | 0.0009 | - | |
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| 0.68 | 25500 | 0.0009 | - | |
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| 0.6933 | 26000 | 0.0009 | - | |
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| 0.7067 | 26500 | 0.0009 | - | |
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| 0.72 | 27000 | 0.0009 | - | |
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| 0.7333 | 27500 | 0.0009 | - | |
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| 0.7467 | 28000 | 0.0009 | - | |
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| 0.76 | 28500 | 0.0008 | - | |
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| 0.7733 | 29000 | 0.0008 | - | |
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| 0.7867 | 29500 | 0.0008 | - | |
|
| 0.8 | 30000 | 0.0008 | - | |
|
| 0.8133 | 30500 | 0.0008 | - | |
|
| 0.8267 | 31000 | 0.0008 | - | |
|
| 0.84 | 31500 | 0.0008 | - | |
|
| 0.8533 | 32000 | 0.0008 | - | |
|
| 0.8667 | 32500 | 0.0008 | - | |
|
| 0.88 | 33000 | 0.0007 | - | |
|
| 0.8933 | 33500 | 0.0007 | - | |
|
| 0.9067 | 34000 | 0.0008 | - | |
|
| 0.92 | 34500 | 0.0007 | - | |
|
| 0.9333 | 35000 | 0.0007 | - | |
|
| 0.9467 | 35500 | 0.0007 | - | |
|
| 0.96 | 36000 | 0.0007 | - | |
|
| 0.9733 | 36500 | 0.0007 | - | |
|
| 0.9867 | 37000 | 0.0007 | - | |
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| 1.0 | 37500 | 0.0007 | 0.9799 | |
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| 0.0133 | 500 | 0.0009 | - | |
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| 0.0267 | 1000 | 0.0011 | - | |
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| 0.04 | 1500 | 0.0011 | - | |
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| 0.0533 | 2000 | 0.001 | - | |
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| 0.0667 | 2500 | 0.001 | - | |
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| 0.08 | 3000 | 0.001 | - | |
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| 0.0933 | 3500 | 0.001 | - | |
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| 0.1067 | 4000 | 0.001 | - | |
|
| 0.12 | 4500 | 0.001 | - | |
|
| 0.1333 | 5000 | 0.001 | - | |
|
| 0.1467 | 5500 | 0.001 | - | |
|
| 0.16 | 6000 | 0.0009 | - | |
|
| 0.1733 | 6500 | 0.0009 | - | |
|
| 0.1867 | 7000 | 0.0009 | - | |
|
| 0.2 | 7500 | 0.0009 | - | |
|
| 0.2133 | 8000 | 0.001 | - | |
|
| 0.2267 | 8500 | 0.0009 | - | |
|
| 0.24 | 9000 | 0.0009 | - | |
|
| 0.2533 | 9500 | 0.0009 | - | |
|
| 0.2667 | 10000 | 0.0008 | - | |
|
| 0.28 | 10500 | 0.0009 | - | |
|
| 0.2933 | 11000 | 0.0008 | - | |
|
| 0.3067 | 11500 | 0.0008 | - | |
|
| 0.32 | 12000 | 0.0008 | - | |
|
| 0.3333 | 12500 | 0.0008 | - | |
|
| 0.3467 | 13000 | 0.0008 | - | |
|
| 0.36 | 13500 | 0.0008 | - | |
|
| 0.3733 | 14000 | 0.0008 | - | |
|
| 0.3867 | 14500 | 0.0008 | - | |
|
| 0.4 | 15000 | 0.0008 | - | |
|
| 0.4133 | 15500 | 0.0007 | - | |
|
| 0.4267 | 16000 | 0.0007 | - | |
|
| 0.44 | 16500 | 0.0008 | - | |
|
| 0.4533 | 17000 | 0.0007 | - | |
|
| 0.4667 | 17500 | 0.0007 | - | |
|
| 0.48 | 18000 | 0.0007 | - | |
|
| 0.4933 | 18500 | 0.0007 | - | |
|
| 0.5067 | 19000 | 0.0007 | - | |
|
| 0.52 | 19500 | 0.0007 | - | |
|
| 0.5333 | 20000 | 0.0007 | - | |
|
| 0.5467 | 20500 | 0.0007 | - | |
|
| 0.56 | 21000 | 0.0007 | - | |
|
| 0.5733 | 21500 | 0.0006 | - | |
|
| 0.5867 | 22000 | 0.0007 | - | |
|
| 0.6 | 22500 | 0.0006 | - | |
|
| 0.6133 | 23000 | 0.0006 | - | |
|
| 0.6267 | 23500 | 0.0006 | - | |
|
| 0.64 | 24000 | 0.0006 | - | |
|
| 0.6533 | 24500 | 0.0006 | - | |
|
| 0.6667 | 25000 | 0.0006 | - | |
|
| 0.68 | 25500 | 0.0006 | - | |
|
| 0.6933 | 26000 | 0.0006 | - | |
|
| 0.7067 | 26500 | 0.0006 | - | |
|
| 0.72 | 27000 | 0.0006 | - | |
|
| 0.7333 | 27500 | 0.0006 | - | |
|
| 0.7467 | 28000 | 0.0006 | - | |
|
| 0.76 | 28500 | 0.0005 | - | |
|
| 0.7733 | 29000 | 0.0005 | - | |
|
| 0.7867 | 29500 | 0.0006 | - | |
|
| 0.8 | 30000 | 0.0005 | - | |
|
| 0.8133 | 30500 | 0.0005 | - | |
|
| 0.8267 | 31000 | 0.0005 | - | |
|
| 0.84 | 31500 | 0.0005 | - | |
|
| 0.8533 | 32000 | 0.0005 | - | |
|
| 0.8667 | 32500 | 0.0005 | - | |
|
| 0.88 | 33000 | 0.0005 | - | |
|
| 0.8933 | 33500 | 0.0005 | - | |
|
| 0.9067 | 34000 | 0.0005 | - | |
|
| 0.92 | 34500 | 0.0005 | - | |
|
| 0.9333 | 35000 | 0.0005 | - | |
|
| 0.9467 | 35500 | 0.0005 | - | |
|
| 0.96 | 36000 | 0.0005 | - | |
|
| 0.9733 | 36500 | 0.0005 | - | |
|
| 0.9867 | 37000 | 0.0005 | - | |
|
| 1.0 | 37500 | 0.0005 | 0.9850 | |
|
| 0.0133 | 500 | 0.0004 | - | |
|
| 0.0267 | 1000 | 0.0005 | - | |
|
| 0.04 | 1500 | 0.0005 | - | |
|
| 0.0533 | 2000 | 0.0005 | - | |
|
| 0.0667 | 2500 | 0.0005 | - | |
|
| 0.08 | 3000 | 0.0005 | - | |
|
| 0.0933 | 3500 | 0.0005 | - | |
|
| 0.1067 | 4000 | 0.0004 | - | |
|
| 0.12 | 4500 | 0.0004 | - | |
|
| 0.1333 | 5000 | 0.0004 | - | |
|
| 0.1467 | 5500 | 0.0004 | - | |
|
| 0.16 | 6000 | 0.0004 | - | |
|
| 0.1733 | 6500 | 0.0004 | - | |
|
| 0.1867 | 7000 | 0.0004 | - | |
|
| 0.2 | 7500 | 0.0004 | - | |
|
| 0.2133 | 8000 | 0.0004 | - | |
|
| 0.2267 | 8500 | 0.0004 | - | |
|
| 0.24 | 9000 | 0.0004 | - | |
|
| 0.2533 | 9500 | 0.0004 | - | |
|
| 0.2667 | 10000 | 0.0004 | - | |
|
| 0.28 | 10500 | 0.0004 | - | |
|
| 0.2933 | 11000 | 0.0004 | - | |
|
| 0.3067 | 11500 | 0.0004 | - | |
|
| 0.32 | 12000 | 0.0004 | - | |
|
| 0.3333 | 12500 | 0.0004 | - | |
|
| 0.3467 | 13000 | 0.0004 | - | |
|
| 0.36 | 13500 | 0.0004 | - | |
|
| 0.3733 | 14000 | 0.0004 | - | |
|
| 0.3867 | 14500 | 0.0004 | - | |
|
| 0.4 | 15000 | 0.0004 | - | |
|
| 0.4133 | 15500 | 0.0004 | - | |
|
| 0.4267 | 16000 | 0.0004 | - | |
|
| 0.44 | 16500 | 0.0004 | - | |
|
| 0.4533 | 17000 | 0.0004 | - | |
|
| 0.4667 | 17500 | 0.0004 | - | |
|
| 0.48 | 18000 | 0.0004 | - | |
|
| 0.4933 | 18500 | 0.0004 | - | |
|
| 0.5067 | 19000 | 0.0004 | - | |
|
| 0.52 | 19500 | 0.0004 | - | |
|
| 0.5333 | 20000 | 0.0004 | - | |
|
| 0.5467 | 20500 | 0.0004 | - | |
|
| 0.56 | 21000 | 0.0004 | - | |
|
| 0.5733 | 21500 | 0.0004 | - | |
|
| 0.5867 | 22000 | 0.0004 | - | |
|
| 0.6 | 22500 | 0.0004 | - | |
|
| 0.6133 | 23000 | 0.0004 | - | |
|
| 0.6267 | 23500 | 0.0004 | - | |
|
| 0.64 | 24000 | 0.0004 | - | |
|
| 0.6533 | 24500 | 0.0004 | - | |
|
| 0.6667 | 25000 | 0.0004 | - | |
|
| 0.68 | 25500 | 0.0004 | - | |
|
| 0.6933 | 26000 | 0.0004 | - | |
|
| 0.7067 | 26500 | 0.0004 | - | |
|
| 0.72 | 27000 | 0.0004 | - | |
|
| 0.7333 | 27500 | 0.0004 | - | |
|
| 0.7467 | 28000 | 0.0004 | - | |
|
| 0.76 | 28500 | 0.0004 | - | |
|
| 0.7733 | 29000 | 0.0004 | - | |
|
| 0.7867 | 29500 | 0.0004 | - | |
|
| 0.8 | 30000 | 0.0004 | - | |
|
| 0.8133 | 30500 | 0.0004 | - | |
|
| 0.8267 | 31000 | 0.0004 | - | |
|
| 0.84 | 31500 | 0.0004 | - | |
|
| 0.8533 | 32000 | 0.0004 | - | |
|
| 0.8667 | 32500 | 0.0004 | - | |
|
| 0.88 | 33000 | 0.0004 | - | |
|
| 0.8933 | 33500 | 0.0004 | - | |
|
| 0.9067 | 34000 | 0.0004 | - | |
|
| 0.92 | 34500 | 0.0004 | - | |
|
| 0.9333 | 35000 | 0.0004 | - | |
|
| 0.9467 | 35500 | 0.0004 | - | |
|
| 0.96 | 36000 | 0.0004 | - | |
|
| 0.9733 | 36500 | 0.0004 | - | |
|
| 0.9867 | 37000 | 0.0004 | - | |
|
| 1.0 | 37500 | 0.0004 | 0.9868 | |
|
|
|
</details> |
|
|
|
### Framework Versions |
|
- Python: 3.11.3 |
|
- Sentence Transformers: 3.3.1 |
|
- Transformers: 4.48.0 |
|
- PyTorch: 2.5.1+cu124 |
|
- 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", |
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} |
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``` |
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