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--- |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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language: |
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- en |
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library_name: sentence-transformers |
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license: apache-2.0 |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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- pearson_manhattan |
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- spearman_manhattan |
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- pearson_euclidean |
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- spearman_euclidean |
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- pearson_dot |
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- spearman_dot |
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- pearson_max |
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- spearman_max |
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pipeline_tag: sentence-similarity |
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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:757962 |
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- loss:CoSENTLoss |
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widget: |
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- source_sentence: cozy beanbag |
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sentences: |
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- black tee |
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- bag |
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- beige pants |
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- source_sentence: lace up top |
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sentences: |
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- necklace |
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- tote bag |
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- swimsuit |
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- source_sentence: soft beanbag |
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sentences: |
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- burlap bag |
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- bag |
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- bag |
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- source_sentence: chair beanbag |
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sentences: |
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- bag |
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- cotton cap |
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- women swimsuit |
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- source_sentence: foam beanbag |
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sentences: |
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- stretchable luggage cover |
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- cycling shorts |
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- bag |
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model-index: |
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- name: all-MiniLM-L6-v2-five_scores |
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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: sts dev |
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type: sts-dev |
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metrics: |
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- type: pearson_cosine |
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value: 0.17485149631639327 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.09988414865893605 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.17514973425719355 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.10129077593840645 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.17476246568808063 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.09988419374813992 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.17485150231012306 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.09988419086282155 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.17514973425719355 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.10129077593840645 |
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name: Spearman Max |
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--- |
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# all-MiniLM-L6-v2-five_scores |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2) <!-- at revision ea78891063587eb050ed4166b20062eaf978037c --> |
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- **Maximum Sequence Length:** 256 tokens |
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- **Output Dimensionality:** 384 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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- **Language:** en |
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- **License:** apache-2.0 |
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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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``` |
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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': 384, '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("sentence_transformers_model_id") |
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# Run inference |
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sentences = [ |
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'foam beanbag', |
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'bag', |
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'cycling shorts', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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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: `sts-dev` |
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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 | 0.1749 | |
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| **spearman_cosine** | **0.0999** | |
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| pearson_manhattan | 0.1751 | |
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| spearman_manhattan | 0.1013 | |
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| pearson_euclidean | 0.1748 | |
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| spearman_euclidean | 0.0999 | |
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| pearson_dot | 0.1749 | |
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| spearman_dot | 0.0999 | |
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| pearson_max | 0.1751 | |
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| spearman_max | 0.1013 | |
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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 Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 4 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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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`: steps |
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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`: 2e-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`: 1.0 |
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- `num_train_epochs`: 4 |
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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`: 0.1 |
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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`: True |
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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`: False |
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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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- `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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- `batch_sampler`: batch_sampler |
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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 | loss | sts-dev_spearman_cosine | |
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|:------:|:-----:|:-------------:|:-------:|:-----------------------:| |
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| 0 | 0 | - | - | 0.0999 | |
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| 0.0042 | 100 | 12.0647 | - | - | |
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| 0.0084 | 200 | 11.7727 | - | - | |
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| 0.0127 | 300 | 11.1315 | - | - | |
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| 0.0169 | 400 | 10.8852 | - | - | |
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| 0.0211 | 500 | 9.9168 | 10.4208 | - | |
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| 0.0253 | 600 | 9.4099 | - | - | |
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| 0.0296 | 700 | 8.5361 | - | - | |
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| 0.0338 | 800 | 7.7286 | - | - | |
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| 0.0380 | 900 | 7.0852 | - | - | |
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| 0.0422 | 1000 | 6.3646 | 6.6350 | - | |
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| 0.0464 | 1100 | 6.1673 | - | - | |
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| 0.0507 | 1200 | 5.5683 | - | - | |
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| 0.0549 | 1300 | 5.4462 | - | - | |
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| 0.0591 | 1400 | 5.303 | - | - | |
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| 0.0633 | 1500 | 5.1935 | 5.2429 | - | |
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| 0.0675 | 1600 | 5.1856 | - | - | |
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| 0.0718 | 1700 | 5.0136 | - | - | |
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| 0.0760 | 1800 | 5.0667 | - | - | |
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| 0.0802 | 1900 | 4.9982 | - | - | |
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| 0.0844 | 2000 | 5.0429 | 5.0099 | - | |
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| 0.0887 | 2100 | 4.8719 | - | - | |
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| 0.0929 | 2200 | 4.8579 | - | - | |
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| 0.0971 | 2300 | 4.9282 | - | - | |
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| 0.1013 | 2400 | 4.9848 | - | - | |
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| 0.1055 | 2500 | 4.8974 | 4.9078 | - | |
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| 0.1098 | 2600 | 4.9103 | - | - | |
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| 0.1140 | 2700 | 4.7459 | - | - | |
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| 0.1182 | 2800 | 4.8084 | - | - | |
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| 0.1224 | 2900 | 4.8221 | - | - | |
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| 0.1267 | 3000 | 4.7622 | 4.8170 | - | |
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| 0.1309 | 3100 | 4.7004 | - | - | |
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| 0.1351 | 3200 | 4.6912 | - | - | |
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| 0.1393 | 3300 | 4.6595 | - | - | |
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| 0.1435 | 3400 | 4.7322 | - | - | |
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| 0.1478 | 3500 | 4.7575 | 4.7199 | - | |
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| 0.1520 | 3600 | 4.6443 | - | - | |
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| 0.1562 | 3700 | 4.6638 | - | - | |
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| 0.1604 | 3800 | 4.5958 | - | - | |
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| 0.1646 | 3900 | 4.6285 | - | - | |
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| 0.1689 | 4000 | 4.6347 | 4.6554 | - | |
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| 0.1731 | 4100 | 4.6558 | - | - | |
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| 0.1773 | 4200 | 4.6712 | - | - | |
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| 0.1815 | 4300 | 4.6126 | - | - | |
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| 0.1858 | 4400 | 4.6219 | - | - | |
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| 0.1900 | 4500 | 4.6101 | 4.6206 | - | |
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| 0.1942 | 4600 | 4.7682 | - | - | |
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| 0.1984 | 4700 | 4.5385 | - | - | |
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| 0.2026 | 4800 | 4.6744 | - | - | |
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| 0.2069 | 4900 | 4.5383 | - | - | |
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| 0.2111 | 5000 | 4.6095 | 4.6262 | - | |
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| 0.2153 | 5100 | 4.6807 | - | - | |
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| 0.2195 | 5200 | 4.4866 | - | - | |
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| 0.2238 | 5300 | 4.5353 | - | - | |
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| 0.2280 | 5400 | 4.5285 | - | - | |
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| 0.2322 | 5500 | 4.5416 | 4.5914 | - | |
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| 0.2364 | 5600 | 4.623 | - | - | |
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| 0.2406 | 5700 | 4.5337 | - | - | |
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| 0.2449 | 5800 | 4.5726 | - | - | |
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| 0.2491 | 5900 | 4.5467 | - | - | |
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| 0.2533 | 6000 | 4.3986 | 4.6011 | - | |
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| 0.2575 | 6100 | 4.559 | - | - | |
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| 0.2617 | 6200 | 4.6066 | - | - | |
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| 0.2660 | 6300 | 4.4445 | - | - | |
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| 0.2702 | 6400 | 4.4518 | - | - | |
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| 0.2744 | 6500 | 4.4761 | 4.5093 | - | |
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| 0.2786 | 6600 | 4.3362 | - | - | |
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| 0.2829 | 6700 | 4.4936 | - | - | |
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| 0.2871 | 6800 | 4.2397 | - | - | |
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| 0.2913 | 6900 | 4.5243 | - | - | |
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| 0.2955 | 7000 | 4.496 | 4.3969 | - | |
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| 0.2997 | 7100 | 4.2558 | - | - | |
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| 0.3040 | 7200 | 4.4691 | - | - | |
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| 0.3082 | 7300 | 4.4819 | - | - | |
|
| 0.3124 | 7400 | 4.3785 | - | - | |
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| 0.3166 | 7500 | 4.4214 | 4.4199 | - | |
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| 0.3209 | 7600 | 4.4935 | - | - | |
|
| 0.3251 | 7700 | 4.4238 | - | - | |
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| 0.3293 | 7800 | 4.5361 | - | - | |
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| 0.3335 | 7900 | 4.4284 | - | - | |
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| 0.3377 | 8000 | 4.3918 | 4.3976 | - | |
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| 0.3420 | 8100 | 4.4622 | - | - | |
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| 0.3462 | 8200 | 4.4128 | - | - | |
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| 0.3504 | 8300 | 4.1565 | - | - | |
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| 0.3546 | 8400 | 4.3241 | - | - | |
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| 0.3588 | 8500 | 4.2764 | 4.4261 | - | |
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| 0.3631 | 8600 | 4.2101 | - | - | |
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| 0.3673 | 8700 | 4.4044 | - | - | |
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| 0.3715 | 8800 | 4.254 | - | - | |
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| 0.3757 | 8900 | 4.362 | - | - | |
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| 0.3800 | 9000 | 4.3424 | 4.4409 | - | |
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| 0.3842 | 9100 | 4.3383 | - | - | |
|
| 0.3884 | 9200 | 4.4713 | - | - | |
|
| 0.3926 | 9300 | 4.2773 | - | - | |
|
| 0.3968 | 9400 | 4.2842 | - | - | |
|
| 0.4011 | 9500 | 4.3301 | 4.3454 | - | |
|
| 0.4053 | 9600 | 4.3224 | - | - | |
|
| 0.4095 | 9700 | 4.3878 | - | - | |
|
| 0.4137 | 9800 | 4.3614 | - | - | |
|
| 0.4180 | 9900 | 4.3423 | - | - | |
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| 0.4222 | 10000 | 4.3576 | 4.3549 | - | |
|
| 0.4264 | 10100 | 4.1451 | - | - | |
|
| 0.4306 | 10200 | 4.3326 | - | - | |
|
| 0.4348 | 10300 | 4.2761 | - | - | |
|
| 0.4391 | 10400 | 4.2421 | - | - | |
|
| 0.4433 | 10500 | 4.262 | 4.3493 | - | |
|
| 0.4475 | 10600 | 4.1227 | - | - | |
|
| 0.4517 | 10700 | 4.2365 | - | - | |
|
| 0.4559 | 10800 | 4.3528 | - | - | |
|
| 0.4602 | 10900 | 4.077 | - | - | |
|
| 0.4644 | 11000 | 4.0878 | 4.3349 | - | |
|
| 0.4686 | 11100 | 4.4246 | - | - | |
|
| 0.4728 | 11200 | 4.1019 | - | - | |
|
| 0.4771 | 11300 | 4.2565 | - | - | |
|
| 0.4813 | 11400 | 4.3177 | - | - | |
|
| 0.4855 | 11500 | 4.1283 | 4.4236 | - | |
|
| 0.4897 | 11600 | 4.2232 | - | - | |
|
| 0.4939 | 11700 | 4.2347 | - | - | |
|
| 0.4982 | 11800 | 4.082 | - | - | |
|
| 0.5024 | 11900 | 4.2026 | - | - | |
|
| 0.5066 | 12000 | 4.2687 | 4.2691 | - | |
|
| 0.5108 | 12100 | 4.302 | - | - | |
|
| 0.5151 | 12200 | 4.0474 | - | - | |
|
| 0.5193 | 12300 | 4.1286 | - | - | |
|
| 0.5235 | 12400 | 4.3888 | - | - | |
|
| 0.5277 | 12500 | 4.2339 | 4.2414 | - | |
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| 0.5319 | 12600 | 4.1976 | - | - | |
|
| 0.5362 | 12700 | 4.1851 | - | - | |
|
| 0.5404 | 12800 | 4.3969 | - | - | |
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| 0.5446 | 12900 | 4.5229 | - | - | |
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| 0.5488 | 13000 | 4.2242 | 4.1389 | - | |
|
| 0.5530 | 13100 | 4.2804 | - | - | |
|
| 0.5573 | 13200 | 4.2097 | - | - | |
|
| 0.5615 | 13300 | 3.9226 | - | - | |
|
| 0.5657 | 13400 | 4.2274 | - | - | |
|
| 0.5699 | 13500 | 4.0309 | 4.2421 | - | |
|
| 0.5742 | 13600 | 4.3429 | - | - | |
|
| 0.5784 | 13700 | 4.0352 | - | - | |
|
| 0.5826 | 13800 | 4.2926 | - | - | |
|
| 0.5868 | 13900 | 4.3063 | - | - | |
|
| 0.5910 | 14000 | 4.3172 | 4.2267 | - | |
|
| 0.5953 | 14100 | 4.0057 | - | - | |
|
| 0.5995 | 14200 | 4.2081 | - | - | |
|
| 0.6037 | 14300 | 4.2408 | - | - | |
|
| 0.6079 | 14400 | 4.1066 | - | - | |
|
| 0.6122 | 14500 | 4.1997 | 4.1798 | - | |
|
| 0.6164 | 14600 | 4.2364 | - | - | |
|
| 0.6206 | 14700 | 4.1135 | - | - | |
|
| 0.6248 | 14800 | 4.0561 | - | - | |
|
| 0.6290 | 14900 | 4.0347 | - | - | |
|
| 0.6333 | 15000 | 4.1979 | 4.2409 | - | |
|
| 0.6375 | 15100 | 4.0132 | - | - | |
|
| 0.6417 | 15200 | 4.1131 | - | - | |
|
| 0.6459 | 15300 | 3.8049 | - | - | |
|
| 0.6501 | 15400 | 3.9468 | - | - | |
|
| 0.6544 | 15500 | 4.17 | 4.1938 | - | |
|
| 0.6586 | 15600 | 4.2369 | - | - | |
|
| 0.6628 | 15700 | 4.159 | - | - | |
|
| 0.6670 | 15800 | 4.1172 | - | - | |
|
| 0.6713 | 15900 | 4.01 | - | - | |
|
| 0.6755 | 16000 | 4.0204 | 4.2796 | - | |
|
| 0.6797 | 16100 | 4.0013 | - | - | |
|
| 0.6839 | 16200 | 4.0174 | - | - | |
|
| 0.6881 | 16300 | 4.0616 | - | - | |
|
| 0.6924 | 16400 | 3.9944 | - | - | |
|
| 0.6966 | 16500 | 4.05 | 4.2132 | - | |
|
| 0.7008 | 16600 | 4.0769 | - | - | |
|
| 0.7050 | 16700 | 4.1289 | - | - | |
|
| 0.7092 | 16800 | 4.0941 | - | - | |
|
| 0.7135 | 16900 | 4.2556 | - | - | |
|
| 0.7177 | 17000 | 4.3075 | 4.1288 | - | |
|
| 0.7219 | 17100 | 4.0751 | - | - | |
|
| 0.7261 | 17200 | 4.0711 | - | - | |
|
| 0.7304 | 17300 | 3.9483 | - | - | |
|
| 0.7346 | 17400 | 4.3186 | - | - | |
|
| 0.7388 | 17500 | 3.932 | 4.1148 | - | |
|
| 0.7430 | 17600 | 3.8774 | - | - | |
|
| 0.7472 | 17700 | 4.2312 | - | - | |
|
| 0.7515 | 17800 | 3.9327 | - | - | |
|
| 0.7557 | 17900 | 4.2264 | - | - | |
|
| 0.7599 | 18000 | 3.9723 | 4.1061 | - | |
|
| 0.7641 | 18100 | 4.1206 | - | - | |
|
| 0.7684 | 18200 | 4.1744 | - | - | |
|
| 0.7726 | 18300 | 3.89 | - | - | |
|
| 0.7768 | 18400 | 4.1414 | - | - | |
|
| 0.7810 | 18500 | 4.0286 | 4.1405 | - | |
|
| 0.7852 | 18600 | 3.885 | - | - | |
|
| 0.7895 | 18700 | 4.3785 | - | - | |
|
| 0.7937 | 18800 | 3.9304 | - | - | |
|
| 0.7979 | 18900 | 4.0831 | - | - | |
|
| 0.8021 | 19000 | 4.1698 | 4.0998 | - | |
|
| 0.8063 | 19100 | 3.9876 | - | - | |
|
| 0.8106 | 19200 | 3.9194 | - | - | |
|
| 0.8148 | 19300 | 3.9222 | - | - | |
|
| 0.8190 | 19400 | 4.1863 | - | - | |
|
| 0.8232 | 19500 | 4.0315 | 4.0778 | - | |
|
| 0.8275 | 19600 | 3.9286 | - | - | |
|
| 0.8317 | 19700 | 3.9605 | - | - | |
|
| 0.8359 | 19800 | 4.1991 | - | - | |
|
| 0.8401 | 19900 | 4.0311 | - | - | |
|
| 0.8443 | 20000 | 3.7869 | 4.1749 | - | |
|
| 0.8486 | 20100 | 3.9232 | - | - | |
|
| 0.8528 | 20200 | 4.034 | - | - | |
|
| 0.8570 | 20300 | 4.2625 | - | - | |
|
| 0.8612 | 20400 | 3.983 | - | - | |
|
| 0.8655 | 20500 | 4.2154 | 4.1057 | - | |
|
| 0.8697 | 20600 | 4.1696 | - | - | |
|
| 0.8739 | 20700 | 3.8989 | - | - | |
|
| 0.8781 | 20800 | 3.9004 | - | - | |
|
| 0.8823 | 20900 | 4.2134 | - | - | |
|
| 0.8866 | 21000 | 3.9789 | 4.0880 | - | |
|
| 0.8908 | 21100 | 4.2438 | - | - | |
|
| 0.8950 | 21200 | 3.9271 | - | - | |
|
| 0.8992 | 21300 | 3.9693 | - | - | |
|
| 0.9034 | 21400 | 4.0197 | - | - | |
|
| 0.9077 | 21500 | 4.1802 | 4.0145 | - | |
|
| 0.9119 | 21600 | 3.8818 | - | - | |
|
| 0.9161 | 21700 | 4.1069 | - | - | |
|
| 0.9203 | 21800 | 3.7999 | - | - | |
|
| 0.9246 | 21900 | 3.8949 | - | - | |
|
| 0.9288 | 22000 | 3.9893 | 4.1313 | - | |
|
| 0.9330 | 22100 | 4.0918 | - | - | |
|
| 0.9372 | 22200 | 4.0451 | - | - | |
|
| 0.9414 | 22300 | 3.9312 | - | - | |
|
| 0.9457 | 22400 | 4.117 | - | - | |
|
| 0.9499 | 22500 | 3.883 | 4.1090 | - | |
|
| 0.9541 | 22600 | 3.6942 | - | - | |
|
| 0.9583 | 22700 | 4.1196 | - | - | |
|
| 0.9626 | 22800 | 3.9292 | - | - | |
|
| 0.9668 | 22900 | 3.9081 | - | - | |
|
| 0.9710 | 23000 | 3.8169 | 4.1232 | - | |
|
| 0.9752 | 23100 | 3.8342 | - | - | |
|
| 0.9794 | 23200 | 4.078 | - | - | |
|
| 0.9837 | 23300 | 4.0002 | - | - | |
|
| 0.9879 | 23400 | 3.9373 | - | - | |
|
| 0.9921 | 23500 | 3.8344 | 4.1565 | - | |
|
| 0.9963 | 23600 | 4.2827 | - | - | |
|
| 1.0005 | 23700 | 4.0298 | - | - | |
|
| 1.0048 | 23800 | 3.9967 | - | - | |
|
| 1.0090 | 23900 | 3.7508 | - | - | |
|
| 1.0132 | 24000 | 3.8919 | 4.0790 | - | |
|
| 1.0174 | 24100 | 4.0181 | - | - | |
|
| 1.0217 | 24200 | 3.7934 | - | - | |
|
| 1.0259 | 24300 | 3.8986 | - | - | |
|
| 1.0301 | 24400 | 3.9275 | - | - | |
|
| 1.0343 | 24500 | 3.6911 | 4.1602 | - | |
|
| 1.0385 | 24600 | 3.5855 | - | - | |
|
| 1.0428 | 24700 | 3.7875 | - | - | |
|
| 1.0470 | 24800 | 3.7999 | - | - | |
|
| 1.0512 | 24900 | 3.7718 | - | - | |
|
| 1.0554 | 25000 | 3.8362 | 4.0381 | - | |
|
| 1.0597 | 25100 | 3.8076 | - | - | |
|
| 1.0639 | 25200 | 3.8875 | - | - | |
|
| 1.0681 | 25300 | 3.9675 | - | - | |
|
| 1.0723 | 25400 | 3.8451 | - | - | |
|
| 1.0765 | 25500 | 3.4346 | 4.1996 | - | |
|
| 1.0808 | 25600 | 4.0584 | - | - | |
|
| 1.0850 | 25700 | 3.602 | - | - | |
|
| 1.0892 | 25800 | 3.673 | - | - | |
|
| 1.0934 | 25900 | 3.976 | - | - | |
|
| 1.0976 | 26000 | 3.8768 | 3.9983 | - | |
|
| 1.1019 | 26100 | 3.7575 | - | - | |
|
| 1.1061 | 26200 | 3.8101 | - | - | |
|
| 1.1103 | 26300 | 4.104 | - | - | |
|
| 1.1145 | 26400 | 3.7139 | - | - | |
|
| 1.1188 | 26500 | 4.0391 | 4.0018 | - | |
|
| 1.1230 | 26600 | 3.8449 | - | - | |
|
| 1.1272 | 26700 | 3.7146 | - | - | |
|
| 1.1314 | 26800 | 4.0576 | - | - | |
|
| 1.1356 | 26900 | 3.8831 | - | - | |
|
| 1.1399 | 27000 | 3.8161 | 4.0019 | - | |
|
| 1.1441 | 27100 | 3.9283 | - | - | |
|
| 1.1483 | 27200 | 3.8637 | - | - | |
|
| 1.1525 | 27300 | 3.701 | - | - | |
|
| 1.1568 | 27400 | 3.9364 | - | - | |
|
| 1.1610 | 27500 | 3.7305 | 3.9959 | - | |
|
| 1.1652 | 27600 | 3.8542 | - | - | |
|
| 1.1694 | 27700 | 3.7249 | - | - | |
|
| 1.1736 | 27800 | 3.7223 | - | - | |
|
| 1.1779 | 27900 | 3.9777 | - | - | |
|
| 1.1821 | 28000 | 3.8036 | 4.0547 | - | |
|
| 1.1863 | 28100 | 3.8635 | - | - | |
|
| 1.1905 | 28200 | 3.8523 | - | - | |
|
| 1.1947 | 28300 | 3.6757 | - | - | |
|
| 1.1990 | 28400 | 3.7519 | - | - | |
|
| 1.2032 | 28500 | 3.983 | 4.0389 | - | |
|
| 1.2074 | 28600 | 3.8288 | - | - | |
|
| 1.2116 | 28700 | 3.8074 | - | - | |
|
| 1.2159 | 28800 | 3.714 | - | - | |
|
| 1.2201 | 28900 | 3.6594 | - | - | |
|
| 1.2243 | 29000 | 3.9452 | 4.0274 | - | |
|
| 1.2285 | 29100 | 3.9906 | - | - | |
|
| 1.2327 | 29200 | 3.9826 | - | - | |
|
| 1.2370 | 29300 | 3.8635 | - | - | |
|
| 1.2412 | 29400 | 3.9888 | - | - | |
|
| 1.2454 | 29500 | 3.7248 | 4.0287 | - | |
|
| 1.2496 | 29600 | 3.7484 | - | - | |
|
| 1.2539 | 29700 | 3.9694 | - | - | |
|
| 1.2581 | 29800 | 4.059 | - | - | |
|
| 1.2623 | 29900 | 3.9358 | - | - | |
|
| 1.2665 | 30000 | 3.8575 | 3.9484 | - | |
|
| 1.2707 | 30100 | 3.8382 | - | - | |
|
| 1.2750 | 30200 | 3.73 | - | - | |
|
| 1.2792 | 30300 | 4.0439 | - | - | |
|
| 1.2834 | 30400 | 3.8426 | - | - | |
|
| 1.2876 | 30500 | 3.7062 | 4.0188 | - | |
|
| 1.2918 | 30600 | 3.8926 | - | - | |
|
| 1.2961 | 30700 | 4.0276 | - | - | |
|
| 1.3003 | 30800 | 3.6359 | - | - | |
|
| 1.3045 | 30900 | 4.0006 | - | - | |
|
| 1.3087 | 31000 | 3.8485 | 4.0019 | - | |
|
| 1.3130 | 31100 | 3.7892 | - | - | |
|
| 1.3172 | 31200 | 3.5783 | - | - | |
|
| 1.3214 | 31300 | 4.0018 | - | - | |
|
| 1.3256 | 31400 | 3.9542 | - | - | |
|
| 1.3298 | 31500 | 3.7739 | 3.9875 | - | |
|
| 1.3341 | 31600 | 3.8806 | - | - | |
|
| 1.3383 | 31700 | 4.176 | - | - | |
|
| 1.3425 | 31800 | 3.826 | - | - | |
|
| 1.3467 | 31900 | 3.8514 | - | - | |
|
| 1.3510 | 32000 | 3.8261 | 3.9716 | - | |
|
| 1.3552 | 32100 | 3.8825 | - | - | |
|
| 1.3594 | 32200 | 3.6388 | - | - | |
|
| 1.3636 | 32300 | 3.7851 | - | - | |
|
| 1.3678 | 32400 | 3.5687 | - | - | |
|
| 1.3721 | 32500 | 3.5408 | 3.9371 | - | |
|
| 1.3763 | 32600 | 3.6995 | - | - | |
|
| 1.3805 | 32700 | 3.882 | - | - | |
|
| 1.3847 | 32800 | 3.8703 | - | - | |
|
| 1.3889 | 32900 | 3.806 | - | - | |
|
| 1.3932 | 33000 | 3.7826 | 3.8901 | - | |
|
| 1.3974 | 33100 | 3.7853 | - | - | |
|
| 1.4016 | 33200 | 3.5745 | - | - | |
|
| 1.4058 | 33300 | 3.5884 | - | - | |
|
| 1.4101 | 33400 | 3.8678 | - | - | |
|
| 1.4143 | 33500 | 4.0917 | 3.9332 | - | |
|
| 1.4185 | 33600 | 3.7125 | - | - | |
|
| 1.4227 | 33700 | 3.7298 | - | - | |
|
| 1.4269 | 33800 | 3.9447 | - | - | |
|
| 1.4312 | 33900 | 3.7176 | - | - | |
|
| 1.4354 | 34000 | 3.6765 | 4.0302 | - | |
|
| 1.4396 | 34100 | 3.9847 | - | - | |
|
| 1.4438 | 34200 | 3.7364 | - | - | |
|
| 1.4481 | 34300 | 3.8246 | - | - | |
|
| 1.4523 | 34400 | 3.575 | - | - | |
|
| 1.4565 | 34500 | 3.814 | 3.9519 | - | |
|
| 1.4607 | 34600 | 3.8708 | - | - | |
|
| 1.4649 | 34700 | 3.7277 | - | - | |
|
| 1.4692 | 34800 | 3.7758 | - | - | |
|
| 1.4734 | 34900 | 3.6727 | - | - | |
|
| 1.4776 | 35000 | 3.773 | 3.9528 | - | |
|
| 1.4818 | 35100 | 4.0004 | - | - | |
|
| 1.4860 | 35200 | 3.8468 | - | - | |
|
| 1.4903 | 35300 | 3.6814 | - | - | |
|
| 1.4945 | 35400 | 3.8993 | - | - | |
|
| 1.4987 | 35500 | 3.8841 | 3.9402 | - | |
|
| 1.5029 | 35600 | 3.8272 | - | - | |
|
| 1.5072 | 35700 | 3.584 | - | - | |
|
| 1.5114 | 35800 | 3.8424 | - | - | |
|
| 1.5156 | 35900 | 3.7274 | - | - | |
|
| 1.5198 | 36000 | 3.9671 | 3.9035 | - | |
|
| 1.5240 | 36100 | 3.7078 | - | - | |
|
| 1.5283 | 36200 | 3.7524 | - | - | |
|
| 1.5325 | 36300 | 3.6992 | - | - | |
|
| 1.5367 | 36400 | 3.8152 | - | - | |
|
| 1.5409 | 36500 | 3.9007 | 3.9785 | - | |
|
| 1.5452 | 36600 | 3.6302 | - | - | |
|
| 1.5494 | 36700 | 3.6208 | - | - | |
|
| 1.5536 | 36800 | 3.6039 | - | - | |
|
| 1.5578 | 36900 | 3.7039 | - | - | |
|
| 1.5620 | 37000 | 3.7069 | 3.9215 | - | |
|
| 1.5663 | 37100 | 3.7246 | - | - | |
|
| 1.5705 | 37200 | 3.7269 | - | - | |
|
| 1.5747 | 37300 | 3.6822 | - | - | |
|
| 1.5789 | 37400 | 3.7083 | - | - | |
|
| 1.5831 | 37500 | 3.7095 | 3.9668 | - | |
|
| 1.5874 | 37600 | 3.4556 | - | - | |
|
| 1.5916 | 37700 | 4.0595 | - | - | |
|
| 1.5958 | 37800 | 3.6583 | - | - | |
|
| 1.6000 | 37900 | 3.5662 | - | - | |
|
| 1.6043 | 38000 | 3.6365 | 3.9035 | - | |
|
| 1.6085 | 38100 | 3.6313 | - | - | |
|
| 1.6127 | 38200 | 3.8767 | - | - | |
|
| 1.6169 | 38300 | 3.9992 | - | - | |
|
| 1.6211 | 38400 | 3.554 | - | - | |
|
| 1.6254 | 38500 | 3.6862 | 3.8900 | - | |
|
| 1.6296 | 38600 | 3.7638 | - | - | |
|
| 1.6338 | 38700 | 3.6716 | - | - | |
|
| 1.6380 | 38800 | 3.8667 | - | - | |
|
| 1.6423 | 38900 | 3.5304 | - | - | |
|
| 1.6465 | 39000 | 3.955 | 3.8894 | - | |
|
| 1.6507 | 39100 | 3.4049 | - | - | |
|
| 1.6549 | 39200 | 3.663 | - | - | |
|
| 1.6591 | 39300 | 4.0267 | - | - | |
|
| 1.6634 | 39400 | 3.8868 | - | - | |
|
| 1.6676 | 39500 | 3.8984 | 3.9277 | - | |
|
| 1.6718 | 39600 | 3.575 | - | - | |
|
| 1.6760 | 39700 | 3.6966 | - | - | |
|
| 1.6802 | 39800 | 4.0533 | - | - | |
|
| 1.6845 | 39900 | 3.6106 | - | - | |
|
| 1.6887 | 40000 | 3.6468 | 3.9425 | - | |
|
| 1.6929 | 40100 | 3.7145 | - | - | |
|
| 1.6971 | 40200 | 3.6602 | - | - | |
|
| 1.7014 | 40300 | 3.5531 | - | - | |
|
| 1.7056 | 40400 | 3.7857 | - | - | |
|
| 1.7098 | 40500 | 3.5586 | 3.8484 | - | |
|
| 1.7140 | 40600 | 3.7711 | - | - | |
|
| 1.7182 | 40700 | 3.7135 | - | - | |
|
| 1.7225 | 40800 | 3.8785 | - | - | |
|
| 1.7267 | 40900 | 3.5577 | - | - | |
|
| 1.7309 | 41000 | 3.5783 | 3.9013 | - | |
|
| 1.7351 | 41100 | 3.7346 | - | - | |
|
| 1.7394 | 41200 | 3.5098 | - | - | |
|
| 1.7436 | 41300 | 4.0181 | - | - | |
|
| 1.7478 | 41400 | 3.8404 | - | - | |
|
| 1.7520 | 41500 | 3.6327 | 3.8684 | - | |
|
| 1.7562 | 41600 | 3.7503 | - | - | |
|
| 1.7605 | 41700 | 3.45 | - | - | |
|
| 1.7647 | 41800 | 3.9138 | - | - | |
|
| 1.7689 | 41900 | 3.6061 | - | - | |
|
| 1.7731 | 42000 | 3.6603 | 3.7956 | - | |
|
| 1.7773 | 42100 | 3.6722 | - | - | |
|
| 1.7816 | 42200 | 3.678 | - | - | |
|
| 1.7858 | 42300 | 3.5802 | - | - | |
|
| 1.7900 | 42400 | 3.8253 | - | - | |
|
| 1.7942 | 42500 | 3.7815 | 3.8192 | - | |
|
| 1.7985 | 42600 | 3.7021 | - | - | |
|
| 1.8027 | 42700 | 3.4263 | - | - | |
|
| 1.8069 | 42800 | 3.8781 | - | - | |
|
| 1.8111 | 42900 | 3.5784 | - | - | |
|
| 1.8153 | 43000 | 3.9405 | 3.8100 | - | |
|
| 1.8196 | 43100 | 3.5516 | - | - | |
|
| 1.8238 | 43200 | 3.8322 | - | - | |
|
| 1.8280 | 43300 | 3.7948 | - | - | |
|
| 1.8322 | 43400 | 3.6175 | - | - | |
|
| 1.8365 | 43500 | 3.5256 | 3.8552 | - | |
|
| 1.8407 | 43600 | 3.8199 | - | - | |
|
| 1.8449 | 43700 | 3.6168 | - | - | |
|
| 1.8491 | 43800 | 3.5648 | - | - | |
|
| 1.8533 | 43900 | 3.5584 | - | - | |
|
| 1.8576 | 44000 | 3.7623 | 3.8202 | - | |
|
| 1.8618 | 44100 | 3.7884 | - | - | |
|
| 1.8660 | 44200 | 3.6241 | - | - | |
|
| 1.8702 | 44300 | 3.4533 | - | - | |
|
| 1.8744 | 44400 | 3.575 | - | - | |
|
| 1.8787 | 44500 | 3.6981 | 3.9080 | - | |
|
| 1.8829 | 44600 | 3.6384 | - | - | |
|
| 1.8871 | 44700 | 3.8267 | - | - | |
|
| 1.8913 | 44800 | 3.5696 | - | - | |
|
| 1.8956 | 44900 | 3.5189 | - | - | |
|
| 1.8998 | 45000 | 3.7528 | 3.8759 | - | |
|
| 1.9040 | 45100 | 3.7572 | - | - | |
|
| 1.9082 | 45200 | 3.7283 | - | - | |
|
| 1.9124 | 45300 | 3.6185 | - | - | |
|
| 1.9167 | 45400 | 3.5348 | - | - | |
|
| 1.9209 | 45500 | 3.5366 | 3.9713 | - | |
|
| 1.9251 | 45600 | 3.8358 | - | - | |
|
| 1.9293 | 45700 | 3.7831 | - | - | |
|
| 1.9336 | 45800 | 3.7524 | - | - | |
|
| 1.9378 | 45900 | 3.4533 | - | - | |
|
| 1.9420 | 46000 | 3.4622 | 3.8907 | - | |
|
| 1.9462 | 46100 | 3.7096 | - | - | |
|
| 1.9504 | 46200 | 3.5447 | - | - | |
|
| 1.9547 | 46300 | 3.601 | - | - | |
|
| 1.9589 | 46400 | 3.6369 | - | - | |
|
| 1.9631 | 46500 | 3.8619 | 3.8416 | - | |
|
| 1.9673 | 46600 | 3.6623 | - | - | |
|
| 1.9715 | 46700 | 3.8225 | - | - | |
|
| 1.9758 | 46800 | 3.9126 | - | - | |
|
| 1.9800 | 46900 | 3.8429 | - | - | |
|
| 1.9842 | 47000 | 3.6607 | 3.8103 | - | |
|
| 1.9884 | 47100 | 3.5708 | - | - | |
|
| 1.9927 | 47200 | 3.6346 | - | - | |
|
| 1.9969 | 47300 | 3.4577 | - | - | |
|
| 2.0011 | 47400 | 3.4326 | - | - | |
|
| 2.0053 | 47500 | 3.5431 | 3.8311 | - | |
|
| 2.0095 | 47600 | 3.4852 | - | - | |
|
| 2.0138 | 47700 | 3.4037 | - | - | |
|
| 2.0180 | 47800 | 3.5685 | - | - | |
|
| 2.0222 | 47900 | 3.2866 | - | - | |
|
| 2.0264 | 48000 | 3.3943 | 3.9447 | - | |
|
| 2.0306 | 48100 | 3.3675 | - | - | |
|
| 2.0349 | 48200 | 3.7605 | - | - | |
|
| 2.0391 | 48300 | 4.0646 | - | - | |
|
| 2.0433 | 48400 | 3.4634 | - | - | |
|
| 2.0475 | 48500 | 3.5041 | 3.8682 | - | |
|
| 2.0518 | 48600 | 3.6862 | - | - | |
|
| 2.0560 | 48700 | 3.5486 | - | - | |
|
| 2.0602 | 48800 | 3.6148 | - | - | |
|
| 2.0644 | 48900 | 3.3776 | - | - | |
|
| 2.0686 | 49000 | 3.4514 | 3.8841 | - | |
|
| 2.0729 | 49100 | 3.4575 | - | - | |
|
| 2.0771 | 49200 | 3.4984 | - | - | |
|
| 2.0813 | 49300 | 3.3978 | - | - | |
|
| 2.0855 | 49400 | 3.644 | - | - | |
|
| 2.0898 | 49500 | 3.6412 | 3.8866 | - | |
|
| 2.0940 | 49600 | 3.322 | - | - | |
|
| 2.0982 | 49700 | 3.7186 | - | - | |
|
| 2.1024 | 49800 | 3.3604 | - | - | |
|
| 2.1066 | 49900 | 3.7262 | - | - | |
|
| 2.1109 | 50000 | 3.541 | 3.8573 | - | |
|
| 2.1151 | 50100 | 3.4695 | - | - | |
|
| 2.1193 | 50200 | 3.3366 | - | - | |
|
| 2.1235 | 50300 | 3.6556 | - | - | |
|
| 2.1277 | 50400 | 3.4016 | - | - | |
|
| 2.1320 | 50500 | 3.6733 | 3.9452 | - | |
|
| 2.1362 | 50600 | 3.7553 | - | - | |
|
| 2.1404 | 50700 | 3.6238 | - | - | |
|
| 2.1446 | 50800 | 3.4566 | - | - | |
|
| 2.1489 | 50900 | 3.7676 | - | - | |
|
| 2.1531 | 51000 | 3.4482 | 3.8958 | - | |
|
| 2.1573 | 51100 | 3.409 | - | - | |
|
| 2.1615 | 51200 | 3.9074 | - | - | |
|
| 2.1657 | 51300 | 3.613 | - | - | |
|
| 2.1700 | 51400 | 3.4835 | - | - | |
|
| 2.1742 | 51500 | 3.5494 | 3.7883 | - | |
|
| 2.1784 | 51600 | 3.5205 | - | - | |
|
| 2.1826 | 51700 | 3.4232 | - | - | |
|
| 2.1869 | 51800 | 3.6539 | - | - | |
|
| 2.1911 | 51900 | 3.6586 | - | - | |
|
| 2.1953 | 52000 | 3.3321 | 3.8934 | - | |
|
| 2.1995 | 52100 | 3.7712 | - | - | |
|
| 2.2037 | 52200 | 3.4973 | - | - | |
|
| 2.2080 | 52300 | 3.5404 | - | - | |
|
| 2.2122 | 52400 | 3.4815 | - | - | |
|
| 2.2164 | 52500 | 3.5586 | 3.8741 | - | |
|
| 2.2206 | 52600 | 3.5463 | - | - | |
|
| 2.2248 | 52700 | 3.537 | - | - | |
|
| 2.2291 | 52800 | 3.5299 | - | - | |
|
| 2.2333 | 52900 | 3.5967 | - | - | |
|
| 2.2375 | 53000 | 3.5156 | 3.8977 | - | |
|
| 2.2417 | 53100 | 3.6079 | - | - | |
|
| 2.2460 | 53200 | 3.4492 | - | - | |
|
| 2.2502 | 53300 | 3.4611 | - | - | |
|
| 2.2544 | 53400 | 3.1611 | - | - | |
|
| 2.2586 | 53500 | 3.6554 | 3.8444 | - | |
|
| 2.2628 | 53600 | 3.473 | - | - | |
|
| 2.2671 | 53700 | 3.8362 | - | - | |
|
| 2.2713 | 53800 | 3.8243 | - | - | |
|
| 2.2755 | 53900 | 3.2957 | - | - | |
|
| 2.2797 | 54000 | 3.4227 | 3.8651 | - | |
|
| 2.2840 | 54100 | 3.6771 | - | - | |
|
| 2.2882 | 54200 | 3.5287 | - | - | |
|
| 2.2924 | 54300 | 3.6393 | - | - | |
|
| 2.2966 | 54400 | 3.6447 | - | - | |
|
| 2.3008 | 54500 | 3.1714 | 3.8695 | - | |
|
| 2.3051 | 54600 | 3.5703 | - | - | |
|
| 2.3093 | 54700 | 3.6058 | - | - | |
|
| 2.3135 | 54800 | 3.3485 | - | - | |
|
| 2.3177 | 54900 | 3.4143 | - | - | |
|
| 2.3219 | 55000 | 3.461 | 3.8285 | - | |
|
| 2.3262 | 55100 | 3.7676 | - | - | |
|
| 2.3304 | 55200 | 3.7192 | - | - | |
|
| 2.3346 | 55300 | 3.3955 | - | - | |
|
| 2.3388 | 55400 | 3.5533 | - | - | |
|
| 2.3431 | 55500 | 3.7335 | 3.7877 | - | |
|
| 2.3473 | 55600 | 3.3743 | - | - | |
|
| 2.3515 | 55700 | 3.5598 | - | - | |
|
| 2.3557 | 55800 | 3.5939 | - | - | |
|
| 2.3599 | 55900 | 3.6577 | - | - | |
|
| 2.3642 | 56000 | 3.407 | 3.7897 | - | |
|
| 2.3684 | 56100 | 3.5838 | - | - | |
|
| 2.3726 | 56200 | 3.5182 | - | - | |
|
| 2.3768 | 56300 | 3.7881 | - | - | |
|
| 2.3811 | 56400 | 3.772 | - | - | |
|
| 2.3853 | 56500 | 3.5779 | 3.8028 | - | |
|
| 2.3895 | 56600 | 3.4765 | - | - | |
|
| 2.3937 | 56700 | 3.5122 | - | - | |
|
| 2.3979 | 56800 | 3.8646 | - | - | |
|
| 2.4022 | 56900 | 3.4861 | - | - | |
|
| 2.4064 | 57000 | 3.4486 | 3.8668 | - | |
|
| 2.4106 | 57100 | 3.4319 | - | - | |
|
| 2.4148 | 57200 | 3.5801 | - | - | |
|
| 2.4190 | 57300 | 3.4412 | - | - | |
|
| 2.4233 | 57400 | 3.4917 | - | - | |
|
| 2.4275 | 57500 | 3.8994 | 3.7693 | - | |
|
| 2.4317 | 57600 | 3.4321 | - | - | |
|
| 2.4359 | 57700 | 3.4605 | - | - | |
|
| 2.4402 | 57800 | 3.5348 | - | - | |
|
| 2.4444 | 57900 | 3.6156 | - | - | |
|
| 2.4486 | 58000 | 3.6726 | 3.7413 | - | |
|
| 2.4528 | 58100 | 3.4447 | - | - | |
|
| 2.4570 | 58200 | 3.5318 | - | - | |
|
| 2.4613 | 58300 | 3.4284 | - | - | |
|
| 2.4655 | 58400 | 3.3426 | - | - | |
|
| 2.4697 | 58500 | 3.5549 | 3.8398 | - | |
|
| 2.4739 | 58600 | 3.7305 | - | - | |
|
| 2.4782 | 58700 | 3.4777 | - | - | |
|
| 2.4824 | 58800 | 3.625 | - | - | |
|
| 2.4866 | 58900 | 3.8084 | - | - | |
|
| 2.4908 | 59000 | 3.6772 | 3.7805 | - | |
|
| 2.4950 | 59100 | 3.4634 | - | - | |
|
| 2.4993 | 59200 | 3.5926 | - | - | |
|
| 2.5035 | 59300 | 3.303 | - | - | |
|
| 2.5077 | 59400 | 3.5749 | - | - | |
|
| 2.5119 | 59500 | 3.7852 | 3.8255 | - | |
|
| 2.5161 | 59600 | 3.6317 | - | - | |
|
| 2.5204 | 59700 | 3.3228 | - | - | |
|
| 2.5246 | 59800 | 3.4541 | - | - | |
|
| 2.5288 | 59900 | 3.5879 | - | - | |
|
| 2.5330 | 60000 | 3.6403 | 3.7744 | - | |
|
| 2.5373 | 60100 | 3.5289 | - | - | |
|
| 2.5415 | 60200 | 3.4 | - | - | |
|
| 2.5457 | 60300 | 3.4026 | - | - | |
|
| 2.5499 | 60400 | 3.6348 | - | - | |
|
| 2.5541 | 60500 | 3.3963 | 3.7309 | - | |
|
| 2.5584 | 60600 | 3.2838 | - | - | |
|
| 2.5626 | 60700 | 3.8537 | - | - | |
|
| 2.5668 | 60800 | 3.3861 | - | - | |
|
| 2.5710 | 60900 | 3.5289 | - | - | |
|
| 2.5753 | 61000 | 3.7611 | 3.7404 | - | |
|
| 2.5795 | 61100 | 3.3036 | - | - | |
|
| 2.5837 | 61200 | 3.4874 | - | - | |
|
| 2.5879 | 61300 | 3.3885 | - | - | |
|
| 2.5921 | 61400 | 3.6008 | - | - | |
|
| 2.5964 | 61500 | 3.7175 | 3.7685 | - | |
|
| 2.6006 | 61600 | 3.589 | - | - | |
|
| 2.6048 | 61700 | 3.6725 | - | - | |
|
| 2.6090 | 61800 | 3.3397 | - | - | |
|
| 2.6132 | 61900 | 3.562 | - | - | |
|
| 2.6175 | 62000 | 3.5421 | 3.7556 | - | |
|
| 2.6217 | 62100 | 3.462 | - | - | |
|
| 2.6259 | 62200 | 3.329 | - | - | |
|
| 2.6301 | 62300 | 3.4274 | - | - | |
|
| 2.6344 | 62400 | 3.4622 | - | - | |
|
| 2.6386 | 62500 | 3.2924 | 3.8314 | - | |
|
| 2.6428 | 62600 | 3.5465 | - | - | |
|
| 2.6470 | 62700 | 3.3618 | - | - | |
|
| 2.6512 | 62800 | 3.2383 | - | - | |
|
| 2.6555 | 62900 | 3.3542 | - | - | |
|
| 2.6597 | 63000 | 3.5209 | 3.7942 | - | |
|
| 2.6639 | 63100 | 3.4416 | - | - | |
|
| 2.6681 | 63200 | 3.5881 | - | - | |
|
| 2.6724 | 63300 | 3.7347 | - | - | |
|
| 2.6766 | 63400 | 3.815 | - | - | |
|
| 2.6808 | 63500 | 3.4667 | 3.7791 | - | |
|
| 2.6850 | 63600 | 3.342 | - | - | |
|
| 2.6892 | 63700 | 3.6695 | - | - | |
|
| 2.6935 | 63800 | 3.4863 | - | - | |
|
| 2.6977 | 63900 | 3.741 | - | - | |
|
| 2.7019 | 64000 | 3.6267 | 3.8010 | - | |
|
| 2.7061 | 64100 | 3.2939 | - | - | |
|
| 2.7103 | 64200 | 3.4422 | - | - | |
|
| 2.7146 | 64300 | 3.7278 | - | - | |
|
| 2.7188 | 64400 | 3.8691 | - | - | |
|
| 2.7230 | 64500 | 3.611 | 3.7172 | - | |
|
| 2.7272 | 64600 | 3.5474 | - | - | |
|
| 2.7315 | 64700 | 3.5087 | - | - | |
|
| 2.7357 | 64800 | 3.4489 | - | - | |
|
| 2.7399 | 64900 | 3.6549 | - | - | |
|
| 2.7441 | 65000 | 3.3956 | 3.7356 | - | |
|
| 2.7483 | 65100 | 3.5116 | - | - | |
|
| 2.7526 | 65200 | 3.1777 | - | - | |
|
| 2.7568 | 65300 | 3.7644 | - | - | |
|
| 2.7610 | 65400 | 3.6376 | - | - | |
|
| 2.7652 | 65500 | 3.6153 | 3.7486 | - | |
|
| 2.7695 | 65600 | 3.2564 | - | - | |
|
| 2.7737 | 65700 | 3.5547 | - | - | |
|
| 2.7779 | 65800 | 3.3283 | - | - | |
|
| 2.7821 | 65900 | 3.4592 | - | - | |
|
| 2.7863 | 66000 | 3.7505 | 3.7252 | - | |
|
| 2.7906 | 66100 | 3.3761 | - | - | |
|
| 2.7948 | 66200 | 3.6223 | - | - | |
|
| 2.7990 | 66300 | 3.4702 | - | - | |
|
| 2.8032 | 66400 | 3.8666 | - | - | |
|
| 2.8074 | 66500 | 3.2927 | 3.7299 | - | |
|
| 2.8117 | 66600 | 3.5424 | - | - | |
|
| 2.8159 | 66700 | 3.5487 | - | - | |
|
| 2.8201 | 66800 | 3.3343 | - | - | |
|
| 2.8243 | 66900 | 3.3005 | - | - | |
|
| 2.8286 | 67000 | 3.5036 | 3.7752 | - | |
|
| 2.8328 | 67100 | 3.4419 | - | - | |
|
| 2.8370 | 67200 | 3.3805 | - | - | |
|
| 2.8412 | 67300 | 3.3591 | - | - | |
|
| 2.8454 | 67400 | 3.738 | - | - | |
|
| 2.8497 | 67500 | 3.268 | 3.7657 | - | |
|
| 2.8539 | 67600 | 3.4224 | - | - | |
|
| 2.8581 | 67700 | 3.5734 | - | - | |
|
| 2.8623 | 67800 | 3.3804 | - | - | |
|
| 2.8666 | 67900 | 3.594 | - | - | |
|
| 2.8708 | 68000 | 3.6526 | 3.7796 | - | |
|
| 2.8750 | 68100 | 3.7921 | - | - | |
|
| 2.8792 | 68200 | 3.352 | - | - | |
|
| 2.8834 | 68300 | 3.7122 | - | - | |
|
| 2.8877 | 68400 | 3.5739 | - | - | |
|
| 2.8919 | 68500 | 3.3912 | 3.7359 | - | |
|
| 2.8961 | 68600 | 3.8863 | - | - | |
|
| 2.9003 | 68700 | 3.6851 | - | - | |
|
| 2.9045 | 68800 | 3.1867 | - | - | |
|
| 2.9088 | 68900 | 3.2456 | - | - | |
|
| 2.9130 | 69000 | 3.447 | 3.7272 | - | |
|
| 2.9172 | 69100 | 3.3142 | - | - | |
|
| 2.9214 | 69200 | 3.8019 | - | - | |
|
| 2.9257 | 69300 | 3.6041 | - | - | |
|
| 2.9299 | 69400 | 3.6291 | - | - | |
|
| 2.9341 | 69500 | 3.5412 | 3.7041 | - | |
|
| 2.9383 | 69600 | 3.5873 | - | - | |
|
| 2.9425 | 69700 | 3.6207 | - | - | |
|
| 2.9468 | 69800 | 3.5858 | - | - | |
|
| 2.9510 | 69900 | 3.5341 | - | - | |
|
| 2.9552 | 70000 | 3.6644 | 3.7406 | - | |
|
| 2.9594 | 70100 | 3.4947 | - | - | |
|
| 2.9637 | 70200 | 3.5763 | - | - | |
|
| 2.9679 | 70300 | 3.6131 | - | - | |
|
| 2.9721 | 70400 | 3.509 | - | - | |
|
| 2.9763 | 70500 | 3.4352 | 3.7116 | - | |
|
| 2.9805 | 70600 | 3.3115 | - | - | |
|
| 2.9848 | 70700 | 3.2393 | - | - | |
|
| 2.9890 | 70800 | 3.3738 | - | - | |
|
| 2.9932 | 70900 | 3.424 | - | - | |
|
| 2.9974 | 71000 | 3.7252 | 3.7152 | - | |
|
| 3.0016 | 71100 | 3.6013 | - | - | |
|
| 3.0059 | 71200 | 3.407 | - | - | |
|
| 3.0101 | 71300 | 3.2695 | - | - | |
|
| 3.0143 | 71400 | 3.3632 | - | - | |
|
| 3.0185 | 71500 | 2.95 | 3.7985 | - | |
|
| 3.0228 | 71600 | 3.164 | - | - | |
|
| 3.0270 | 71700 | 3.4829 | - | - | |
|
| 3.0312 | 71800 | 3.7491 | - | - | |
|
| 3.0354 | 71900 | 3.7257 | - | - | |
|
| 3.0396 | 72000 | 3.701 | 3.7497 | - | |
|
| 3.0439 | 72100 | 3.105 | - | - | |
|
| 3.0481 | 72200 | 3.43 | - | - | |
|
| 3.0523 | 72300 | 3.3014 | - | - | |
|
| 3.0565 | 72400 | 3.2578 | - | - | |
|
| 3.0608 | 72500 | 3.4971 | 3.7966 | - | |
|
| 3.0650 | 72600 | 3.4542 | - | - | |
|
| 3.0692 | 72700 | 3.4634 | - | - | |
|
| 3.0734 | 72800 | 3.6576 | - | - | |
|
| 3.0776 | 72900 | 3.38 | - | - | |
|
| 3.0819 | 73000 | 3.4615 | 3.7337 | - | |
|
| 3.0861 | 73100 | 3.6838 | - | - | |
|
| 3.0903 | 73200 | 3.2183 | - | - | |
|
| 3.0945 | 73300 | 3.3281 | - | - | |
|
| 3.0987 | 73400 | 3.2304 | - | - | |
|
| 3.1030 | 73500 | 3.3314 | 3.7586 | - | |
|
| 3.1072 | 73600 | 3.0549 | - | - | |
|
| 3.1114 | 73700 | 3.4578 | - | - | |
|
| 3.1156 | 73800 | 3.3797 | - | - | |
|
| 3.1199 | 73900 | 3.4435 | - | - | |
|
| 3.1241 | 74000 | 3.1522 | 3.7355 | - | |
|
| 3.1283 | 74100 | 3.3775 | - | - | |
|
| 3.1325 | 74200 | 3.5751 | - | - | |
|
| 3.1367 | 74300 | 3.5017 | - | - | |
|
| 3.1410 | 74400 | 3.3353 | - | - | |
|
| 3.1452 | 74500 | 3.5746 | 3.7411 | - | |
|
| 3.1494 | 74600 | 3.7003 | - | - | |
|
| 3.1536 | 74700 | 3.0499 | - | - | |
|
| 3.1579 | 74800 | 3.3735 | - | - | |
|
| 3.1621 | 74900 | 3.5844 | - | - | |
|
| 3.1663 | 75000 | 3.3551 | 3.8240 | - | |
|
| 3.1705 | 75100 | 3.262 | - | - | |
|
| 3.1747 | 75200 | 3.4301 | - | - | |
|
| 3.1790 | 75300 | 3.464 | - | - | |
|
| 3.1832 | 75400 | 3.4751 | - | - | |
|
| 3.1874 | 75500 | 3.5351 | 3.7465 | - | |
|
| 3.1916 | 75600 | 3.2933 | - | - | |
|
| 3.1958 | 75700 | 4.0448 | - | - | |
|
| 3.2001 | 75800 | 3.4882 | - | - | |
|
| 3.2043 | 75900 | 3.615 | - | - | |
|
| 3.2085 | 76000 | 3.1492 | 3.7142 | - | |
|
| 3.2127 | 76100 | 3.0458 | - | - | |
|
| 3.2170 | 76200 | 3.6002 | - | - | |
|
| 3.2212 | 76300 | 3.3197 | - | - | |
|
| 3.2254 | 76400 | 3.3113 | - | - | |
|
| 3.2296 | 76500 | 3.3607 | 3.7780 | - | |
|
| 3.2338 | 76600 | 3.3242 | - | - | |
|
| 3.2381 | 76700 | 3.6477 | - | - | |
|
| 3.2423 | 76800 | 3.1657 | - | - | |
|
| 3.2465 | 76900 | 3.0839 | - | - | |
|
| 3.2507 | 77000 | 3.599 | 3.7618 | - | |
|
| 3.2549 | 77100 | 3.1563 | - | - | |
|
| 3.2592 | 77200 | 3.1867 | - | - | |
|
| 3.2634 | 77300 | 3.5676 | - | - | |
|
| 3.2676 | 77400 | 3.6313 | - | - | |
|
| 3.2718 | 77500 | 3.2504 | 3.7301 | - | |
|
| 3.2761 | 77600 | 3.2488 | - | - | |
|
| 3.2803 | 77700 | 3.0412 | - | - | |
|
| 3.2845 | 77800 | 3.1514 | - | - | |
|
| 3.2887 | 77900 | 2.9742 | - | - | |
|
| 3.2929 | 78000 | 3.395 | 3.7579 | - | |
|
| 3.2972 | 78100 | 3.5513 | - | - | |
|
| 3.3014 | 78200 | 3.3194 | - | - | |
|
| 3.3056 | 78300 | 3.2702 | - | - | |
|
| 3.3098 | 78400 | 3.322 | - | - | |
|
| 3.3141 | 78500 | 3.5357 | 3.7827 | - | |
|
| 3.3183 | 78600 | 3.3831 | - | - | |
|
| 3.3225 | 78700 | 3.3878 | - | - | |
|
| 3.3267 | 78800 | 3.2869 | - | - | |
|
| 3.3309 | 78900 | 3.7636 | - | - | |
|
| 3.3352 | 79000 | 3.4089 | 3.7984 | - | |
|
| 3.3394 | 79100 | 3.3371 | - | - | |
|
| 3.3436 | 79200 | 3.5966 | - | - | |
|
| 3.3478 | 79300 | 3.8318 | - | - | |
|
| 3.3520 | 79400 | 3.4452 | - | - | |
|
| 3.3563 | 79500 | 3.1789 | 3.7290 | - | |
|
| 3.3605 | 79600 | 3.1829 | - | - | |
|
| 3.3647 | 79700 | 3.4624 | - | - | |
|
| 3.3689 | 79800 | 3.3163 | - | - | |
|
| 3.3732 | 79900 | 3.2591 | - | - | |
|
| 3.3774 | 80000 | 3.2375 | 3.7153 | - | |
|
| 3.3816 | 80100 | 3.0596 | - | - | |
|
| 3.3858 | 80200 | 3.2673 | - | - | |
|
| 3.3900 | 80300 | 3.8284 | - | - | |
|
| 3.3943 | 80400 | 3.2518 | - | - | |
|
| 3.3985 | 80500 | 3.4214 | 3.7572 | - | |
|
| 3.4027 | 80600 | 3.3534 | - | - | |
|
| 3.4069 | 80700 | 3.7609 | - | - | |
|
| 3.4112 | 80800 | 3.7096 | - | - | |
|
| 3.4154 | 80900 | 2.9755 | - | - | |
|
| 3.4196 | 81000 | 3.4585 | 3.7362 | - | |
|
| 3.4238 | 81100 | 3.5315 | - | - | |
|
| 3.4280 | 81200 | 3.4276 | - | - | |
|
| 3.4323 | 81300 | 3.5303 | - | - | |
|
| 3.4365 | 81400 | 3.2272 | - | - | |
|
| 3.4407 | 81500 | 2.9614 | 3.7592 | - | |
|
| 3.4449 | 81600 | 3.3272 | - | - | |
|
| 3.4491 | 81700 | 3.548 | - | - | |
|
| 3.4534 | 81800 | 3.5806 | - | - | |
|
| 3.4576 | 81900 | 3.2915 | - | - | |
|
| 3.4618 | 82000 | 3.4571 | 3.7447 | - | |
|
| 3.4660 | 82100 | 3.2471 | - | - | |
|
| 3.4703 | 82200 | 3.3675 | - | - | |
|
| 3.4745 | 82300 | 3.039 | - | - | |
|
| 3.4787 | 82400 | 3.1737 | - | - | |
|
| 3.4829 | 82500 | 3.5937 | 3.7527 | - | |
|
| 3.4871 | 82600 | 3.3723 | - | - | |
|
| 3.4914 | 82700 | 3.5835 | - | - | |
|
| 3.4956 | 82800 | 3.3739 | - | - | |
|
| 3.4998 | 82900 | 3.3891 | - | - | |
|
| 3.5040 | 83000 | 3.5204 | 3.7398 | - | |
|
| 3.5083 | 83100 | 3.0925 | - | - | |
|
| 3.5125 | 83200 | 3.2285 | - | - | |
|
| 3.5167 | 83300 | 3.4032 | - | - | |
|
| 3.5209 | 83400 | 3.5367 | - | - | |
|
| 3.5251 | 83500 | 3.1513 | 3.7454 | - | |
|
| 3.5294 | 83600 | 3.292 | - | - | |
|
| 3.5336 | 83700 | 3.2018 | - | - | |
|
| 3.5378 | 83800 | 3.4814 | - | - | |
|
| 3.5420 | 83900 | 3.2591 | - | - | |
|
| 3.5462 | 84000 | 3.179 | 3.7722 | - | |
|
| 3.5505 | 84100 | 3.3408 | - | - | |
|
| 3.5547 | 84200 | 3.6131 | - | - | |
|
| 3.5589 | 84300 | 3.2299 | - | - | |
|
| 3.5631 | 84400 | 3.3005 | - | - | |
|
| 3.5674 | 84500 | 3.4731 | 3.7458 | - | |
|
| 3.5716 | 84600 | 3.2288 | - | - | |
|
| 3.5758 | 84700 | 3.4384 | - | - | |
|
| 3.5800 | 84800 | 3.6438 | - | - | |
|
| 3.5842 | 84900 | 3.3293 | - | - | |
|
| 3.5885 | 85000 | 3.3555 | 3.7534 | - | |
|
| 3.5927 | 85100 | 3.4791 | - | - | |
|
| 3.5969 | 85200 | 3.1024 | - | - | |
|
| 3.6011 | 85300 | 3.4605 | - | - | |
|
| 3.6054 | 85400 | 3.4317 | - | - | |
|
| 3.6096 | 85500 | 3.2913 | 3.7037 | - | |
|
| 3.6138 | 85600 | 3.3377 | - | - | |
|
| 3.6180 | 85700 | 3.2746 | - | - | |
|
| 3.6222 | 85800 | 3.4173 | - | - | |
|
| 3.6265 | 85900 | 3.4623 | - | - | |
|
| 3.6307 | 86000 | 3.2596 | 3.7052 | - | |
|
| 3.6349 | 86100 | 3.2322 | - | - | |
|
| 3.6391 | 86200 | 3.2082 | - | - | |
|
| 3.6433 | 86300 | 3.4993 | - | - | |
|
| 3.6476 | 86400 | 3.3922 | - | - | |
|
| 3.6518 | 86500 | 3.2275 | 3.6793 | - | |
|
| 3.6560 | 86600 | 3.3031 | - | - | |
|
| 3.6602 | 86700 | 3.2876 | - | - | |
|
| 3.6645 | 86800 | 3.5403 | - | - | |
|
| 3.6687 | 86900 | 3.3889 | - | - | |
|
| 3.6729 | 87000 | 3.3938 | 3.6927 | - | |
|
| 3.6771 | 87100 | 3.3169 | - | - | |
|
| 3.6813 | 87200 | 3.1372 | - | - | |
|
| 3.6856 | 87300 | 3.0958 | - | - | |
|
| 3.6898 | 87400 | 3.1186 | - | - | |
|
| 3.6940 | 87500 | 3.5419 | 3.6852 | - | |
|
| 3.6982 | 87600 | 3.6208 | - | - | |
|
| 3.7025 | 87700 | 3.3815 | - | - | |
|
| 3.7067 | 87800 | 2.9388 | - | - | |
|
| 3.7109 | 87900 | 3.2111 | - | - | |
|
| 3.7151 | 88000 | 3.4742 | 3.6905 | - | |
|
| 3.7193 | 88100 | 3.2668 | - | - | |
|
| 3.7236 | 88200 | 3.439 | - | - | |
|
| 3.7278 | 88300 | 3.3342 | - | - | |
|
| 3.7320 | 88400 | 3.5079 | - | - | |
|
| 3.7362 | 88500 | 3.4446 | 3.7126 | - | |
|
| 3.7404 | 88600 | 3.3036 | - | - | |
|
| 3.7447 | 88700 | 3.323 | - | - | |
|
| 3.7489 | 88800 | 3.2921 | - | - | |
|
| 3.7531 | 88900 | 3.3972 | - | - | |
|
| 3.7573 | 89000 | 3.3132 | 3.7031 | - | |
|
| 3.7616 | 89100 | 3.6181 | - | - | |
|
| 3.7658 | 89200 | 3.41 | - | - | |
|
| 3.7700 | 89300 | 3.2602 | - | - | |
|
| 3.7742 | 89400 | 3.3742 | - | - | |
|
| 3.7784 | 89500 | 3.2929 | 3.7064 | - | |
|
| 3.7827 | 89600 | 3.1366 | - | - | |
|
| 3.7869 | 89700 | 3.5312 | - | - | |
|
| 3.7911 | 89800 | 3.2735 | - | - | |
|
| 3.7953 | 89900 | 3.3797 | - | - | |
|
| 3.7996 | 90000 | 3.3003 | 3.7135 | - | |
|
| 3.8038 | 90100 | 3.483 | - | - | |
|
| 3.8080 | 90200 | 3.2309 | - | - | |
|
| 3.8122 | 90300 | 3.3767 | - | - | |
|
| 3.8164 | 90400 | 2.8296 | - | - | |
|
| 3.8207 | 90500 | 3.361 | 3.7145 | - | |
|
| 3.8249 | 90600 | 3.3726 | - | - | |
|
| 3.8291 | 90700 | 3.2925 | - | - | |
|
| 3.8333 | 90800 | 3.5113 | - | - | |
|
| 3.8375 | 90900 | 3.6037 | - | - | |
|
| 3.8418 | 91000 | 3.0925 | 3.7223 | - | |
|
| 3.8460 | 91100 | 3.4363 | - | - | |
|
| 3.8502 | 91200 | 3.3181 | - | - | |
|
| 3.8544 | 91300 | 3.4216 | - | - | |
|
| 3.8587 | 91400 | 3.1301 | - | - | |
|
| 3.8629 | 91500 | 3.5791 | 3.7144 | - | |
|
| 3.8671 | 91600 | 3.0492 | - | - | |
|
| 3.8713 | 91700 | 3.4513 | - | - | |
|
| 3.8755 | 91800 | 3.7442 | - | - | |
|
| 3.8798 | 91900 | 3.1566 | - | - | |
|
| 3.8840 | 92000 | 3.3871 | 3.7025 | - | |
|
| 3.8882 | 92100 | 3.3478 | - | - | |
|
| 3.8924 | 92200 | 3.2922 | - | - | |
|
| 3.8967 | 92300 | 3.0988 | - | - | |
|
| 3.9009 | 92400 | 3.4383 | - | - | |
|
| 3.9051 | 92500 | 3.175 | 3.7026 | - | |
|
| 3.9093 | 92600 | 3.3831 | - | - | |
|
| 3.9135 | 92700 | 3.3871 | - | - | |
|
| 3.9178 | 92800 | 3.5747 | - | - | |
|
| 3.9220 | 92900 | 3.272 | - | - | |
|
| 3.9262 | 93000 | 3.4294 | 3.6876 | - | |
|
| 3.9304 | 93100 | 3.6332 | - | - | |
|
| 3.9346 | 93200 | 3.626 | - | - | |
|
| 3.9389 | 93300 | 3.5402 | - | - | |
|
| 3.9431 | 93400 | 3.348 | - | - | |
|
| 3.9473 | 93500 | 3.2556 | 3.6902 | - | |
|
| 3.9515 | 93600 | 3.5298 | - | - | |
|
| 3.9558 | 93700 | 3.5247 | - | - | |
|
| 3.9600 | 93800 | 3.0763 | - | - | |
|
| 3.9642 | 93900 | 3.2457 | - | - | |
|
| 3.9684 | 94000 | 3.1805 | 3.6897 | - | |
|
| 3.9726 | 94100 | 3.3959 | - | - | |
|
| 3.9769 | 94200 | 3.205 | - | - | |
|
| 3.9811 | 94300 | 3.3307 | - | - | |
|
| 3.9853 | 94400 | 3.0448 | - | - | |
|
| 3.9895 | 94500 | 3.0447 | 3.6906 | - | |
|
| 3.9938 | 94600 | 3.3314 | - | - | |
|
| 3.9980 | 94700 | 3.4516 | - | - | |
|
|
|
</details> |
|
|
|
### Framework Versions |
|
- Python: 3.8.10 |
|
- Sentence Transformers: 3.1.1 |
|
- Transformers: 4.45.1 |
|
- PyTorch: 2.4.0+cu121 |
|
- Accelerate: 0.34.2 |
|
- Datasets: 3.0.1 |
|
- Tokenizers: 0.20.0 |
|
|
|
## 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", |
|
} |
|
``` |
|
|
|
#### CoSENTLoss |
|
```bibtex |
|
@online{kexuefm-8847, |
|
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, |
|
author={Su Jianlin}, |
|
year={2022}, |
|
month={Jan}, |
|
url={https://kexue.fm/archives/8847}, |
|
} |
|
``` |
|
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