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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:1000000 |
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- loss:MultipleNegativesRankingLoss |
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base_model: sentence-transformers/LaBSE |
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widget: |
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- source_sentence: Акӑ ӗнтӗ Чакак кимӗ ҫине сикрӗ, Коля пӗр-икӗ хут шнуртан туртрӗ |
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те, мотор кӗрлесе те кайрӗ, унтан кимӗ утрав еннелле вӗҫтерчӗ. |
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sentences: |
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- Вот Сорока вскочил в лодку, Коля дернул за шнур, раз, другой, мотор затрещал, |
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и лодка понеслась к острову. |
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- Победа римского флота в гавани Эвносте. |
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- Повесть Бориса Горбатова о подвиге и героизме советских людей во время Великой |
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Отечественной войны. |
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- source_sentence: Ун патне пысӑках мар хырӑмлӑ, шурӑ сӑнлӑ, хӗрлӗ питлӗ, лутра ҫын |
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килсе кӗчӗ. |
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sentences: |
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- Антонов, Семён Михеевич |
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- Явился низенький человек, с умеренным брюшком, с белым лицом, румяными щеками |
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- Чёрно-белые фильмы СССР |
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- source_sentence: '3. Анчах Гаваон ҫыннисем, Иисус Иерихонпа Гай хулисене епле пӗтерсе |
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тӑкни ҫинчен илтсессӗн, 4. акӑ мӗнле чеелӗх тупнӑ: ашакӗсем ҫине ҫул валли кивӗ |
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михӗсемпе ҫӑкӑр янтӑласа хунӑ, ҫӗтӗлсе пӗтнӗ, саплӑклӑ тир хутаҫпа эрех илнӗ; |
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5. ури сырри те вӗсен кивӗ, саплӑклӑ пулнӑ, ҫийӗнчи тумтирӗсем те ҫӗтӗк пулнӑ; |
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ҫул ҫине илнӗ ҫӑкӑрӗ те пӗтӗмпех типсе-кӑвакарса кайнӑскер, [тӗпренсе] пӗтнӗскер |
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пулнӑ.' |
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sentences: |
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- '3. Но жители Гаваона, услышав, что Иисус сделал с Иерихоном и Гаем, 4. употребили |
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хитрость: пошли, запаслись хлебом на дорогу и положили ветхие мешки на ослов своих |
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и ветхие, изорванные и заплатанные мехи вина; 5. и обувь на ногах их была ветхая |
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с заплатами, и одежда на них ветхая; и весь дорожный хлеб их был сухой и заплесневелый |
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[и раскрошенный].' |
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- «Черти бы их дули!..» — в отчаянии вскричал Щукарь и кинулся к цыганскому табору, |
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но, выскочив на пригорок, обнаружил, что ни шатров, ни кибиток возле речки уже |
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нет. |
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- 9. И сделаю над тобою то, чего Я никогда не делал и чему подобного впредь не буду |
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делать, за все твои мерзости. |
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- source_sentence: Эпӗ кӗпер айӗпе укҫасӑрах, ахалех вӗҫсе тухрӑм. |
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sentences: |
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- У меня в экипаже был механик — что называется, «палец в рот не клади». |
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- А я под мост даром слетал. |
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- Я пользовался этим и прогуливал школу, чтобы проводить время в компании более |
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старших ребят. |
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- source_sentence: Генри Джастис Форд |
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sentences: |
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- — Вижу, по одному делу? — спросила она, взглянув на Сашу и его приятелей. |
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- Я вышел из ванны свеж и бодр, как будто собирался на бал. |
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- Форд, Генри Джастис |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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--- |
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# SentenceTransformer based on sentence-transformers/LaBSE |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision 836121a0533e5664b21c7aacc5d22951f2b8b25b --> |
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- **Maximum Sequence Length:** 256 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) |
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(3): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("sentence_transformers_model_id") |
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# Run inference |
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sentences = [ |
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'Генри Джастис Форд', |
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'Форд, Генри Джастис', |
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'Я вышел из ванны свеж и бодр, как будто собирался на бал.', |
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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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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 1,000,000 training samples |
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* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | label | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------| |
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| type | string | string | float | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 21.82 tokens</li><li>max: 127 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 21.16 tokens</li><li>max: 136 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | label | |
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|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------|:-----------------| |
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| <code>Темех мар.</code> | <code>Дело десятое.</code> | <code>1.0</code> | |
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| <code>Уругвайӑн тĕн ĕҫченĕсем</code> | <code>Религиозные деятели Уругвая</code> | <code>1.0</code> | |
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| <code>Эп аванах ас тӑватӑп, пилӗк ҫул каялла пахчана эпир лайӑх тасатнӑччӗ.</code> | <code>А пять лет тому назад я знал, что сад был чищен.</code> | <code>1.0</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) 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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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 12 |
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- `per_device_eval_batch_size`: 12 |
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- `num_train_epochs`: 1 |
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- `fp16`: True |
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- `multi_dataset_batch_sampler`: round_robin |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 12 |
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- `per_device_eval_batch_size`: 12 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1 |
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- `num_train_epochs`: 1 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.0 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `tp_size`: 0 |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: round_robin |
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</details> |
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### Training Logs |
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<details><summary>Click to expand</summary> |
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| Epoch | Step | Training Loss | |
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|:------:|:-----:|:-------------:| |
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| 0.0012 | 100 | - | |
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| 0.0024 | 200 | - | |
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| 0.0036 | 300 | - | |
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| 0.0048 | 400 | - | |
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| 0.0060 | 500 | 0.5331 | |
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| 0.0072 | 600 | - | |
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| 0.0084 | 700 | - | |
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| 0.0096 | 800 | - | |
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| 0.0108 | 900 | - | |
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| 0.0120 | 1000 | 0.3694 | |
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| 0.0132 | 1100 | - | |
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| 0.0144 | 1200 | - | |
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| 0.0156 | 1300 | - | |
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| 0.0168 | 1400 | - | |
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| 0.0180 | 1500 | 0.3141 | |
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| 0.0192 | 1600 | - | |
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| 0.0204 | 1700 | - | |
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| 0.0216 | 1800 | - | |
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| 0.0228 | 1900 | - | |
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| 0.0240 | 2000 | 0.2836 | |
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| 0.0252 | 2100 | - | |
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| 0.0264 | 2200 | - | |
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| 0.0276 | 2300 | - | |
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| 0.0288 | 2400 | - | |
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| 0.0300 | 2500 | 0.2823 | |
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| 0.0312 | 2600 | - | |
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| 0.0324 | 2700 | - | |
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| 0.0336 | 2800 | - | |
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| 0.0348 | 2900 | - | |
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| 0.0360 | 3000 | 0.265 | |
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| 0.0372 | 3100 | - | |
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| 0.0384 | 3200 | - | |
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| 0.0396 | 3300 | - | |
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| 0.0408 | 3400 | - | |
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| 0.0420 | 3500 | 0.2599 | |
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| 0.0432 | 3600 | - | |
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| 0.0444 | 3700 | - | |
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| 0.0456 | 3800 | - | |
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| 0.0468 | 3900 | - | |
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| 0.0480 | 4000 | 0.234 | |
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| 0.0492 | 4100 | - | |
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| 0.0504 | 4200 | - | |
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| 0.0516 | 4300 | - | |
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| 0.0528 | 4400 | - | |
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| 0.0540 | 4500 | 0.1966 | |
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| 0.0552 | 4600 | - | |
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| 0.0564 | 4700 | - | |
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| 0.0576 | 4800 | - | |
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| 0.0588 | 4900 | - | |
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| 0.0600 | 5000 | 0.2204 | |
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| 0.0612 | 5100 | - | |
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| 0.0624 | 5200 | - | |
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| 0.0636 | 5300 | - | |
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| 0.0648 | 5400 | - | |
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| 0.0660 | 5500 | 0.2272 | |
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| 0.0672 | 5600 | - | |
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| 0.0684 | 5700 | - | |
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| 0.0696 | 5800 | - | |
|
|
| 0.0708 | 5900 | - | |
|
|
| 0.0720 | 6000 | 0.2256 | |
|
|
| 0.0732 | 6100 | - | |
|
|
| 0.0744 | 6200 | - | |
|
|
| 0.0756 | 6300 | - | |
|
|
| 0.0768 | 6400 | - | |
|
|
| 0.0780 | 6500 | 0.2071 | |
|
|
| 0.0792 | 6600 | - | |
|
|
| 0.0804 | 6700 | - | |
|
|
| 0.0816 | 6800 | - | |
|
|
| 0.0828 | 6900 | - | |
|
|
| 0.0840 | 7000 | 0.2113 | |
|
|
| 0.0852 | 7100 | - | |
|
|
| 0.0864 | 7200 | - | |
|
|
| 0.0876 | 7300 | - | |
|
|
| 0.0888 | 7400 | - | |
|
|
| 0.0900 | 7500 | 0.2222 | |
|
|
| 0.0912 | 7600 | - | |
|
|
| 0.0924 | 7700 | - | |
|
|
| 0.0936 | 7800 | - | |
|
|
| 0.0948 | 7900 | - | |
|
|
| 0.0960 | 8000 | 0.2186 | |
|
|
| 0.0972 | 8100 | - | |
|
|
| 0.0984 | 8200 | - | |
|
|
| 0.0996 | 8300 | - | |
|
|
| 0.1008 | 8400 | - | |
|
|
| 0.1020 | 8500 | 0.2137 | |
|
|
| 0.1032 | 8600 | - | |
|
|
| 0.1044 | 8700 | - | |
|
|
| 0.1056 | 8800 | - | |
|
|
| 0.1068 | 8900 | - | |
|
|
| 0.1080 | 9000 | 0.1928 | |
|
|
| 0.1092 | 9100 | - | |
|
|
| 0.1104 | 9200 | - | |
|
|
| 0.1116 | 9300 | - | |
|
|
| 0.1128 | 9400 | - | |
|
|
| 0.1140 | 9500 | 0.2117 | |
|
|
| 0.1152 | 9600 | - | |
|
|
| 0.1164 | 9700 | - | |
|
|
| 0.1176 | 9800 | - | |
|
|
| 0.1188 | 9900 | - | |
|
|
| 0.1200 | 10000 | 0.1987 | |
|
|
| 0.1212 | 10100 | - | |
|
|
| 0.1224 | 10200 | - | |
|
|
| 0.1236 | 10300 | - | |
|
|
| 0.1248 | 10400 | - | |
|
|
| 0.1260 | 10500 | 0.2011 | |
|
|
| 0.1272 | 10600 | - | |
|
|
| 0.1284 | 10700 | - | |
|
|
| 0.1296 | 10800 | - | |
|
|
| 0.1308 | 10900 | - | |
|
|
| 0.1320 | 11000 | 0.1775 | |
|
|
| 0.1332 | 11100 | - | |
|
|
| 0.1344 | 11200 | - | |
|
|
| 0.1356 | 11300 | - | |
|
|
| 0.1368 | 11400 | - | |
|
|
| 0.1380 | 11500 | 0.2048 | |
|
|
| 0.1392 | 11600 | - | |
|
|
| 0.1404 | 11700 | - | |
|
|
| 0.1416 | 11800 | - | |
|
|
| 0.1428 | 11900 | - | |
|
|
| 0.1440 | 12000 | 0.2064 | |
|
|
| 0.1452 | 12100 | - | |
|
|
| 0.1464 | 12200 | - | |
|
|
| 0.1476 | 12300 | - | |
|
|
| 0.1488 | 12400 | - | |
|
|
| 0.1500 | 12500 | 0.1883 | |
|
|
| 0.1512 | 12600 | - | |
|
|
| 0.1524 | 12700 | - | |
|
|
| 0.1536 | 12800 | - | |
|
|
| 0.1548 | 12900 | - | |
|
|
| 0.1560 | 13000 | 0.2084 | |
|
|
| 0.1572 | 13100 | - | |
|
|
| 0.1584 | 13200 | - | |
|
|
| 0.1596 | 13300 | - | |
|
|
| 0.1608 | 13400 | - | |
|
|
| 0.1620 | 13500 | 0.2077 | |
|
|
| 0.1632 | 13600 | - | |
|
|
| 0.1644 | 13700 | - | |
|
|
| 0.1656 | 13800 | - | |
|
|
| 0.1668 | 13900 | - | |
|
|
| 0.1680 | 14000 | 0.1866 | |
|
|
| 0.1692 | 14100 | - | |
|
|
| 0.1704 | 14200 | - | |
|
|
| 0.1716 | 14300 | - | |
|
|
| 0.1728 | 14400 | - | |
|
|
| 0.1740 | 14500 | 0.1859 | |
|
|
| 0.1752 | 14600 | - | |
|
|
| 0.1764 | 14700 | - | |
|
|
| 0.1776 | 14800 | - | |
|
|
| 0.1788 | 14900 | - | |
|
|
| 0.1800 | 15000 | 0.1735 | |
|
|
| 0.1812 | 15100 | - | |
|
|
| 0.1824 | 15200 | - | |
|
|
| 0.1836 | 15300 | - | |
|
|
| 0.1848 | 15400 | - | |
|
|
| 0.1860 | 15500 | 0.171 | |
|
|
| 0.1872 | 15600 | - | |
|
|
| 0.1884 | 15700 | - | |
|
|
| 0.1896 | 15800 | - | |
|
|
| 0.1908 | 15900 | - | |
|
|
| 0.1920 | 16000 | 0.1465 | |
|
|
| 0.1932 | 16100 | - | |
|
|
| 0.1944 | 16200 | - | |
|
|
| 0.1956 | 16300 | - | |
|
|
| 0.1968 | 16400 | - | |
|
|
| 0.1980 | 16500 | 0.1921 | |
|
|
| 0.1992 | 16600 | - | |
|
|
| 0.2004 | 16700 | - | |
|
|
| 0.2016 | 16800 | - | |
|
|
| 0.2028 | 16900 | - | |
|
|
| 0.2040 | 17000 | 0.1669 | |
|
|
| 0.2052 | 17100 | - | |
|
|
| 0.2064 | 17200 | - | |
|
|
| 0.2076 | 17300 | - | |
|
|
| 0.2088 | 17400 | - | |
|
|
| 0.2100 | 17500 | 0.1656 | |
|
|
| 0.2112 | 17600 | - | |
|
|
| 0.2124 | 17700 | - | |
|
|
| 0.2136 | 17800 | - | |
|
|
| 0.2148 | 17900 | - | |
|
|
| 0.2160 | 18000 | 0.1952 | |
|
|
| 0.2172 | 18100 | - | |
|
|
| 0.2184 | 18200 | - | |
|
|
| 0.2196 | 18300 | - | |
|
|
| 0.2208 | 18400 | - | |
|
|
| 0.2220 | 18500 | 0.1658 | |
|
|
| 0.2232 | 18600 | - | |
|
|
| 0.2244 | 18700 | - | |
|
|
| 0.2256 | 18800 | - | |
|
|
| 0.2268 | 18900 | - | |
|
|
| 0.2280 | 19000 | 0.1774 | |
|
|
| 0.2292 | 19100 | - | |
|
|
| 0.2304 | 19200 | - | |
|
|
| 0.2316 | 19300 | - | |
|
|
| 0.2328 | 19400 | - | |
|
|
| 0.2340 | 19500 | 0.1802 | |
|
|
| 0.2352 | 19600 | - | |
|
|
| 0.2364 | 19700 | - | |
|
|
| 0.2376 | 19800 | - | |
|
|
| 0.2388 | 19900 | - | |
|
|
| 0.2400 | 20000 | 0.1724 | |
|
|
| 0.2412 | 20100 | - | |
|
|
| 0.2424 | 20200 | - | |
|
|
| 0.2436 | 20300 | - | |
|
|
| 0.2448 | 20400 | - | |
|
|
| 0.2460 | 20500 | 0.1653 | |
|
|
| 0.2472 | 20600 | - | |
|
|
| 0.2484 | 20700 | - | |
|
|
| 0.2496 | 20800 | - | |
|
|
| 0.2508 | 20900 | - | |
|
|
| 0.2520 | 21000 | 0.1484 | |
|
|
| 0.2532 | 21100 | - | |
|
|
| 0.2544 | 21200 | - | |
|
|
| 0.2556 | 21300 | - | |
|
|
| 0.2568 | 21400 | - | |
|
|
| 0.2580 | 21500 | 0.1544 | |
|
|
| 0.2592 | 21600 | - | |
|
|
| 0.2604 | 21700 | - | |
|
|
| 0.2616 | 21800 | - | |
|
|
| 0.2628 | 21900 | - | |
|
|
| 0.2640 | 22000 | 0.174 | |
|
|
| 0.2652 | 22100 | - | |
|
|
| 0.2664 | 22200 | - | |
|
|
| 0.2676 | 22300 | - | |
|
|
| 0.2688 | 22400 | - | |
|
|
| 0.2700 | 22500 | 0.1488 | |
|
|
| 0.2712 | 22600 | - | |
|
|
| 0.2724 | 22700 | - | |
|
|
| 0.2736 | 22800 | - | |
|
|
| 0.2748 | 22900 | - | |
|
|
| 0.2760 | 23000 | 0.1696 | |
|
|
| 0.2772 | 23100 | - | |
|
|
| 0.2784 | 23200 | - | |
|
|
| 0.2796 | 23300 | - | |
|
|
| 0.2808 | 23400 | - | |
|
|
| 0.2820 | 23500 | 0.1468 | |
|
|
| 0.2832 | 23600 | - | |
|
|
| 0.2844 | 23700 | - | |
|
|
| 0.2856 | 23800 | - | |
|
|
| 0.2868 | 23900 | - | |
|
|
| 0.2880 | 24000 | 0.1738 | |
|
|
| 0.2892 | 24100 | - | |
|
|
| 0.2904 | 24200 | - | |
|
|
| 0.2916 | 24300 | - | |
|
|
| 0.2928 | 24400 | - | |
|
|
| 0.2940 | 24500 | 0.1667 | |
|
|
| 0.2952 | 24600 | - | |
|
|
| 0.2964 | 24700 | - | |
|
|
| 0.2976 | 24800 | - | |
|
|
| 0.2988 | 24900 | - | |
|
|
| 0.3000 | 25000 | 0.1562 | |
|
|
| 0.3012 | 25100 | - | |
|
|
| 0.3024 | 25200 | - | |
|
|
| 0.3036 | 25300 | - | |
|
|
| 0.3048 | 25400 | - | |
|
|
| 0.3060 | 25500 | 0.1628 | |
|
|
| 0.3072 | 25600 | - | |
|
|
| 0.3084 | 25700 | - | |
|
|
| 0.3096 | 25800 | - | |
|
|
| 0.3108 | 25900 | - | |
|
|
| 0.3120 | 26000 | 0.1392 | |
|
|
| 0.3132 | 26100 | - | |
|
|
| 0.3144 | 26200 | - | |
|
|
| 0.3156 | 26300 | - | |
|
|
| 0.3168 | 26400 | - | |
|
|
| 0.3180 | 26500 | 0.1507 | |
|
|
| 0.3192 | 26600 | - | |
|
|
| 0.3204 | 26700 | - | |
|
|
| 0.3216 | 26800 | - | |
|
|
| 0.3228 | 26900 | - | |
|
|
| 0.3240 | 27000 | 0.1646 | |
|
|
| 0.3252 | 27100 | - | |
|
|
| 0.3264 | 27200 | - | |
|
|
| 0.3276 | 27300 | - | |
|
|
| 0.3288 | 27400 | - | |
|
|
| 0.3300 | 27500 | 0.1433 | |
|
|
| 0.3312 | 27600 | - | |
|
|
| 0.3324 | 27700 | - | |
|
|
| 0.3336 | 27800 | - | |
|
|
| 0.3348 | 27900 | - | |
|
|
| 0.3360 | 28000 | 0.1689 | |
|
|
| 0.3372 | 28100 | - | |
|
|
| 0.3384 | 28200 | - | |
|
|
| 0.3396 | 28300 | - | |
|
|
| 0.3408 | 28400 | - | |
|
|
| 0.3420 | 28500 | 0.1432 | |
|
|
| 0.3432 | 28600 | - | |
|
|
| 0.3444 | 28700 | - | |
|
|
| 0.3456 | 28800 | - | |
|
|
| 0.3468 | 28900 | - | |
|
|
| 0.3480 | 29000 | 0.1534 | |
|
|
| 0.3492 | 29100 | - | |
|
|
| 0.3504 | 29200 | - | |
|
|
| 0.3516 | 29300 | - | |
|
|
| 0.3528 | 29400 | - | |
|
|
| 0.3540 | 29500 | 0.1487 | |
|
|
| 0.3552 | 29600 | - | |
|
|
| 0.3564 | 29700 | - | |
|
|
| 0.3576 | 29800 | - | |
|
|
| 0.3588 | 29900 | - | |
|
|
| 0.3600 | 30000 | 0.1439 | |
|
|
| 0.3612 | 30100 | - | |
|
|
| 0.3624 | 30200 | - | |
|
|
| 0.3636 | 30300 | - | |
|
|
| 0.3648 | 30400 | - | |
|
|
| 0.3660 | 30500 | 0.1397 | |
|
|
| 0.3672 | 30600 | - | |
|
|
| 0.3684 | 30700 | - | |
|
|
| 0.3696 | 30800 | - | |
|
|
| 0.3708 | 30900 | - | |
|
|
| 0.3720 | 31000 | 0.1542 | |
|
|
| 0.3732 | 31100 | - | |
|
|
| 0.3744 | 31200 | - | |
|
|
| 0.3756 | 31300 | - | |
|
|
| 0.3768 | 31400 | - | |
|
|
| 0.3780 | 31500 | 0.1448 | |
|
|
| 0.3792 | 31600 | - | |
|
|
| 0.3804 | 31700 | - | |
|
|
| 0.3816 | 31800 | - | |
|
|
| 0.3828 | 31900 | - | |
|
|
| 0.3840 | 32000 | 0.1608 | |
|
|
| 0.3852 | 32100 | - | |
|
|
| 0.3864 | 32200 | - | |
|
|
| 0.3876 | 32300 | - | |
|
|
| 0.3888 | 32400 | - | |
|
|
| 0.3900 | 32500 | 0.1486 | |
|
|
| 0.3912 | 32600 | - | |
|
|
| 0.3924 | 32700 | - | |
|
|
| 0.3936 | 32800 | - | |
|
|
| 0.3948 | 32900 | - | |
|
|
| 0.3960 | 33000 | 0.1274 | |
|
|
| 0.3972 | 33100 | - | |
|
|
| 0.3984 | 33200 | - | |
|
|
| 0.3996 | 33300 | - | |
|
|
| 0.4008 | 33400 | - | |
|
|
| 0.4020 | 33500 | 0.1451 | |
|
|
| 0.4032 | 33600 | - | |
|
|
| 0.4044 | 33700 | - | |
|
|
| 0.4056 | 33800 | - | |
|
|
| 0.4068 | 33900 | - | |
|
|
| 0.4080 | 34000 | 0.1316 | |
|
|
| 0.4092 | 34100 | - | |
|
|
| 0.4104 | 34200 | - | |
|
|
| 0.4116 | 34300 | - | |
|
|
| 0.4128 | 34400 | - | |
|
|
| 0.4140 | 34500 | 0.1306 | |
|
|
| 0.4152 | 34600 | - | |
|
|
| 0.4164 | 34700 | - | |
|
|
| 0.4176 | 34800 | - | |
|
|
| 0.4188 | 34900 | - | |
|
|
| 0.4200 | 35000 | 0.1382 | |
|
|
| 0.4212 | 35100 | - | |
|
|
| 0.4224 | 35200 | - | |
|
|
| 0.4236 | 35300 | - | |
|
|
| 0.4248 | 35400 | - | |
|
|
| 0.4260 | 35500 | 0.1322 | |
|
|
| 0.4272 | 35600 | - | |
|
|
| 0.4284 | 35700 | - | |
|
|
| 0.4296 | 35800 | - | |
|
|
| 0.4308 | 35900 | - | |
|
|
| 0.4320 | 36000 | 0.1617 | |
|
|
| 0.4332 | 36100 | - | |
|
|
| 0.4344 | 36200 | - | |
|
|
| 0.4356 | 36300 | - | |
|
|
| 0.4368 | 36400 | - | |
|
|
| 0.4380 | 36500 | 0.14 | |
|
|
| 0.4392 | 36600 | - | |
|
|
| 0.4404 | 36700 | - | |
|
|
| 0.4416 | 36800 | - | |
|
|
| 0.4428 | 36900 | - | |
|
|
| 0.4440 | 37000 | 0.1321 | |
|
|
| 0.4452 | 37100 | - | |
|
|
| 0.4464 | 37200 | - | |
|
|
| 0.4476 | 37300 | - | |
|
|
| 0.4488 | 37400 | - | |
|
|
| 0.4500 | 37500 | 0.1464 | |
|
|
| 0.4512 | 37600 | - | |
|
|
| 0.4524 | 37700 | - | |
|
|
| 0.4536 | 37800 | - | |
|
|
| 0.4548 | 37900 | - | |
|
|
| 0.4560 | 38000 | 0.1236 | |
|
|
| 0.4572 | 38100 | - | |
|
|
| 0.4584 | 38200 | - | |
|
|
| 0.4596 | 38300 | - | |
|
|
| 0.4608 | 38400 | - | |
|
|
| 0.4620 | 38500 | 0.147 | |
|
|
| 0.4632 | 38600 | - | |
|
|
| 0.4644 | 38700 | - | |
|
|
| 0.4656 | 38800 | - | |
|
|
| 0.4668 | 38900 | - | |
|
|
| 0.4680 | 39000 | 0.1376 | |
|
|
| 0.4692 | 39100 | - | |
|
|
| 0.4704 | 39200 | - | |
|
|
| 0.4716 | 39300 | - | |
|
|
| 0.4728 | 39400 | - | |
|
|
| 0.4740 | 39500 | 0.1342 | |
|
|
| 0.4752 | 39600 | - | |
|
|
| 0.4764 | 39700 | - | |
|
|
| 0.4776 | 39800 | - | |
|
|
| 0.4788 | 39900 | - | |
|
|
| 0.4800 | 40000 | 0.123 | |
|
|
| 0.4812 | 40100 | - | |
|
|
| 0.4824 | 40200 | - | |
|
|
| 0.4836 | 40300 | - | |
|
|
| 0.4848 | 40400 | - | |
|
|
| 0.4860 | 40500 | 0.1312 | |
|
|
| 0.4872 | 40600 | - | |
|
|
| 0.4884 | 40700 | - | |
|
|
| 0.4896 | 40800 | - | |
|
|
| 0.4908 | 40900 | - | |
|
|
| 0.4920 | 41000 | 0.1325 | |
|
|
| 0.4932 | 41100 | - | |
|
|
| 0.4944 | 41200 | - | |
|
|
| 0.4956 | 41300 | - | |
|
|
| 0.4968 | 41400 | - | |
|
|
| 0.4980 | 41500 | 0.1203 | |
|
|
| 0.4992 | 41600 | - | |
|
|
| 0.5004 | 41700 | - | |
|
|
| 0.5016 | 41800 | - | |
|
|
| 0.5028 | 41900 | - | |
|
|
| 0.5040 | 42000 | 0.1258 | |
|
|
| 0.5052 | 42100 | - | |
|
|
| 0.5064 | 42200 | - | |
|
|
| 0.5076 | 42300 | - | |
|
|
| 0.5088 | 42400 | - | |
|
|
| 0.5100 | 42500 | 0.141 | |
|
|
| 0.5112 | 42600 | - | |
|
|
| 0.5124 | 42700 | - | |
|
|
| 0.5136 | 42800 | - | |
|
|
| 0.5148 | 42900 | - | |
|
|
| 0.5160 | 43000 | 0.1473 | |
|
|
| 0.5172 | 43100 | - | |
|
|
| 0.5184 | 43200 | - | |
|
|
| 0.5196 | 43300 | - | |
|
|
| 0.5208 | 43400 | - | |
|
|
| 0.5220 | 43500 | 0.1247 | |
|
|
| 0.5232 | 43600 | - | |
|
|
| 0.5244 | 43700 | - | |
|
|
| 0.5256 | 43800 | - | |
|
|
| 0.5268 | 43900 | - | |
|
|
| 0.5280 | 44000 | 0.1259 | |
|
|
| 0.5292 | 44100 | - | |
|
|
| 0.5304 | 44200 | - | |
|
|
| 0.5316 | 44300 | - | |
|
|
| 0.5328 | 44400 | - | |
|
|
| 0.5340 | 44500 | 0.1372 | |
|
|
| 0.5352 | 44600 | - | |
|
|
| 0.5364 | 44700 | - | |
|
|
| 0.5376 | 44800 | - | |
|
|
| 0.5388 | 44900 | - | |
|
|
| 0.5400 | 45000 | 0.1413 | |
|
|
| 0.5412 | 45100 | - | |
|
|
| 0.5424 | 45200 | - | |
|
|
| 0.5436 | 45300 | - | |
|
|
| 0.5448 | 45400 | - | |
|
|
| 0.5460 | 45500 | 0.1157 | |
|
|
| 0.5472 | 45600 | - | |
|
|
| 0.5484 | 45700 | - | |
|
|
| 0.5496 | 45800 | - | |
|
|
| 0.5508 | 45900 | - | |
|
|
| 0.5520 | 46000 | 0.127 | |
|
|
| 0.5532 | 46100 | - | |
|
|
| 0.5544 | 46200 | - | |
|
|
| 0.5556 | 46300 | - | |
|
|
| 0.5568 | 46400 | - | |
|
|
| 0.5580 | 46500 | 0.1202 | |
|
|
| 0.5592 | 46600 | - | |
|
|
| 0.5604 | 46700 | - | |
|
|
| 0.5616 | 46800 | - | |
|
|
| 0.5628 | 46900 | - | |
|
|
| 0.5640 | 47000 | 0.1199 | |
|
|
| 0.5652 | 47100 | - | |
|
|
| 0.5664 | 47200 | - | |
|
|
| 0.5676 | 47300 | - | |
|
|
| 0.5688 | 47400 | - | |
|
|
| 0.5700 | 47500 | 0.1309 | |
|
|
| 0.5712 | 47600 | - | |
|
|
| 0.5724 | 47700 | - | |
|
|
| 0.5736 | 47800 | - | |
|
|
| 0.5748 | 47900 | - | |
|
|
| 0.5760 | 48000 | 0.1276 | |
|
|
| 0.5772 | 48100 | - | |
|
|
| 0.5784 | 48200 | - | |
|
|
| 0.5796 | 48300 | - | |
|
|
| 0.5808 | 48400 | - | |
|
|
| 0.5820 | 48500 | 0.1278 | |
|
|
| 0.5832 | 48600 | - | |
|
|
| 0.5844 | 48700 | - | |
|
|
| 0.5856 | 48800 | - | |
|
|
| 0.5868 | 48900 | - | |
|
|
| 0.5880 | 49000 | 0.1175 | |
|
|
| 0.5892 | 49100 | - | |
|
|
| 0.5904 | 49200 | - | |
|
|
| 0.5916 | 49300 | - | |
|
|
| 0.5928 | 49400 | - | |
|
|
| 0.5940 | 49500 | 0.1327 | |
|
|
| 0.5952 | 49600 | - | |
|
|
| 0.5964 | 49700 | - | |
|
|
| 0.5976 | 49800 | - | |
|
|
| 0.5988 | 49900 | - | |
|
|
| 0.6000 | 50000 | 0.1109 | |
|
|
| 0.6012 | 50100 | - | |
|
|
| 0.6024 | 50200 | - | |
|
|
| 0.6036 | 50300 | - | |
|
|
| 0.6048 | 50400 | - | |
|
|
| 0.6060 | 50500 | 0.1248 | |
|
|
| 0.6072 | 50600 | - | |
|
|
| 0.6084 | 50700 | - | |
|
|
| 0.6096 | 50800 | - | |
|
|
| 0.6108 | 50900 | - | |
|
|
| 0.6120 | 51000 | 0.1296 | |
|
|
| 0.6132 | 51100 | - | |
|
|
| 0.6144 | 51200 | - | |
|
|
| 0.6156 | 51300 | - | |
|
|
| 0.6168 | 51400 | - | |
|
|
| 0.6180 | 51500 | 0.1323 | |
|
|
| 0.6192 | 51600 | - | |
|
|
| 0.6204 | 51700 | - | |
|
|
| 0.6216 | 51800 | - | |
|
|
| 0.6228 | 51900 | - | |
|
|
| 0.6240 | 52000 | 0.1155 | |
|
|
| 0.6252 | 52100 | - | |
|
|
| 0.6264 | 52200 | - | |
|
|
| 0.6276 | 52300 | - | |
|
|
| 0.6288 | 52400 | - | |
|
|
| 0.6300 | 52500 | 0.1245 | |
|
|
| 0.6312 | 52600 | - | |
|
|
| 0.6324 | 52700 | - | |
|
|
| 0.6336 | 52800 | - | |
|
|
| 0.6348 | 52900 | - | |
|
|
| 0.6360 | 53000 | 0.1238 | |
|
|
| 0.6372 | 53100 | - | |
|
|
| 0.6384 | 53200 | - | |
|
|
| 0.6396 | 53300 | - | |
|
|
| 0.6408 | 53400 | - | |
|
|
| 0.6420 | 53500 | 0.12 | |
|
|
| 0.6432 | 53600 | - | |
|
|
| 0.6444 | 53700 | - | |
|
|
| 0.6456 | 53800 | - | |
|
|
| 0.6468 | 53900 | - | |
|
|
| 0.6480 | 54000 | 0.1116 | |
|
|
| 0.6492 | 54100 | - | |
|
|
| 0.6504 | 54200 | - | |
|
|
| 0.6516 | 54300 | - | |
|
|
| 0.6528 | 54400 | - | |
|
|
| 0.6540 | 54500 | 0.1305 | |
|
|
| 0.6552 | 54600 | - | |
|
|
| 0.6564 | 54700 | - | |
|
|
| 0.6576 | 54800 | - | |
|
|
| 0.6588 | 54900 | - | |
|
|
| 0.6600 | 55000 | 0.1355 | |
|
|
| 0.6612 | 55100 | - | |
|
|
| 0.6624 | 55200 | - | |
|
|
| 0.6636 | 55300 | - | |
|
|
| 0.6648 | 55400 | - | |
|
|
| 0.6660 | 55500 | 0.1139 | |
|
|
| 0.6672 | 55600 | - | |
|
|
| 0.6684 | 55700 | - | |
|
|
| 0.6696 | 55800 | - | |
|
|
| 0.6708 | 55900 | - | |
|
|
| 0.6720 | 56000 | 0.1251 | |
|
|
| 0.6732 | 56100 | - | |
|
|
| 0.6744 | 56200 | - | |
|
|
| 0.6756 | 56300 | - | |
|
|
| 0.6768 | 56400 | - | |
|
|
| 0.6780 | 56500 | 0.1211 | |
|
|
| 0.6792 | 56600 | - | |
|
|
| 0.6804 | 56700 | - | |
|
|
| 0.6816 | 56800 | - | |
|
|
| 0.6828 | 56900 | - | |
|
|
| 0.6840 | 57000 | 0.1123 | |
|
|
| 0.6852 | 57100 | - | |
|
|
| 0.6864 | 57200 | - | |
|
|
| 0.6876 | 57300 | - | |
|
|
| 0.6888 | 57400 | - | |
|
|
| 0.6900 | 57500 | 0.1071 | |
|
|
| 0.6912 | 57600 | - | |
|
|
| 0.6924 | 57700 | - | |
|
|
| 0.6936 | 57800 | - | |
|
|
| 0.6948 | 57900 | - | |
|
|
| 0.6960 | 58000 | 0.112 | |
|
|
| 0.6972 | 58100 | - | |
|
|
| 0.6984 | 58200 | - | |
|
|
| 0.6996 | 58300 | - | |
|
|
| 0.7008 | 58400 | - | |
|
|
| 0.7020 | 58500 | 0.1038 | |
|
|
| 0.7032 | 58600 | - | |
|
|
| 0.7044 | 58700 | - | |
|
|
| 0.7056 | 58800 | - | |
|
|
| 0.7068 | 58900 | - | |
|
|
| 0.7080 | 59000 | 0.1238 | |
|
|
| 0.7092 | 59100 | - | |
|
|
| 0.7104 | 59200 | - | |
|
|
| 0.7116 | 59300 | - | |
|
|
| 0.7128 | 59400 | - | |
|
|
| 0.7140 | 59500 | 0.1001 | |
|
|
| 0.7152 | 59600 | - | |
|
|
| 0.7164 | 59700 | - | |
|
|
| 0.7176 | 59800 | - | |
|
|
| 0.7188 | 59900 | - | |
|
|
| 0.7200 | 60000 | 0.0948 | |
|
|
| 0.7212 | 60100 | - | |
|
|
| 0.7224 | 60200 | - | |
|
|
| 0.7236 | 60300 | - | |
|
|
| 0.7248 | 60400 | - | |
|
|
| 0.7260 | 60500 | 0.1271 | |
|
|
| 0.7272 | 60600 | - | |
|
|
| 0.7284 | 60700 | - | |
|
|
| 0.7296 | 60800 | - | |
|
|
| 0.7308 | 60900 | - | |
|
|
| 0.7320 | 61000 | 0.1117 | |
|
|
| 0.7332 | 61100 | - | |
|
|
| 0.7344 | 61200 | - | |
|
|
| 0.7356 | 61300 | - | |
|
|
| 0.7368 | 61400 | - | |
|
|
| 0.7380 | 61500 | 0.1122 | |
|
|
| 0.7392 | 61600 | - | |
|
|
| 0.7404 | 61700 | - | |
|
|
| 0.7416 | 61800 | - | |
|
|
| 0.7428 | 61900 | - | |
|
|
| 0.7440 | 62000 | 0.0972 | |
|
|
| 0.7452 | 62100 | - | |
|
|
| 0.7464 | 62200 | - | |
|
|
| 0.7476 | 62300 | - | |
|
|
| 0.7488 | 62400 | - | |
|
|
| 0.7500 | 62500 | 0.1135 | |
|
|
| 0.7512 | 62600 | - | |
|
|
| 0.7524 | 62700 | - | |
|
|
| 0.7536 | 62800 | - | |
|
|
| 0.7548 | 62900 | - | |
|
|
| 0.7560 | 63000 | 0.1092 | |
|
|
| 0.7572 | 63100 | - | |
|
|
| 0.7584 | 63200 | - | |
|
|
| 0.7596 | 63300 | - | |
|
|
| 0.7608 | 63400 | - | |
|
|
| 0.7620 | 63500 | 0.1155 | |
|
|
| 0.7632 | 63600 | - | |
|
|
| 0.7644 | 63700 | - | |
|
|
| 0.7656 | 63800 | - | |
|
|
| 0.7668 | 63900 | - | |
|
|
| 0.7680 | 64000 | 0.1065 | |
|
|
| 0.7692 | 64100 | - | |
|
|
| 0.7704 | 64200 | - | |
|
|
| 0.7716 | 64300 | - | |
|
|
| 0.7728 | 64400 | - | |
|
|
| 0.7740 | 64500 | 0.1211 | |
|
|
| 0.7752 | 64600 | - | |
|
|
| 0.7764 | 64700 | - | |
|
|
| 0.7776 | 64800 | - | |
|
|
| 0.7788 | 64900 | - | |
|
|
| 0.7800 | 65000 | 0.116 | |
|
|
| 0.7812 | 65100 | - | |
|
|
| 0.7824 | 65200 | - | |
|
|
| 0.7836 | 65300 | - | |
|
|
| 0.7848 | 65400 | - | |
|
|
| 0.7860 | 65500 | 0.1138 | |
|
|
| 0.7872 | 65600 | - | |
|
|
| 0.7884 | 65700 | - | |
|
|
| 0.7896 | 65800 | - | |
|
|
| 0.7908 | 65900 | - | |
|
|
| 0.7920 | 66000 | 0.1155 | |
|
|
| 0.7932 | 66100 | - | |
|
|
| 0.7944 | 66200 | - | |
|
|
| 0.7956 | 66300 | - | |
|
|
| 0.7968 | 66400 | - | |
|
|
| 0.7980 | 66500 | 0.1059 | |
|
|
| 0.7992 | 66600 | - | |
|
|
| 0.8004 | 66700 | - | |
|
|
| 0.8016 | 66800 | - | |
|
|
| 0.8028 | 66900 | - | |
|
|
| 0.8040 | 67000 | 0.1189 | |
|
|
| 0.8052 | 67100 | - | |
|
|
| 0.8064 | 67200 | - | |
|
|
| 0.8076 | 67300 | - | |
|
|
| 0.8088 | 67400 | - | |
|
|
| 0.8100 | 67500 | 0.1089 | |
|
|
| 0.8112 | 67600 | - | |
|
|
| 0.8124 | 67700 | - | |
|
|
| 0.8136 | 67800 | - | |
|
|
| 0.8148 | 67900 | - | |
|
|
| 0.8160 | 68000 | 0.1016 | |
|
|
| 0.8172 | 68100 | - | |
|
|
| 0.8184 | 68200 | - | |
|
|
| 0.8196 | 68300 | - | |
|
|
| 0.8208 | 68400 | - | |
|
|
| 0.8220 | 68500 | 0.121 | |
|
|
| 0.8232 | 68600 | - | |
|
|
| 0.8244 | 68700 | - | |
|
|
| 0.8256 | 68800 | - | |
|
|
| 0.8268 | 68900 | - | |
|
|
| 0.8280 | 69000 | 0.1185 | |
|
|
| 0.8292 | 69100 | - | |
|
|
| 0.8304 | 69200 | - | |
|
|
| 0.8316 | 69300 | - | |
|
|
| 0.8328 | 69400 | - | |
|
|
| 0.8340 | 69500 | 0.1026 | |
|
|
| 0.8352 | 69600 | - | |
|
|
| 0.8364 | 69700 | - | |
|
|
| 0.8376 | 69800 | - | |
|
|
| 0.8388 | 69900 | - | |
|
|
| 0.8400 | 70000 | 0.1209 | |
|
|
| 0.8412 | 70100 | - | |
|
|
| 0.8424 | 70200 | - | |
|
|
| 0.8436 | 70300 | - | |
|
|
| 0.8448 | 70400 | - | |
|
|
| 0.8460 | 70500 | 0.1103 | |
|
|
| 0.8472 | 70600 | - | |
|
|
| 0.8484 | 70700 | - | |
|
|
| 0.8496 | 70800 | - | |
|
|
| 0.8508 | 70900 | - | |
|
|
| 0.8520 | 71000 | 0.1098 | |
|
|
| 0.8532 | 71100 | - | |
|
|
| 0.8544 | 71200 | - | |
|
|
| 0.8556 | 71300 | - | |
|
|
| 0.8568 | 71400 | - | |
|
|
| 0.8580 | 71500 | 0.1055 | |
|
|
| 0.8592 | 71600 | - | |
|
|
| 0.8604 | 71700 | - | |
|
|
| 0.8616 | 71800 | - | |
|
|
| 0.8628 | 71900 | - | |
|
|
| 0.8640 | 72000 | 0.1045 | |
|
|
| 0.8652 | 72100 | - | |
|
|
| 0.8664 | 72200 | - | |
|
|
| 0.8676 | 72300 | - | |
|
|
| 0.8688 | 72400 | - | |
|
|
| 0.8700 | 72500 | 0.1126 | |
|
|
| 0.8712 | 72600 | - | |
|
|
| 0.8724 | 72700 | - | |
|
|
| 0.8736 | 72800 | - | |
|
|
| 0.8748 | 72900 | - | |
|
|
| 0.8760 | 73000 | 0.1058 | |
|
|
| 0.8772 | 73100 | - | |
|
|
| 0.8784 | 73200 | - | |
|
|
| 0.8796 | 73300 | - | |
|
|
| 0.8808 | 73400 | - | |
|
|
| 0.8820 | 73500 | 0.1138 | |
|
|
| 0.8832 | 73600 | - | |
|
|
| 0.8844 | 73700 | - | |
|
|
| 0.8856 | 73800 | - | |
|
|
| 0.8868 | 73900 | - | |
|
|
| 0.8880 | 74000 | 0.1071 | |
|
|
| 0.8892 | 74100 | - | |
|
|
| 0.8904 | 74200 | - | |
|
|
| 0.8916 | 74300 | - | |
|
|
| 0.8928 | 74400 | - | |
|
|
| 0.8940 | 74500 | 0.1091 | |
|
|
| 0.8952 | 74600 | - | |
|
|
| 0.8964 | 74700 | - | |
|
|
| 0.8976 | 74800 | - | |
|
|
| 0.8988 | 74900 | - | |
|
|
| 0.9000 | 75000 | 0.1143 | |
|
|
| 0.9012 | 75100 | - | |
|
|
| 0.9024 | 75200 | - | |
|
|
|
|
|
</details> |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.12.10 |
|
|
- Sentence Transformers: 4.1.0 |
|
|
- Transformers: 4.51.3 |
|
|
- PyTorch: 2.6.0+cu124 |
|
|
- Accelerate: 1.8.1 |
|
|
- Datasets: 3.6.0 |
|
|
- Tokenizers: 0.21.1 |
|
|
|
|
|
## 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", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### MultipleNegativesRankingLoss |
|
|
```bibtex |
|
|
@misc{henderson2017efficient, |
|
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
|
year={2017}, |
|
|
eprint={1705.00652}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CL} |
|
|
} |
|
|
``` |
|
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|
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