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--- |
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language: [] |
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library_name: sentence-transformers |
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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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- dataset_size:1M<n<10M |
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- loss:CachedMultipleNegativesRankingLoss |
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- loss:AnglELoss |
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base_model: nomic-ai/nomic-embed-text-v1.5 |
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metrics: |
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- cosine_accuracy |
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- dot_accuracy |
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- manhattan_accuracy |
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- euclidean_accuracy |
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- max_accuracy |
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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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widget: |
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- source_sentence: cloths |
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sentences: |
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- kitchen washcloths |
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- single-line kite |
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- airpods 2 |
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- source_sentence: syosin |
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sentences: |
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- wrogn |
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- tv wire hide kit |
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- camp jupiter t shirt |
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- source_sentence: range |
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sentences: |
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- rifle scope |
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- 3 mm pearl beads |
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- purple seashell top |
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- source_sentence: 'search_query: snuggie' |
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sentences: |
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- 'search_query: pink floyd fleece' |
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- 'search_query: a70 case shockproof' |
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- 'search_query: ceramic oil diffusers' |
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- source_sentence: 'search_query: makeup' |
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sentences: |
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- 'search_query: make up' |
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- 'search_query: hyundai tucson rims' |
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- 'search_query: headphone extension cable' |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5 |
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results: |
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- task: |
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type: triplet |
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name: Triplet |
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dataset: |
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name: Unknown |
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type: unknown |
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metrics: |
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- type: cosine_accuracy |
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value: 0.724 |
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name: Cosine Accuracy |
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- type: dot_accuracy |
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value: 0.2849 |
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name: Dot Accuracy |
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- type: manhattan_accuracy |
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value: 0.7206 |
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name: Manhattan Accuracy |
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- type: euclidean_accuracy |
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value: 0.7224 |
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name: Euclidean Accuracy |
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- type: max_accuracy |
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value: 0.724 |
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name: Max Accuracy |
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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: Unknown |
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type: unknown |
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metrics: |
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- type: pearson_cosine |
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value: 0.5105281570849078 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.49158759889472853 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.4555624073784202 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.4447225365747482 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.45721175758447263 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.44664446679502867 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.49380575455876385 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.4818963204509048 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.5105281570849078 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.49158759889472853 |
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name: Spearman Max |
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--- |
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|
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# SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) on the triplets and pairs datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) <!-- at revision 91d2d6bfdddf0b0da840f901b533e99bae30d757 --> |
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- **Maximum Sequence Length:** 8192 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
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- **Training Datasets:** |
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- triplets |
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- pairs |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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|
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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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|
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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': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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) |
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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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'search_query: makeup', |
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'search_query: make up', |
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'search_query: hyundai tucson rims', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Triplet |
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
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| Metric | Value | |
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|:--------------------|:----------| |
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| **cosine_accuracy** | **0.724** | |
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| dot_accuracy | 0.2849 | |
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| manhattan_accuracy | 0.7206 | |
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| euclidean_accuracy | 0.7224 | |
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| max_accuracy | 0.724 | |
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#### Semantic Similarity |
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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 | 0.5105 | |
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| **spearman_cosine** | **0.4916** | |
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| pearson_manhattan | 0.4556 | |
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| spearman_manhattan | 0.4447 | |
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| pearson_euclidean | 0.4572 | |
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| spearman_euclidean | 0.4466 | |
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| pearson_dot | 0.4938 | |
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| spearman_dot | 0.4819 | |
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| pearson_max | 0.5105 | |
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| spearman_max | 0.4916 | |
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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 Datasets |
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#### triplets |
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* Dataset: triplets |
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* Size: 684,084 training samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 7 tokens</li><li>mean: 11.1 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 42.75 tokens</li><li>max: 95 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 43.8 tokens</li><li>max: 127 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:----------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>search_query: tarps heavy duty waterproof 8x10</code> | <code>search_document: 8' x 10' Super Heavy Duty 16 Mil Brown Poly Tarp Cover - Thick Waterproof, UV Resistant, Rip and Tear Proof Tarpaulin with Grommets and Reinforced Edges - by Xpose Safety, Xpose Safety, Brown</code> | <code>search_document: Grillkid 6'X8' 4.5 Mil Thick General Purpose Waterproof Poly Tarp, Grillkid, All Purpose</code> | |
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| <code>search_query: wireless keyboard without number pad</code> | <code>search_document: Macally 2.4G Small Wireless Keyboard - Ergonomic & Comfortable Computer Keyboard - Compact Keyboard for Laptop or Windows PC Desktop, Tablet, Smart TV - Plug & Play Mini Keyboard with 12 Hot Keys, Macally, Black</code> | <code>search_document: Wireless Keyboard - iClever GKA22S Rechargeable Keyboard with Number Pad, Full-Size Stainless Steel Ultra Slim Keyboard, 2.4G Stable Connection Wireless Keyboard for iMac, Mackbook, PC, Laptop, iClever, Silver</code> | |
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| <code>search_query: geometry earrings</code> | <code>search_document: Simple Stud Earrings for Women, Geometric Minimalist Stud Earring Set Tiny Circle Triangle Square Bar Stud Earrings Mini Cartilage Tragus Earrings, choice of all, B:Circle Sliver</code> | <code>search_document: BONALUNA Bohemian Wood And Marble Effect Oblong Shaped Drop Statement Earrings (VIVID TURQUOISE), BONALUNA, VIVID TURQUOISE</code> | |
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* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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|
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#### pairs |
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* Dataset: pairs |
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* Size: 498,114 training samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | score | |
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|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
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| type | string | string | float | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 6.73 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 40.14 tokens</li><li>max: 98 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.81</li><li>max: 1.0</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | score | |
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|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| |
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| <code>I would choose a medium weight waterproof fabric, hip length jacket or longer, long sleeves, zip front, with a hood and deep pockets with zips</code> | <code>ZSHOW Men's Winter Hooded Packable Down Jacket(Blue, XX-Large), ZSHOW, Blue</code> | <code>1.0</code> | |
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| <code>sequin dance costume girls</code> | <code>Yeahdor Big Girls' Lyrical Latin Ballet Dance Costumes Dresses Halter Sequins Irregular Tutu Skirted Leotard Dancewear Pink 12-14, Yeahdor, Pink</code> | <code>1.0</code> | |
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| <code>paint easel bulk</code> | <code>Artecho Artist Easel Display Easel Stand, 2 Pack Metal Tripod Stand Easel for Painting, Hold Canvas from 21" to 66", Floor and Tabletop Displaying, Painting with Portable Bag, Artecho, Black</code> | <code>1.0</code> | |
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* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "pairwise_angle_sim" |
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} |
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``` |
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|
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### Evaluation Datasets |
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|
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#### triplets |
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* Dataset: triplets |
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* Size: 10,000 evaluation samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 7 tokens</li><li>mean: 11.13 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 43.11 tokens</li><li>max: 107 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 43.56 tokens</li><li>max: 99 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>search_query: hitch fifth wheel</code> | <code>search_document: ENIXWILL 5th Wheel Trailer Hitch Lifting Device Bracket Pin Fit for Hitch Companion and Patriot Series Hitch, ENIXWILL, Black</code> | <code>search_document: ECOTRIC Fifth 5th Wheel Trailer Hitch Mount Rails and Installation Kits for Full-Size Trucks, ECOTRIC, black</code> | |
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| <code>search_query: dek pro</code> | <code>search_document: Cubiker Computer Desk 47 inch Home Office Writing Study Desk, Modern Simple Style Laptop Table with Storage Bag, Brown, Cubiker, Brown</code> | <code>search_document: FEZIBO Dual Motor L Shaped Electric Standing Desk, 48 Inches Stand Up Corner Desk, Home Office Sit Stand Desk with Rustic Brown Top and Black Frame, FEZIBO, Rustic Brown</code> | |
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| <code>search_query: 1 year baby mouth without teeth cleaner</code> | <code>search_document: Baby Toothbrush,Infant Toothbrush,Baby Tongue Cleaner,Infant Toothbrush,Baby Tongue Cleaner Newborn,Toothbrush Tongue Cleaner Dental Care for 0-36 Month Baby,36 Pcs + Free 4 Pcs, Babycolor, Blue</code> | <code>search_document: Slotic Baby Toothbrush for 0-2 Years, Safe and Sturdy, Toddler Oral Care Teether Brush, Extra Soft Bristle for Baby Teeth and Infant Gums, Dentist Recommended (4-Pack), Slotic, 4 Pack</code> | |
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* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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#### pairs |
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* Dataset: pairs |
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* Size: 10,000 evaluation samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | score | |
|
|:--------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
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| type | string | string | float | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 6.8 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 39.7 tokens</li><li>max: 101 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.77</li><li>max: 1.0</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | score | |
|
|:------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| |
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| <code>outdoor ceiling fans without light</code> | <code>44" Plaza Industrial Indoor Outdoor Ceiling Fan with Remote Control Oil Rubbed Bronze Damp Rated for Patio Porch - Casa Vieja, Casa Vieja, No Light Kit - Bronze</code> | <code>1.0</code> | |
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| <code>bathroom cabinet</code> | <code>Homfa Bathroom Floor Cabinet Free Standing with Single Door Multifunctional Bathroom Storage Organizer Toiletries(Ivory White), Homfa, White</code> | <code>1.0</code> | |
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| <code>fitbit charge 3</code> | <code>TreasureMax Compatible with Fitbit Charge 2 Bands for Women/Men,Silicone Fadeless Pattern Printed Replacement Floral Bands for Fitbit Charge 2 HR Wristbands, TreasureMax, Paw 2</code> | <code>0.4</code> | |
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* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "pairwise_angle_sim" |
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} |
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``` |
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|
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `per_device_train_batch_size`: 4 |
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- `per_device_eval_batch_size`: 4 |
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- `gradient_accumulation_steps`: 4 |
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- `learning_rate`: 1e-06 |
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- `num_train_epochs`: 5 |
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- `lr_scheduler_type`: cosine_with_restarts |
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- `lr_scheduler_kwargs`: {'num_cycles': 4} |
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- `warmup_ratio`: 0.01 |
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- `dataloader_drop_last`: True |
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- `dataloader_num_workers`: 4 |
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- `dataloader_prefetch_factor`: 2 |
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- `load_best_model_at_end`: True |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 4 |
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- `per_device_eval_batch_size`: 4 |
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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`: 4 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 1e-06 |
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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`: 5 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine_with_restarts |
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- `lr_scheduler_kwargs`: {'num_cycles': 4} |
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- `warmup_ratio`: 0.01 |
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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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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: True |
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- `dataloader_num_workers`: 4 |
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- `dataloader_prefetch_factor`: 2 |
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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`: True |
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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} |
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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 |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
<details><summary>Click to expand</summary> |
|
|
|
| Epoch | Step | Training Loss | pairs loss | triplets loss | cosine_accuracy | spearman_cosine | |
|
|:------:|:-----:|:-------------:|:----------:|:-------------:|:---------------:|:---------------:| |
|
| 0.0014 | 100 | 0.8207 | - | - | - | - | |
|
| 0.0027 | 200 | 0.9003 | - | - | - | - | |
|
| 0.0041 | 300 | 0.8379 | - | - | - | - | |
|
| 0.0054 | 400 | 0.815 | - | - | - | - | |
|
| 0.0068 | 500 | 0.8981 | - | - | - | - | |
|
| 0.0081 | 600 | 0.9957 | - | - | - | - | |
|
| 0.0095 | 700 | 0.8284 | - | - | - | - | |
|
| 0.0108 | 800 | 0.8095 | - | - | - | - | |
|
| 0.0122 | 900 | 0.9307 | - | - | - | - | |
|
| 0.0135 | 1000 | 0.9906 | 1.3590 | 0.6927 | 0.694 | 0.3576 | |
|
| 0.0149 | 1100 | 0.8519 | - | - | - | - | |
|
| 0.0162 | 1200 | 0.738 | - | - | - | - | |
|
| 0.0176 | 1300 | 0.9221 | - | - | - | - | |
|
| 0.0189 | 1400 | 0.8652 | - | - | - | - | |
|
| 0.0203 | 1500 | 0.8599 | - | - | - | - | |
|
| 0.0217 | 1600 | 0.8376 | - | - | - | - | |
|
| 0.0230 | 1700 | 0.8015 | - | - | - | - | |
|
| 0.0244 | 1800 | 0.8402 | - | - | - | - | |
|
| 0.0257 | 1900 | 0.8278 | - | - | - | - | |
|
| 0.0271 | 2000 | 0.9169 | 1.2825 | 0.6685 | 0.6984 | 0.3827 | |
|
| 0.0284 | 2100 | 0.8237 | - | - | - | - | |
|
| 0.0298 | 2200 | 0.6999 | - | - | - | - | |
|
| 0.0311 | 2300 | 0.8482 | - | - | - | - | |
|
| 0.0325 | 2400 | 0.7317 | - | - | - | - | |
|
| 0.0338 | 2500 | 0.8562 | - | - | - | - | |
|
| 0.0352 | 2600 | 0.7919 | - | - | - | - | |
|
| 0.0365 | 2700 | 0.8009 | - | - | - | - | |
|
| 0.0379 | 2800 | 0.7552 | - | - | - | - | |
|
| 0.0392 | 2900 | 0.8148 | - | - | - | - | |
|
| 0.0406 | 3000 | 0.7556 | 1.2029 | 0.6480 | 0.7064 | 0.4045 | |
|
| 0.0420 | 3100 | 0.6813 | - | - | - | - | |
|
| 0.0433 | 3200 | 0.7406 | - | - | - | - | |
|
| 0.0447 | 3300 | 0.8198 | - | - | - | - | |
|
| 0.0460 | 3400 | 0.7842 | - | - | - | - | |
|
| 0.0474 | 3500 | 0.74 | - | - | - | - | |
|
| 0.0487 | 3600 | 0.7117 | - | - | - | - | |
|
| 0.0501 | 3700 | 0.7404 | - | - | - | - | |
|
| 0.0514 | 3800 | 0.6719 | - | - | - | - | |
|
| 0.0528 | 3900 | 0.6728 | - | - | - | - | |
|
| 0.0541 | 4000 | 0.7189 | 1.0997 | 0.6337 | 0.7146 | 0.4284 | |
|
| 0.0555 | 4100 | 0.7812 | - | - | - | - | |
|
| 0.0568 | 4200 | 0.7474 | - | - | - | - | |
|
| 0.0582 | 4300 | 0.6556 | - | - | - | - | |
|
| 0.0596 | 4400 | 0.8303 | - | - | - | - | |
|
| 0.0609 | 4500 | 0.6796 | - | - | - | - | |
|
| 0.0623 | 4600 | 0.7077 | - | - | - | - | |
|
| 0.0636 | 4700 | 0.6863 | - | - | - | - | |
|
| 0.0650 | 4800 | 0.6756 | - | - | - | - | |
|
| 0.0663 | 4900 | 0.6955 | - | - | - | - | |
|
| 0.0677 | 5000 | 0.7199 | 1.0589 | 0.6257 | 0.7161 | 0.4426 | |
|
| 0.0690 | 5100 | 0.6744 | - | - | - | - | |
|
| 0.0704 | 5200 | 0.7609 | - | - | - | - | |
|
| 0.0717 | 5300 | 0.6707 | - | - | - | - | |
|
| 0.0731 | 5400 | 0.6796 | - | - | - | - | |
|
| 0.0744 | 5500 | 0.6842 | - | - | - | - | |
|
| 0.0758 | 5600 | 0.7358 | - | - | - | - | |
|
| 0.0771 | 5700 | 0.7578 | - | - | - | - | |
|
| 0.0785 | 5800 | 0.6822 | - | - | - | - | |
|
| 0.0799 | 5900 | 0.6847 | - | - | - | - | |
|
| 0.0812 | 6000 | 0.7556 | 1.0383 | 0.6199 | 0.7168 | 0.4488 | |
|
| 0.0826 | 6100 | 0.7013 | - | - | - | - | |
|
| 0.0839 | 6200 | 0.6728 | - | - | - | - | |
|
| 0.0853 | 6300 | 0.6418 | - | - | - | - | |
|
| 0.0866 | 6400 | 0.6918 | - | - | - | - | |
|
| 0.0880 | 6500 | 0.7399 | - | - | - | - | |
|
| 0.0893 | 6600 | 0.7896 | - | - | - | - | |
|
| 0.0907 | 6700 | 0.6771 | - | - | - | - | |
|
| 0.0920 | 6800 | 0.6429 | - | - | - | - | |
|
| 0.0934 | 6900 | 0.6806 | - | - | - | - | |
|
| 0.0947 | 7000 | 0.6931 | 1.0354 | 0.6176 | 0.7195 | 0.4561 | |
|
| 0.0961 | 7100 | 0.7115 | - | - | - | - | |
|
| 0.0974 | 7200 | 0.6108 | - | - | - | - | |
|
| 0.0988 | 7300 | 0.6889 | - | - | - | - | |
|
| 0.1002 | 7400 | 0.6451 | - | - | - | - | |
|
| 0.1015 | 7500 | 0.6501 | - | - | - | - | |
|
| 0.1029 | 7600 | 0.699 | - | - | - | - | |
|
| 0.1042 | 7700 | 0.6624 | - | - | - | - | |
|
| 0.1056 | 7800 | 0.7075 | - | - | - | - | |
|
| 0.1069 | 7900 | 0.6789 | - | - | - | - | |
|
| 0.1083 | 8000 | 0.6572 | 1.0391 | 0.6183 | 0.7211 | 0.4544 | |
|
| 0.1096 | 8100 | 0.6754 | - | - | - | - | |
|
| 0.1110 | 8200 | 0.6404 | - | - | - | - | |
|
| 0.1123 | 8300 | 0.6816 | - | - | - | - | |
|
| 0.1137 | 8400 | 0.6485 | - | - | - | - | |
|
| 0.1150 | 8500 | 0.6794 | - | - | - | - | |
|
| 0.1164 | 8600 | 0.693 | - | - | - | - | |
|
| 0.1177 | 8700 | 0.5798 | - | - | - | - | |
|
| 0.1191 | 8800 | 0.7063 | - | - | - | - | |
|
| 0.1205 | 8900 | 0.6192 | - | - | - | - | |
|
| 0.1218 | 9000 | 0.6889 | 1.0438 | 0.6175 | 0.7243 | 0.4580 | |
|
| 0.1232 | 9100 | 0.6881 | - | - | - | - | |
|
| 0.1245 | 9200 | 0.6369 | - | - | - | - | |
|
| 0.1259 | 9300 | 0.6451 | - | - | - | - | |
|
| 0.1272 | 9400 | 0.644 | - | - | - | - | |
|
| 0.1286 | 9500 | 0.7059 | - | - | - | - | |
|
| 0.1299 | 9600 | 0.5983 | - | - | - | - | |
|
| 0.1313 | 9700 | 0.5935 | - | - | - | - | |
|
| 0.1326 | 9800 | 0.634 | - | - | - | - | |
|
| 0.1340 | 9900 | 0.6716 | - | - | - | - | |
|
| 0.1353 | 10000 | 0.6591 | 1.0213 | 0.6132 | 0.7231 | 0.4640 | |
|
| 0.1367 | 10100 | 0.6886 | - | - | - | - | |
|
| 0.1380 | 10200 | 0.6133 | - | - | - | - | |
|
| 0.1394 | 10300 | 0.5871 | - | - | - | - | |
|
| 0.1408 | 10400 | 0.5949 | - | - | - | - | |
|
| 0.1421 | 10500 | 0.6356 | - | - | - | - | |
|
| 0.1435 | 10600 | 0.6379 | - | - | - | - | |
|
| 0.1448 | 10700 | 0.6288 | - | - | - | - | |
|
| 0.1462 | 10800 | 0.6732 | - | - | - | - | |
|
| 0.1475 | 10900 | 0.6515 | - | - | - | - | |
|
| 0.1489 | 11000 | 0.7013 | 1.0164 | 0.6123 | 0.7257 | 0.4629 | |
|
| 0.1502 | 11100 | 0.5848 | - | - | - | - | |
|
| 0.1516 | 11200 | 0.5988 | - | - | - | - | |
|
| 0.1529 | 11300 | 0.7331 | - | - | - | - | |
|
| 0.1543 | 11400 | 0.6089 | - | - | - | - | |
|
| 0.1556 | 11500 | 0.6553 | - | - | - | - | |
|
| 0.1570 | 11600 | 0.654 | - | - | - | - | |
|
| 0.1583 | 11700 | 0.6509 | - | - | - | - | |
|
| 0.1597 | 11800 | 0.6187 | - | - | - | - | |
|
| 0.1611 | 11900 | 0.6448 | - | - | - | - | |
|
| 0.1624 | 12000 | 0.6775 | 1.0087 | 0.6137 | 0.7257 | 0.4687 | |
|
| 0.1638 | 12100 | 0.5793 | - | - | - | - | |
|
| 0.1651 | 12200 | 0.6827 | - | - | - | - | |
|
| 0.1665 | 12300 | 0.6002 | - | - | - | - | |
|
| 0.1678 | 12400 | 0.583 | - | - | - | - | |
|
| 0.1692 | 12500 | 0.6342 | - | - | - | - | |
|
| 0.1705 | 12600 | 0.6378 | - | - | - | - | |
|
| 0.1719 | 12700 | 0.6008 | - | - | - | - | |
|
| 0.1732 | 12800 | 0.6778 | - | - | - | - | |
|
| 0.1746 | 12900 | 0.6637 | - | - | - | - | |
|
| 0.1759 | 13000 | 0.6419 | 1.0117 | 0.6126 | 0.7234 | 0.4705 | |
|
| 0.1773 | 13100 | 0.663 | - | - | - | - | |
|
| 0.1787 | 13200 | 0.5404 | - | - | - | - | |
|
| 0.1800 | 13300 | 0.6427 | - | - | - | - | |
|
| 0.1814 | 13400 | 0.6907 | - | - | - | - | |
|
| 0.1827 | 13500 | 0.63 | - | - | - | - | |
|
| 0.1841 | 13600 | 0.6501 | - | - | - | - | |
|
| 0.1854 | 13700 | 0.6124 | - | - | - | - | |
|
| 0.1868 | 13800 | 0.6381 | - | - | - | - | |
|
| 0.1881 | 13900 | 0.6324 | - | - | - | - | |
|
| 0.1895 | 14000 | 0.6542 | 1.0119 | 0.6126 | 0.7253 | 0.4641 | |
|
| 0.1908 | 14100 | 0.6292 | - | - | - | - | |
|
| 0.1922 | 14200 | 0.6214 | - | - | - | - | |
|
| 0.1935 | 14300 | 0.643 | - | - | - | - | |
|
| 0.1949 | 14400 | 0.6094 | - | - | - | - | |
|
| 0.1962 | 14500 | 0.5929 | - | - | - | - | |
|
| 0.1976 | 14600 | 0.7236 | - | - | - | - | |
|
| 0.1990 | 14700 | 0.5857 | - | - | - | - | |
|
| 0.2003 | 14800 | 0.7177 | - | - | - | - | |
|
| 0.2017 | 14900 | 0.6651 | - | - | - | - | |
|
| 0.2030 | 15000 | 0.6197 | 1.0012 | 0.6098 | 0.727 | 0.4724 | |
|
| 0.2044 | 15100 | 0.6128 | - | - | - | - | |
|
| 0.2057 | 15200 | 0.6281 | - | - | - | - | |
|
| 0.2071 | 15300 | 0.7106 | - | - | - | - | |
|
| 0.2084 | 15400 | 0.6095 | - | - | - | - | |
|
| 0.2098 | 15500 | 0.5855 | - | - | - | - | |
|
| 0.2111 | 15600 | 0.6124 | - | - | - | - | |
|
| 0.2125 | 15700 | 0.6233 | - | - | - | - | |
|
| 0.2138 | 15800 | 0.6511 | - | - | - | - | |
|
| 0.2152 | 15900 | 0.5701 | - | - | - | - | |
|
| 0.2165 | 16000 | 0.6011 | 0.9990 | 0.6083 | 0.7261 | 0.4756 | |
|
| 0.2179 | 16100 | 0.5907 | - | - | - | - | |
|
| 0.2193 | 16200 | 0.599 | - | - | - | - | |
|
| 0.2206 | 16300 | 0.5879 | - | - | - | - | |
|
| 0.2220 | 16400 | 0.5505 | - | - | - | - | |
|
| 0.2233 | 16500 | 0.721 | - | - | - | - | |
|
| 0.2247 | 16600 | 0.6972 | - | - | - | - | |
|
| 0.2260 | 16700 | 0.6147 | - | - | - | - | |
|
| 0.2274 | 16800 | 0.6147 | - | - | - | - | |
|
| 0.2287 | 16900 | 0.6217 | - | - | - | - | |
|
| 0.2301 | 17000 | 0.6048 | 1.0026 | 0.6097 | 0.7284 | 0.4700 | |
|
| 0.2314 | 17100 | 0.6233 | - | - | - | - | |
|
| 0.2328 | 17200 | 0.5569 | - | - | - | - | |
|
| 0.2341 | 17300 | 0.6158 | - | - | - | - | |
|
| 0.2355 | 17400 | 0.6483 | - | - | - | - | |
|
| 0.2368 | 17500 | 0.5811 | - | - | - | - | |
|
| 0.2382 | 17600 | 0.5988 | - | - | - | - | |
|
| 0.2396 | 17700 | 0.5472 | - | - | - | - | |
|
| 0.2409 | 17800 | 0.515 | - | - | - | - | |
|
| 0.2423 | 17900 | 0.6188 | - | - | - | - | |
|
| 0.2436 | 18000 | 0.6179 | 1.0068 | 0.6109 | 0.727 | 0.4749 | |
|
| 0.2450 | 18100 | 0.6492 | - | - | - | - | |
|
| 0.2463 | 18200 | 0.6303 | - | - | - | - | |
|
| 0.2477 | 18300 | 0.6875 | - | - | - | - | |
|
| 0.2490 | 18400 | 0.6421 | - | - | - | - | |
|
| 0.2504 | 18500 | 0.5463 | - | - | - | - | |
|
| 0.2517 | 18600 | 0.6061 | - | - | - | - | |
|
| 0.2531 | 18700 | 0.6271 | - | - | - | - | |
|
| 0.2544 | 18800 | 0.5899 | - | - | - | - | |
|
| 0.2558 | 18900 | 0.583 | - | - | - | - | |
|
| 0.2571 | 19000 | 0.5725 | 1.0107 | 0.6102 | 0.7282 | 0.4717 | |
|
| 0.2585 | 19100 | 0.578 | - | - | - | - | |
|
| 0.2599 | 19200 | 0.649 | - | - | - | - | |
|
| 0.2612 | 19300 | 0.5673 | - | - | - | - | |
|
| 0.2626 | 19400 | 0.6736 | - | - | - | - | |
|
| 0.2639 | 19500 | 0.6257 | - | - | - | - | |
|
| 0.2653 | 19600 | 0.6759 | - | - | - | - | |
|
| 0.2666 | 19700 | 0.5767 | - | - | - | - | |
|
| 0.2680 | 19800 | 0.6644 | - | - | - | - | |
|
| 0.2693 | 19900 | 0.6232 | - | - | - | - | |
|
| 0.2707 | 20000 | 0.5403 | 1.0150 | 0.6096 | 0.7279 | 0.4799 | |
|
| 0.2720 | 20100 | 0.6195 | - | - | - | - | |
|
| 0.2734 | 20200 | 0.6111 | - | - | - | - | |
|
| 0.2747 | 20300 | 0.6524 | - | - | - | - | |
|
| 0.2761 | 20400 | 0.5863 | - | - | - | - | |
|
| 0.2774 | 20500 | 0.5788 | - | - | - | - | |
|
| 0.2788 | 20600 | 0.5401 | - | - | - | - | |
|
| 0.2802 | 20700 | 0.6166 | - | - | - | - | |
|
| 0.2815 | 20800 | 0.5687 | - | - | - | - | |
|
| 0.2829 | 20900 | 0.6352 | - | - | - | - | |
|
| 0.2842 | 21000 | 0.6574 | 1.0086 | 0.6104 | 0.7291 | 0.4772 | |
|
| 0.2856 | 21100 | 0.633 | - | - | - | - | |
|
| 0.2869 | 21200 | 0.6008 | - | - | - | - | |
|
| 0.2883 | 21300 | 0.5929 | - | - | - | - | |
|
| 0.2896 | 21400 | 0.6791 | - | - | - | - | |
|
| 0.2910 | 21500 | 0.6044 | - | - | - | - | |
|
| 0.2923 | 21600 | 0.5487 | - | - | - | - | |
|
| 0.2937 | 21700 | 0.5302 | - | - | - | - | |
|
| 0.2950 | 21800 | 0.5842 | - | - | - | - | |
|
| 0.2964 | 21900 | 0.5931 | - | - | - | - | |
|
| 0.2978 | 22000 | 0.5376 | 1.0130 | 0.6114 | 0.7292 | 0.4803 | |
|
| 0.2991 | 22100 | 0.511 | - | - | - | - | |
|
| 0.3005 | 22200 | 0.5989 | - | - | - | - | |
|
| 0.3018 | 22300 | 0.6184 | - | - | - | - | |
|
| 0.3032 | 22400 | 0.5367 | - | - | - | - | |
|
| 0.3045 | 22500 | 0.6855 | - | - | - | - | |
|
| 0.3059 | 22600 | 0.6058 | - | - | - | - | |
|
| 0.3072 | 22700 | 0.582 | - | - | - | - | |
|
| 0.3086 | 22800 | 0.5601 | - | - | - | - | |
|
| 0.3099 | 22900 | 0.6476 | - | - | - | - | |
|
| 0.3113 | 23000 | 0.5905 | 1.0174 | 0.6103 | 0.7294 | 0.4818 | |
|
| 0.3126 | 23100 | 0.6215 | - | - | - | - | |
|
| 0.3140 | 23200 | 0.5134 | - | - | - | - | |
|
| 0.3153 | 23300 | 0.5508 | - | - | - | - | |
|
| 0.3167 | 23400 | 0.5855 | - | - | - | - | |
|
| 0.3181 | 23500 | 0.604 | - | - | - | - | |
|
| 0.3194 | 23600 | 0.6711 | - | - | - | - | |
|
| 0.3208 | 23700 | 0.6602 | - | - | - | - | |
|
| 0.3221 | 23800 | 0.5083 | - | - | - | - | |
|
| 0.3235 | 23900 | 0.5928 | - | - | - | - | |
|
| 0.3248 | 24000 | 0.5756 | 1.0079 | 0.6117 | 0.7304 | 0.4850 | |
|
| 0.3262 | 24100 | 0.5659 | - | - | - | - | |
|
| 0.3275 | 24200 | 0.5664 | - | - | - | - | |
|
| 0.3289 | 24300 | 0.5622 | - | - | - | - | |
|
| 0.3302 | 24400 | 0.6685 | - | - | - | - | |
|
| 0.3316 | 24500 | 0.5807 | - | - | - | - | |
|
| 0.3329 | 24600 | 0.5583 | - | - | - | - | |
|
| 0.3343 | 24700 | 0.5634 | - | - | - | - | |
|
| 0.3356 | 24800 | 0.6452 | - | - | - | - | |
|
| 0.3370 | 24900 | 0.5716 | - | - | - | - | |
|
| 0.3384 | 25000 | 0.5411 | 1.0043 | 0.6116 | 0.7289 | 0.4851 | |
|
| 0.3397 | 25100 | 0.583 | - | - | - | - | |
|
| 0.3411 | 25200 | 0.5801 | - | - | - | - | |
|
| 0.3424 | 25300 | 0.52 | - | - | - | - | |
|
| 0.3438 | 25400 | 0.5882 | - | - | - | - | |
|
| 0.3451 | 25500 | 0.5788 | - | - | - | - | |
|
| 0.3465 | 25600 | 0.6031 | - | - | - | - | |
|
| 0.3478 | 25700 | 0.5806 | - | - | - | - | |
|
| 0.3492 | 25800 | 0.541 | - | - | - | - | |
|
| 0.3505 | 25900 | 0.6236 | - | - | - | - | |
|
| 0.3519 | 26000 | 0.5642 | 1.0042 | 0.6124 | 0.7283 | 0.4798 | |
|
| 0.3532 | 26100 | 0.5681 | - | - | - | - | |
|
| 0.3546 | 26200 | 0.5849 | - | - | - | - | |
|
| 0.3559 | 26300 | 0.5879 | - | - | - | - | |
|
| 0.3573 | 26400 | 0.5634 | - | - | - | - | |
|
| 0.3587 | 26500 | 0.5681 | - | - | - | - | |
|
| 0.3600 | 26600 | 0.6432 | - | - | - | - | |
|
| 0.3614 | 26700 | 0.5447 | - | - | - | - | |
|
| 0.3627 | 26800 | 0.5574 | - | - | - | - | |
|
| 0.3641 | 26900 | 0.5698 | - | - | - | - | |
|
| 0.3654 | 27000 | 0.6691 | 1.0087 | 0.6126 | 0.7286 | 0.4829 | |
|
| 0.3668 | 27100 | 0.6235 | - | - | - | - | |
|
| 0.3681 | 27200 | 0.5478 | - | - | - | - | |
|
| 0.3695 | 27300 | 0.586 | - | - | - | - | |
|
| 0.3708 | 27400 | 0.5454 | - | - | - | - | |
|
| 0.3722 | 27500 | 0.5608 | - | - | - | - | |
|
| 0.3735 | 27600 | 0.6274 | - | - | - | - | |
|
| 0.3749 | 27700 | 0.5939 | - | - | - | - | |
|
| 0.3762 | 27800 | 0.5673 | - | - | - | - | |
|
| 0.3776 | 27900 | 0.5784 | - | - | - | - | |
|
| 0.3790 | 28000 | 0.6069 | 1.0183 | 0.6126 | 0.7295 | 0.4798 | |
|
| 0.3803 | 28100 | 0.5733 | - | - | - | - | |
|
| 0.3817 | 28200 | 0.6075 | - | - | - | - | |
|
| 0.3830 | 28300 | 0.5933 | - | - | - | - | |
|
| 0.3844 | 28400 | 0.5907 | - | - | - | - | |
|
| 0.3857 | 28500 | 0.5869 | - | - | - | - | |
|
| 0.3871 | 28600 | 0.5781 | - | - | - | - | |
|
| 0.3884 | 28700 | 0.6056 | - | - | - | - | |
|
| 0.3898 | 28800 | 0.5676 | - | - | - | - | |
|
| 0.3911 | 28900 | 0.5997 | - | - | - | - | |
|
| 0.3925 | 29000 | 0.5936 | 1.0096 | 0.6135 | 0.7269 | 0.4866 | |
|
| 0.3938 | 29100 | 0.5261 | - | - | - | - | |
|
| 0.3952 | 29200 | 0.53 | - | - | - | - | |
|
| 0.3966 | 29300 | 0.5153 | - | - | - | - | |
|
| 0.3979 | 29400 | 0.5161 | - | - | - | - | |
|
| 0.3993 | 29500 | 0.5723 | - | - | - | - | |
|
| 0.4006 | 29600 | 0.6247 | - | - | - | - | |
|
| 0.4020 | 29700 | 0.5521 | - | - | - | - | |
|
| 0.4033 | 29800 | 0.5528 | - | - | - | - | |
|
| 0.4047 | 29900 | 0.5917 | - | - | - | - | |
|
| 0.4060 | 30000 | 0.5267 | 1.0133 | 0.6117 | 0.7258 | 0.4869 | |
|
| 0.4074 | 30100 | 0.6074 | - | - | - | - | |
|
| 0.4087 | 30200 | 0.5774 | - | - | - | - | |
|
| 0.4101 | 30300 | 0.5645 | - | - | - | - | |
|
| 0.4114 | 30400 | 0.5908 | - | - | - | - | |
|
| 0.4128 | 30500 | 0.5364 | - | - | - | - | |
|
| 0.4141 | 30600 | 0.5945 | - | - | - | - | |
|
| 0.4155 | 30700 | 0.5497 | - | - | - | - | |
|
| 0.4169 | 30800 | 0.5291 | - | - | - | - | |
|
| 0.4182 | 30900 | 0.5701 | - | - | - | - | |
|
| 0.4196 | 31000 | 0.5788 | 1.0041 | 0.6143 | 0.727 | 0.4870 | |
|
| 0.4209 | 31100 | 0.6269 | - | - | - | - | |
|
| 0.4223 | 31200 | 0.4914 | - | - | - | - | |
|
| 0.4236 | 31300 | 0.5144 | - | - | - | - | |
|
| 0.4250 | 31400 | 0.6026 | - | - | - | - | |
|
| 0.4263 | 31500 | 0.5646 | - | - | - | - | |
|
| 0.4277 | 31600 | 0.6424 | - | - | - | - | |
|
| 0.4290 | 31700 | 0.5755 | - | - | - | - | |
|
| 0.4304 | 31800 | 0.5646 | - | - | - | - | |
|
| 0.4317 | 31900 | 0.573 | - | - | - | - | |
|
| 0.4331 | 32000 | 0.5648 | 1.0000 | 0.6133 | 0.7258 | 0.4867 | |
|
| 0.4344 | 32100 | 0.5113 | - | - | - | - | |
|
| 0.4358 | 32200 | 0.5836 | - | - | - | - | |
|
| 0.4372 | 32300 | 0.6013 | - | - | - | - | |
|
| 0.4385 | 32400 | 0.5698 | - | - | - | - | |
|
| 0.4399 | 32500 | 0.5731 | - | - | - | - | |
|
| 0.4412 | 32600 | 0.489 | - | - | - | - | |
|
| 0.4426 | 32700 | 0.5728 | - | - | - | - | |
|
| 0.4439 | 32800 | 0.4829 | - | - | - | - | |
|
| 0.4453 | 32900 | 0.5783 | - | - | - | - | |
|
| 0.4466 | 33000 | 0.6191 | 1.0009 | 0.6162 | 0.7239 | 0.4863 | |
|
| 0.4480 | 33100 | 0.5383 | - | - | - | - | |
|
| 0.4493 | 33200 | 0.5611 | - | - | - | - | |
|
| 0.4507 | 33300 | 0.5346 | - | - | - | - | |
|
| 0.4520 | 33400 | 0.5451 | - | - | - | - | |
|
| 0.4534 | 33500 | 0.5719 | - | - | - | - | |
|
| 0.4547 | 33600 | 0.5272 | - | - | - | - | |
|
| 0.4561 | 33700 | 0.5747 | - | - | - | - | |
|
| 0.4575 | 33800 | 0.509 | - | - | - | - | |
|
| 0.4588 | 33900 | 0.5746 | - | - | - | - | |
|
| 0.4602 | 34000 | 0.5873 | 0.9978 | 0.6142 | 0.7257 | 0.4914 | |
|
| 0.4615 | 34100 | 0.5948 | - | - | - | - | |
|
| 0.4629 | 34200 | 0.5344 | - | - | - | - | |
|
| 0.4642 | 34300 | 0.5398 | - | - | - | - | |
|
| 0.4656 | 34400 | 0.6095 | - | - | - | - | |
|
| 0.4669 | 34500 | 0.5878 | - | - | - | - | |
|
| 0.4683 | 34600 | 0.5372 | - | - | - | - | |
|
| 0.4696 | 34700 | 0.5113 | - | - | - | - | |
|
| 0.4710 | 34800 | 0.5675 | - | - | - | - | |
|
| 0.4723 | 34900 | 0.5268 | - | - | - | - | |
|
| 0.4737 | 35000 | 0.4527 | 1.0195 | 0.6185 | 0.7254 | 0.4918 | |
|
| 0.4750 | 35100 | 0.5625 | - | - | - | - | |
|
| 0.4764 | 35200 | 0.5786 | - | - | - | - | |
|
| 0.4778 | 35300 | 0.5327 | - | - | - | - | |
|
| 0.4791 | 35400 | 0.568 | - | - | - | - | |
|
| 0.4805 | 35500 | 0.5652 | - | - | - | - | |
|
| 0.4818 | 35600 | 0.61 | - | - | - | - | |
|
| 0.4832 | 35700 | 0.604 | - | - | - | - | |
|
| 0.4845 | 35800 | 0.6238 | - | - | - | - | |
|
| 0.4859 | 35900 | 0.5492 | - | - | - | - | |
|
| 0.4872 | 36000 | 0.5459 | 1.0140 | 0.6201 | 0.7237 | 0.4877 | |
|
| 0.4886 | 36100 | 0.5833 | - | - | - | - | |
|
| 0.4899 | 36200 | 0.5663 | - | - | - | - | |
|
| 0.4913 | 36300 | 0.5248 | - | - | - | - | |
|
| 0.4926 | 36400 | 0.5352 | - | - | - | - | |
|
| 0.4940 | 36500 | 0.5271 | - | - | - | - | |
|
| 0.4953 | 36600 | 0.5142 | - | - | - | - | |
|
| 0.4967 | 36700 | 0.5173 | - | - | - | - | |
|
| 0.4981 | 36800 | 0.6029 | - | - | - | - | |
|
| 0.4994 | 36900 | 0.5732 | - | - | - | - | |
|
| 0.5008 | 37000 | 0.5887 | 1.0166 | 0.6182 | 0.7276 | 0.4938 | |
|
| 0.5021 | 37100 | 0.529 | - | - | - | - | |
|
| 0.5035 | 37200 | 0.6251 | - | - | - | - | |
|
| 0.5048 | 37300 | 0.4641 | - | - | - | - | |
|
| 0.5062 | 37400 | 0.5818 | - | - | - | - | |
|
| 0.5075 | 37500 | 0.6206 | - | - | - | - | |
|
| 0.5089 | 37600 | 0.4771 | - | - | - | - | |
|
| 0.5102 | 37700 | 0.5578 | - | - | - | - | |
|
| 0.5116 | 37800 | 0.5857 | - | - | - | - | |
|
| 0.5129 | 37900 | 0.5658 | - | - | - | - | |
|
| 0.5143 | 38000 | 0.5514 | 1.0124 | 0.6188 | 0.727 | 0.4904 | |
|
| 0.5157 | 38100 | 0.5092 | - | - | - | - | |
|
| 0.5170 | 38200 | 0.5495 | - | - | - | - | |
|
| 0.5184 | 38300 | 0.5263 | - | - | - | - | |
|
| 0.5197 | 38400 | 0.5399 | - | - | - | - | |
|
| 0.5211 | 38500 | 0.5643 | - | - | - | - | |
|
| 0.5224 | 38600 | 0.5608 | - | - | - | - | |
|
| 0.5238 | 38700 | 0.4812 | - | - | - | - | |
|
| 0.5251 | 38800 | 0.4792 | - | - | - | - | |
|
| 0.5265 | 38900 | 0.5185 | - | - | - | - | |
|
| 0.5278 | 39000 | 0.4966 | 1.0211 | 0.6196 | 0.7251 | 0.4902 | |
|
| 0.5292 | 39100 | 0.6323 | - | - | - | - | |
|
| 0.5305 | 39200 | 0.4468 | - | - | - | - | |
|
| 0.5319 | 39300 | 0.6048 | - | - | - | - | |
|
| 0.5332 | 39400 | 0.4753 | - | - | - | - | |
|
| 0.5346 | 39500 | 0.5749 | - | - | - | - | |
|
| 0.5360 | 39600 | 0.5466 | - | - | - | - | |
|
| 0.5373 | 39700 | 0.5235 | - | - | - | - | |
|
| 0.5387 | 39800 | 0.5608 | - | - | - | - | |
|
| 0.5400 | 39900 | 0.5072 | - | - | - | - | |
|
| 0.5414 | 40000 | 0.5574 | 1.0107 | 0.6220 | 0.7272 | 0.4924 | |
|
| 0.5427 | 40100 | 0.5694 | - | - | - | - | |
|
| 0.5441 | 40200 | 0.5462 | - | - | - | - | |
|
| 0.5454 | 40300 | 0.6253 | - | - | - | - | |
|
| 0.5468 | 40400 | 0.5736 | - | - | - | - | |
|
| 0.5481 | 40500 | 0.5225 | - | - | - | - | |
|
| 0.5495 | 40600 | 0.5313 | - | - | - | - | |
|
| 0.5508 | 40700 | 0.4789 | - | - | - | - | |
|
| 0.5522 | 40800 | 0.5424 | - | - | - | - | |
|
| 0.5535 | 40900 | 0.5282 | - | - | - | - | |
|
| 0.5549 | 41000 | 0.4923 | 1.0111 | 0.6215 | 0.7258 | 0.4906 | |
|
| 0.5563 | 41100 | 0.5614 | - | - | - | - | |
|
| 0.5576 | 41200 | 0.552 | - | - | - | - | |
|
| 0.5590 | 41300 | 0.5455 | - | - | - | - | |
|
| 0.5603 | 41400 | 0.5593 | - | - | - | - | |
|
| 0.5617 | 41500 | 0.527 | - | - | - | - | |
|
| 0.5630 | 41600 | 0.5886 | - | - | - | - | |
|
| 0.5644 | 41700 | 0.5066 | - | - | - | - | |
|
| 0.5657 | 41800 | 0.6026 | - | - | - | - | |
|
| 0.5671 | 41900 | 0.5673 | - | - | - | - | |
|
| 0.5684 | 42000 | 0.5392 | 1.0095 | 0.6220 | 0.7261 | 0.4906 | |
|
| 0.5698 | 42100 | 0.5483 | - | - | - | - | |
|
| 0.5711 | 42200 | 0.5596 | - | - | - | - | |
|
| 0.5725 | 42300 | 0.5462 | - | - | - | - | |
|
| 0.5738 | 42400 | 0.495 | - | - | - | - | |
|
| 0.5752 | 42500 | 0.4769 | - | - | - | - | |
|
| 0.5766 | 42600 | 0.6079 | - | - | - | - | |
|
| 0.5779 | 42700 | 0.5764 | - | - | - | - | |
|
| 0.5793 | 42800 | 0.5553 | - | - | - | - | |
|
| 0.5806 | 42900 | 0.4955 | - | - | - | - | |
|
| 0.5820 | 43000 | 0.568 | 1.0159 | 0.6221 | 0.7276 | 0.4926 | |
|
| 0.5833 | 43100 | 0.4474 | - | - | - | - | |
|
| 0.5847 | 43200 | 0.5976 | - | - | - | - | |
|
| 0.5860 | 43300 | 0.5831 | - | - | - | - | |
|
| 0.5874 | 43400 | 0.4641 | - | - | - | - | |
|
| 0.5887 | 43500 | 0.5126 | - | - | - | - | |
|
| 0.5901 | 43600 | 0.5044 | - | - | - | - | |
|
| 0.5914 | 43700 | 0.5308 | - | - | - | - | |
|
| 0.5928 | 43800 | 0.5399 | - | - | - | - | |
|
| 0.5941 | 43900 | 0.5638 | - | - | - | - | |
|
| 0.5955 | 44000 | 0.5718 | 1.0135 | 0.6226 | 0.7268 | 0.4925 | |
|
| 0.5969 | 44100 | 0.4601 | - | - | - | - | |
|
| 0.5982 | 44200 | 0.5542 | - | - | - | - | |
|
| 0.5996 | 44300 | 0.5645 | - | - | - | - | |
|
| 0.6009 | 44400 | 0.5284 | - | - | - | - | |
|
| 0.6023 | 44500 | 0.5632 | - | - | - | - | |
|
| 0.6036 | 44600 | 0.4867 | - | - | - | - | |
|
| 0.6050 | 44700 | 0.5773 | - | - | - | - | |
|
| 0.6063 | 44800 | 0.4619 | - | - | - | - | |
|
| 0.6077 | 44900 | 0.5044 | - | - | - | - | |
|
| 0.6090 | 45000 | 0.5379 | 1.0204 | 0.6268 | 0.7246 | 0.4889 | |
|
| 0.6104 | 45100 | 0.4914 | - | - | - | - | |
|
| 0.6117 | 45200 | 0.5678 | - | - | - | - | |
|
| 0.6131 | 45300 | 0.5516 | - | - | - | - | |
|
| 0.6144 | 45400 | 0.5519 | - | - | - | - | |
|
| 0.6158 | 45500 | 0.4939 | - | - | - | - | |
|
| 0.6172 | 45600 | 0.4991 | - | - | - | - | |
|
| 0.6185 | 45700 | 0.4988 | - | - | - | - | |
|
| 0.6199 | 45800 | 0.5275 | - | - | - | - | |
|
| 0.6212 | 45900 | 0.51 | - | - | - | - | |
|
| 0.6226 | 46000 | 0.5478 | 1.0193 | 0.6250 | 0.726 | 0.4880 | |
|
| 0.6239 | 46100 | 0.532 | - | - | - | - | |
|
| 0.6253 | 46200 | 0.5847 | - | - | - | - | |
|
| 0.6266 | 46300 | 0.5285 | - | - | - | - | |
|
| 0.6280 | 46400 | 0.4651 | - | - | - | - | |
|
| 0.6293 | 46500 | 0.5035 | - | - | - | - | |
|
| 0.6307 | 46600 | 0.6693 | - | - | - | - | |
|
| 0.6320 | 46700 | 0.4864 | - | - | - | - | |
|
| 0.6334 | 46800 | 0.5401 | - | - | - | - | |
|
| 0.6348 | 46900 | 0.5968 | - | - | - | - | |
|
| 0.6361 | 47000 | 0.5339 | 1.0217 | 0.6255 | 0.7261 | 0.4912 | |
|
| 0.6375 | 47100 | 0.5183 | - | - | - | - | |
|
| 0.6388 | 47200 | 0.4989 | - | - | - | - | |
|
| 0.6402 | 47300 | 0.5263 | - | - | - | - | |
|
| 0.6415 | 47400 | 0.4698 | - | - | - | - | |
|
| 0.6429 | 47500 | 0.5878 | - | - | - | - | |
|
| 0.6442 | 47600 | 0.5186 | - | - | - | - | |
|
| 0.6456 | 47700 | 0.4365 | - | - | - | - | |
|
| 0.6469 | 47800 | 0.5596 | - | - | - | - | |
|
| 0.6483 | 47900 | 0.4989 | - | - | - | - | |
|
| 0.6496 | 48000 | 0.4629 | 1.0253 | 0.6279 | 0.7267 | 0.4903 | |
|
| 0.6510 | 48100 | 0.4798 | - | - | - | - | |
|
| 0.6523 | 48200 | 0.541 | - | - | - | - | |
|
| 0.6537 | 48300 | 0.4916 | - | - | - | - | |
|
| 0.6551 | 48400 | 0.5228 | - | - | - | - | |
|
| 0.6564 | 48500 | 0.5612 | - | - | - | - | |
|
| 0.6578 | 48600 | 0.4756 | - | - | - | - | |
|
| 0.6591 | 48700 | 0.4542 | - | - | - | - | |
|
| 0.6605 | 48800 | 0.5226 | - | - | - | - | |
|
| 0.6618 | 48900 | 0.4651 | - | - | - | - | |
|
| 0.6632 | 49000 | 0.5673 | 1.0208 | 0.6264 | 0.7259 | 0.4934 | |
|
| 0.6645 | 49100 | 0.6201 | - | - | - | - | |
|
| 0.6659 | 49200 | 0.5079 | - | - | - | - | |
|
| 0.6672 | 49300 | 0.5184 | - | - | - | - | |
|
| 0.6686 | 49400 | 0.4925 | - | - | - | - | |
|
| 0.6699 | 49500 | 0.5116 | - | - | - | - | |
|
| 0.6713 | 49600 | 0.5157 | - | - | - | - | |
|
| 0.6726 | 49700 | 0.5521 | - | - | - | - | |
|
| 0.6740 | 49800 | 0.5871 | - | - | - | - | |
|
| 0.6754 | 49900 | 0.5028 | - | - | - | - | |
|
| 0.6767 | 50000 | 0.4776 | 1.0173 | 0.6305 | 0.724 | 0.4916 | |
|
|
|
</details> |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.0 |
|
- Transformers: 4.38.2 |
|
- PyTorch: 2.1.2+cu121 |
|
- Accelerate: 0.27.2 |
|
- Datasets: 2.19.1 |
|
- Tokenizers: 0.15.2 |
|
|
|
## 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", |
|
} |
|
``` |
|
|
|
#### CachedMultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{gao2021scaling, |
|
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, |
|
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan}, |
|
year={2021}, |
|
eprint={2101.06983}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### AnglELoss |
|
```bibtex |
|
@misc{li2023angleoptimized, |
|
title={AnglE-optimized Text Embeddings}, |
|
author={Xianming Li and Jing Li}, |
|
year={2023}, |
|
eprint={2309.12871}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
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