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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:257886 |
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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: 'Karwa Chauth is a festival celebrated by Hindu women of Northern |
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and Western India on the fourth day after Purnima in the month of Kartika. |
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' |
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sentences: |
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- 'तस्याः युग्मभ्रातुः वंशानुगत-राजकुमारस्य जाक् इत्यस्य निमेषद्वयात् प्राक् सा |
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अजायत। |
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' |
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- '"तथापि, Internet Explorer नोपयोक्तव्यम् । यतो हि तत् सम्यक् डिस्प्ले न करोति |
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।"' |
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- 'कर्वा-चौथ् इति उत्सवः उत्तर-पश्चिम-भारतस्य हिन्दु-महिलाभिः कार्तिकमासे पूर्णिमायाः |
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अनन्तरं चतुर्थदिने आचर्यते। |
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' |
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- source_sentence: '"""And if any man will hurt them, fire proceedeth out of their |
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mouth, and devoureth their enemies: and if any man will hurt them, he must in |
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this manner be killed."""' |
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sentences: |
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- '"C तथा C++ उभयोः मध्येऽपि, इदं समानं मार्गं इम्प्लिमेण्ट् कर्तुमनुसरति ।"' |
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- यदि केचित् तौ हिंसितुं चेष्टन्ते तर्हि तयो र्वदनाभ्याम् अग्नि र्निर्गत्य तयोः |
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शत्रून् भस्मीकरिष्यति। यः कश्चित् तौ हिंसितुं चेष्टते तेनैवमेव विनष्टव्यं। |
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- यवक्रीत उवाच नायं शक्यस्त्वया बड़े महानोघस्तपोधन। अशक्याद् विनिवर्तस्व शक्यमर्थं |
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समारभ॥ |
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- source_sentence: 'It tarnishes in air to produce a whitish oxidized layer on the |
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surface. |
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' |
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sentences: |
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- उपस्थितानां रत्नानां श्रेष्ठानामर्घहारिणाम्। नादृश्यत परः पारो नापरस्तत्र भारत॥ |
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- 'इदं वायौ कलङ्कितं भवति, येन तले श्वेतवर्णीयं आक्सिडैस्ड्-आस्तरणं निर्मीयते। |
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' |
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- आचार्येणाभ्यनुज्ञातश्चतुर्णामेकमाश्रमम्। आविमोक्षाच्छरीरस्य सोऽवतिष्ठेद् यथाविधि॥ |
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- source_sentence: 'If you''re planning to fund part or all of your child''s higher |
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education, it''s best to start saving early on. |
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' |
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sentences: |
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- समयं वाजिमेधस्य विदित्वा पुरुषर्षभः। यथोक्तो धर्मपुत्रेण प्रव्रजन् स्वपुरी प्रति॥ |
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- 'यदि भवान् भवतः सन्ततेः उच्चशिक्षायाः कृते, आंशिकं वा सम्पूर्णं वा शुल्कं दातुम् |
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इच्छति तर्हि तदर्थं पूर्वमेव धनसञ्चयस्य आरम्भः क्षेमकरः भवेत्। |
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' |
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- '"""तदनन्तरं तेषां सप्तकंसधारिणां सप्तदूतानाम् एक आगत्य मां सम्भाष्यावदत्, अत्रागच्छ, |
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मेदिन्या नरपतयो यया वेश्यया सार्द्धं व्यभिचारकर्म्म कृतवन्तः,"""' |
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- source_sentence: In spite of these, Dhananjaya made Drona's son carless by cutting |
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off the out-stretched bow of his foe with three shafts, killing his driver with |
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a razor like shaft and making away with his banner with three and his four horses |
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with four other shafts. |
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sentences: |
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- तथापि तं प्रस्फुरदात्तकार्मुकं त्रिभिः शरैर्यन्तृशिरः क्षुरेणा हयांश्चतुर्भिश्च |
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पुनस्त्रिभिर्ध्वज धनंजयो द्रौणिरथादपातयत्॥ |
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- एकवारं पूरितं चेत् एतां प्रक्रियां undo कर्तुं न शक्नुमः । |
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- क्रीडां तथा कूर्दनं विना शिक्षा अपूर्णा अस्ति । |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- src2trg_accuracy |
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- trg2src_accuracy |
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- mean_accuracy |
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model-index: |
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- name: SentenceTransformer based on sentence-transformers/LaBSE |
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results: |
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- task: |
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type: translation |
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name: Translation |
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dataset: |
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name: eval en sa |
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type: eval-en-sa |
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metrics: |
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- type: src2trg_accuracy |
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value: 0.944 |
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name: Src2Trg Accuracy |
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- type: trg2src_accuracy |
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value: 0.947 |
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name: Trg2Src Accuracy |
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- type: mean_accuracy |
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value: 0.9455 |
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name: Mean Accuracy |
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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:** 128 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': 128, '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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"In spite of these, Dhananjaya made Drona's son carless by cutting off the out-stretched bow of his foe with three shafts, killing his driver with a razor like shaft and making away with his banner with three and his four horses with four other shafts.", |
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'तथापि तं प्रस्फुरदात्तकार्मुकं त्रिभिः शरैर्यन्तृशिरः क्षुरेणा हयांश्चतुर्भिश्च पुनस्त्रिभिर्ध्वज धनंजयो द्रौणिरथादपातयत्॥', |
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'क्रीडां तथा कूर्दनं विना शिक्षा अपूर्णा अस्ति ।', |
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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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#### Translation |
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* Dataset: `eval-en-sa` |
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* Evaluated with [<code>TranslationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator) |
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| Metric | Value | |
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|:------------------|:-----------| |
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| src2trg_accuracy | 0.944 | |
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| trg2src_accuracy | 0.947 | |
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| **mean_accuracy** | **0.9455** | |
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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: 257,886 training samples |
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* Columns: <code>sentence_0</code> and <code>sentence_1</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | |
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|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 31.6 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 40.18 tokens</li><li>max: 128 tokens</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | |
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|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>It normally connects to port 80 on a computer.<br></code> | <code>इदं सामान्यतः एकस्मिन् सङ्गणके पोर्ट् ८० इत्यनेन सम्पर्कं साधयति।<br></code> | |
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| <code>He who gives to a Brahmana a good bed perfumed with fragrant scents, covered with an excellent sheet, and pillows, gets without any effort on his part a beautiful wife, belonging to a respectable family and of agreeable manners.</code> | <code>सुगन्धचित्रास्तरणोपधानं दद्यान्नरो यः शयनं द्विजाय। रूपान्वितां पक्षवती मनोज्ञां भार्यामयत्नोपगतां लभेत् सः।</code> | |
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| <code>By mid-1665, with the fortress at Purandar besieged and near capture, Shivaji was forced to come to terms with Jai Singh.<br></code> | <code>१६६५ तमवर्षस्य मध्यभागे यावत् पुरन्दरस्थस्य दुर्गस्य परिवेष्टनं कृत्वा, ग्रहणस्य समीपे, शिवाजी जयसिङ्घेन सह सन्धानं कर्तुं बाध्यः अभवत्।<br></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`: 4 |
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- `per_device_eval_batch_size`: 4 |
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- `num_train_epochs`: 15 |
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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`: 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`: 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`: 15 |
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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`: 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`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `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 |
|
|
- `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 |
|
|
- `optim_target_modules`: None |
|
|
- `batch_eval_metrics`: False |
|
|
- `eval_on_start`: False |
|
|
- `use_liger_kernel`: False |
|
|
- `eval_use_gather_object`: False |
|
|
- `average_tokens_across_devices`: False |
|
|
- `prompts`: None |
|
|
- `batch_sampler`: batch_sampler |
|
|
- `multi_dataset_batch_sampler`: round_robin |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
<details><summary>Click to expand</summary> |
|
|
|
|
|
| Epoch | Step | Training Loss | eval-en-sa_mean_accuracy | |
|
|
|:-------:|:------:|:-------------:|:------------------------:| |
|
|
| 0.0310 | 500 | 0.4289 | - | |
|
|
| 0.0620 | 1000 | 0.182 | - | |
|
|
| 0.0931 | 1500 | 0.1405 | - | |
|
|
| 0.1241 | 2000 | 0.1097 | - | |
|
|
| 0.1551 | 2500 | 0.0911 | - | |
|
|
| 0.1861 | 3000 | 0.0791 | - | |
|
|
| 0.2171 | 3500 | 0.0725 | - | |
|
|
| 0.2482 | 4000 | 0.067 | - | |
|
|
| 0.2792 | 4500 | 0.0594 | - | |
|
|
| 0.3102 | 5000 | 0.0629 | - | |
|
|
| 0.3412 | 5500 | 0.0535 | - | |
|
|
| 0.3723 | 6000 | 0.0512 | - | |
|
|
| 0.4033 | 6500 | 0.0456 | - | |
|
|
| 0.4343 | 7000 | 0.0462 | - | |
|
|
| 0.4653 | 7500 | 0.043 | - | |
|
|
| 0.4963 | 8000 | 0.0425 | - | |
|
|
| 0.5274 | 8500 | 0.0412 | - | |
|
|
| 0.5584 | 9000 | 0.0418 | - | |
|
|
| 0.5894 | 9500 | 0.0415 | - | |
|
|
| 0.6204 | 10000 | 0.0409 | - | |
|
|
| 0.6514 | 10500 | 0.04 | - | |
|
|
| 0.6825 | 11000 | 0.032 | - | |
|
|
| 0.7135 | 11500 | 0.0323 | - | |
|
|
| 0.7445 | 12000 | 0.0325 | - | |
|
|
| 0.7755 | 12500 | 0.0355 | - | |
|
|
| 0.8066 | 13000 | 0.0285 | - | |
|
|
| 0.8376 | 13500 | 0.0281 | - | |
|
|
| 0.8686 | 14000 | 0.0289 | - | |
|
|
| 0.8996 | 14500 | 0.033 | - | |
|
|
| 0.9306 | 15000 | 0.0336 | - | |
|
|
| 0.9617 | 15500 | 0.0335 | - | |
|
|
| 0.9927 | 16000 | 0.0278 | - | |
|
|
| 1.0 | 16118 | - | 0.913 | |
|
|
| 1.0237 | 16500 | 0.0312 | - | |
|
|
| 1.0547 | 17000 | 0.0294 | - | |
|
|
| 1.0857 | 17500 | 0.0288 | - | |
|
|
| 1.1168 | 18000 | 0.0287 | - | |
|
|
| 1.1478 | 18500 | 0.0245 | - | |
|
|
| 1.1788 | 19000 | 0.0243 | - | |
|
|
| 1.2098 | 19500 | 0.022 | - | |
|
|
| 1.2408 | 20000 | 0.0266 | - | |
|
|
| 1.2719 | 20500 | 0.0224 | - | |
|
|
| 1.3029 | 21000 | 0.0283 | - | |
|
|
| 1.3339 | 21500 | 0.02 | - | |
|
|
| 1.3649 | 22000 | 0.0212 | - | |
|
|
| 1.3960 | 22500 | 0.0197 | - | |
|
|
| 1.4270 | 23000 | 0.0174 | - | |
|
|
| 1.4580 | 23500 | 0.0179 | - | |
|
|
| 1.4890 | 24000 | 0.0187 | - | |
|
|
| 1.5200 | 24500 | 0.0191 | - | |
|
|
| 1.5511 | 25000 | 0.0151 | - | |
|
|
| 1.5821 | 25500 | 0.0161 | - | |
|
|
| 1.6131 | 26000 | 0.0182 | - | |
|
|
| 1.6441 | 26500 | 0.0155 | - | |
|
|
| 1.6751 | 27000 | 0.013 | - | |
|
|
| 1.7062 | 27500 | 0.0119 | - | |
|
|
| 1.7372 | 28000 | 0.0119 | - | |
|
|
| 1.7682 | 28500 | 0.0133 | - | |
|
|
| 1.7992 | 29000 | 0.0113 | - | |
|
|
| 1.8303 | 29500 | 0.011 | - | |
|
|
| 1.8613 | 30000 | 0.0133 | - | |
|
|
| 1.8923 | 30500 | 0.0114 | - | |
|
|
| 1.9233 | 31000 | 0.0139 | - | |
|
|
| 1.9543 | 31500 | 0.0131 | - | |
|
|
| 1.9854 | 32000 | 0.0115 | - | |
|
|
| 2.0 | 32236 | - | 0.9345 | |
|
|
| 2.0164 | 32500 | 0.01 | - | |
|
|
| 2.0474 | 33000 | 0.01 | - | |
|
|
| 2.0784 | 33500 | 0.0091 | - | |
|
|
| 2.1094 | 34000 | 0.0131 | - | |
|
|
| 2.1405 | 34500 | 0.0096 | - | |
|
|
| 2.1715 | 35000 | 0.0095 | - | |
|
|
| 2.2025 | 35500 | 0.0103 | - | |
|
|
| 2.2335 | 36000 | 0.0101 | - | |
|
|
| 2.2645 | 36500 | 0.0102 | - | |
|
|
| 2.2956 | 37000 | 0.0102 | - | |
|
|
| 2.3266 | 37500 | 0.0085 | - | |
|
|
| 2.3576 | 38000 | 0.0087 | - | |
|
|
| 2.3886 | 38500 | 0.0103 | - | |
|
|
| 2.4197 | 39000 | 0.0058 | - | |
|
|
| 2.4507 | 39500 | 0.0086 | - | |
|
|
| 2.4817 | 40000 | 0.0088 | - | |
|
|
| 2.5127 | 40500 | 0.0088 | - | |
|
|
| 2.5437 | 41000 | 0.007 | - | |
|
|
| 2.5748 | 41500 | 0.0082 | - | |
|
|
| 2.6058 | 42000 | 0.0069 | - | |
|
|
| 2.6368 | 42500 | 0.0071 | - | |
|
|
| 2.6678 | 43000 | 0.0058 | - | |
|
|
| 2.6988 | 43500 | 0.0075 | - | |
|
|
| 2.7299 | 44000 | 0.0064 | - | |
|
|
| 2.7609 | 44500 | 0.0053 | - | |
|
|
| 2.7919 | 45000 | 0.0055 | - | |
|
|
| 2.8229 | 45500 | 0.0061 | - | |
|
|
| 2.8540 | 46000 | 0.0059 | - | |
|
|
| 2.8850 | 46500 | 0.0062 | - | |
|
|
| 2.9160 | 47000 | 0.0046 | - | |
|
|
| 2.9470 | 47500 | 0.0064 | - | |
|
|
| 2.9780 | 48000 | 0.0053 | - | |
|
|
| 3.0 | 48354 | - | 0.941 | |
|
|
| 3.0091 | 48500 | 0.0048 | - | |
|
|
| 3.0401 | 49000 | 0.0059 | - | |
|
|
| 3.0711 | 49500 | 0.005 | - | |
|
|
| 3.1021 | 50000 | 0.005 | 0.9415 | |
|
|
| 3.1331 | 50500 | 0.0046 | - | |
|
|
| 3.1642 | 51000 | 0.005 | - | |
|
|
| 3.1952 | 51500 | 0.0051 | - | |
|
|
| 3.2262 | 52000 | 0.0041 | - | |
|
|
| 3.2572 | 52500 | 0.0052 | - | |
|
|
| 3.2882 | 53000 | 0.0052 | - | |
|
|
| 3.3193 | 53500 | 0.0053 | - | |
|
|
| 3.3503 | 54000 | 0.0041 | - | |
|
|
| 3.3813 | 54500 | 0.0042 | - | |
|
|
| 3.4123 | 55000 | 0.0026 | - | |
|
|
| 3.4434 | 55500 | 0.0045 | - | |
|
|
| 3.4744 | 56000 | 0.0045 | - | |
|
|
| 3.5054 | 56500 | 0.0054 | - | |
|
|
| 3.5364 | 57000 | 0.0055 | - | |
|
|
| 3.5674 | 57500 | 0.0046 | - | |
|
|
| 3.5985 | 58000 | 0.0045 | - | |
|
|
| 3.6295 | 58500 | 0.0041 | - | |
|
|
| 3.6605 | 59000 | 0.0037 | - | |
|
|
| 3.6915 | 59500 | 0.003 | - | |
|
|
| 3.7225 | 60000 | 0.0039 | - | |
|
|
| 3.7536 | 60500 | 0.0027 | - | |
|
|
| 3.7846 | 61000 | 0.0041 | - | |
|
|
| 3.8156 | 61500 | 0.003 | - | |
|
|
| 3.8466 | 62000 | 0.0027 | - | |
|
|
| 3.8777 | 62500 | 0.0039 | - | |
|
|
| 3.9087 | 63000 | 0.0038 | - | |
|
|
| 3.9397 | 63500 | 0.0029 | - | |
|
|
| 3.9707 | 64000 | 0.0037 | - | |
|
|
| 4.0 | 64472 | - | 0.9365 | |
|
|
| 4.0017 | 64500 | 0.0023 | - | |
|
|
| 4.0328 | 65000 | 0.0034 | - | |
|
|
| 4.0638 | 65500 | 0.0033 | - | |
|
|
| 4.0948 | 66000 | 0.0033 | - | |
|
|
| 4.1258 | 66500 | 0.004 | - | |
|
|
| 4.1568 | 67000 | 0.0026 | - | |
|
|
| 4.1879 | 67500 | 0.0026 | - | |
|
|
| 4.2189 | 68000 | 0.0025 | - | |
|
|
| 4.2499 | 68500 | 0.0037 | - | |
|
|
| 4.2809 | 69000 | 0.0041 | - | |
|
|
| 4.3119 | 69500 | 0.0031 | - | |
|
|
| 4.3430 | 70000 | 0.0025 | - | |
|
|
| 4.3740 | 70500 | 0.0025 | - | |
|
|
| 4.4050 | 71000 | 0.0022 | - | |
|
|
| 4.4360 | 71500 | 0.0016 | - | |
|
|
| 4.4671 | 72000 | 0.003 | - | |
|
|
| 4.4981 | 72500 | 0.0029 | - | |
|
|
| 4.5291 | 73000 | 0.003 | - | |
|
|
| 4.5601 | 73500 | 0.0025 | - | |
|
|
| 4.5911 | 74000 | 0.0027 | - | |
|
|
| 4.6222 | 74500 | 0.0028 | - | |
|
|
| 4.6532 | 75000 | 0.003 | - | |
|
|
| 4.6842 | 75500 | 0.002 | - | |
|
|
| 4.7152 | 76000 | 0.0028 | - | |
|
|
| 4.7462 | 76500 | 0.0016 | - | |
|
|
| 4.7773 | 77000 | 0.0022 | - | |
|
|
| 4.8083 | 77500 | 0.0019 | - | |
|
|
| 4.8393 | 78000 | 0.0019 | - | |
|
|
| 4.8703 | 78500 | 0.0026 | - | |
|
|
| 4.9014 | 79000 | 0.0023 | - | |
|
|
| 4.9324 | 79500 | 0.0016 | - | |
|
|
| 4.9634 | 80000 | 0.0019 | - | |
|
|
| 4.9944 | 80500 | 0.0018 | - | |
|
|
| 5.0 | 80590 | - | 0.937 | |
|
|
| 5.0254 | 81000 | 0.0028 | - | |
|
|
| 5.0565 | 81500 | 0.0019 | - | |
|
|
| 5.0875 | 82000 | 0.0024 | - | |
|
|
| 5.1185 | 82500 | 0.0016 | - | |
|
|
| 5.1495 | 83000 | 0.0015 | - | |
|
|
| 5.1805 | 83500 | 0.0017 | - | |
|
|
| 5.2116 | 84000 | 0.0016 | - | |
|
|
| 5.2426 | 84500 | 0.0026 | - | |
|
|
| 5.2736 | 85000 | 0.0029 | - | |
|
|
| 5.3046 | 85500 | 0.0027 | - | |
|
|
| 5.3356 | 86000 | 0.002 | - | |
|
|
| 5.3667 | 86500 | 0.002 | - | |
|
|
| 5.3977 | 87000 | 0.0021 | - | |
|
|
| 5.4287 | 87500 | 0.0011 | - | |
|
|
| 5.4597 | 88000 | 0.0016 | - | |
|
|
| 5.4908 | 88500 | 0.0019 | - | |
|
|
| 5.5218 | 89000 | 0.0027 | - | |
|
|
| 5.5528 | 89500 | 0.0012 | - | |
|
|
| 5.5838 | 90000 | 0.0012 | - | |
|
|
| 5.6148 | 90500 | 0.0016 | - | |
|
|
| 5.6459 | 91000 | 0.0019 | - | |
|
|
| 5.6769 | 91500 | 0.0016 | - | |
|
|
| 5.7079 | 92000 | 0.0027 | - | |
|
|
| 5.7389 | 92500 | 0.0013 | - | |
|
|
| 5.7699 | 93000 | 0.0013 | - | |
|
|
| 5.8010 | 93500 | 0.0015 | - | |
|
|
| 5.8320 | 94000 | 0.0016 | - | |
|
|
| 5.8630 | 94500 | 0.002 | - | |
|
|
| 5.8940 | 95000 | 0.001 | - | |
|
|
| 5.9251 | 95500 | 0.0014 | - | |
|
|
| 5.9561 | 96000 | 0.0021 | - | |
|
|
| 5.9871 | 96500 | 0.0022 | - | |
|
|
| 6.0 | 96708 | - | 0.933 | |
|
|
| 6.0181 | 97000 | 0.0016 | - | |
|
|
| 6.0491 | 97500 | 0.0015 | - | |
|
|
| 6.0802 | 98000 | 0.0011 | - | |
|
|
| 6.1112 | 98500 | 0.0016 | - | |
|
|
| 6.1422 | 99000 | 0.001 | - | |
|
|
| 6.1732 | 99500 | 0.0013 | - | |
|
|
| 6.2042 | 100000 | 0.0015 | 0.9365 | |
|
|
| 6.2353 | 100500 | 0.0017 | - | |
|
|
| 6.2663 | 101000 | 0.0015 | - | |
|
|
| 6.2973 | 101500 | 0.0016 | - | |
|
|
| 6.3283 | 102000 | 0.001 | - | |
|
|
| 6.3593 | 102500 | 0.0013 | - | |
|
|
| 6.3904 | 103000 | 0.0013 | - | |
|
|
| 6.4214 | 103500 | 0.0011 | - | |
|
|
| 6.4524 | 104000 | 0.0007 | - | |
|
|
| 6.4834 | 104500 | 0.0013 | - | |
|
|
| 6.5145 | 105000 | 0.0011 | - | |
|
|
| 6.5455 | 105500 | 0.0011 | - | |
|
|
| 6.5765 | 106000 | 0.0015 | - | |
|
|
| 6.6075 | 106500 | 0.002 | - | |
|
|
| 6.6385 | 107000 | 0.0011 | - | |
|
|
| 6.6696 | 107500 | 0.0013 | - | |
|
|
| 6.7006 | 108000 | 0.0017 | - | |
|
|
| 6.7316 | 108500 | 0.0008 | - | |
|
|
| 6.7626 | 109000 | 0.0011 | - | |
|
|
| 6.7936 | 109500 | 0.0008 | - | |
|
|
| 6.8247 | 110000 | 0.0009 | - | |
|
|
| 6.8557 | 110500 | 0.0014 | - | |
|
|
| 6.8867 | 111000 | 0.0014 | - | |
|
|
| 6.9177 | 111500 | 0.0014 | - | |
|
|
| 6.9488 | 112000 | 0.0014 | - | |
|
|
| 6.9798 | 112500 | 0.0013 | - | |
|
|
| 7.0 | 112826 | - | 0.9390 | |
|
|
| 7.0108 | 113000 | 0.0011 | - | |
|
|
| 7.0418 | 113500 | 0.0013 | - | |
|
|
| 7.0728 | 114000 | 0.0012 | - | |
|
|
| 7.1039 | 114500 | 0.001 | - | |
|
|
| 7.1349 | 115000 | 0.0016 | - | |
|
|
| 7.1659 | 115500 | 0.0009 | - | |
|
|
| 7.1969 | 116000 | 0.0009 | - | |
|
|
| 7.2279 | 116500 | 0.0007 | - | |
|
|
| 7.2590 | 117000 | 0.0008 | - | |
|
|
| 7.2900 | 117500 | 0.0014 | - | |
|
|
| 7.3210 | 118000 | 0.0012 | - | |
|
|
| 7.3520 | 118500 | 0.0007 | - | |
|
|
| 7.3831 | 119000 | 0.001 | - | |
|
|
| 7.4141 | 119500 | 0.001 | - | |
|
|
| 7.4451 | 120000 | 0.0007 | - | |
|
|
| 7.4761 | 120500 | 0.0008 | - | |
|
|
| 7.5071 | 121000 | 0.0009 | - | |
|
|
| 7.5382 | 121500 | 0.0009 | - | |
|
|
| 7.5692 | 122000 | 0.001 | - | |
|
|
| 7.6002 | 122500 | 0.0009 | - | |
|
|
| 7.6312 | 123000 | 0.0007 | - | |
|
|
| 7.6622 | 123500 | 0.0009 | - | |
|
|
| 7.6933 | 124000 | 0.0007 | - | |
|
|
| 7.7243 | 124500 | 0.0012 | - | |
|
|
| 7.7553 | 125000 | 0.001 | - | |
|
|
| 7.7863 | 125500 | 0.0005 | - | |
|
|
| 7.8173 | 126000 | 0.0005 | - | |
|
|
| 7.8484 | 126500 | 0.0008 | - | |
|
|
| 7.8794 | 127000 | 0.0014 | - | |
|
|
| 7.9104 | 127500 | 0.0014 | - | |
|
|
| 7.9414 | 128000 | 0.0009 | - | |
|
|
| 7.9725 | 128500 | 0.0008 | - | |
|
|
| 8.0 | 128944 | - | 0.94 | |
|
|
| 8.0035 | 129000 | 0.0013 | - | |
|
|
| 8.0345 | 129500 | 0.0007 | - | |
|
|
| 8.0655 | 130000 | 0.0007 | - | |
|
|
| 8.0965 | 130500 | 0.0008 | - | |
|
|
| 8.1276 | 131000 | 0.0009 | - | |
|
|
| 8.1586 | 131500 | 0.0009 | - | |
|
|
| 8.1896 | 132000 | 0.0007 | - | |
|
|
| 8.2206 | 132500 | 0.0008 | - | |
|
|
| 8.2516 | 133000 | 0.0008 | - | |
|
|
| 8.2827 | 133500 | 0.0006 | - | |
|
|
| 8.3137 | 134000 | 0.0008 | - | |
|
|
| 8.3447 | 134500 | 0.001 | - | |
|
|
| 8.3757 | 135000 | 0.0006 | - | |
|
|
| 8.4068 | 135500 | 0.0007 | - | |
|
|
| 8.4378 | 136000 | 0.0007 | - | |
|
|
| 8.4688 | 136500 | 0.0009 | - | |
|
|
| 8.4998 | 137000 | 0.0008 | - | |
|
|
| 8.5308 | 137500 | 0.0006 | - | |
|
|
| 8.5619 | 138000 | 0.0008 | - | |
|
|
| 8.5929 | 138500 | 0.0007 | - | |
|
|
| 8.6239 | 139000 | 0.0008 | - | |
|
|
| 8.6549 | 139500 | 0.0006 | - | |
|
|
| 8.6859 | 140000 | 0.0005 | - | |
|
|
| 8.7170 | 140500 | 0.0006 | - | |
|
|
| 8.7480 | 141000 | 0.0006 | - | |
|
|
| 8.7790 | 141500 | 0.0006 | - | |
|
|
| 8.8100 | 142000 | 0.0005 | - | |
|
|
| 8.8410 | 142500 | 0.0006 | - | |
|
|
| 8.8721 | 143000 | 0.0005 | - | |
|
|
| 8.9031 | 143500 | 0.0006 | - | |
|
|
| 8.9341 | 144000 | 0.0009 | - | |
|
|
| 8.9651 | 144500 | 0.0007 | - | |
|
|
| 8.9962 | 145000 | 0.0007 | - | |
|
|
| 9.0 | 145062 | - | 0.938 | |
|
|
| 9.0272 | 145500 | 0.0007 | - | |
|
|
| 9.0582 | 146000 | 0.0007 | - | |
|
|
| 9.0892 | 146500 | 0.0007 | - | |
|
|
| 9.1202 | 147000 | 0.0007 | - | |
|
|
| 9.1513 | 147500 | 0.0005 | - | |
|
|
| 9.1823 | 148000 | 0.0005 | - | |
|
|
| 9.2133 | 148500 | 0.0005 | - | |
|
|
| 9.2443 | 149000 | 0.0007 | - | |
|
|
| 9.2753 | 149500 | 0.0006 | - | |
|
|
| 9.3064 | 150000 | 0.0005 | 0.938 | |
|
|
| 9.3374 | 150500 | 0.0005 | - | |
|
|
| 9.3684 | 151000 | 0.0004 | - | |
|
|
| 9.3994 | 151500 | 0.0007 | - | |
|
|
| 9.4305 | 152000 | 0.0006 | - | |
|
|
| 9.4615 | 152500 | 0.0006 | - | |
|
|
| 9.4925 | 153000 | 0.0012 | - | |
|
|
| 9.5235 | 153500 | 0.0015 | - | |
|
|
| 9.5545 | 154000 | 0.0006 | - | |
|
|
| 9.5856 | 154500 | 0.0004 | - | |
|
|
| 9.6166 | 155000 | 0.0004 | - | |
|
|
| 9.6476 | 155500 | 0.0007 | - | |
|
|
| 9.6786 | 156000 | 0.0005 | - | |
|
|
| 9.7096 | 156500 | 0.0006 | - | |
|
|
| 9.7407 | 157000 | 0.0004 | - | |
|
|
| 9.7717 | 157500 | 0.0004 | - | |
|
|
| 9.8027 | 158000 | 0.0006 | - | |
|
|
| 9.8337 | 158500 | 0.0004 | - | |
|
|
| 9.8647 | 159000 | 0.0005 | - | |
|
|
| 9.8958 | 159500 | 0.0005 | - | |
|
|
| 9.9268 | 160000 | 0.0004 | - | |
|
|
| 9.9578 | 160500 | 0.0007 | - | |
|
|
| 9.9888 | 161000 | 0.0008 | - | |
|
|
| 10.0 | 161180 | - | 0.9405 | |
|
|
| 10.0199 | 161500 | 0.0009 | - | |
|
|
| 10.0509 | 162000 | 0.0007 | - | |
|
|
| 10.0819 | 162500 | 0.0007 | - | |
|
|
| 10.1129 | 163000 | 0.0007 | - | |
|
|
| 10.1439 | 163500 | 0.0005 | - | |
|
|
| 10.1750 | 164000 | 0.0005 | - | |
|
|
| 10.2060 | 164500 | 0.0004 | - | |
|
|
| 10.2370 | 165000 | 0.0006 | - | |
|
|
| 10.2680 | 165500 | 0.0006 | - | |
|
|
| 10.2990 | 166000 | 0.0005 | - | |
|
|
| 10.3301 | 166500 | 0.0005 | - | |
|
|
| 10.3611 | 167000 | 0.0006 | - | |
|
|
| 10.3921 | 167500 | 0.0006 | - | |
|
|
| 10.4231 | 168000 | 0.0003 | - | |
|
|
| 10.4542 | 168500 | 0.0005 | - | |
|
|
| 10.4852 | 169000 | 0.001 | - | |
|
|
| 10.5162 | 169500 | 0.0007 | - | |
|
|
| 10.5472 | 170000 | 0.0003 | - | |
|
|
| 10.5782 | 170500 | 0.0005 | - | |
|
|
| 10.6093 | 171000 | 0.0003 | - | |
|
|
| 10.6403 | 171500 | 0.0004 | - | |
|
|
| 10.6713 | 172000 | 0.0006 | - | |
|
|
| 10.7023 | 172500 | 0.0006 | - | |
|
|
| 10.7333 | 173000 | 0.0005 | - | |
|
|
| 10.7644 | 173500 | 0.0004 | - | |
|
|
| 10.7954 | 174000 | 0.0003 | - | |
|
|
| 10.8264 | 174500 | 0.0007 | - | |
|
|
| 10.8574 | 175000 | 0.0005 | - | |
|
|
| 10.8884 | 175500 | 0.0003 | - | |
|
|
| 10.9195 | 176000 | 0.0006 | - | |
|
|
| 10.9505 | 176500 | 0.001 | - | |
|
|
| 10.9815 | 177000 | 0.0007 | - | |
|
|
| 11.0 | 177298 | - | 0.9345 | |
|
|
| 11.0125 | 177500 | 0.0003 | - | |
|
|
| 11.0436 | 178000 | 0.0003 | - | |
|
|
| 11.0746 | 178500 | 0.0005 | - | |
|
|
| 11.1056 | 179000 | 0.0005 | - | |
|
|
| 11.1366 | 179500 | 0.0007 | - | |
|
|
| 11.1676 | 180000 | 0.0008 | - | |
|
|
| 11.1987 | 180500 | 0.0004 | - | |
|
|
| 11.2297 | 181000 | 0.0006 | - | |
|
|
| 11.2607 | 181500 | 0.0006 | - | |
|
|
| 11.2917 | 182000 | 0.0009 | - | |
|
|
| 11.3227 | 182500 | 0.0005 | - | |
|
|
| 11.3538 | 183000 | 0.0004 | - | |
|
|
| 11.3848 | 183500 | 0.0004 | - | |
|
|
| 11.4158 | 184000 | 0.0005 | - | |
|
|
| 11.4468 | 184500 | 0.0003 | - | |
|
|
| 11.4779 | 185000 | 0.0002 | - | |
|
|
| 11.5089 | 185500 | 0.0003 | - | |
|
|
| 11.5399 | 186000 | 0.0007 | - | |
|
|
| 11.5709 | 186500 | 0.0003 | - | |
|
|
| 11.6019 | 187000 | 0.0003 | - | |
|
|
| 11.6330 | 187500 | 0.0004 | - | |
|
|
| 11.6640 | 188000 | 0.0007 | - | |
|
|
| 11.6950 | 188500 | 0.0003 | - | |
|
|
| 11.7260 | 189000 | 0.0003 | - | |
|
|
| 11.7570 | 189500 | 0.0004 | - | |
|
|
| 11.7881 | 190000 | 0.0004 | - | |
|
|
| 11.8191 | 190500 | 0.0003 | - | |
|
|
| 11.8501 | 191000 | 0.0003 | - | |
|
|
| 11.8811 | 191500 | 0.0003 | - | |
|
|
| 11.9121 | 192000 | 0.0002 | - | |
|
|
| 11.9432 | 192500 | 0.0008 | - | |
|
|
| 11.9742 | 193000 | 0.0004 | - | |
|
|
| 12.0 | 193416 | - | 0.944 | |
|
|
| 12.0052 | 193500 | 0.0005 | - | |
|
|
| 12.0362 | 194000 | 0.0002 | - | |
|
|
| 12.0673 | 194500 | 0.0003 | - | |
|
|
| 12.0983 | 195000 | 0.0004 | - | |
|
|
| 12.1293 | 195500 | 0.0005 | - | |
|
|
| 12.1603 | 196000 | 0.0004 | - | |
|
|
| 12.1913 | 196500 | 0.0002 | - | |
|
|
| 12.2224 | 197000 | 0.0002 | - | |
|
|
| 12.2534 | 197500 | 0.0003 | - | |
|
|
| 12.2844 | 198000 | 0.0003 | - | |
|
|
| 12.3154 | 198500 | 0.0005 | - | |
|
|
| 12.3464 | 199000 | 0.0004 | - | |
|
|
| 12.3775 | 199500 | 0.0004 | - | |
|
|
| 12.4085 | 200000 | 0.0003 | 0.9435 | |
|
|
| 12.4395 | 200500 | 0.0003 | - | |
|
|
| 12.4705 | 201000 | 0.0004 | - | |
|
|
| 12.5016 | 201500 | 0.0009 | - | |
|
|
| 12.5326 | 202000 | 0.0005 | - | |
|
|
| 12.5636 | 202500 | 0.0003 | - | |
|
|
| 12.5946 | 203000 | 0.0003 | - | |
|
|
| 12.6256 | 203500 | 0.0002 | - | |
|
|
| 12.6567 | 204000 | 0.0003 | - | |
|
|
| 12.6877 | 204500 | 0.0002 | - | |
|
|
| 12.7187 | 205000 | 0.0005 | - | |
|
|
| 12.7497 | 205500 | 0.0003 | - | |
|
|
| 12.7807 | 206000 | 0.0004 | - | |
|
|
| 12.8118 | 206500 | 0.0003 | - | |
|
|
| 12.8428 | 207000 | 0.0003 | - | |
|
|
| 12.8738 | 207500 | 0.0003 | - | |
|
|
| 12.9048 | 208000 | 0.0003 | - | |
|
|
| 12.9358 | 208500 | 0.0006 | - | |
|
|
| 12.9669 | 209000 | 0.0004 | - | |
|
|
| 12.9979 | 209500 | 0.0004 | - | |
|
|
| 13.0 | 209534 | - | 0.9455 | |
|
|
|
|
|
</details> |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.10.17 |
|
|
- Sentence Transformers: 4.1.0 |
|
|
- Transformers: 4.46.3 |
|
|
- PyTorch: 2.2.0+cu121 |
|
|
- Accelerate: 1.1.1 |
|
|
- Datasets: 2.18.0 |
|
|
- Tokenizers: 0.20.3 |
|
|
|
|
|
## 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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