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--- |
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base_model: sentence-transformers/all-MiniLM-L12-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:510287 |
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- loss:CoSENTLoss |
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widget: |
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- source_sentence: bag |
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sentences: |
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- bag |
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- summer colors bag |
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- carry all bag |
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- source_sentence: bean bag |
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sentences: |
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- bag |
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- havan bag |
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- black yellow shoes |
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- source_sentence: pyramid shaped cushion mattress |
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sentences: |
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- dress |
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- silver bag |
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- women shoes |
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- source_sentence: handcrafted rug |
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sentences: |
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- amaga cross bag - white |
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- handcrafted boots |
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- polyester top |
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- source_sentence: bean bag |
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sentences: |
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- bag |
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- v-neck dress |
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- bag |
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model-index: |
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- name: all-MiniLM-L12-v2-pair_score |
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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: -0.10403022864037037 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: -0.1437799564130218 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: -0.10847915569723102 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: -0.14274368509273366 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: -0.11064121359722408 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: -0.14377964610318103 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: -0.10403015819885228 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: -0.14377961300118045 |
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name: Spearman Dot |
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- type: pearson_max |
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value: -0.10403015819885228 |
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name: Pearson Max |
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- type: spearman_max |
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value: -0.14274368509273366 |
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name: Spearman Max |
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--- |
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# all-MiniLM-L12-v2-pair_score |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-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-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) <!-- at revision 30ce63ae64e71b9199b3d2eae9de99f64a26eedc --> |
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- **Maximum Sequence Length:** 128 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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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 128, '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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'bean bag', |
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'bag', |
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'v-neck dress', |
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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 | -0.104 | |
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| **spearman_cosine** | **-0.1438** | |
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| pearson_manhattan | -0.1085 | |
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| spearman_manhattan | -0.1427 | |
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| pearson_euclidean | -0.1106 | |
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| spearman_euclidean | -0.1438 | |
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| pearson_dot | -0.104 | |
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| spearman_dot | -0.1438 | |
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| pearson_max | -0.104 | |
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| spearman_max | -0.1427 | |
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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 | - | - | -0.1438 | |
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| 0.0063 | 100 | 11.9171 | - | - | |
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| 0.0125 | 200 | 11.0074 | - | - | |
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| 0.0188 | 300 | 10.1073 | - | - | |
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| 0.0251 | 400 | 8.6232 | - | - | |
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| 0.0314 | 500 | 7.5947 | 7.2720 | - | |
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| 0.0376 | 600 | 6.3883 | - | - | |
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| 0.0439 | 700 | 5.6165 | - | - | |
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| 0.0502 | 800 | 4.8254 | - | - | |
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| 0.0564 | 900 | 4.5595 | - | - | |
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| 0.0627 | 1000 | 4.2965 | 4.1720 | - | |
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| 0.0690 | 1100 | 4.063 | - | - | |
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| 0.0752 | 1200 | 4.0861 | - | - | |
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| 0.0815 | 1300 | 3.9703 | - | - | |
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| 0.0878 | 1400 | 3.8222 | - | - | |
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| 0.0941 | 1500 | 3.927 | 3.6404 | - | |
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| 0.1003 | 1600 | 3.6892 | - | - | |
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| 0.1066 | 1700 | 3.9166 | - | - | |
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| 0.1129 | 1800 | 3.7162 | - | - | |
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| 0.1191 | 1900 | 3.4866 | - | - | |
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| 0.1254 | 2000 | 3.5202 | 3.4226 | - | |
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| 0.1317 | 2100 | 3.6876 | - | - | |
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| 0.1380 | 2200 | 3.4884 | - | - | |
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| 0.1442 | 2300 | 3.4407 | - | - | |
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| 0.1505 | 2400 | 3.2658 | - | - | |
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| 0.1568 | 2500 | 3.2973 | 3.0777 | - | |
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| 0.1630 | 2600 | 3.2087 | - | - | |
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| 0.1693 | 2700 | 3.4316 | - | - | |
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| 0.1756 | 2800 | 3.3372 | - | - | |
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| 0.1819 | 2900 | 3.161 | - | - | |
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| 0.1881 | 3000 | 3.0232 | 2.8805 | - | |
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| 0.1944 | 3100 | 3.2897 | - | - | |
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| 0.2007 | 3200 | 3.2576 | - | - | |
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| 0.2069 | 3300 | 2.7636 | - | - | |
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| 0.2132 | 3400 | 3.1788 | - | - | |
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| 0.2195 | 3500 | 2.6269 | 2.6237 | - | |
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| 0.2257 | 3600 | 2.9352 | - | - | |
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| 0.2320 | 3700 | 2.847 | - | - | |
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| 0.2383 | 3800 | 2.8001 | - | - | |
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| 0.2446 | 3900 | 2.6048 | - | - | |
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| 0.2508 | 4000 | 2.5976 | 2.5250 | - | |
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| 0.2571 | 4100 | 2.5211 | - | - | |
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| 0.2634 | 4200 | 2.7812 | - | - | |
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| 0.2696 | 4300 | 2.6822 | - | - | |
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| 0.2759 | 4400 | 2.4779 | - | - | |
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| 0.2822 | 4500 | 2.6242 | 2.6365 | - | |
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| 0.2885 | 4600 | 2.5655 | - | - | |
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| 0.2947 | 4700 | 2.9998 | - | - | |
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| 0.3010 | 4800 | 2.679 | - | - | |
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| 0.3073 | 4900 | 2.5719 | - | - | |
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| 0.3135 | 5000 | 2.6913 | 2.6934 | - | |
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| 0.3198 | 5100 | 2.8346 | - | - | |
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| 0.3261 | 5200 | 2.7453 | - | - | |
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| 0.3324 | 5300 | 2.4492 | - | - | |
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| 0.3386 | 5400 | 2.9389 | - | - | |
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| 0.3449 | 5500 | 2.6002 | 2.6182 | - | |
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| 0.3512 | 5600 | 2.2592 | - | - | |
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| 0.3574 | 5700 | 2.3822 | - | - | |
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| 0.3637 | 5800 | 2.4771 | - | - | |
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| 0.3700 | 5900 | 3.5914 | - | - | |
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| 0.3762 | 6000 | 2.3525 | 2.5605 | - | |
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| 0.3825 | 6100 | 2.2667 | - | - | |
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| 0.3888 | 6200 | 2.4671 | - | - | |
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| 0.3951 | 6300 | 2.6816 | - | - | |
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| 0.4013 | 6400 | 2.2303 | - | - | |
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| 0.4076 | 6500 | 2.3153 | 2.4245 | - | |
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| 0.4139 | 6600 | 2.7969 | - | - | |
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| 0.4201 | 6700 | 2.61 | - | - | |
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| 0.4264 | 6800 | 2.5267 | - | - | |
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| 0.4327 | 6900 | 2.532 | - | - | |
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| 0.4390 | 7000 | 2.6088 | 2.4666 | - | |
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| 0.4452 | 7100 | 1.848 | - | - | |
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| 0.4515 | 7200 | 2.1369 | - | - | |
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| 0.4578 | 7300 | 2.185 | - | - | |
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| 0.4640 | 7400 | 2.0279 | - | - | |
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| 0.4703 | 7500 | 2.5593 | 2.3958 | - | |
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| 0.4766 | 7600 | 2.339 | - | - | |
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| 0.4828 | 7700 | 2.2122 | - | - | |
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| 0.4891 | 7800 | 2.7878 | - | - | |
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| 0.4954 | 7900 | 2.3005 | - | - | |
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| 0.5017 | 8000 | 2.2922 | 2.5408 | - | |
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| 0.5079 | 8100 | 2.3731 | - | - | |
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| 0.5142 | 8200 | 2.1879 | - | - | |
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| 0.5205 | 8300 | 2.1598 | - | - | |
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| 0.5267 | 8400 | 2.2292 | - | - | |
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| 0.5330 | 8500 | 1.958 | 2.0935 | - | |
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| 0.5393 | 8600 | 2.1152 | - | - | |
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| 0.5456 | 8700 | 1.9725 | - | - | |
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| 0.5518 | 8800 | 2.1106 | - | - | |
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| 0.5581 | 8900 | 2.06 | - | - | |
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| 0.5644 | 9000 | 1.7624 | 2.1509 | - | |
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| 0.5706 | 9100 | 2.3793 | - | - | |
|
| 0.5769 | 9200 | 1.9322 | - | - | |
|
| 0.5832 | 9300 | 1.8355 | - | - | |
|
| 0.5895 | 9400 | 2.1425 | - | - | |
|
| 0.5957 | 9500 | 2.2191 | 1.9984 | - | |
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| 0.6020 | 9600 | 2.3245 | - | - | |
|
| 0.6083 | 9700 | 2.1206 | - | - | |
|
| 0.6145 | 9800 | 2.0957 | - | - | |
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| 0.6208 | 9900 | 2.5276 | - | - | |
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| 0.6271 | 10000 | 1.5383 | 1.9509 | - | |
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| 0.6333 | 10100 | 2.111 | - | - | |
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| 0.6396 | 10200 | 1.893 | - | - | |
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| 0.6459 | 10300 | 1.8961 | - | - | |
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| 0.6522 | 10400 | 1.6599 | - | - | |
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| 0.6584 | 10500 | 2.3409 | 1.8286 | - | |
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| 0.6647 | 10600 | 1.9741 | - | - | |
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| 0.6710 | 10700 | 2.0438 | - | - | |
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| 0.6772 | 10800 | 1.814 | - | - | |
|
| 0.6835 | 10900 | 2.1819 | - | - | |
|
| 0.6898 | 11000 | 1.8547 | 1.9461 | - | |
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| 0.6961 | 11100 | 2.5979 | - | - | |
|
| 0.7023 | 11200 | 1.9309 | - | - | |
|
| 0.7086 | 11300 | 1.6247 | - | - | |
|
| 0.7149 | 11400 | 2.1107 | - | - | |
|
| 0.7211 | 11500 | 2.1264 | 1.8004 | - | |
|
| 0.7274 | 11600 | 1.7397 | - | - | |
|
| 0.7337 | 11700 | 1.9569 | - | - | |
|
| 0.7400 | 11800 | 1.4769 | - | - | |
|
| 0.7462 | 11900 | 1.6222 | - | - | |
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| 0.7525 | 12000 | 1.5354 | 1.6811 | - | |
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| 0.7588 | 12100 | 2.2645 | - | - | |
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| 0.7650 | 12200 | 1.8662 | - | - | |
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| 0.7713 | 12300 | 1.5327 | - | - | |
|
| 0.7776 | 12400 | 1.9501 | - | - | |
|
| 0.7838 | 12500 | 2.0923 | 1.6134 | - | |
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| 0.7901 | 12600 | 1.8887 | - | - | |
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| 0.7964 | 12700 | 1.7207 | - | - | |
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| 0.8027 | 12800 | 1.8589 | - | - | |
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| 0.8089 | 12900 | 1.7602 | - | - | |
|
| 0.8152 | 13000 | 2.2405 | 1.5030 | - | |
|
| 0.8215 | 13100 | 1.6249 | - | - | |
|
| 0.8277 | 13200 | 1.6814 | - | - | |
|
| 0.8340 | 13300 | 1.4072 | - | - | |
|
| 0.8403 | 13400 | 1.6286 | - | - | |
|
| 0.8466 | 13500 | 2.2081 | 1.6078 | - | |
|
| 0.8528 | 13600 | 1.7387 | - | - | |
|
| 0.8591 | 13700 | 1.5268 | - | - | |
|
| 0.8654 | 13800 | 1.5693 | - | - | |
|
| 0.8716 | 13900 | 1.2473 | - | - | |
|
| 0.8779 | 14000 | 1.361 | 1.7168 | - | |
|
| 0.8842 | 14100 | 1.5246 | - | - | |
|
| 0.8904 | 14200 | 1.7266 | - | - | |
|
| 0.8967 | 14300 | 0.9221 | - | - | |
|
| 0.9030 | 14400 | 1.6397 | - | - | |
|
| 0.9093 | 14500 | 1.3139 | 1.5492 | - | |
|
| 0.9155 | 14600 | 1.7942 | - | - | |
|
| 0.9218 | 14700 | 1.5206 | - | - | |
|
| 0.9281 | 14800 | 1.5868 | - | - | |
|
| 0.9343 | 14900 | 1.2131 | - | - | |
|
| 0.9406 | 15000 | 1.8765 | 1.4192 | - | |
|
| 0.9469 | 15100 | 1.624 | - | - | |
|
| 0.9532 | 15200 | 1.4692 | - | - | |
|
| 0.9594 | 15300 | 1.5426 | - | - | |
|
| 0.9657 | 15400 | 1.3668 | - | - | |
|
| 0.9720 | 15500 | 1.3951 | 1.6835 | - | |
|
| 0.9782 | 15600 | 1.1567 | - | - | |
|
| 0.9845 | 15700 | 1.8634 | - | - | |
|
| 0.9908 | 15800 | 1.641 | - | - | |
|
| 0.9971 | 15900 | 1.6458 | - | - | |
|
| 1.0033 | 16000 | 1.1369 | 1.5746 | - | |
|
| 1.0096 | 16100 | 1.1913 | - | - | |
|
| 1.0159 | 16200 | 1.5563 | - | - | |
|
| 1.0221 | 16300 | 1.4081 | - | - | |
|
| 1.0284 | 16400 | 1.8157 | - | - | |
|
| 1.0347 | 16500 | 1.6405 | 1.5235 | - | |
|
| 1.0409 | 16600 | 0.9207 | - | - | |
|
| 1.0472 | 16700 | 1.4301 | - | - | |
|
| 1.0535 | 16800 | 1.4566 | - | - | |
|
| 1.0598 | 16900 | 1.5397 | - | - | |
|
| 1.0660 | 17000 | 1.3417 | 1.3883 | - | |
|
| 1.0723 | 17100 | 0.9769 | - | - | |
|
| 1.0786 | 17200 | 1.3734 | - | - | |
|
| 1.0848 | 17300 | 1.0874 | - | - | |
|
| 1.0911 | 17400 | 1.2601 | - | - | |
|
| 1.0974 | 17500 | 1.4799 | 1.4361 | - | |
|
| 1.1037 | 17600 | 1.1086 | - | - | |
|
| 1.1099 | 17700 | 1.3731 | - | - | |
|
| 1.1162 | 17800 | 1.0515 | - | - | |
|
| 1.1225 | 17900 | 1.7916 | - | - | |
|
| 1.1287 | 18000 | 1.7606 | 1.3792 | - | |
|
| 1.1350 | 18100 | 1.3844 | - | - | |
|
| 1.1413 | 18200 | 1.3567 | - | - | |
|
| 1.1476 | 18300 | 1.4322 | - | - | |
|
| 1.1538 | 18400 | 1.9509 | - | - | |
|
| 1.1601 | 18500 | 1.0303 | 1.3425 | - | |
|
| 1.1664 | 18600 | 1.6484 | - | - | |
|
| 1.1726 | 18700 | 1.1177 | - | - | |
|
| 1.1789 | 18800 | 1.0295 | - | - | |
|
| 1.1852 | 18900 | 1.4364 | - | - | |
|
| 1.1914 | 19000 | 1.1954 | 1.3385 | - | |
|
| 1.1977 | 19100 | 1.1944 | - | - | |
|
| 1.2040 | 19200 | 0.9109 | - | - | |
|
| 1.2103 | 19300 | 1.4191 | - | - | |
|
| 1.2165 | 19400 | 1.5755 | - | - | |
|
| 1.2228 | 19500 | 1.0958 | 1.2872 | - | |
|
| 1.2291 | 19600 | 0.9054 | - | - | |
|
| 1.2353 | 19700 | 1.0892 | - | - | |
|
| 1.2416 | 19800 | 1.4455 | - | - | |
|
| 1.2479 | 19900 | 1.3273 | - | - | |
|
| 1.2542 | 20000 | 1.6442 | 1.2880 | - | |
|
| 1.2604 | 20100 | 1.1901 | - | - | |
|
| 1.2667 | 20200 | 0.9871 | - | - | |
|
| 1.2730 | 20300 | 1.6448 | - | - | |
|
| 1.2792 | 20400 | 1.1899 | - | - | |
|
| 1.2855 | 20500 | 1.3454 | 1.3303 | - | |
|
| 1.2918 | 20600 | 1.4376 | - | - | |
|
| 1.2980 | 20700 | 1.0356 | - | - | |
|
| 1.3043 | 20800 | 1.7588 | - | - | |
|
| 1.3106 | 20900 | 1.0993 | - | - | |
|
| 1.3169 | 21000 | 1.3673 | 1.2607 | - | |
|
| 1.3231 | 21100 | 1.3326 | - | - | |
|
| 1.3294 | 21200 | 1.3618 | - | - | |
|
| 1.3357 | 21300 | 1.3123 | - | - | |
|
| 1.3419 | 21400 | 0.9771 | - | - | |
|
| 1.3482 | 21500 | 1.1626 | 1.2873 | - | |
|
| 1.3545 | 21600 | 1.41 | - | - | |
|
| 1.3608 | 21700 | 1.6998 | - | - | |
|
| 1.3670 | 21800 | 0.8335 | - | - | |
|
| 1.3733 | 21900 | 1.579 | - | - | |
|
| 1.3796 | 22000 | 1.6073 | 1.2164 | - | |
|
| 1.3858 | 22100 | 1.0534 | - | - | |
|
| 1.3921 | 22200 | 1.0045 | - | - | |
|
| 1.3984 | 22300 | 1.4195 | - | - | |
|
| 1.4047 | 22400 | 1.4409 | - | - | |
|
| 1.4109 | 22500 | 1.3942 | 1.2018 | - | |
|
| 1.4172 | 22600 | 1.6013 | - | - | |
|
| 1.4235 | 22700 | 1.139 | - | - | |
|
| 1.4297 | 22800 | 0.7062 | - | - | |
|
| 1.4360 | 22900 | 1.1948 | - | - | |
|
| 1.4423 | 23000 | 1.6784 | 1.1736 | - | |
|
| 1.4485 | 23100 | 1.1618 | - | - | |
|
| 1.4548 | 23200 | 0.827 | - | - | |
|
| 1.4611 | 23300 | 1.0041 | - | - | |
|
| 1.4674 | 23400 | 0.7447 | - | - | |
|
| 1.4736 | 23500 | 1.1531 | 1.0797 | - | |
|
| 1.4799 | 23600 | 1.0904 | - | - | |
|
| 1.4862 | 23700 | 1.0648 | - | - | |
|
| 1.4924 | 23800 | 1.1863 | - | - | |
|
| 1.4987 | 23900 | 0.893 | - | - | |
|
| 1.5050 | 24000 | 1.2528 | 1.0737 | - | |
|
| 1.5113 | 24100 | 0.9333 | - | - | |
|
| 1.5175 | 24200 | 1.3404 | - | - | |
|
| 1.5238 | 24300 | 0.8959 | - | - | |
|
| 1.5301 | 24400 | 0.6898 | - | - | |
|
| 1.5363 | 24500 | 0.9896 | 1.1813 | - | |
|
| 1.5426 | 24600 | 0.7928 | - | - | |
|
| 1.5489 | 24700 | 1.4153 | - | - | |
|
| 1.5552 | 24800 | 1.2393 | - | - | |
|
| 1.5614 | 24900 | 0.744 | - | - | |
|
| 1.5677 | 25000 | 0.7545 | 1.0823 | - | |
|
| 1.5740 | 25100 | 1.1936 | - | - | |
|
| 1.5802 | 25200 | 0.8755 | - | - | |
|
| 1.5865 | 25300 | 1.063 | - | - | |
|
| 1.5928 | 25400 | 0.8634 | - | - | |
|
| 1.5990 | 25500 | 1.2905 | 1.0718 | - | |
|
| 1.6053 | 25600 | 1.0906 | - | - | |
|
| 1.6116 | 25700 | 1.1594 | - | - | |
|
| 1.6179 | 25800 | 1.108 | - | - | |
|
| 1.6241 | 25900 | 1.2538 | - | - | |
|
| 1.6304 | 26000 | 1.3377 | 1.1370 | - | |
|
| 1.6367 | 26100 | 0.8156 | - | - | |
|
| 1.6429 | 26200 | 0.9753 | - | - | |
|
| 1.6492 | 26300 | 1.0909 | - | - | |
|
| 1.6555 | 26400 | 1.0029 | - | - | |
|
| 1.6618 | 26500 | 0.6841 | 1.0385 | - | |
|
| 1.6680 | 26600 | 1.1673 | - | - | |
|
| 1.6743 | 26700 | 1.3606 | - | - | |
|
| 1.6806 | 26800 | 0.4306 | - | - | |
|
| 1.6868 | 26900 | 1.0989 | - | - | |
|
| 1.6931 | 27000 | 1.3283 | 1.0136 | - | |
|
| 1.6994 | 27100 | 1.0206 | - | - | |
|
| 1.7056 | 27200 | 0.6866 | - | - | |
|
| 1.7119 | 27300 | 0.9168 | - | - | |
|
| 1.7182 | 27400 | 0.9472 | - | - | |
|
| 1.7245 | 27500 | 0.7866 | 1.0890 | - | |
|
| 1.7307 | 27600 | 1.481 | - | - | |
|
| 1.7370 | 27700 | 1.0311 | - | - | |
|
| 1.7433 | 27800 | 1.3346 | - | - | |
|
| 1.7495 | 27900 | 0.8331 | - | - | |
|
| 1.7558 | 28000 | 1.3056 | 0.9919 | - | |
|
| 1.7621 | 28100 | 0.9692 | - | - | |
|
| 1.7684 | 28200 | 0.9337 | - | - | |
|
| 1.7746 | 28300 | 1.1588 | - | - | |
|
| 1.7809 | 28400 | 1.0859 | - | - | |
|
| 1.7872 | 28500 | 0.9939 | 1.0109 | - | |
|
| 1.7934 | 28600 | 1.4019 | - | - | |
|
| 1.7997 | 28700 | 0.9404 | - | - | |
|
| 1.8060 | 28800 | 0.7085 | - | - | |
|
| 1.8123 | 28900 | 1.1423 | - | - | |
|
| 1.8185 | 29000 | 0.8389 | 0.9510 | - | |
|
| 1.8248 | 29100 | 1.3947 | - | - | |
|
| 1.8311 | 29200 | 0.8909 | - | - | |
|
| 1.8373 | 29300 | 1.3824 | - | - | |
|
| 1.8436 | 29400 | 0.6364 | - | - | |
|
| 1.8499 | 29500 | 1.2197 | 0.9501 | - | |
|
| 1.8561 | 29600 | 0.6353 | - | - | |
|
| 1.8624 | 29700 | 1.3453 | - | - | |
|
| 1.8687 | 29800 | 1.1069 | - | - | |
|
| 1.8750 | 29900 | 0.9873 | - | - | |
|
| 1.8812 | 30000 | 0.9291 | 1.0391 | - | |
|
| 1.8875 | 30100 | 1.3971 | - | - | |
|
| 1.8938 | 30200 | 1.0569 | - | - | |
|
| 1.9000 | 30300 | 0.6731 | - | - | |
|
| 1.9063 | 30400 | 1.0216 | - | - | |
|
| 1.9126 | 30500 | 1.295 | 0.9819 | - | |
|
| 1.9189 | 30600 | 1.1641 | - | - | |
|
| 1.9251 | 30700 | 0.9199 | - | - | |
|
| 1.9314 | 30800 | 0.9774 | - | - | |
|
| 1.9377 | 30900 | 0.8242 | - | - | |
|
| 1.9439 | 31000 | 1.4039 | 0.9666 | - | |
|
| 1.9502 | 31100 | 0.7112 | - | - | |
|
| 1.9565 | 31200 | 0.846 | - | - | |
|
| 1.9628 | 31300 | 1.0952 | - | - | |
|
| 1.9690 | 31400 | 1.0372 | - | - | |
|
| 1.9753 | 31500 | 0.9585 | 0.8983 | - | |
|
| 1.9816 | 31600 | 1.1527 | - | - | |
|
| 1.9878 | 31700 | 0.7675 | - | - | |
|
| 1.9941 | 31800 | 0.8359 | - | - | |
|
| 2.0004 | 31900 | 1.1224 | - | - | |
|
| 2.0066 | 32000 | 1.3421 | 0.9575 | - | |
|
| 2.0129 | 32100 | 0.9171 | - | - | |
|
| 2.0192 | 32200 | 0.5865 | - | - | |
|
| 2.0255 | 32300 | 0.9239 | - | - | |
|
| 2.0317 | 32400 | 0.7426 | - | - | |
|
| 2.0380 | 32500 | 0.8965 | 0.9158 | - | |
|
| 2.0443 | 32600 | 0.6605 | - | - | |
|
| 2.0505 | 32700 | 0.8507 | - | - | |
|
| 2.0568 | 32800 | 0.7288 | - | - | |
|
| 2.0631 | 32900 | 0.6888 | - | - | |
|
| 2.0694 | 33000 | 0.8745 | 0.9568 | - | |
|
| 2.0756 | 33100 | 0.7972 | - | - | |
|
| 2.0819 | 33200 | 0.6211 | - | - | |
|
| 2.0882 | 33300 | 1.0126 | - | - | |
|
| 2.0944 | 33400 | 0.8268 | - | - | |
|
| 2.1007 | 33500 | 0.9723 | 0.8551 | - | |
|
| 2.1070 | 33600 | 0.6366 | - | - | |
|
| 2.1133 | 33700 | 0.6773 | - | - | |
|
| 2.1195 | 33800 | 0.7676 | - | - | |
|
| 2.1258 | 33900 | 0.9192 | - | - | |
|
| 2.1321 | 34000 | 0.7054 | 0.8941 | - | |
|
| 2.1383 | 34100 | 0.7349 | - | - | |
|
| 2.1446 | 34200 | 0.6288 | - | - | |
|
| 2.1509 | 34300 | 0.799 | - | - | |
|
| 2.1571 | 34400 | 0.7492 | - | - | |
|
| 2.1634 | 34500 | 1.0967 | 0.8746 | - | |
|
| 2.1697 | 34600 | 0.7628 | - | - | |
|
| 2.1760 | 34700 | 0.7697 | - | - | |
|
| 2.1822 | 34800 | 0.7458 | - | - | |
|
| 2.1885 | 34900 | 0.7868 | - | - | |
|
| 2.1948 | 35000 | 0.9526 | 0.8620 | - | |
|
| 2.2010 | 35100 | 0.6087 | - | - | |
|
| 2.2073 | 35200 | 0.8602 | - | - | |
|
| 2.2136 | 35300 | 0.8906 | - | - | |
|
| 2.2199 | 35400 | 0.6012 | - | - | |
|
| 2.2261 | 35500 | 0.9625 | 0.9094 | - | |
|
| 2.2324 | 35600 | 0.8622 | - | - | |
|
| 2.2387 | 35700 | 0.9015 | - | - | |
|
| 2.2449 | 35800 | 1.0395 | - | - | |
|
| 2.2512 | 35900 | 0.5582 | - | - | |
|
| 2.2575 | 36000 | 0.7266 | 0.8666 | - | |
|
| 2.2637 | 36100 | 0.6806 | - | - | |
|
| 2.2700 | 36200 | 0.9246 | - | - | |
|
| 2.2763 | 36300 | 0.7452 | - | - | |
|
| 2.2826 | 36400 | 0.7886 | - | - | |
|
| 2.2888 | 36500 | 0.9288 | 0.8529 | - | |
|
| 2.2951 | 36600 | 1.2166 | - | - | |
|
| 2.3014 | 36700 | 0.9566 | - | - | |
|
| 2.3076 | 36800 | 0.7842 | - | - | |
|
| 2.3139 | 36900 | 0.6815 | - | - | |
|
| 2.3202 | 37000 | 0.78 | 0.8212 | - | |
|
| 2.3265 | 37100 | 0.8306 | - | - | |
|
| 2.3327 | 37200 | 0.8073 | - | - | |
|
| 2.3390 | 37300 | 0.7565 | - | - | |
|
| 2.3453 | 37400 | 0.8478 | - | - | |
|
| 2.3515 | 37500 | 1.0159 | 0.8735 | - | |
|
| 2.3578 | 37600 | 0.8126 | - | - | |
|
| 2.3641 | 37700 | 0.751 | - | - | |
|
| 2.3704 | 37800 | 0.7185 | - | - | |
|
| 2.3766 | 37900 | 0.7429 | - | - | |
|
| 2.3829 | 38000 | 0.7149 | 0.7997 | - | |
|
| 2.3892 | 38100 | 0.6867 | - | - | |
|
| 2.3954 | 38200 | 0.608 | - | - | |
|
| 2.4017 | 38300 | 0.5687 | - | - | |
|
| 2.4080 | 38400 | 0.6623 | - | - | |
|
| 2.4142 | 38500 | 0.7751 | 0.7834 | - | |
|
| 2.4205 | 38600 | 0.6537 | - | - | |
|
| 2.4268 | 38700 | 0.7121 | - | - | |
|
| 2.4331 | 38800 | 0.7864 | - | - | |
|
| 2.4393 | 38900 | 0.296 | - | - | |
|
| 2.4456 | 39000 | 0.4544 | 0.8051 | - | |
|
| 2.4519 | 39100 | 0.4543 | - | - | |
|
| 2.4581 | 39200 | 0.9965 | - | - | |
|
| 2.4644 | 39300 | 0.4595 | - | - | |
|
| 2.4707 | 39400 | 0.7557 | - | - | |
|
| 2.4770 | 39500 | 0.6006 | 0.8437 | - | |
|
| 2.4832 | 39600 | 0.695 | - | - | |
|
| 2.4895 | 39700 | 0.6292 | - | - | |
|
| 2.4958 | 39800 | 0.7392 | - | - | |
|
| 2.5020 | 39900 | 0.6547 | - | - | |
|
| 2.5083 | 40000 | 0.739 | 0.8443 | - | |
|
| 2.5146 | 40100 | 0.5618 | - | - | |
|
| 2.5209 | 40200 | 0.861 | - | - | |
|
| 2.5271 | 40300 | 0.7318 | - | - | |
|
| 2.5334 | 40400 | 0.9021 | - | - | |
|
| 2.5397 | 40500 | 0.7329 | 0.8595 | - | |
|
| 2.5459 | 40600 | 0.9691 | - | - | |
|
| 2.5522 | 40700 | 1.0524 | - | - | |
|
| 2.5585 | 40800 | 0.4546 | - | - | |
|
| 2.5647 | 40900 | 0.8917 | - | - | |
|
| 2.5710 | 41000 | 0.6644 | 0.8664 | - | |
|
| 2.5773 | 41100 | 0.5167 | - | - | |
|
| 2.5836 | 41200 | 0.6499 | - | - | |
|
| 2.5898 | 41300 | 0.8096 | - | - | |
|
| 2.5961 | 41400 | 0.7269 | - | - | |
|
| 2.6024 | 41500 | 0.8561 | 0.8173 | - | |
|
| 2.6086 | 41600 | 0.761 | - | - | |
|
| 2.6149 | 41700 | 1.0167 | - | - | |
|
| 2.6212 | 41800 | 0.763 | - | - | |
|
| 2.6275 | 41900 | 0.6659 | - | - | |
|
| 2.6337 | 42000 | 0.7299 | 0.8343 | - | |
|
| 2.6400 | 42100 | 0.7045 | - | - | |
|
| 2.6463 | 42200 | 0.9054 | - | - | |
|
| 2.6525 | 42300 | 0.3002 | - | - | |
|
| 2.6588 | 42400 | 0.7728 | - | - | |
|
| 2.6651 | 42500 | 0.8214 | 0.8112 | - | |
|
| 2.6713 | 42600 | 0.6762 | - | - | |
|
| 2.6776 | 42700 | 0.8863 | - | - | |
|
| 2.6839 | 42800 | 0.7438 | - | - | |
|
| 2.6902 | 42900 | 0.5968 | - | - | |
|
| 2.6964 | 43000 | 0.5292 | 0.7920 | - | |
|
| 2.7027 | 43100 | 0.429 | - | - | |
|
| 2.7090 | 43200 | 0.6001 | - | - | |
|
| 2.7152 | 43300 | 0.7253 | - | - | |
|
| 2.7215 | 43400 | 0.9268 | - | - | |
|
| 2.7278 | 43500 | 0.9536 | 0.8434 | - | |
|
| 2.7341 | 43600 | 0.6164 | - | - | |
|
| 2.7403 | 43700 | 0.8411 | - | - | |
|
| 2.7466 | 43800 | 1.0441 | - | - | |
|
| 2.7529 | 43900 | 0.6473 | - | - | |
|
| 2.7591 | 44000 | 0.8697 | 0.8089 | - | |
|
| 2.7654 | 44100 | 0.7743 | - | - | |
|
| 2.7717 | 44200 | 0.9118 | - | - | |
|
| 2.7780 | 44300 | 0.7464 | - | - | |
|
| 2.7842 | 44400 | 0.7195 | - | - | |
|
| 2.7905 | 44500 | 0.9814 | 0.8122 | - | |
|
| 2.7968 | 44600 | 0.5812 | - | - | |
|
| 2.8030 | 44700 | 0.5095 | - | - | |
|
| 2.8093 | 44800 | 0.7771 | - | - | |
|
| 2.8156 | 44900 | 0.6714 | - | - | |
|
| 2.8218 | 45000 | 0.5836 | 0.7786 | - | |
|
| 2.8281 | 45100 | 1.0708 | - | - | |
|
| 2.8344 | 45200 | 0.576 | - | - | |
|
| 2.8407 | 45300 | 0.9657 | - | - | |
|
| 2.8469 | 45400 | 0.8103 | - | - | |
|
| 2.8532 | 45500 | 0.4644 | 0.7895 | - | |
|
| 2.8595 | 45600 | 0.7485 | - | - | |
|
| 2.8657 | 45700 | 0.9843 | - | - | |
|
| 2.8720 | 45800 | 0.8462 | - | - | |
|
| 2.8783 | 45900 | 0.9025 | - | - | |
|
| 2.8846 | 46000 | 0.7014 | 0.8031 | - | |
|
| 2.8908 | 46100 | 0.5638 | - | - | |
|
| 2.8971 | 46200 | 0.6016 | - | - | |
|
| 2.9034 | 46300 | 0.7257 | - | - | |
|
| 2.9096 | 46400 | 1.1182 | - | - | |
|
| 2.9159 | 46500 | 1.0352 | 0.8031 | - | |
|
| 2.9222 | 46600 | 0.8413 | - | - | |
|
| 2.9285 | 46700 | 0.7341 | - | - | |
|
| 2.9347 | 46800 | 0.7115 | - | - | |
|
| 2.9410 | 46900 | 0.9124 | - | - | |
|
| 2.9473 | 47000 | 0.7988 | 0.7591 | - | |
|
| 2.9535 | 47100 | 0.8373 | - | - | |
|
| 2.9598 | 47200 | 0.8587 | - | - | |
|
| 2.9661 | 47300 | 0.4961 | - | - | |
|
| 2.9723 | 47400 | 0.7349 | - | - | |
|
| 2.9786 | 47500 | 0.5285 | 0.7255 | - | |
|
| 2.9849 | 47600 | 0.3715 | - | - | |
|
| 2.9912 | 47700 | 0.811 | - | - | |
|
| 2.9974 | 47800 | 0.6716 | - | - | |
|
| 3.0037 | 47900 | 0.4408 | - | - | |
|
| 3.0100 | 48000 | 0.7449 | 0.7503 | - | |
|
| 3.0162 | 48100 | 0.4491 | - | - | |
|
| 3.0225 | 48200 | 0.5995 | - | - | |
|
| 3.0288 | 48300 | 0.6073 | - | - | |
|
| 3.0351 | 48400 | 0.5753 | - | - | |
|
| 3.0413 | 48500 | 0.6204 | 0.7650 | - | |
|
| 3.0476 | 48600 | 0.9864 | - | - | |
|
| 3.0539 | 48700 | 0.6648 | - | - | |
|
| 3.0601 | 48800 | 0.4662 | - | - | |
|
| 3.0664 | 48900 | 0.5638 | - | - | |
|
| 3.0727 | 49000 | 0.6692 | 0.7381 | - | |
|
| 3.0789 | 49100 | 0.6403 | - | - | |
|
| 3.0852 | 49200 | 0.5042 | - | - | |
|
| 3.0915 | 49300 | 0.4447 | - | - | |
|
| 3.0978 | 49400 | 0.5983 | - | - | |
|
| 3.1040 | 49500 | 0.6961 | 0.7289 | - | |
|
| 3.1103 | 49600 | 0.8092 | - | - | |
|
| 3.1166 | 49700 | 0.4172 | - | - | |
|
| 3.1228 | 49800 | 0.6542 | - | - | |
|
| 3.1291 | 49900 | 0.8016 | - | - | |
|
| 3.1354 | 50000 | 0.3927 | 0.7370 | - | |
|
| 3.1417 | 50100 | 0.4724 | - | - | |
|
| 3.1479 | 50200 | 0.46 | - | - | |
|
| 3.1542 | 50300 | 0.4258 | - | - | |
|
| 3.1605 | 50400 | 0.5053 | - | - | |
|
| 3.1667 | 50500 | 0.3406 | 0.7210 | - | |
|
| 3.1730 | 50600 | 0.6276 | - | - | |
|
| 3.1793 | 50700 | 0.5913 | - | - | |
|
| 3.1856 | 50800 | 0.3902 | - | - | |
|
| 3.1918 | 50900 | 0.5063 | - | - | |
|
| 3.1981 | 51000 | 0.7909 | 0.7442 | - | |
|
| 3.2044 | 51100 | 0.5071 | - | - | |
|
| 3.2106 | 51200 | 0.5611 | - | - | |
|
| 3.2169 | 51300 | 0.545 | - | - | |
|
| 3.2232 | 51400 | 0.4359 | - | - | |
|
| 3.2294 | 51500 | 0.5249 | 0.7148 | - | |
|
| 3.2357 | 51600 | 0.6759 | - | - | |
|
| 3.2420 | 51700 | 0.5458 | - | - | |
|
| 3.2483 | 51800 | 0.5195 | - | - | |
|
| 3.2545 | 51900 | 0.292 | - | - | |
|
| 3.2608 | 52000 | 0.4826 | 0.7129 | - | |
|
| 3.2671 | 52100 | 0.2496 | - | - | |
|
| 3.2733 | 52200 | 0.6702 | - | - | |
|
| 3.2796 | 52300 | 0.3192 | - | - | |
|
| 3.2859 | 52400 | 0.66 | - | - | |
|
| 3.2922 | 52500 | 0.6472 | 0.7146 | - | |
|
| 3.2984 | 52600 | 0.4482 | - | - | |
|
| 3.3047 | 52700 | 0.6618 | - | - | |
|
| 3.3110 | 52800 | 0.4424 | - | - | |
|
| 3.3172 | 52900 | 0.6157 | - | - | |
|
| 3.3235 | 53000 | 0.5087 | 0.7036 | - | |
|
| 3.3298 | 53100 | 0.5148 | - | - | |
|
| 3.3361 | 53200 | 0.386 | - | - | |
|
| 3.3423 | 53300 | 0.3552 | - | - | |
|
| 3.3486 | 53400 | 0.5609 | - | - | |
|
| 3.3549 | 53500 | 0.3549 | 0.7148 | - | |
|
| 3.3611 | 53600 | 0.3099 | - | - | |
|
| 3.3674 | 53700 | 0.2903 | - | - | |
|
| 3.3737 | 53800 | 0.7385 | - | - | |
|
| 3.3799 | 53900 | 0.7025 | - | - | |
|
| 3.3862 | 54000 | 0.5625 | 0.7014 | - | |
|
| 3.3925 | 54100 | 0.7545 | - | - | |
|
| 3.3988 | 54200 | 0.4371 | - | - | |
|
| 3.4050 | 54300 | 0.4588 | - | - | |
|
| 3.4113 | 54400 | 0.4973 | - | - | |
|
| 3.4176 | 54500 | 0.4534 | 0.7010 | - | |
|
| 3.4238 | 54600 | 0.6761 | - | - | |
|
| 3.4301 | 54700 | 0.6559 | - | - | |
|
| 3.4364 | 54800 | 0.6087 | - | - | |
|
| 3.4427 | 54900 | 0.601 | - | - | |
|
| 3.4489 | 55000 | 0.4894 | 0.6706 | - | |
|
| 3.4552 | 55100 | 0.6524 | - | - | |
|
| 3.4615 | 55200 | 0.8268 | - | - | |
|
| 3.4677 | 55300 | 0.1795 | - | - | |
|
| 3.4740 | 55400 | 0.5667 | - | - | |
|
| 3.4803 | 55500 | 0.4185 | 0.6823 | - | |
|
| 3.4865 | 55600 | 0.615 | - | - | |
|
| 3.4928 | 55700 | 0.6231 | - | - | |
|
| 3.4991 | 55800 | 0.3809 | - | - | |
|
| 3.5054 | 55900 | 0.6747 | - | - | |
|
| 3.5116 | 56000 | 0.6484 | 0.6736 | - | |
|
| 3.5179 | 56100 | 0.6208 | - | - | |
|
| 3.5242 | 56200 | 0.2345 | - | - | |
|
| 3.5304 | 56300 | 0.4494 | - | - | |
|
| 3.5367 | 56400 | 0.327 | - | - | |
|
| 3.5430 | 56500 | 0.5614 | 0.6762 | - | |
|
| 3.5493 | 56600 | 0.8796 | - | - | |
|
| 3.5555 | 56700 | 0.6068 | - | - | |
|
| 3.5618 | 56800 | 0.4918 | - | - | |
|
| 3.5681 | 56900 | 0.7352 | - | - | |
|
| 3.5743 | 57000 | 0.4149 | 0.6881 | - | |
|
| 3.5806 | 57100 | 0.3746 | - | - | |
|
| 3.5869 | 57200 | 0.7055 | - | - | |
|
| 3.5932 | 57300 | 0.5557 | - | - | |
|
| 3.5994 | 57400 | 0.7734 | - | - | |
|
| 3.6057 | 57500 | 0.5263 | 0.6800 | - | |
|
| 3.6120 | 57600 | 0.4527 | - | - | |
|
| 3.6182 | 57700 | 0.8339 | - | - | |
|
| 3.6245 | 57800 | 0.7004 | - | - | |
|
| 3.6308 | 57900 | 0.5068 | - | - | |
|
| 3.6370 | 58000 | 0.6601 | 0.6667 | - | |
|
| 3.6433 | 58100 | 0.8452 | - | - | |
|
| 3.6496 | 58200 | 0.2345 | - | - | |
|
| 3.6559 | 58300 | 0.6034 | - | - | |
|
| 3.6621 | 58400 | 0.8962 | - | - | |
|
| 3.6684 | 58500 | 0.5844 | 0.6755 | - | |
|
| 3.6747 | 58600 | 0.6827 | - | - | |
|
| 3.6809 | 58700 | 0.4087 | - | - | |
|
| 3.6872 | 58800 | 0.6221 | - | - | |
|
| 3.6935 | 58900 | 0.777 | - | - | |
|
| 3.6998 | 59000 | 0.572 | 0.6737 | - | |
|
| 3.7060 | 59100 | 0.5479 | - | - | |
|
| 3.7123 | 59200 | 0.5078 | - | - | |
|
| 3.7186 | 59300 | 0.6982 | - | - | |
|
| 3.7248 | 59400 | 0.2223 | - | - | |
|
| 3.7311 | 59500 | 0.5361 | 0.6709 | - | |
|
| 3.7374 | 59600 | 0.6072 | - | - | |
|
| 3.7437 | 59700 | 0.35 | - | - | |
|
| 3.7499 | 59800 | 0.8802 | - | - | |
|
| 3.7562 | 59900 | 0.6216 | - | - | |
|
| 3.7625 | 60000 | 0.2514 | 0.6836 | - | |
|
| 3.7687 | 60100 | 0.6285 | - | - | |
|
| 3.7750 | 60200 | 0.9845 | - | - | |
|
| 3.7813 | 60300 | 0.5355 | - | - | |
|
| 3.7875 | 60400 | 0.495 | - | - | |
|
| 3.7938 | 60500 | 0.6905 | 0.6725 | - | |
|
| 3.8001 | 60600 | 0.563 | - | - | |
|
| 3.8064 | 60700 | 0.6067 | - | - | |
|
| 3.8126 | 60800 | 0.7585 | - | - | |
|
| 3.8189 | 60900 | 0.4283 | - | - | |
|
| 3.8252 | 61000 | 0.4758 | 0.6600 | - | |
|
| 3.8314 | 61100 | 0.5462 | - | - | |
|
| 3.8377 | 61200 | 0.649 | - | - | |
|
| 3.8440 | 61300 | 0.5576 | - | - | |
|
| 3.8503 | 61400 | 0.6717 | - | - | |
|
| 3.8565 | 61500 | 0.2951 | 0.6613 | - | |
|
| 3.8628 | 61600 | 0.457 | - | - | |
|
| 3.8691 | 61700 | 0.473 | - | - | |
|
| 3.8753 | 61800 | 0.5181 | - | - | |
|
| 3.8816 | 61900 | 0.4581 | - | - | |
|
| 3.8879 | 62000 | 0.6875 | 0.6669 | - | |
|
| 3.8941 | 62100 | 0.3821 | - | - | |
|
| 3.9004 | 62200 | 0.5039 | - | - | |
|
| 3.9067 | 62300 | 0.6809 | - | - | |
|
| 3.9130 | 62400 | 0.3591 | - | - | |
|
| 3.9192 | 62500 | 0.6695 | 0.6654 | - | |
|
| 3.9255 | 62600 | 0.5352 | - | - | |
|
| 3.9318 | 62700 | 0.8635 | - | - | |
|
| 3.9380 | 62800 | 0.73 | - | - | |
|
| 3.9443 | 62900 | 0.4138 | - | - | |
|
| 3.9506 | 63000 | 0.3704 | 0.6620 | - | |
|
| 3.9569 | 63100 | 0.4831 | - | - | |
|
| 3.9631 | 63200 | 0.5405 | - | - | |
|
| 3.9694 | 63300 | 0.6123 | - | - | |
|
| 3.9757 | 63400 | 0.5167 | - | - | |
|
| 3.9819 | 63500 | 0.6967 | 0.6613 | - | |
|
| 3.9882 | 63600 | 0.338 | - | - | |
|
| 3.9945 | 63700 | 0.515 | - | - | |
|
|
|
</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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