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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:100K<n<1M |
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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: mw-59 |
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sentences: |
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- i9 |
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- beats headphones |
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- tablet stands for 7in |
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- source_sentence: totod |
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sentences: |
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- torxh |
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- massage warehouse |
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- ferry boats scrub cap |
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- source_sentence: 'search_query: ecloth' |
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sentences: |
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- 'search_query: friend wine stopper' |
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- 'search_query: pants for teen girls' |
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- 'search_query: 11x14 frame without mat' |
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- source_sentence: skull |
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sentences: |
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- dog kennel |
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- duct tape colors |
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- mustard tie |
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- source_sentence: 'search_query: dab rig' |
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sentences: |
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- 'search_query: aga stove' |
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- 'search_query: jerky slicer machine' |
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- 'search_query: womens wallet phone' |
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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: 0.702 |
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name: Cosine Accuracy |
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- type: dot_accuracy |
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value: 0.3047 |
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name: Dot Accuracy |
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- type: manhattan_accuracy |
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value: 0.7038 |
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name: Manhattan Accuracy |
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- type: euclidean_accuracy |
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value: 0.7034 |
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name: Euclidean Accuracy |
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- type: max_accuracy |
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value: 0.7038 |
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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: 0.44005266442024726 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.42992442611334314 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.40023272026373946 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.4006937930339286 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.400264197728783 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.4007885190533924 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.443162859807234 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.435512515703368 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.443162859807234 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.435512515703368 |
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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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### 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: dab rig', |
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'search_query: aga stove', |
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'search_query: jerky slicer machine', |
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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** | **0.702** | |
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| dot_accuracy | 0.3047 | |
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| manhattan_accuracy | 0.7038 | |
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| euclidean_accuracy | 0.7034 | |
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| max_accuracy | 0.7038 | |
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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 | 0.4401 | |
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| **spearman_cosine** | **0.4299** | |
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| pearson_manhattan | 0.4002 | |
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| spearman_manhattan | 0.4007 | |
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| pearson_euclidean | 0.4003 | |
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| spearman_euclidean | 0.4008 | |
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| pearson_dot | 0.4432 | |
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| spearman_dot | 0.4355 | |
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| pearson_max | 0.4432 | |
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| spearman_max | 0.4355 | |
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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: 261,250 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: 11.57 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 43.83 tokens</li><li>max: 119 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 43.31 tokens</li><li>max: 112 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: ear warmers women north face</code> | <code>search_document: The North Face Women's Oh-Mega Fur Pom Beanie, TNF Black, OS, The North Face, Tnf Black</code> | <code>search_document: The North Face Shinsky Beanie, TNF Light Grey Heather, OS, The North Face, Tnf Light Grey Heather</code> | |
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| <code>search_query: natural braided hairstyles without weave for black women</code> | <code>search_document: Baseball Cap Wig Long Ombre Braids Cap Wig Hat with Synthetic Small Box Braiding Hair for Women Girls(B-53), Yunkang, B-53</code> | <code>search_document: K'ryssma Dark Brown Synthetic Wigs for women - Natural Looking Long Wavy Right Side Parting NONE Lace Heat Resistant Replacement Wig Full Machine Made 24 inch (#2), K'ryssma, Dark Brown</code> | |
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| <code>search_query: boy siracha shirt</code> | <code>search_document: Sriracha Distressed Label Graphic T-Shirt, Sriracha, Red</code> | <code>search_document: Pho Sho | Funny Vietnamese Cuisine Vietnam Foodie Chef Cook Food Humor T-Shirt-(Adult,M) Sport Grey, Ann Arbor T-shirt Co., Sport Grey</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: 261,250 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: 0.77</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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#### 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: 11.52 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 42.45 tokens</li><li>max: 113 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 42.7 tokens</li><li>max: 116 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: non damaging eyelash glue</code> | <code>search_document: Professional Eyelash Extension Remover Gel - Quickly And Easily Remove Individual Semi Permanent False Lashes - Works With Even The Strongest Fake Eyelash Glue or Adhesive, BEAU LASHES, Clear</code> | <code>search_document: Premade Volume Eyelash Extensions 4D-D-0.10-14 Long Stem Premade Fans Eyelash Extensions C D Curl Volume Lash Extensions Pre made Lash Fans(4D-D-0.10, 14mm), B&Qaugen, 4D-0.10-D</code> | |
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| <code>search_query: christmas tablecloths for rectangle tables 60 x 120 gold</code> | <code>search_document: Aquazolax Damask Tablecloth for Rectangle Table 60 x 120 Damask Foliate Pattern Jacquard Heavy Weight Fabric Table Overlay, Gold, Aquazolax, 02 - Gold</code> | <code>search_document: Benson Mills Harmony Scroll Woven Damask Fabric Tablecloth (60" X 104" Rectangular, Gold), Benson Mills, Gold</code> | |
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| <code>search_query: #10 standard no tint no window not self seal</code> | <code>search_document: #10 Security Tinted Self-Seal Envelopes - No Window - EnveGuard, Size 4-1/8 X 9-1/2 Inches - White - 24 LB - 100 Count (34100), Aimoh, White</code> | <code>search_document: Chalktastic Liquid Chalk Markers for Kids - Set of 8 Washable, Dry Erase Pens for School, Menu Board & Car Window Glass - Neon, Erasable Chalkboard Pen Pack - Gifts for Artists, Chalktastic, Classic</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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|:--------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
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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: 0.73</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>0.2</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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- `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-05 |
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- `num_train_epochs`: 5 |
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- `lr_scheduler_type`: cosine_with_restarts |
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- `warmup_ratio`: 0.1 |
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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-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`: 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`: {} |
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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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- `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 |
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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 |
|
- `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 | |
|
|:------:|:-----:|:-------------:|:----------:|:-------------:|:---------------:|:---------------:| |
|
| 0.0031 | 100 | 0.9224 | - | - | - | - | |
|
| 0.0061 | 200 | 0.9823 | - | - | - | - | |
|
| 0.0092 | 300 | 0.906 | - | - | - | - | |
|
| 0.0122 | 400 | 0.9692 | - | - | - | - | |
|
| 0.0153 | 500 | 1.0174 | - | - | - | - | |
|
| 0.0184 | 600 | 0.9488 | - | - | - | - | |
|
| 0.0214 | 700 | 0.9094 | - | - | - | - | |
|
| 0.0245 | 800 | 1.086 | - | - | - | - | |
|
| 0.0276 | 900 | 0.9104 | - | - | - | - | |
|
| 0.0306 | 1000 | 0.8288 | 1.3267 | 0.7466 | 0.6776 | 0.3661 | |
|
| 0.0337 | 1100 | 0.9905 | - | - | - | - | |
|
| 0.0367 | 1200 | 0.9511 | - | - | - | - | |
|
| 0.0398 | 1300 | 0.894 | - | - | - | - | |
|
| 0.0429 | 1400 | 0.7935 | - | - | - | - | |
|
| 0.0459 | 1500 | 0.9212 | - | - | - | - | |
|
| 0.0490 | 1600 | 0.846 | - | - | - | - | |
|
| 0.0521 | 1700 | 0.9323 | - | - | - | - | |
|
| 0.0551 | 1800 | 0.8216 | - | - | - | - | |
|
| 0.0582 | 1900 | 0.7616 | - | - | - | - | |
|
| 0.0612 | 2000 | 0.8028 | 1.1716 | 0.6940 | 0.6942 | 0.4072 | |
|
| 0.0643 | 2100 | 0.8196 | - | - | - | - | |
|
| 0.0674 | 2200 | 0.8022 | - | - | - | - | |
|
| 0.0704 | 2300 | 0.814 | - | - | - | - | |
|
| 0.0735 | 2400 | 0.8388 | - | - | - | - | |
|
| 0.0766 | 2500 | 0.7658 | - | - | - | - | |
|
| 0.0796 | 2600 | 0.7226 | - | - | - | - | |
|
| 0.0827 | 2700 | 0.7802 | - | - | - | - | |
|
| 0.0857 | 2800 | 0.8148 | - | - | - | - | |
|
| 0.0888 | 2900 | 0.7444 | - | - | - | - | |
|
| 0.0919 | 3000 | 0.7463 | 1.0475 | 0.6718 | 0.7019 | 0.4410 | |
|
| 0.0949 | 3100 | 0.7129 | - | - | - | - | |
|
| 0.0980 | 3200 | 0.6884 | - | - | - | - | |
|
| 0.1011 | 3300 | 0.7072 | - | - | - | - | |
|
| 0.1041 | 3400 | 0.7956 | - | - | - | - | |
|
| 0.1072 | 3500 | 0.7932 | - | - | - | - | |
|
| 0.1102 | 3600 | 0.6843 | - | - | - | - | |
|
| 0.1133 | 3700 | 0.8722 | - | - | - | - | |
|
| 0.1164 | 3800 | 0.6767 | - | - | - | - | |
|
| 0.1194 | 3900 | 0.6905 | - | - | - | - | |
|
| 0.1225 | 4000 | 0.7022 | 1.0538 | 0.6663 | 0.706 | 0.4501 | |
|
| 0.1256 | 4100 | 0.6574 | - | - | - | - | |
|
| 0.1286 | 4200 | 0.8011 | - | - | - | - | |
|
| 0.1317 | 4300 | 0.6902 | - | - | - | - | |
|
| 0.1347 | 4400 | 0.836 | - | - | - | - | |
|
| 0.1378 | 4500 | 0.6457 | - | - | - | - | |
|
| 0.1409 | 4600 | 0.6786 | - | - | - | - | |
|
| 0.1439 | 4700 | 0.7356 | - | - | - | - | |
|
| 0.1470 | 4800 | 0.8078 | - | - | - | - | |
|
| 0.1500 | 4900 | 0.7157 | - | - | - | - | |
|
| 0.1531 | 5000 | 0.6629 | 1.0507 | 0.6669 | 0.7108 | 0.4493 | |
|
| 0.1562 | 5100 | 0.7387 | - | - | - | - | |
|
| 0.1592 | 5200 | 0.7108 | - | - | - | - | |
|
| 0.1623 | 5300 | 0.6361 | - | - | - | - | |
|
| 0.1654 | 5400 | 0.6931 | - | - | - | - | |
|
| 0.1684 | 5500 | 0.7409 | - | - | - | - | |
|
| 0.1715 | 5600 | 0.7645 | - | - | - | - | |
|
| 0.1745 | 5700 | 0.6577 | - | - | - | - | |
|
| 0.1776 | 5800 | 0.7284 | - | - | - | - | |
|
| 0.1807 | 5900 | 0.6774 | - | - | - | - | |
|
| 0.1837 | 6000 | 0.7187 | 1.0089 | 0.6612 | 0.7112 | 0.4569 | |
|
| 0.1868 | 6100 | 0.6003 | - | - | - | - | |
|
| 0.1899 | 6200 | 0.7028 | - | - | - | - | |
|
| 0.1929 | 6300 | 0.7195 | - | - | - | - | |
|
| 0.1960 | 6400 | 0.6823 | - | - | - | - | |
|
| 0.1990 | 6500 | 0.6665 | - | - | - | - | |
|
| 0.2021 | 6600 | 0.6206 | - | - | - | - | |
|
| 0.2052 | 6700 | 0.6442 | - | - | - | - | |
|
| 0.2082 | 6800 | 0.7191 | - | - | - | - | |
|
| 0.2113 | 6900 | 0.6074 | - | - | - | - | |
|
| 0.2144 | 7000 | 0.6311 | 1.0315 | 0.6657 | 0.7109 | 0.4451 | |
|
| 0.2174 | 7100 | 0.6444 | - | - | - | - | |
|
| 0.2205 | 7200 | 0.6475 | - | - | - | - | |
|
| 0.2235 | 7300 | 0.5911 | - | - | - | - | |
|
| 0.2266 | 7400 | 0.6709 | - | - | - | - | |
|
| 0.2297 | 7500 | 0.6306 | - | - | - | - | |
|
| 0.2327 | 7600 | 0.7122 | - | - | - | - | |
|
| 0.2358 | 7700 | 0.6461 | - | - | - | - | |
|
| 0.2389 | 7800 | 0.6899 | - | - | - | - | |
|
| 0.2419 | 7900 | 0.6413 | - | - | - | - | |
|
| 0.2450 | 8000 | 0.691 | 1.0058 | 0.6705 | 0.7158 | 0.4520 | |
|
| 0.2480 | 8100 | 0.609 | - | - | - | - | |
|
| 0.2511 | 8200 | 0.6054 | - | - | - | - | |
|
| 0.2542 | 8300 | 0.61 | - | - | - | - | |
|
| 0.2572 | 8400 | 0.596 | - | - | - | - | |
|
| 0.2603 | 8500 | 0.6999 | - | - | - | - | |
|
| 0.2634 | 8600 | 0.5909 | - | - | - | - | |
|
| 0.2664 | 8700 | 0.5965 | - | - | - | - | |
|
| 0.2695 | 8800 | 0.5951 | - | - | - | - | |
|
| 0.2725 | 8900 | 0.6058 | - | - | - | - | |
|
| 0.2756 | 9000 | 0.5979 | 1.0287 | 0.6904 | 0.7142 | 0.4628 | |
|
| 0.2787 | 9100 | 0.6249 | - | - | - | - | |
|
| 0.2817 | 9200 | 0.6261 | - | - | - | - | |
|
| 0.2848 | 9300 | 0.6365 | - | - | - | - | |
|
| 0.2878 | 9400 | 0.5699 | - | - | - | - | |
|
| 0.2909 | 9500 | 0.6675 | - | - | - | - | |
|
| 0.2940 | 9600 | 0.5806 | - | - | - | - | |
|
| 0.2970 | 9700 | 0.5832 | - | - | - | - | |
|
| 0.3001 | 9800 | 0.6135 | - | - | - | - | |
|
| 0.3032 | 9900 | 0.6005 | - | - | - | - | |
|
| 0.3062 | 10000 | 0.6079 | 1.0232 | 0.7137 | 0.7163 | 0.4686 | |
|
| 0.3093 | 10100 | 0.6452 | - | - | - | - | |
|
| 0.3123 | 10200 | 0.5765 | - | - | - | - | |
|
| 0.3154 | 10300 | 0.619 | - | - | - | - | |
|
| 0.3185 | 10400 | 0.5154 | - | - | - | - | |
|
| 0.3215 | 10500 | 0.6142 | - | - | - | - | |
|
| 0.3246 | 10600 | 0.574 | - | - | - | - | |
|
| 0.3277 | 10700 | 0.5569 | - | - | - | - | |
|
| 0.3307 | 10800 | 0.6233 | - | - | - | - | |
|
| 0.3338 | 10900 | 0.6183 | - | - | - | - | |
|
| 0.3368 | 11000 | 0.5953 | 1.0279 | 0.7040 | 0.71 | 0.4571 | |
|
| 0.3399 | 11100 | 0.5146 | - | - | - | - | |
|
| 0.3430 | 11200 | 0.6029 | - | - | - | - | |
|
| 0.3460 | 11300 | 0.6054 | - | - | - | - | |
|
| 0.3491 | 11400 | 0.6324 | - | - | - | - | |
|
| 0.3522 | 11500 | 0.5459 | - | - | - | - | |
|
| 0.3552 | 11600 | 0.5721 | - | - | - | - | |
|
| 0.3583 | 11700 | 0.5224 | - | - | - | - | |
|
| 0.3613 | 11800 | 0.5979 | - | - | - | - | |
|
| 0.3644 | 11900 | 0.5832 | - | - | - | - | |
|
| 0.3675 | 12000 | 0.5638 | 1.0372 | 0.7184 | 0.7127 | 0.4475 | |
|
| 0.3705 | 12100 | 0.4945 | - | - | - | - | |
|
| 0.3736 | 12200 | 0.6368 | - | - | - | - | |
|
| 0.3767 | 12300 | 0.5071 | - | - | - | - | |
|
| 0.3797 | 12400 | 0.626 | - | - | - | - | |
|
| 0.3828 | 12500 | 0.5986 | - | - | - | - | |
|
| 0.3858 | 12600 | 0.539 | - | - | - | - | |
|
| 0.3889 | 12700 | 0.5167 | - | - | - | - | |
|
| 0.3920 | 12800 | 0.657 | - | - | - | - | |
|
| 0.3950 | 12900 | 0.5264 | - | - | - | - | |
|
| 0.3981 | 13000 | 0.4996 | 1.0362 | 0.7276 | 0.7162 | 0.4534 | |
|
| 0.4012 | 13100 | 0.4991 | - | - | - | - | |
|
| 0.4042 | 13200 | 0.5725 | - | - | - | - | |
|
| 0.4073 | 13300 | 0.5788 | - | - | - | - | |
|
| 0.4103 | 13400 | 0.5293 | - | - | - | - | |
|
| 0.4134 | 13500 | 0.5192 | - | - | - | - | |
|
| 0.4165 | 13600 | 0.5477 | - | - | - | - | |
|
| 0.4195 | 13700 | 0.5151 | - | - | - | - | |
|
| 0.4226 | 13800 | 0.5121 | - | - | - | - | |
|
| 0.4256 | 13900 | 0.6849 | - | - | - | - | |
|
| 0.4287 | 14000 | 0.5508 | 1.0062 | 0.7397 | 0.7103 | 0.4571 | |
|
| 0.4318 | 14100 | 0.55 | - | - | - | - | |
|
| 0.4348 | 14200 | 0.5041 | - | - | - | - | |
|
| 0.4379 | 14300 | 0.5041 | - | - | - | - | |
|
| 0.4410 | 14400 | 0.5198 | - | - | - | - | |
|
| 0.4440 | 14500 | 0.5354 | - | - | - | - | |
|
| 0.4471 | 14600 | 0.5535 | - | - | - | - | |
|
| 0.4501 | 14700 | 0.5368 | - | - | - | - | |
|
| 0.4532 | 14800 | 0.5379 | - | - | - | - | |
|
| 0.4563 | 14900 | 0.47 | - | - | - | - | |
|
| 0.4593 | 15000 | 0.5567 | 1.0441 | 0.7531 | 0.715 | 0.4516 | |
|
| 0.4624 | 15100 | 0.5157 | - | - | - | - | |
|
| 0.4655 | 15200 | 0.5698 | - | - | - | - | |
|
| 0.4685 | 15300 | 0.5436 | - | - | - | - | |
|
| 0.4716 | 15400 | 0.6344 | - | - | - | - | |
|
| 0.4746 | 15500 | 0.4351 | - | - | - | - | |
|
| 0.4777 | 15600 | 0.5286 | - | - | - | - | |
|
| 0.4808 | 15700 | 0.552 | - | - | - | - | |
|
| 0.4838 | 15800 | 0.508 | - | - | - | - | |
|
| 0.4869 | 15900 | 0.5111 | - | - | - | - | |
|
| 0.4900 | 16000 | 0.5411 | 1.0264 | 0.7570 | 0.7106 | 0.4506 | |
|
| 0.4930 | 16100 | 0.5363 | - | - | - | - | |
|
| 0.4961 | 16200 | 0.5259 | - | - | - | - | |
|
| 0.4991 | 16300 | 0.5722 | - | - | - | - | |
|
| 0.5022 | 16400 | 0.5059 | - | - | - | - | |
|
| 0.5053 | 16500 | 0.5194 | - | - | - | - | |
|
| 0.5083 | 16600 | 0.5099 | - | - | - | - | |
|
| 0.5114 | 16700 | 0.4857 | - | - | - | - | |
|
| 0.5145 | 16800 | 0.4585 | - | - | - | - | |
|
| 0.5175 | 16900 | 0.5366 | - | - | - | - | |
|
| 0.5206 | 17000 | 0.4825 | 1.0299 | 0.7740 | 0.7048 | 0.4504 | |
|
| 0.5236 | 17100 | 0.543 | - | - | - | - | |
|
| 0.5267 | 17200 | 0.5022 | - | - | - | - | |
|
| 0.5298 | 17300 | 0.4399 | - | - | - | - | |
|
| 0.5328 | 17400 | 0.5342 | - | - | - | - | |
|
| 0.5359 | 17500 | 0.5064 | - | - | - | - | |
|
| 0.5390 | 17600 | 0.5978 | - | - | - | - | |
|
| 0.5420 | 17700 | 0.4947 | - | - | - | - | |
|
| 0.5451 | 17800 | 0.4974 | - | - | - | - | |
|
| 0.5481 | 17900 | 0.5555 | - | - | - | - | |
|
| 0.5512 | 18000 | 0.5397 | 1.0885 | 0.7564 | 0.7143 | 0.4493 | |
|
| 0.5543 | 18100 | 0.4415 | - | - | - | - | |
|
| 0.5573 | 18200 | 0.3887 | - | - | - | - | |
|
| 0.5604 | 18300 | 0.4956 | - | - | - | - | |
|
| 0.5634 | 18400 | 0.471 | - | - | - | - | |
|
| 0.5665 | 18500 | 0.4671 | - | - | - | - | |
|
| 0.5696 | 18600 | 0.4279 | - | - | - | - | |
|
| 0.5726 | 18700 | 0.5509 | - | - | - | - | |
|
| 0.5757 | 18800 | 0.5135 | - | - | - | - | |
|
| 0.5788 | 18900 | 0.595 | - | - | - | - | |
|
| 0.5818 | 19000 | 0.4531 | 1.0569 | 0.7628 | 0.708 | 0.4528 | |
|
| 0.5849 | 19100 | 0.4926 | - | - | - | - | |
|
| 0.5879 | 19200 | 0.5718 | - | - | - | - | |
|
| 0.5910 | 19300 | 0.4963 | - | - | - | - | |
|
| 0.5941 | 19400 | 0.5222 | - | - | - | - | |
|
| 0.5971 | 19500 | 0.4079 | - | - | - | - | |
|
| 0.6002 | 19600 | 0.4662 | - | - | - | - | |
|
| 0.6033 | 19700 | 0.4838 | - | - | - | - | |
|
| 0.6063 | 19800 | 0.5238 | - | - | - | - | |
|
| 0.6094 | 19900 | 0.5475 | - | - | - | - | |
|
| 0.6124 | 20000 | 0.4 | 1.0716 | 0.7965 | 0.709 | 0.4691 | |
|
| 0.6155 | 20100 | 0.5323 | - | - | - | - | |
|
| 0.6186 | 20200 | 0.4544 | - | - | - | - | |
|
| 0.6216 | 20300 | 0.4556 | - | - | - | - | |
|
| 0.6247 | 20400 | 0.5716 | - | - | - | - | |
|
| 0.6278 | 20500 | 0.5538 | - | - | - | - | |
|
| 0.6308 | 20600 | 0.4546 | - | - | - | - | |
|
| 0.6339 | 20700 | 0.4146 | - | - | - | - | |
|
| 0.6369 | 20800 | 0.4811 | - | - | - | - | |
|
| 0.6400 | 20900 | 0.4577 | - | - | - | - | |
|
| 0.6431 | 21000 | 0.4901 | 1.0721 | 0.7903 | 0.7084 | 0.4594 | |
|
| 0.6461 | 21100 | 0.4999 | - | - | - | - | |
|
| 0.6492 | 21200 | 0.3999 | - | - | - | - | |
|
| 0.6523 | 21300 | 0.4587 | - | - | - | - | |
|
| 0.6553 | 21400 | 0.4737 | - | - | - | - | |
|
| 0.6584 | 21500 | 0.4913 | - | - | - | - | |
|
| 0.6614 | 21600 | 0.4612 | - | - | - | - | |
|
| 0.6645 | 21700 | 0.432 | - | - | - | - | |
|
| 0.6676 | 21800 | 0.4627 | - | - | - | - | |
|
| 0.6706 | 21900 | 0.5023 | - | - | - | - | |
|
| 0.6737 | 22000 | 0.4486 | 1.0888 | 0.7913 | 0.7056 | 0.4618 | |
|
| 0.6768 | 22100 | 0.5068 | - | - | - | - | |
|
| 0.6798 | 22200 | 0.4843 | - | - | - | - | |
|
| 0.6829 | 22300 | 0.4687 | - | - | - | - | |
|
| 0.6859 | 22400 | 0.5123 | - | - | - | - | |
|
| 0.6890 | 22500 | 0.3802 | - | - | - | - | |
|
| 0.6921 | 22600 | 0.4883 | - | - | - | - | |
|
| 0.6951 | 22700 | 0.5069 | - | - | - | - | |
|
| 0.6982 | 22800 | 0.4859 | - | - | - | - | |
|
| 0.7012 | 22900 | 0.3931 | - | - | - | - | |
|
| 0.7043 | 23000 | 0.4675 | 1.1026 | 0.8213 | 0.7051 | 0.4513 | |
|
| 0.7074 | 23100 | 0.4948 | - | - | - | - | |
|
| 0.7104 | 23200 | 0.4561 | - | - | - | - | |
|
| 0.7135 | 23300 | 0.3874 | - | - | - | - | |
|
| 0.7166 | 23400 | 0.4909 | - | - | - | - | |
|
| 0.7196 | 23500 | 0.521 | - | - | - | - | |
|
| 0.7227 | 23600 | 0.4997 | - | - | - | - | |
|
| 0.7257 | 23700 | 0.4104 | - | - | - | - | |
|
| 0.7288 | 23800 | 0.4801 | - | - | - | - | |
|
| 0.7319 | 23900 | 0.5237 | - | - | - | - | |
|
| 0.7349 | 24000 | 0.3782 | 1.0715 | 0.7962 | 0.7098 | 0.4583 | |
|
| 0.7380 | 24100 | 0.493 | - | - | - | - | |
|
| 0.7411 | 24200 | 0.489 | - | - | - | - | |
|
| 0.7441 | 24300 | 0.4797 | - | - | - | - | |
|
| 0.7472 | 24400 | 0.4636 | - | - | - | - | |
|
| 0.7502 | 24500 | 0.406 | - | - | - | - | |
|
| 0.7533 | 24600 | 0.3765 | - | - | - | - | |
|
| 0.7564 | 24700 | 0.4746 | - | - | - | - | |
|
| 0.7594 | 24800 | 0.447 | - | - | - | - | |
|
| 0.7625 | 24900 | 0.5286 | - | - | - | - | |
|
| 0.7656 | 25000 | 0.4814 | 1.0794 | 0.7977 | 0.7134 | 0.4614 | |
|
| 0.7686 | 25100 | 0.505 | - | - | - | - | |
|
| 0.7717 | 25200 | 0.4508 | - | - | - | - | |
|
| 0.7747 | 25300 | 0.4317 | - | - | - | - | |
|
| 0.7778 | 25400 | 0.5088 | - | - | - | - | |
|
| 0.7809 | 25500 | 0.3931 | - | - | - | - | |
|
| 0.7839 | 25600 | 0.4516 | - | - | - | - | |
|
| 0.7870 | 25700 | 0.4394 | - | - | - | - | |
|
| 0.7901 | 25800 | 0.4825 | - | - | - | - | |
|
| 0.7931 | 25900 | 0.4248 | - | - | - | - | |
|
| 0.7962 | 26000 | 0.4215 | 1.0887 | 0.8159 | 0.7065 | 0.4719 | |
|
| 0.7992 | 26100 | 0.4674 | - | - | - | - | |
|
| 0.8023 | 26200 | 0.4634 | - | - | - | - | |
|
| 0.8054 | 26300 | 0.3975 | - | - | - | - | |
|
| 0.8084 | 26400 | 0.402 | - | - | - | - | |
|
| 0.8115 | 26500 | 0.4652 | - | - | - | - | |
|
| 0.8146 | 26600 | 0.487 | - | - | - | - | |
|
| 0.8176 | 26700 | 0.4677 | - | - | - | - | |
|
| 0.8207 | 26800 | 0.4662 | - | - | - | - | |
|
| 0.8237 | 26900 | 0.4658 | - | - | - | - | |
|
| 0.8268 | 27000 | 0.4922 | 1.0792 | 0.8019 | 0.7126 | 0.4544 | |
|
| 0.8299 | 27100 | 0.4551 | - | - | - | - | |
|
| 0.8329 | 27200 | 0.4052 | - | - | - | - | |
|
| 0.8360 | 27300 | 0.3713 | - | - | - | - | |
|
| 0.8390 | 27400 | 0.4247 | - | - | - | - | |
|
| 0.8421 | 27500 | 0.4167 | - | - | - | - | |
|
| 0.8452 | 27600 | 0.4035 | - | - | - | - | |
|
| 0.8482 | 27700 | 0.5203 | - | - | - | - | |
|
| 0.8513 | 27800 | 0.4768 | - | - | - | - | |
|
| 0.8544 | 27900 | 0.4085 | - | - | - | - | |
|
| 0.8574 | 28000 | 0.3793 | 1.0920 | 0.7942 | 0.7146 | 0.4630 | |
|
| 0.8605 | 28100 | 0.4188 | - | - | - | - | |
|
| 0.8635 | 28200 | 0.4492 | - | - | - | - | |
|
| 0.8666 | 28300 | 0.4534 | - | - | - | - | |
|
| 0.8697 | 28400 | 0.4188 | - | - | - | - | |
|
| 0.8727 | 28500 | 0.5298 | - | - | - | - | |
|
| 0.8758 | 28600 | 0.4907 | - | - | - | - | |
|
| 0.8789 | 28700 | 0.4415 | - | - | - | - | |
|
| 0.8819 | 28800 | 0.4436 | - | - | - | - | |
|
| 0.8850 | 28900 | 0.4105 | - | - | - | - | |
|
| 0.8880 | 29000 | 0.5498 | 1.0937 | 0.8023 | 0.7127 | 0.4492 | |
|
| 0.8911 | 29100 | 0.4478 | - | - | - | - | |
|
| 0.8942 | 29200 | 0.4467 | - | - | - | - | |
|
| 0.8972 | 29300 | 0.3691 | - | - | - | - | |
|
| 0.9003 | 29400 | 0.358 | - | - | - | - | |
|
| 0.9034 | 29500 | 0.4101 | - | - | - | - | |
|
| 0.9064 | 29600 | 0.4568 | - | - | - | - | |
|
| 0.9095 | 29700 | 0.4776 | - | - | - | - | |
|
| 0.9125 | 29800 | 0.3909 | - | - | - | - | |
|
| 0.9156 | 29900 | 0.4731 | - | - | - | - | |
|
| 0.9187 | 30000 | 0.4407 | 1.1511 | 0.8187 | 0.7131 | 0.4423 | |
|
| 0.9217 | 30100 | 0.5712 | - | - | - | - | |
|
| 0.9248 | 30200 | 0.457 | - | - | - | - | |
|
| 0.9279 | 30300 | 0.4141 | - | - | - | - | |
|
| 0.9309 | 30400 | 0.4779 | - | - | - | - | |
|
| 0.9340 | 30500 | 0.418 | - | - | - | - | |
|
| 0.9370 | 30600 | 0.4377 | - | - | - | - | |
|
| 0.9401 | 30700 | 0.3997 | - | - | - | - | |
|
| 0.9432 | 30800 | 0.3443 | - | - | - | - | |
|
| 0.9462 | 30900 | 0.5006 | - | - | - | - | |
|
| 0.9493 | 31000 | 0.4728 | 1.1302 | 0.8141 | 0.7137 | 0.4555 | |
|
| 0.9524 | 31100 | 0.5103 | - | - | - | - | |
|
| 0.9554 | 31200 | 0.3898 | - | - | - | - | |
|
| 0.9585 | 31300 | 0.4132 | - | - | - | - | |
|
| 0.9615 | 31400 | 0.4567 | - | - | - | - | |
|
| 0.9646 | 31500 | 0.4226 | - | - | - | - | |
|
| 0.9677 | 31600 | 0.3669 | - | - | - | - | |
|
| 0.9707 | 31700 | 0.4707 | - | - | - | - | |
|
| 0.9738 | 31800 | 0.5012 | - | - | - | - | |
|
| 0.9768 | 31900 | 0.4114 | - | - | - | - | |
|
| 0.9799 | 32000 | 0.3666 | 1.1309 | 0.8225 | 0.7102 | 0.4632 | |
|
| 0.9830 | 32100 | 0.4514 | - | - | - | - | |
|
| 0.9860 | 32200 | 0.4329 | - | - | - | - | |
|
| 0.9891 | 32300 | 0.4559 | - | - | - | - | |
|
| 0.9922 | 32400 | 0.412 | - | - | - | - | |
|
| 0.9952 | 32500 | 0.3883 | - | - | - | - | |
|
| 0.9983 | 32600 | 0.3854 | - | - | - | - | |
|
| 1.0013 | 32700 | 0.3886 | - | - | - | - | |
|
| 1.0044 | 32800 | 0.41 | - | - | - | - | |
|
| 1.0075 | 32900 | 0.4494 | - | - | - | - | |
|
| 1.0105 | 33000 | 0.4862 | 1.1124 | 0.8362 | 0.7079 | 0.4494 | |
|
| 1.0136 | 33100 | 0.3951 | - | - | - | - | |
|
| 1.0167 | 33200 | 0.4714 | - | - | - | - | |
|
| 1.0197 | 33300 | 0.4037 | - | - | - | - | |
|
| 1.0228 | 33400 | 0.4534 | - | - | - | - | |
|
| 1.0258 | 33500 | 0.5265 | - | - | - | - | |
|
| 1.0289 | 33600 | 0.4432 | - | - | - | - | |
|
| 1.0320 | 33700 | 0.3665 | - | - | - | - | |
|
| 1.0350 | 33800 | 0.4235 | - | - | - | - | |
|
| 1.0381 | 33900 | 0.3905 | - | - | - | - | |
|
| 1.0412 | 34000 | 0.3532 | 1.1693 | 0.8203 | 0.7142 | 0.4420 | |
|
| 1.0442 | 34100 | 0.3472 | - | - | - | - | |
|
| 1.0473 | 34200 | 0.4316 | - | - | - | - | |
|
| 1.0503 | 34300 | 0.3811 | - | - | - | - | |
|
| 1.0534 | 34400 | 0.4753 | - | - | - | - | |
|
| 1.0565 | 34500 | 0.3757 | - | - | - | - | |
|
| 1.0595 | 34600 | 0.417 | - | - | - | - | |
|
| 1.0626 | 34700 | 0.3727 | - | - | - | - | |
|
| 1.0657 | 34800 | 0.4127 | - | - | - | - | |
|
| 1.0687 | 34900 | 0.4487 | - | - | - | - | |
|
| 1.0718 | 35000 | 0.3786 | 1.1073 | 0.8310 | 0.7159 | 0.4678 | |
|
| 1.0748 | 35100 | 0.4043 | - | - | - | - | |
|
| 1.0779 | 35200 | 0.4226 | - | - | - | - | |
|
| 1.0810 | 35300 | 0.3585 | - | - | - | - | |
|
| 1.0840 | 35400 | 0.407 | - | - | - | - | |
|
| 1.0871 | 35500 | 0.4682 | - | - | - | - | |
|
| 1.0902 | 35600 | 0.3273 | - | - | - | - | |
|
| 1.0932 | 35700 | 0.3594 | - | - | - | - | |
|
| 1.0963 | 35800 | 0.3795 | - | - | - | - | |
|
| 1.0993 | 35900 | 0.3633 | - | - | - | - | |
|
| 1.1024 | 36000 | 0.3729 | 1.1356 | 0.8248 | 0.7169 | 0.4525 | |
|
| 1.1055 | 36100 | 0.4179 | - | - | - | - | |
|
| 1.1085 | 36200 | 0.3907 | - | - | - | - | |
|
| 1.1116 | 36300 | 0.4495 | - | - | - | - | |
|
| 1.1146 | 36400 | 0.4093 | - | - | - | - | |
|
| 1.1177 | 36500 | 0.327 | - | - | - | - | |
|
| 1.1208 | 36600 | 0.2868 | - | - | - | - | |
|
| 1.1238 | 36700 | 0.2917 | - | - | - | - | |
|
| 1.1269 | 36800 | 0.3753 | - | - | - | - | |
|
| 1.1300 | 36900 | 0.3508 | - | - | - | - | |
|
| 1.1330 | 37000 | 0.4483 | 1.1865 | 0.8488 | 0.7035 | 0.4559 | |
|
| 1.1361 | 37100 | 0.4439 | - | - | - | - | |
|
| 1.1391 | 37200 | 0.3225 | - | - | - | - | |
|
| 1.1422 | 37300 | 0.401 | - | - | - | - | |
|
| 1.1453 | 37400 | 0.3858 | - | - | - | - | |
|
| 1.1483 | 37500 | 0.4877 | - | - | - | - | |
|
| 1.1514 | 37600 | 0.3456 | - | - | - | - | |
|
| 1.1545 | 37700 | 0.3827 | - | - | - | - | |
|
| 1.1575 | 37800 | 0.4412 | - | - | - | - | |
|
| 1.1606 | 37900 | 0.3679 | - | - | - | - | |
|
| 1.1636 | 38000 | 0.3465 | 1.1654 | 0.8383 | 0.7095 | 0.4498 | |
|
| 1.1667 | 38100 | 0.3433 | - | - | - | - | |
|
| 1.1698 | 38200 | 0.3745 | - | - | - | - | |
|
| 1.1728 | 38300 | 0.3902 | - | - | - | - | |
|
| 1.1759 | 38400 | 0.2779 | - | - | - | - | |
|
| 1.1790 | 38500 | 0.3916 | - | - | - | - | |
|
| 1.1820 | 38600 | 0.346 | - | - | - | - | |
|
| 1.1851 | 38700 | 0.3742 | - | - | - | - | |
|
| 1.1881 | 38800 | 0.3424 | - | - | - | - | |
|
| 1.1912 | 38900 | 0.4042 | - | - | - | - | |
|
| 1.1943 | 39000 | 0.2993 | 1.2051 | 0.8313 | 0.7106 | 0.4571 | |
|
| 1.1973 | 39100 | 0.3167 | - | - | - | - | |
|
| 1.2004 | 39200 | 0.3291 | - | - | - | - | |
|
| 1.2035 | 39300 | 0.245 | - | - | - | - | |
|
| 1.2065 | 39400 | 0.3289 | - | - | - | - | |
|
| 1.2096 | 39500 | 0.3969 | - | - | - | - | |
|
| 1.2126 | 39600 | 0.2511 | - | - | - | - | |
|
| 1.2157 | 39700 | 0.2972 | - | - | - | - | |
|
| 1.2188 | 39800 | 0.3434 | - | - | - | - | |
|
| 1.2218 | 39900 | 0.324 | - | - | - | - | |
|
| 1.2249 | 40000 | 0.2837 | 1.2372 | 0.8453 | 0.7121 | 0.4562 | |
|
| 1.2280 | 40100 | 0.2727 | - | - | - | - | |
|
| 1.2310 | 40200 | 0.3327 | - | - | - | - | |
|
| 1.2341 | 40300 | 0.3468 | - | - | - | - | |
|
| 1.2371 | 40400 | 0.3029 | - | - | - | - | |
|
| 1.2402 | 40500 | 0.3583 | - | - | - | - | |
|
| 1.2433 | 40600 | 0.3664 | - | - | - | - | |
|
| 1.2463 | 40700 | 0.2661 | - | - | - | - | |
|
| 1.2494 | 40800 | 0.2768 | - | - | - | - | |
|
| 1.2524 | 40900 | 0.3065 | - | - | - | - | |
|
| 1.2555 | 41000 | 0.309 | 1.2609 | 0.8644 | 0.704 | 0.4467 | |
|
| 1.2586 | 41100 | 0.377 | - | - | - | - | |
|
| 1.2616 | 41200 | 0.3031 | - | - | - | - | |
|
| 1.2647 | 41300 | 0.2317 | - | - | - | - | |
|
| 1.2678 | 41400 | 0.2504 | - | - | - | - | |
|
| 1.2708 | 41500 | 0.2546 | - | - | - | - | |
|
| 1.2739 | 41600 | 0.2859 | - | - | - | - | |
|
| 1.2769 | 41700 | 0.3507 | - | - | - | - | |
|
| 1.2800 | 41800 | 0.2578 | - | - | - | - | |
|
| 1.2831 | 41900 | 0.297 | - | - | - | - | |
|
| 1.2861 | 42000 | 0.3016 | 1.2546 | 0.8479 | 0.7115 | 0.4578 | |
|
| 1.2892 | 42100 | 0.2067 | - | - | - | - | |
|
| 1.2923 | 42200 | 0.3729 | - | - | - | - | |
|
| 1.2953 | 42300 | 0.2365 | - | - | - | - | |
|
| 1.2984 | 42400 | 0.2855 | - | - | - | - | |
|
| 1.3014 | 42500 | 0.2272 | - | - | - | - | |
|
| 1.3045 | 42600 | 0.2688 | - | - | - | - | |
|
| 1.3076 | 42700 | 0.2285 | - | - | - | - | |
|
| 1.3106 | 42800 | 0.2615 | - | - | - | - | |
|
| 1.3137 | 42900 | 0.2599 | - | - | - | - | |
|
| 1.3168 | 43000 | 0.2968 | 1.2860 | 0.8800 | 0.7071 | 0.4599 | |
|
| 1.3198 | 43100 | 0.2464 | - | - | - | - | |
|
| 1.3229 | 43200 | 0.2673 | - | - | - | - | |
|
| 1.3259 | 43300 | 0.2108 | - | - | - | - | |
|
| 1.3290 | 43400 | 0.2353 | - | - | - | - | |
|
| 1.3321 | 43500 | 0.2396 | - | - | - | - | |
|
| 1.3351 | 43600 | 0.237 | - | - | - | - | |
|
| 1.3382 | 43700 | 0.2083 | - | - | - | - | |
|
| 1.3413 | 43800 | 0.2638 | - | - | - | - | |
|
| 1.3443 | 43900 | 0.2888 | - | - | - | - | |
|
| 1.3474 | 44000 | 0.3166 | 1.2710 | 0.8729 | 0.7052 | 0.4501 | |
|
| 1.3504 | 44100 | 0.1949 | - | - | - | - | |
|
| 1.3535 | 44200 | 0.2285 | - | - | - | - | |
|
| 1.3566 | 44300 | 0.1923 | - | - | - | - | |
|
| 1.3596 | 44400 | 0.1875 | - | - | - | - | |
|
| 1.3627 | 44500 | 0.2736 | - | - | - | - | |
|
| 1.3658 | 44600 | 0.2154 | - | - | - | - | |
|
| 1.3688 | 44700 | 0.1975 | - | - | - | - | |
|
| 1.3719 | 44800 | 0.1799 | - | - | - | - | |
|
| 1.3749 | 44900 | 0.2417 | - | - | - | - | |
|
| 1.3780 | 45000 | 0.3224 | 1.3032 | 0.8788 | 0.7072 | 0.4521 | |
|
| 1.3811 | 45100 | 0.2433 | - | - | - | - | |
|
| 1.3841 | 45200 | 0.269 | - | - | - | - | |
|
| 1.3872 | 45300 | 0.2034 | - | - | - | - | |
|
| 1.3902 | 45400 | 0.236 | - | - | - | - | |
|
| 1.3933 | 45500 | 0.2599 | - | - | - | - | |
|
| 1.3964 | 45600 | 0.1798 | - | - | - | - | |
|
| 1.3994 | 45700 | 0.1412 | - | - | - | - | |
|
| 1.4025 | 45800 | 0.215 | - | - | - | - | |
|
| 1.4056 | 45900 | 0.2081 | - | - | - | - | |
|
| 1.4086 | 46000 | 0.2277 | 1.2555 | 0.8621 | 0.7075 | 0.4577 | |
|
| 1.4117 | 46100 | 0.2005 | - | - | - | - | |
|
| 1.4147 | 46200 | 0.2051 | - | - | - | - | |
|
| 1.4178 | 46300 | 0.1588 | - | - | - | - | |
|
| 1.4209 | 46400 | 0.2318 | - | - | - | - | |
|
| 1.4239 | 46500 | 0.205 | - | - | - | - | |
|
| 1.4270 | 46600 | 0.2404 | - | - | - | - | |
|
| 1.4301 | 46700 | 0.2167 | - | - | - | - | |
|
| 1.4331 | 46800 | 0.1729 | - | - | - | - | |
|
| 1.4362 | 46900 | 0.1866 | - | - | - | - | |
|
| 1.4392 | 47000 | 0.2168 | 1.3006 | 0.8624 | 0.7094 | 0.4562 | |
|
| 1.4423 | 47100 | 0.1615 | - | - | - | - | |
|
| 1.4454 | 47200 | 0.2104 | - | - | - | - | |
|
| 1.4484 | 47300 | 0.2051 | - | - | - | - | |
|
| 1.4515 | 47400 | 0.1904 | - | - | - | - | |
|
| 1.4546 | 47500 | 0.1773 | - | - | - | - | |
|
| 1.4576 | 47600 | 0.1494 | - | - | - | - | |
|
| 1.4607 | 47700 | 0.1668 | - | - | - | - | |
|
| 1.4637 | 47800 | 0.1527 | - | - | - | - | |
|
| 1.4668 | 47900 | 0.1724 | - | - | - | - | |
|
| 1.4699 | 48000 | 0.1707 | 1.3098 | 0.8911 | 0.7093 | 0.4421 | |
|
| 1.4729 | 48100 | 0.2147 | - | - | - | - | |
|
| 1.4760 | 48200 | 0.1513 | - | - | - | - | |
|
| 1.4791 | 48300 | 0.2049 | - | - | - | - | |
|
| 1.4821 | 48400 | 0.171 | - | - | - | - | |
|
| 1.4852 | 48500 | 0.1283 | - | - | - | - | |
|
| 1.4882 | 48600 | 0.1768 | - | - | - | - | |
|
| 1.4913 | 48700 | 0.172 | - | - | - | - | |
|
| 1.4944 | 48800 | 0.2131 | - | - | - | - | |
|
| 1.4974 | 48900 | 0.1621 | - | - | - | - | |
|
| 1.5005 | 49000 | 0.1941 | 1.3623 | 0.8859 | 0.7091 | 0.4501 | |
|
| 1.5036 | 49100 | 0.1493 | - | - | - | - | |
|
| 1.5066 | 49200 | 0.1544 | - | - | - | - | |
|
| 1.5097 | 49300 | 0.1524 | - | - | - | - | |
|
| 1.5127 | 49400 | 0.1137 | - | - | - | - | |
|
| 1.5158 | 49500 | 0.1611 | - | - | - | - | |
|
| 1.5189 | 49600 | 0.1396 | - | - | - | - | |
|
| 1.5219 | 49700 | 0.1462 | - | - | - | - | |
|
| 1.5250 | 49800 | 0.1261 | - | - | - | - | |
|
| 1.5280 | 49900 | 0.122 | - | - | - | - | |
|
| 1.5311 | 50000 | 0.1478 | 1.3027 | 0.8766 | 0.7093 | 0.4555 | |
|
| 1.5342 | 50100 | 0.1324 | - | - | - | - | |
|
| 1.5372 | 50200 | 0.1468 | - | - | - | - | |
|
| 1.5403 | 50300 | 0.1795 | - | - | - | - | |
|
| 1.5434 | 50400 | 0.1308 | - | - | - | - | |
|
| 1.5464 | 50500 | 0.1796 | - | - | - | - | |
|
| 1.5495 | 50600 | 0.2207 | - | - | - | - | |
|
| 1.5525 | 50700 | 0.1383 | - | - | - | - | |
|
| 1.5556 | 50800 | 0.0884 | - | - | - | - | |
|
| 1.5587 | 50900 | 0.1208 | - | - | - | - | |
|
| 1.5617 | 51000 | 0.1139 | 1.4073 | 0.9156 | 0.7061 | 0.4502 | |
|
| 1.5648 | 51100 | 0.169 | - | - | - | - | |
|
| 1.5679 | 51200 | 0.1142 | - | - | - | - | |
|
| 1.5709 | 51300 | 0.1269 | - | - | - | - | |
|
| 1.5740 | 51400 | 0.1664 | - | - | - | - | |
|
| 1.5770 | 51500 | 0.1191 | - | - | - | - | |
|
| 1.5801 | 51600 | 0.2078 | - | - | - | - | |
|
| 1.5832 | 51700 | 0.1045 | - | - | - | - | |
|
| 1.5862 | 51800 | 0.1564 | - | - | - | - | |
|
| 1.5893 | 51900 | 0.219 | - | - | - | - | |
|
| 1.5924 | 52000 | 0.1308 | 1.3284 | 0.9085 | 0.7003 | 0.4439 | |
|
| 1.5954 | 52100 | 0.1002 | - | - | - | - | |
|
| 1.5985 | 52200 | 0.1133 | - | - | - | - | |
|
| 1.6015 | 52300 | 0.1612 | - | - | - | - | |
|
| 1.6046 | 52400 | 0.1216 | - | - | - | - | |
|
| 1.6077 | 52500 | 0.1767 | - | - | - | - | |
|
| 1.6107 | 52600 | 0.1198 | - | - | - | - | |
|
| 1.6138 | 52700 | 0.1426 | - | - | - | - | |
|
| 1.6169 | 52800 | 0.1505 | - | - | - | - | |
|
| 1.6199 | 52900 | 0.1503 | - | - | - | - | |
|
| 1.6230 | 53000 | 0.161 | 1.3557 | 0.9038 | 0.7073 | 0.4517 | |
|
| 1.6260 | 53100 | 0.1799 | - | - | - | - | |
|
| 1.6291 | 53200 | 0.1794 | - | - | - | - | |
|
| 1.6322 | 53300 | 0.1527 | - | - | - | - | |
|
| 1.6352 | 53400 | 0.1093 | - | - | - | - | |
|
| 1.6383 | 53500 | 0.1338 | - | - | - | - | |
|
| 1.6414 | 53600 | 0.1515 | - | - | - | - | |
|
| 1.6444 | 53700 | 0.1415 | - | - | - | - | |
|
| 1.6475 | 53800 | 0.1083 | - | - | - | - | |
|
| 1.6505 | 53900 | 0.0896 | - | - | - | - | |
|
| 1.6536 | 54000 | 0.1524 | 1.4412 | 0.9069 | 0.7047 | 0.4428 | |
|
| 1.6567 | 54100 | 0.1153 | - | - | - | - | |
|
| 1.6597 | 54200 | 0.1643 | - | - | - | - | |
|
| 1.6628 | 54300 | 0.0891 | - | - | - | - | |
|
| 1.6659 | 54400 | 0.1331 | - | - | - | - | |
|
| 1.6689 | 54500 | 0.14 | - | - | - | - | |
|
| 1.6720 | 54600 | 0.2027 | - | - | - | - | |
|
| 1.6750 | 54700 | 0.112 | - | - | - | - | |
|
| 1.6781 | 54800 | 0.1932 | - | - | - | - | |
|
| 1.6812 | 54900 | 0.1298 | - | - | - | - | |
|
| 1.6842 | 55000 | 0.1509 | 1.3844 | 0.8949 | 0.7094 | 0.4458 | |
|
| 1.6873 | 55100 | 0.113 | - | - | - | - | |
|
| 1.6903 | 55200 | 0.1516 | - | - | - | - | |
|
| 1.6934 | 55300 | 0.1523 | - | - | - | - | |
|
| 1.6965 | 55400 | 0.1627 | - | - | - | - | |
|
| 1.6995 | 55500 | 0.1142 | - | - | - | - | |
|
| 1.7026 | 55600 | 0.1054 | - | - | - | - | |
|
| 1.7057 | 55700 | 0.1438 | - | - | - | - | |
|
| 1.7087 | 55800 | 0.0908 | - | - | - | - | |
|
| 1.7118 | 55900 | 0.1311 | - | - | - | - | |
|
| 1.7148 | 56000 | 0.0691 | 1.4079 | 0.9229 | 0.7051 | 0.4484 | |
|
| 1.7179 | 56100 | 0.1617 | - | - | - | - | |
|
| 1.7210 | 56200 | 0.1709 | - | - | - | - | |
|
| 1.7240 | 56300 | 0.102 | - | - | - | - | |
|
| 1.7271 | 56400 | 0.1384 | - | - | - | - | |
|
| 1.7302 | 56500 | 0.1339 | - | - | - | - | |
|
| 1.7332 | 56600 | 0.1961 | - | - | - | - | |
|
| 1.7363 | 56700 | 0.1549 | - | - | - | - | |
|
| 1.7393 | 56800 | 0.1545 | - | - | - | - | |
|
| 1.7424 | 56900 | 0.1175 | - | - | - | - | |
|
| 1.7455 | 57000 | 0.1447 | 1.4055 | 0.9385 | 0.7006 | 0.4433 | |
|
| 1.7485 | 57100 | 0.1392 | - | - | - | - | |
|
| 1.7516 | 57200 | 0.0765 | - | - | - | - | |
|
| 1.7547 | 57300 | 0.1444 | - | - | - | - | |
|
| 1.7577 | 57400 | 0.1617 | - | - | - | - | |
|
| 1.7608 | 57500 | 0.164 | - | - | - | - | |
|
| 1.7638 | 57600 | 0.1584 | - | - | - | - | |
|
| 1.7669 | 57700 | 0.1613 | - | - | - | - | |
|
| 1.7700 | 57800 | 0.1381 | - | - | - | - | |
|
| 1.7730 | 57900 | 0.132 | - | - | - | - | |
|
| 1.7761 | 58000 | 0.1373 | 1.4008 | 0.9141 | 0.7088 | 0.4456 | |
|
| 1.7792 | 58100 | 0.1018 | - | - | - | - | |
|
| 1.7822 | 58200 | 0.0882 | - | - | - | - | |
|
| 1.7853 | 58300 | 0.1232 | - | - | - | - | |
|
| 1.7883 | 58400 | 0.1111 | - | - | - | - | |
|
| 1.7914 | 58500 | 0.0985 | - | - | - | - | |
|
| 1.7945 | 58600 | 0.1063 | - | - | - | - | |
|
| 1.7975 | 58700 | 0.0696 | - | - | - | - | |
|
| 1.8006 | 58800 | 0.113 | - | - | - | - | |
|
| 1.8037 | 58900 | 0.1048 | - | - | - | - | |
|
| 1.8067 | 59000 | 0.1305 | 1.4202 | 0.9253 | 0.7046 | 0.4450 | |
|
| 1.8098 | 59100 | 0.1203 | - | - | - | - | |
|
| 1.8128 | 59200 | 0.0975 | - | - | - | - | |
|
| 1.8159 | 59300 | 0.1163 | - | - | - | - | |
|
| 1.8190 | 59400 | 0.163 | - | - | - | - | |
|
| 1.8220 | 59500 | 0.1438 | - | - | - | - | |
|
| 1.8251 | 59600 | 0.1465 | - | - | - | - | |
|
| 1.8281 | 59700 | 0.1345 | - | - | - | - | |
|
| 1.8312 | 59800 | 0.1726 | - | - | - | - | |
|
| 1.8343 | 59900 | 0.1268 | - | - | - | - | |
|
| 1.8373 | 60000 | 0.0755 | 1.4523 | 0.9355 | 0.7059 | 0.4424 | |
|
| 1.8404 | 60100 | 0.1033 | - | - | - | - | |
|
| 1.8435 | 60200 | 0.1231 | - | - | - | - | |
|
| 1.8465 | 60300 | 0.1272 | - | - | - | - | |
|
| 1.8496 | 60400 | 0.1233 | - | - | - | - | |
|
| 1.8526 | 60500 | 0.1144 | - | - | - | - | |
|
| 1.8557 | 60600 | 0.1158 | - | - | - | - | |
|
| 1.8588 | 60700 | 0.1266 | - | - | - | - | |
|
| 1.8618 | 60800 | 0.0837 | - | - | - | - | |
|
| 1.8649 | 60900 | 0.1247 | - | - | - | - | |
|
| 1.8680 | 61000 | 0.1297 | 1.4443 | 0.9315 | 0.7037 | 0.4498 | |
|
| 1.8710 | 61100 | 0.1014 | - | - | - | - | |
|
| 1.8741 | 61200 | 0.127 | - | - | - | - | |
|
| 1.8771 | 61300 | 0.128 | - | - | - | - | |
|
| 1.8802 | 61400 | 0.1021 | - | - | - | - | |
|
| 1.8833 | 61500 | 0.1625 | - | - | - | - | |
|
| 1.8863 | 61600 | 0.1177 | - | - | - | - | |
|
| 1.8894 | 61700 | 0.1241 | - | - | - | - | |
|
| 1.8925 | 61800 | 0.1289 | - | - | - | - | |
|
| 1.8955 | 61900 | 0.1144 | - | - | - | - | |
|
| 1.8986 | 62000 | 0.0968 | 1.4650 | 0.9320 | 0.7012 | 0.4421 | |
|
| 1.9016 | 62100 | 0.0951 | - | - | - | - | |
|
| 1.9047 | 62200 | 0.1262 | - | - | - | - | |
|
| 1.9078 | 62300 | 0.1387 | - | - | - | - | |
|
| 1.9108 | 62400 | 0.129 | - | - | - | - | |
|
| 1.9139 | 62500 | 0.088 | - | - | - | - | |
|
| 1.9170 | 62600 | 0.1166 | - | - | - | - | |
|
| 1.9200 | 62700 | 0.1536 | - | - | - | - | |
|
| 1.9231 | 62800 | 0.1216 | - | - | - | - | |
|
| 1.9261 | 62900 | 0.1326 | - | - | - | - | |
|
| 1.9292 | 63000 | 0.1014 | 1.4315 | 0.9462 | 0.6982 | 0.4377 | |
|
| 1.9323 | 63100 | 0.1152 | - | - | - | - | |
|
| 1.9353 | 63200 | 0.0821 | - | - | - | - | |
|
| 1.9384 | 63300 | 0.1374 | - | - | - | - | |
|
| 1.9415 | 63400 | 0.0827 | - | - | - | - | |
|
| 1.9445 | 63500 | 0.1104 | - | - | - | - | |
|
| 1.9476 | 63600 | 0.1578 | - | - | - | - | |
|
| 1.9506 | 63700 | 0.1232 | - | - | - | - | |
|
| 1.9537 | 63800 | 0.1482 | - | - | - | - | |
|
| 1.9568 | 63900 | 0.1156 | - | - | - | - | |
|
| 1.9598 | 64000 | 0.1177 | 1.4263 | 0.9434 | 0.706 | 0.4420 | |
|
| 1.9629 | 64100 | 0.1074 | - | - | - | - | |
|
| 1.9659 | 64200 | 0.1385 | - | - | - | - | |
|
| 1.9690 | 64300 | 0.1083 | - | - | - | - | |
|
| 1.9721 | 64400 | 0.1138 | - | - | - | - | |
|
| 1.9751 | 64500 | 0.1383 | - | - | - | - | |
|
| 1.9782 | 64600 | 0.0786 | - | - | - | - | |
|
| 1.9813 | 64700 | 0.1043 | - | - | - | - | |
|
| 1.9843 | 64800 | 0.1112 | - | - | - | - | |
|
| 1.9874 | 64900 | 0.1237 | - | - | - | - | |
|
| 1.9904 | 65000 | 0.1073 | 1.3901 | 0.9587 | 0.7002 | 0.4438 | |
|
| 1.9935 | 65100 | 0.1174 | - | - | - | - | |
|
| 1.9966 | 65200 | 0.1091 | - | - | - | - | |
|
| 1.9996 | 65300 | 0.1143 | - | - | - | - | |
|
| 2.0027 | 65400 | 0.1044 | - | - | - | - | |
|
| 2.0058 | 65500 | 0.1279 | - | - | - | - | |
|
| 2.0088 | 65600 | 0.13 | - | - | - | - | |
|
| 2.0119 | 65700 | 0.1299 | - | - | - | - | |
|
| 2.0149 | 65800 | 0.1017 | - | - | - | - | |
|
| 2.0180 | 65900 | 0.124 | - | - | - | - | |
|
| 2.0211 | 66000 | 0.1062 | 1.4390 | 0.9290 | 0.704 | 0.4440 | |
|
| 2.0241 | 66100 | 0.1634 | - | - | - | - | |
|
| 2.0272 | 66200 | 0.1149 | - | - | - | - | |
|
| 2.0303 | 66300 | 0.0682 | - | - | - | - | |
|
| 2.0333 | 66400 | 0.1386 | - | - | - | - | |
|
| 2.0364 | 66500 | 0.0861 | - | - | - | - | |
|
| 2.0394 | 66600 | 0.0669 | - | - | - | - | |
|
| 2.0425 | 66700 | 0.0944 | - | - | - | - | |
|
| 2.0456 | 66800 | 0.1332 | - | - | - | - | |
|
| 2.0486 | 66900 | 0.0884 | - | - | - | - | |
|
| 2.0517 | 67000 | 0.122 | 1.5088 | 0.9513 | 0.7063 | 0.4492 | |
|
| 2.0548 | 67100 | 0.0934 | - | - | - | - | |
|
| 2.0578 | 67200 | 0.102 | - | - | - | - | |
|
| 2.0609 | 67300 | 0.1402 | - | - | - | - | |
|
| 2.0639 | 67400 | 0.1394 | - | - | - | - | |
|
| 2.0670 | 67500 | 0.1067 | - | - | - | - | |
|
| 2.0701 | 67600 | 0.1052 | - | - | - | - | |
|
| 2.0731 | 67700 | 0.1267 | - | - | - | - | |
|
| 2.0762 | 67800 | 0.1048 | - | - | - | - | |
|
| 2.0793 | 67900 | 0.0962 | - | - | - | - | |
|
| 2.0823 | 68000 | 0.0929 | 1.4530 | 0.9247 | 0.7109 | 0.4491 | |
|
| 2.0854 | 68100 | 0.1298 | - | - | - | - | |
|
| 2.0884 | 68200 | 0.1332 | - | - | - | - | |
|
| 2.0915 | 68300 | 0.0913 | - | - | - | - | |
|
| 2.0946 | 68400 | 0.0843 | - | - | - | - | |
|
| 2.0976 | 68500 | 0.0846 | - | - | - | - | |
|
| 2.1007 | 68600 | 0.1142 | - | - | - | - | |
|
| 2.1037 | 68700 | 0.1403 | - | - | - | - | |
|
| 2.1068 | 68800 | 0.0961 | - | - | - | - | |
|
| 2.1099 | 68900 | 0.0984 | - | - | - | - | |
|
| 2.1129 | 69000 | 0.1509 | 1.4343 | 0.9552 | 0.7039 | 0.4382 | |
|
| 2.1160 | 69100 | 0.0947 | - | - | - | - | |
|
| 2.1191 | 69200 | 0.0877 | - | - | - | - | |
|
| 2.1221 | 69300 | 0.0786 | - | - | - | - | |
|
| 2.1252 | 69400 | 0.0754 | - | - | - | - | |
|
| 2.1282 | 69500 | 0.0765 | - | - | - | - | |
|
| 2.1313 | 69600 | 0.0632 | - | - | - | - | |
|
| 2.1344 | 69700 | 0.1792 | - | - | - | - | |
|
| 2.1374 | 69800 | 0.0666 | - | - | - | - | |
|
| 2.1405 | 69900 | 0.1225 | - | - | - | - | |
|
| 2.1436 | 70000 | 0.0922 | 1.4291 | 0.9393 | 0.7053 | 0.4357 | |
|
| 2.1466 | 70100 | 0.126 | - | - | - | - | |
|
| 2.1497 | 70200 | 0.0991 | - | - | - | - | |
|
| 2.1527 | 70300 | 0.0759 | - | - | - | - | |
|
| 2.1558 | 70400 | 0.1024 | - | - | - | - | |
|
| 2.1589 | 70500 | 0.0894 | - | - | - | - | |
|
| 2.1619 | 70600 | 0.113 | - | - | - | - | |
|
| 2.1650 | 70700 | 0.1084 | - | - | - | - | |
|
| 2.1681 | 70800 | 0.1013 | - | - | - | - | |
|
| 2.1711 | 70900 | 0.111 | - | - | - | - | |
|
| 2.1742 | 71000 | 0.0965 | 1.3915 | 0.9477 | 0.7052 | 0.4437 | |
|
| 2.1772 | 71100 | 0.0837 | - | - | - | - | |
|
| 2.1803 | 71200 | 0.0347 | - | - | - | - | |
|
| 2.1834 | 71300 | 0.1215 | - | - | - | - | |
|
| 2.1864 | 71400 | 0.0799 | - | - | - | - | |
|
| 2.1895 | 71500 | 0.1173 | - | - | - | - | |
|
| 2.1926 | 71600 | 0.0964 | - | - | - | - | |
|
| 2.1956 | 71700 | 0.1036 | - | - | - | - | |
|
| 2.1987 | 71800 | 0.0952 | - | - | - | - | |
|
| 2.2017 | 71900 | 0.0752 | - | - | - | - | |
|
| 2.2048 | 72000 | 0.0824 | 1.4657 | 0.9593 | 0.7011 | 0.4397 | |
|
| 2.2079 | 72100 | 0.1081 | - | - | - | - | |
|
| 2.2109 | 72200 | 0.0718 | - | - | - | - | |
|
| 2.2140 | 72300 | 0.0644 | - | - | - | - | |
|
| 2.2171 | 72400 | 0.0919 | - | - | - | - | |
|
| 2.2201 | 72500 | 0.1099 | - | - | - | - | |
|
| 2.2232 | 72600 | 0.072 | - | - | - | - | |
|
| 2.2262 | 72700 | 0.0675 | - | - | - | - | |
|
| 2.2293 | 72800 | 0.0568 | - | - | - | - | |
|
| 2.2324 | 72900 | 0.0664 | - | - | - | - | |
|
| 2.2354 | 73000 | 0.0926 | 1.4526 | 0.9607 | 0.701 | 0.4383 | |
|
| 2.2385 | 73100 | 0.1089 | - | - | - | - | |
|
| 2.2415 | 73200 | 0.1208 | - | - | - | - | |
|
| 2.2446 | 73300 | 0.0583 | - | - | - | - | |
|
| 2.2477 | 73400 | 0.0546 | - | - | - | - | |
|
| 2.2507 | 73500 | 0.086 | - | - | - | - | |
|
| 2.2538 | 73600 | 0.1029 | - | - | - | - | |
|
| 2.2569 | 73700 | 0.0803 | - | - | - | - | |
|
| 2.2599 | 73800 | 0.114 | - | - | - | - | |
|
| 2.2630 | 73900 | 0.0542 | - | - | - | - | |
|
| 2.2660 | 74000 | 0.0732 | 1.4112 | 0.9411 | 0.7028 | 0.4454 | |
|
| 2.2691 | 74100 | 0.0641 | - | - | - | - | |
|
| 2.2722 | 74200 | 0.072 | - | - | - | - | |
|
| 2.2752 | 74300 | 0.0806 | - | - | - | - | |
|
| 2.2783 | 74400 | 0.0845 | - | - | - | - | |
|
| 2.2814 | 74500 | 0.0599 | - | - | - | - | |
|
| 2.2844 | 74600 | 0.069 | - | - | - | - | |
|
| 2.2875 | 74700 | 0.0808 | - | - | - | - | |
|
| 2.2905 | 74800 | 0.0903 | - | - | - | - | |
|
| 2.2936 | 74900 | 0.0693 | - | - | - | - | |
|
| 2.2967 | 75000 | 0.074 | 1.4487 | 0.9686 | 0.6945 | 0.4495 | |
|
| 2.2997 | 75100 | 0.1261 | - | - | - | - | |
|
| 2.3028 | 75200 | 0.055 | - | - | - | - | |
|
| 2.3059 | 75300 | 0.0828 | - | - | - | - | |
|
| 2.3089 | 75400 | 0.0735 | - | - | - | - | |
|
| 2.3120 | 75500 | 0.0539 | - | - | - | - | |
|
| 2.3150 | 75600 | 0.0763 | - | - | - | - | |
|
| 2.3181 | 75700 | 0.0598 | - | - | - | - | |
|
| 2.3212 | 75800 | 0.0782 | - | - | - | - | |
|
| 2.3242 | 75900 | 0.0681 | - | - | - | - | |
|
| 2.3273 | 76000 | 0.0497 | 1.4493 | 0.9589 | 0.6972 | 0.4354 | |
|
| 2.3304 | 76100 | 0.0353 | - | - | - | - | |
|
| 2.3334 | 76200 | 0.0669 | - | - | - | - | |
|
| 2.3365 | 76300 | 0.064 | - | - | - | - | |
|
| 2.3395 | 76400 | 0.0814 | - | - | - | - | |
|
| 2.3426 | 76500 | 0.0786 | - | - | - | - | |
|
| 2.3457 | 76600 | 0.091 | - | - | - | - | |
|
| 2.3487 | 76700 | 0.0861 | - | - | - | - | |
|
| 2.3518 | 76800 | 0.0445 | - | - | - | - | |
|
| 2.3549 | 76900 | 0.0589 | - | - | - | - | |
|
| 2.3579 | 77000 | 0.0318 | 1.4455 | 0.9647 | 0.7012 | 0.4390 | |
|
| 2.3610 | 77100 | 0.0425 | - | - | - | - | |
|
| 2.3640 | 77200 | 0.0605 | - | - | - | - | |
|
| 2.3671 | 77300 | 0.0523 | - | - | - | - | |
|
| 2.3702 | 77400 | 0.0715 | - | - | - | - | |
|
| 2.3732 | 77500 | 0.0756 | - | - | - | - | |
|
| 2.3763 | 77600 | 0.0911 | - | - | - | - | |
|
| 2.3793 | 77700 | 0.1023 | - | - | - | - | |
|
| 2.3824 | 77800 | 0.0538 | - | - | - | - | |
|
| 2.3855 | 77900 | 0.0571 | - | - | - | - | |
|
| 2.3885 | 78000 | 0.0505 | 1.4434 | 0.9554 | 0.7048 | 0.4411 | |
|
| 2.3916 | 78100 | 0.1114 | - | - | - | - | |
|
| 2.3947 | 78200 | 0.0368 | - | - | - | - | |
|
| 2.3977 | 78300 | 0.0636 | - | - | - | - | |
|
| 2.4008 | 78400 | 0.0419 | - | - | - | - | |
|
| 2.4038 | 78500 | 0.0691 | - | - | - | - | |
|
| 2.4069 | 78600 | 0.0814 | - | - | - | - | |
|
| 2.4100 | 78700 | 0.0644 | - | - | - | - | |
|
| 2.4130 | 78800 | 0.0584 | - | - | - | - | |
|
| 2.4161 | 78900 | 0.0745 | - | - | - | - | |
|
| 2.4192 | 79000 | 0.0558 | 1.4218 | 0.9631 | 0.7024 | 0.4399 | |
|
| 2.4222 | 79100 | 0.0478 | - | - | - | - | |
|
| 2.4253 | 79200 | 0.1116 | - | - | - | - | |
|
| 2.4283 | 79300 | 0.0487 | - | - | - | - | |
|
| 2.4314 | 79400 | 0.0457 | - | - | - | - | |
|
| 2.4345 | 79500 | 0.0441 | - | - | - | - | |
|
| 2.4375 | 79600 | 0.037 | - | - | - | - | |
|
| 2.4406 | 79700 | 0.0382 | - | - | - | - | |
|
| 2.4437 | 79800 | 0.0453 | - | - | - | - | |
|
| 2.4467 | 79900 | 0.0625 | - | - | - | - | |
|
| 2.4498 | 80000 | 0.0649 | 1.4950 | 0.9400 | 0.7053 | 0.4516 | |
|
| 2.4528 | 80100 | 0.0417 | - | - | - | - | |
|
| 2.4559 | 80200 | 0.03 | - | - | - | - | |
|
| 2.4590 | 80300 | 0.0281 | - | - | - | - | |
|
| 2.4620 | 80400 | 0.0637 | - | - | - | - | |
|
| 2.4651 | 80500 | 0.0415 | - | - | - | - | |
|
| 2.4682 | 80600 | 0.048 | - | - | - | - | |
|
| 2.4712 | 80700 | 0.0653 | - | - | - | - | |
|
| 2.4743 | 80800 | 0.0382 | - | - | - | - | |
|
| 2.4773 | 80900 | 0.0524 | - | - | - | - | |
|
| 2.4804 | 81000 | 0.0699 | 1.4317 | 0.9833 | 0.7024 | 0.4487 | |
|
| 2.4835 | 81100 | 0.0728 | - | - | - | - | |
|
| 2.4865 | 81200 | 0.0346 | - | - | - | - | |
|
| 2.4896 | 81300 | 0.0448 | - | - | - | - | |
|
| 2.4927 | 81400 | 0.0425 | - | - | - | - | |
|
| 2.4957 | 81500 | 0.0941 | - | - | - | - | |
|
| 2.4988 | 81600 | 0.0385 | - | - | - | - | |
|
| 2.5018 | 81700 | 0.0802 | - | - | - | - | |
|
| 2.5049 | 81800 | 0.033 | - | - | - | - | |
|
| 2.5080 | 81900 | 0.0653 | - | - | - | - | |
|
| 2.5110 | 82000 | 0.0435 | 1.4630 | 0.9643 | 0.7006 | 0.4467 | |
|
| 2.5141 | 82100 | 0.0301 | - | - | - | - | |
|
| 2.5171 | 82200 | 0.0392 | - | - | - | - | |
|
| 2.5202 | 82300 | 0.0402 | - | - | - | - | |
|
| 2.5233 | 82400 | 0.0584 | - | - | - | - | |
|
| 2.5263 | 82500 | 0.0334 | - | - | - | - | |
|
| 2.5294 | 82600 | 0.0409 | - | - | - | - | |
|
| 2.5325 | 82700 | 0.0587 | - | - | - | - | |
|
| 2.5355 | 82800 | 0.0412 | - | - | - | - | |
|
| 2.5386 | 82900 | 0.0477 | - | - | - | - | |
|
| 2.5416 | 83000 | 0.0485 | 1.4052 | 0.9665 | 0.7021 | 0.4382 | |
|
| 2.5447 | 83100 | 0.0501 | - | - | - | - | |
|
| 2.5478 | 83200 | 0.0492 | - | - | - | - | |
|
| 2.5508 | 83300 | 0.0829 | - | - | - | - | |
|
| 2.5539 | 83400 | 0.0571 | - | - | - | - | |
|
| 2.5570 | 83500 | 0.0353 | - | - | - | - | |
|
| 2.5600 | 83600 | 0.0439 | - | - | - | - | |
|
| 2.5631 | 83700 | 0.0264 | - | - | - | - | |
|
| 2.5661 | 83800 | 0.0743 | - | - | - | - | |
|
| 2.5692 | 83900 | 0.0467 | - | - | - | - | |
|
| 2.5723 | 84000 | 0.0442 | 1.4663 | 0.9854 | 0.7022 | 0.4380 | |
|
| 2.5753 | 84100 | 0.0461 | - | - | - | - | |
|
| 2.5784 | 84200 | 0.0542 | - | - | - | - | |
|
| 2.5815 | 84300 | 0.0842 | - | - | - | - | |
|
| 2.5845 | 84400 | 0.0361 | - | - | - | - | |
|
| 2.5876 | 84500 | 0.0577 | - | - | - | - | |
|
| 2.5906 | 84600 | 0.0345 | - | - | - | - | |
|
| 2.5937 | 84700 | 0.0296 | - | - | - | - | |
|
| 2.5968 | 84800 | 0.025 | - | - | - | - | |
|
| 2.5998 | 84900 | 0.0575 | - | - | - | - | |
|
| 2.6029 | 85000 | 0.0567 | 1.5179 | 0.9760 | 0.7008 | 0.4469 | |
|
| 2.6060 | 85100 | 0.0528 | - | - | - | - | |
|
| 2.6090 | 85200 | 0.0437 | - | - | - | - | |
|
| 2.6121 | 85300 | 0.0291 | - | - | - | - | |
|
| 2.6151 | 85400 | 0.0689 | - | - | - | - | |
|
| 2.6182 | 85500 | 0.0328 | - | - | - | - | |
|
| 2.6213 | 85600 | 0.0473 | - | - | - | - | |
|
| 2.6243 | 85700 | 0.0682 | - | - | - | - | |
|
| 2.6274 | 85800 | 0.0544 | - | - | - | - | |
|
| 2.6305 | 85900 | 0.0641 | - | - | - | - | |
|
| 2.6335 | 86000 | 0.029 | 1.4558 | 0.9576 | 0.7072 | 0.4448 | |
|
| 2.6366 | 86100 | 0.0364 | - | - | - | - | |
|
| 2.6396 | 86200 | 0.0365 | - | - | - | - | |
|
| 2.6427 | 86300 | 0.0514 | - | - | - | - | |
|
| 2.6458 | 86400 | 0.0417 | - | - | - | - | |
|
| 2.6488 | 86500 | 0.0309 | - | - | - | - | |
|
| 2.6519 | 86600 | 0.035 | - | - | - | - | |
|
| 2.6549 | 86700 | 0.044 | - | - | - | - | |
|
| 2.6580 | 86800 | 0.0694 | - | - | - | - | |
|
| 2.6611 | 86900 | 0.0194 | - | - | - | - | |
|
| 2.6641 | 87000 | 0.0373 | 1.5556 | 0.9819 | 0.7052 | 0.4339 | |
|
| 2.6672 | 87100 | 0.0349 | - | - | - | - | |
|
| 2.6703 | 87200 | 0.0561 | - | - | - | - | |
|
| 2.6733 | 87300 | 0.0487 | - | - | - | - | |
|
| 2.6764 | 87400 | 0.0722 | - | - | - | - | |
|
| 2.6794 | 87500 | 0.0501 | - | - | - | - | |
|
| 2.6825 | 87600 | 0.0404 | - | - | - | - | |
|
| 2.6856 | 87700 | 0.0533 | - | - | - | - | |
|
| 2.6886 | 87800 | 0.0371 | - | - | - | - | |
|
| 2.6917 | 87900 | 0.0585 | - | - | - | - | |
|
| 2.6948 | 88000 | 0.0482 | 1.4305 | 0.9550 | 0.7053 | 0.4397 | |
|
| 2.6978 | 88100 | 0.0731 | - | - | - | - | |
|
| 2.7009 | 88200 | 0.0312 | - | - | - | - | |
|
| 2.7039 | 88300 | 0.0339 | - | - | - | - | |
|
| 2.7070 | 88400 | 0.0348 | - | - | - | - | |
|
| 2.7101 | 88500 | 0.0509 | - | - | - | - | |
|
| 2.7131 | 88600 | 0.0343 | - | - | - | - | |
|
| 2.7162 | 88700 | 0.0282 | - | - | - | - | |
|
| 2.7193 | 88800 | 0.0518 | - | - | - | - | |
|
| 2.7223 | 88900 | 0.0569 | - | - | - | - | |
|
| 2.7254 | 89000 | 0.0427 | 1.4783 | 0.9810 | 0.7066 | 0.4263 | |
|
| 2.7284 | 89100 | 0.0554 | - | - | - | - | |
|
| 2.7315 | 89200 | 0.0368 | - | - | - | - | |
|
| 2.7346 | 89300 | 0.0301 | - | - | - | - | |
|
| 2.7376 | 89400 | 0.0469 | - | - | - | - | |
|
| 2.7407 | 89500 | 0.0479 | - | - | - | - | |
|
| 2.7438 | 89600 | 0.0586 | - | - | - | - | |
|
| 2.7468 | 89700 | 0.0687 | - | - | - | - | |
|
| 2.7499 | 89800 | 0.0427 | - | - | - | - | |
|
| 2.7529 | 89900 | 0.0551 | - | - | - | - | |
|
| 2.7560 | 90000 | 0.0255 | 1.4759 | 0.9758 | 0.7011 | 0.4434 | |
|
| 2.7591 | 90100 | 0.0348 | - | - | - | - | |
|
| 2.7621 | 90200 | 0.0536 | - | - | - | - | |
|
| 2.7652 | 90300 | 0.0554 | - | - | - | - | |
|
| 2.7683 | 90400 | 0.0367 | - | - | - | - | |
|
| 2.7713 | 90500 | 0.0185 | - | - | - | - | |
|
| 2.7744 | 90600 | 0.0498 | - | - | - | - | |
|
| 2.7774 | 90700 | 0.0296 | - | - | - | - | |
|
| 2.7805 | 90800 | 0.0132 | - | - | - | - | |
|
| 2.7836 | 90900 | 0.0607 | - | - | - | - | |
|
| 2.7866 | 91000 | 0.0303 | 1.4869 | 0.9817 | 0.704 | 0.4442 | |
|
| 2.7897 | 91100 | 0.0463 | - | - | - | - | |
|
| 2.7927 | 91200 | 0.0302 | - | - | - | - | |
|
| 2.7958 | 91300 | 0.04 | - | - | - | - | |
|
| 2.7989 | 91400 | 0.0406 | - | - | - | - | |
|
| 2.8019 | 91500 | 0.0202 | - | - | - | - | |
|
| 2.8050 | 91600 | 0.0397 | - | - | - | - | |
|
| 2.8081 | 91700 | 0.0313 | - | - | - | - | |
|
| 2.8111 | 91800 | 0.0419 | - | - | - | - | |
|
| 2.8142 | 91900 | 0.055 | - | - | - | - | |
|
| 2.8172 | 92000 | 0.0377 | 1.5091 | 0.9752 | 0.7071 | 0.4336 | |
|
| 2.8203 | 92100 | 0.0386 | - | - | - | - | |
|
| 2.8234 | 92200 | 0.0497 | - | - | - | - | |
|
| 2.8264 | 92300 | 0.061 | - | - | - | - | |
|
| 2.8295 | 92400 | 0.0683 | - | - | - | - | |
|
| 2.8326 | 92500 | 0.0846 | - | - | - | - | |
|
| 2.8356 | 92600 | 0.0121 | - | - | - | - | |
|
| 2.8387 | 92700 | 0.0268 | - | - | - | - | |
|
| 2.8417 | 92800 | 0.0205 | - | - | - | - | |
|
| 2.8448 | 92900 | 0.0414 | - | - | - | - | |
|
| 2.8479 | 93000 | 0.0443 | 1.5421 | 0.9798 | 0.708 | 0.4298 | |
|
| 2.8509 | 93100 | 0.0365 | - | - | - | - | |
|
| 2.8540 | 93200 | 0.0431 | - | - | - | - | |
|
| 2.8571 | 93300 | 0.0254 | - | - | - | - | |
|
| 2.8601 | 93400 | 0.0348 | - | - | - | - | |
|
| 2.8632 | 93500 | 0.0408 | - | - | - | - | |
|
| 2.8662 | 93600 | 0.0481 | - | - | - | - | |
|
| 2.8693 | 93700 | 0.0303 | - | - | - | - | |
|
| 2.8724 | 93800 | 0.0512 | - | - | - | - | |
|
| 2.8754 | 93900 | 0.0563 | - | - | - | - | |
|
| 2.8785 | 94000 | 0.0506 | 1.6061 | 0.9909 | 0.7043 | 0.4392 | |
|
| 2.8816 | 94100 | 0.0224 | - | - | - | - | |
|
| 2.8846 | 94200 | 0.0652 | - | - | - | - | |
|
| 2.8877 | 94300 | 0.0313 | - | - | - | - | |
|
| 2.8907 | 94400 | 0.0657 | - | - | - | - | |
|
| 2.8938 | 94500 | 0.0605 | - | - | - | - | |
|
| 2.8969 | 94600 | 0.0332 | - | - | - | - | |
|
| 2.8999 | 94700 | 0.0126 | - | - | - | - | |
|
| 2.9030 | 94800 | 0.0374 | - | - | - | - | |
|
| 2.9061 | 94900 | 0.051 | - | - | - | - | |
|
| 2.9091 | 95000 | 0.0477 | 1.5915 | 1.0124 | 0.702 | 0.4299 | |
|
|
|
</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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