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saikasyap
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xlm-roberta-base-multilingual-en-sa

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:257886
loss:MSELoss
Eval Results
text-embeddings-inference
Model card Files
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Add new SentenceTransformer model

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Files changed (12) hide show
  1. .gitattributes +1 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +1248 -0
  4. config.json +28 -0
  5. config_sentence_transformers.json +10 -0
  6. model.safetensors +3 -0
  7. modules.json +14 -0
  8. sentence_bert_config.json +4 -0
  9. sentencepiece.bpe.model +3 -0
  10. special_tokens_map.json +15 -0
  11. tokenizer.json +3 -0
  12. tokenizer_config.json +54 -0
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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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:MSELoss
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+ base_model: FacebookAI/xlm-roberta-base
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+ widget:
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+ - source_sentence: Click on Save.Make sure the file is saved with the extension .cpp.
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+ sentences:
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+ - अधुना लेयर्-पेलेट्-मध्ये त्रीणि लेयर्स् सन्ति ।
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+ - '"save नुदन्तु । भवतां फैल् इत्येतत् ,डाट् सि पि पि एक्स्टेन्षन् इत्यनेन समं सेव्
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+ कृतं वर्तते इति निश्चयं कुर्वन्तु ।"'
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+ - '"प्रारम्भे,,initial amount इतीदं Rs 5000 इति लिखाम।"'
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+ - source_sentence: '"Variable initialisation, Operators,"'
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+ sentences:
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+ - '"''वेरियेबल् इनिशियलैसेशन्'', ओपरेटर्स्,"'
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+ - यस्य मानुषस्यैतत् स्वास्थ्यकरणम् आश्चर्य्यं कर्म्माक्रियत तस्य वयश्चत्वारिंशद्वत्सरा
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+ व्यतीताः।
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+ - '"यदि, भवद्भिः टेम्प्लेट्-स्ट्याण्ड्-द्वारा आरब्धं चेत्, तद् टर्मिनल्-मध्ये यथा
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+ दर्श्यते तथा, ‘रिवर्स् काम्प्लिमेण्ड् मेथड्’ इत्येतत् उपयुज्य, ‘कोडिङ्ग् स्ट्राण्ड्’
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+ प्रति परिवर्तयन्तु ।"'
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+ - source_sentence: '"""The same day went Jesus out of the house, and sat by the sea
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+ side."""'
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+ sentences:
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+ - ‘Tag deleted’ इति सन्देशेन सह अन्यत् विण्डो दृश्यते । Ok उपरि क्लिक् कुर्वन्तु
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+ ।
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+ - अपरञ्च तस्मिन् दिने यीशुः सद्मनो गत्वा सरित्पते रोधसि समुपविवेश।
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+ - '"MySQL सर्वर्-प्रोपर्टिस् इत्यस्य कोन्फिगरिङ्ग् कर्तुं ,"'
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+ - source_sentence: '"The next thing that I am going do is to rename these layers,
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+ So that I can get an idea about which layer I am working on."'
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+ sentences:
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+ - स्तराणां पुनर्नामकरणम् एव मम अग्रिमकार्यम् अस्ति । अतः यस्य स्तरस्य विषये कार्यं
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+ कुर्वन् अस्मि तद्विषयक-कल्पनां प्राप्तुं शक्नोमि।
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+ - अस्माकं स्लैड् प्रति गच्छामः।
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+ - plus New framework उपरि क्लिक् कुर्वन्तु ।
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+ - source_sentence: Magazines and Periodicals that are published periodically.
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+ sentences:
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+ - दण्डजनिका सेवा यदि तेजोयुक्ता भवेत् तर्हि पुण्यजनिका सेवा ततोऽधिकं बहुतेजोयुक्ता
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+ भविष्यति।
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+ - '"अस्योपरि नुदामश्चेत्, इदं पेन्-ड्रैव् मध्ये, विद्यमानानि सर्वाणि फैल्स् फोल्डर्स्
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+ च दर्शयति ।"'
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+ - पत्रिकाणां (Magazines) तथा नियतकालिकानां च (Periodicals) ग्राहकत्वस्य निर्वहणार्थम्
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+ उपयुज्यन्ते ।
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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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+ - negative_mse
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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 FacebookAI/xlm-roberta-base
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+ results:
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+ - task:
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+ type: knowledge-distillation
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+ name: Knowledge Distillation
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+ dataset:
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+ name: en sa
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+ type: en-sa
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+ metrics:
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+ - type: negative_mse
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+ value: -13.024808466434479
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+ name: Negative Mse
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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: en sa
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+ type: en-sa
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+ metrics:
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+ - type: src2trg_accuracy
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+ value: 0.927
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+ name: Src2Trg Accuracy
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+ - type: trg2src_accuracy
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+ value: 0.903
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+ name: Trg2Src Accuracy
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+ - type: mean_accuracy
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+ value: 0.915
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+ name: Mean Accuracy
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+ ---
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+
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+ # SentenceTransformer based on FacebookAI/xlm-roberta-base
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the en-sa dataset. 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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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ - **Base model:** [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) <!-- at revision e73636d4f797dec63c3081bb6ed5c7b0bb3f2089 -->
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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:**
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+ - en-sa
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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+
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+ ### Full Model Architecture
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+
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+ ```
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+ SentenceTransformer(
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+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
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+ (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
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+ )
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+ ```
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+
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+ ## Usage
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+
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+ ### Direct Usage (Sentence Transformers)
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+
121
+ First install the Sentence Transformers library:
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+
123
+ ```bash
124
+ pip install -U sentence-transformers
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+ ```
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+
127
+ 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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+
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+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("saikasyap/xlm-roberta-base-multilingual-en-sa")
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+ # Run inference
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+ sentences = [
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+ 'Magazines and Periodicals that are published periodically.',
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+ 'पत्रिकाणां (Magazines) तथा नियतकालिकानां च (Periodicals) ग्राहकत्वस्य निर्वहणार्थम् उपयुज्यन्ते ।',
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+ '"अस्योपरि नुदामश्चेत्, इदं पेन्-ड्रैव् मध्ये, विद्यमानानि सर्वाणि फैल्स् फोल्डर्स् च दर्शयति ।"',
138
+ ]
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+ embeddings = model.encode(sentences)
140
+ print(embeddings.shape)
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+ # [3, 768]
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+
143
+ # Get the similarity scores for the embeddings
144
+ similarities = model.similarity(embeddings, embeddings)
145
+ print(similarities.shape)
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+ # [3, 3]
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+ ```
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+
149
+ <!--
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+ ### Direct Usage (Transformers)
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+
152
+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
154
+ </details>
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+ -->
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+
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+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
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+ You can finetune this model on your own dataset.
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+
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+ <details><summary>Click to expand</summary>
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+
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+ </details>
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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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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+
173
+ ## Evaluation
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+
175
+ ### Metrics
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+
177
+ #### Knowledge Distillation
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+
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+ * Dataset: `en-sa`
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+ * Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
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+
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+ | Metric | Value |
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+ |:-----------------|:-------------|
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+ | **negative_mse** | **-13.0248** |
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+
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+ #### Translation
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+
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+ * Dataset: `en-sa`
189
+ * Evaluated with [<code>TranslationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
190
+
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+ | Metric | Value |
192
+ |:------------------|:----------|
193
+ | src2trg_accuracy | 0.927 |
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+ | trg2src_accuracy | 0.903 |
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+ | **mean_accuracy** | **0.915** |
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+
197
+ <!--
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+ ## Bias, Risks and Limitations
199
+
200
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
201
+ -->
202
+
203
+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
207
+ -->
208
+
209
+ ## Training Details
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+
211
+ ### Training Dataset
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+
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+ #### en-sa
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+
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+ * Dataset: en-sa
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+ * Size: 257,886 training samples
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+ * Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | english | non_english | label |
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+ |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------|
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+ | type | string | string | list |
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+ | details | <ul><li>min: 12 tokens</li><li>mean: 34.23 tokens</li><li>max: 113 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 49.72 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
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+ * Samples:
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+ | english | non_english | label |
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+ |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------|
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+ | <code>There was no Mughal tradition of primogeniture, the systematic passing of rule, upon an emperor's death, to his eldest son.<br></code> | <code>चक्रवर्तिनः मृत्योः अनन्तरं तस्य शासनस्य व्यवस्थितरूपेण सङ्क्रमणस्य, मुघलपरम्परायाः ज्येष्ठपुत्राधिकारपद्धतिः नासीत्।<br></code> | <code>[-0.5880301594734192, -0.20026817917823792, 0.372330904006958, -0.9807565808296204, -0.35607191920280457, ...]</code> |
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+ | <code>The four sons of Shah Jahan all held governorships during their father's reign.<br></code> | <code>शाह्-जहाँ-नामकस्य चत्वारः पुत्राः, सर्वे पितुः शासनकाले शासकपदम् अधारयन्।<br></code> | <code>[-0.5090229511260986, 0.33517003059387207, 0.27507224678993225, -0.05707915127277374, -0.5126022100448608, ...]</code> |
228
+ | <code>In this regard he discusses the correlation between social opportunities of education and health and how both of these complement economic and political freedoms as a healthy and well-educated person is better suited to make informed economic decisions and be involved in fruitful political demonstrations etc.<br></code> | <code>अस्मिन् विषये सः शिक्षणस्य स्वास्थ्यस्य च सामाजिकावकाशानाम् अन्योन्य-सम्बन्धस्य, तथा च एतद्द्वयम् अपि आर्थिक-राजनैतिक-स्वातन्त्र्ययोः कथं पूरकं भवतः इति च चर्चां करोति, यतोहि स्वस्था सुशिक्षिता च व्यक्तिः ज्ञानपूर्वम् आर्थिकविषयान् निर्णेतुं तथा फलप्रदेषु राजनैतिकेषु प्रतिपादनादिषु संलग्नः भवितुं च अधिकारी भवति इति।<br></code> | <code>[0.16507332026958466, -0.1722974181175232, 0.02585001103579998, 0.36087149381637573, -0.6401643753051758, ...]</code> |
229
+ * Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
230
+
231
+ ### Evaluation Dataset
232
+
233
+ #### en-sa
234
+
235
+ * Dataset: en-sa
236
+ * Size: 1,000 evaluation samples
237
+ * Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
238
+ * Approximate statistics based on the first 1000 samples:
239
+ | | english | non_english | label |
240
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------|
241
+ | type | string | string | list |
242
+ | details | <ul><li>min: 4 tokens</li><li>mean: 21.38 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 27.89 tokens</li><li>max: 91 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
243
+ * Samples:
244
+ | english | non_english | label |
245
+ |:-------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------|
246
+ | <code>"""So they cast him out of the vineyard, and killed him. What therefore shall the lord of the vineyard do unto them?"""</code> | <code>ततस्ते तं क्षेत्राद् बहि र्निपात्य जघ्नुस्तस्मात् स क्षेत्रपतिस्तान् प्रति किं करिष्यति?</code> | <code>[-0.06878167390823364, -0.5150429606437683, -0.09011576324701309, -0.7458725571632385, 0.050420328974723816, ...]</code> |
247
+ | <code>Avogadro application window opens.</code> | <code>Avogadro एप्लिकेशन् विण्डो उद्घट्यते ।</code> | <code>[0.9054689407348633, -0.2203768789768219, -0.19827595353126526, 0.23870715498924255, -0.3162331283092499, ...]</code> |
248
+ | <code>Svangah: One whose limbs are beautiful.</code> | <code>स्वंग:यस्य अङ्गानि सुन्दराणि सन्ति</code> | <code>[0.6443825960159302, 0.4850354492664337, -0.4563218355178833, -0.4771449863910675, 0.6588209867477417, ...]</code> |
249
+ * Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
250
+
251
+ ### Training Hyperparameters
252
+ #### Non-Default Hyperparameters
253
+
254
+ - `eval_strategy`: steps
255
+ - `learning_rate`: 2e-05
256
+ - `num_train_epochs`: 10
257
+ - `warmup_ratio`: 0.1
258
+
259
+ #### All Hyperparameters
260
+ <details><summary>Click to expand</summary>
261
+
262
+ - `overwrite_output_dir`: False
263
+ - `do_predict`: False
264
+ - `eval_strategy`: steps
265
+ - `prediction_loss_only`: True
266
+ - `per_device_train_batch_size`: 8
267
+ - `per_device_eval_batch_size`: 8
268
+ - `per_gpu_train_batch_size`: None
269
+ - `per_gpu_eval_batch_size`: None
270
+ - `gradient_accumulation_steps`: 1
271
+ - `eval_accumulation_steps`: None
272
+ - `torch_empty_cache_steps`: None
273
+ - `learning_rate`: 2e-05
274
+ - `weight_decay`: 0.0
275
+ - `adam_beta1`: 0.9
276
+ - `adam_beta2`: 0.999
277
+ - `adam_epsilon`: 1e-08
278
+ - `max_grad_norm`: 1.0
279
+ - `num_train_epochs`: 10
280
+ - `max_steps`: -1
281
+ - `lr_scheduler_type`: linear
282
+ - `lr_scheduler_kwargs`: {}
283
+ - `warmup_ratio`: 0.1
284
+ - `warmup_steps`: 0
285
+ - `log_level`: passive
286
+ - `log_level_replica`: warning
287
+ - `log_on_each_node`: True
288
+ - `logging_nan_inf_filter`: True
289
+ - `save_safetensors`: True
290
+ - `save_on_each_node`: False
291
+ - `save_only_model`: False
292
+ - `restore_callback_states_from_checkpoint`: False
293
+ - `no_cuda`: False
294
+ - `use_cpu`: False
295
+ - `use_mps_device`: False
296
+ - `seed`: 42
297
+ - `data_seed`: None
298
+ - `jit_mode_eval`: False
299
+ - `use_ipex`: False
300
+ - `bf16`: False
301
+ - `fp16`: False
302
+ - `fp16_opt_level`: O1
303
+ - `half_precision_backend`: auto
304
+ - `bf16_full_eval`: False
305
+ - `fp16_full_eval`: False
306
+ - `tf32`: None
307
+ - `local_rank`: 0
308
+ - `ddp_backend`: None
309
+ - `tpu_num_cores`: None
310
+ - `tpu_metrics_debug`: False
311
+ - `debug`: []
312
+ - `dataloader_drop_last`: False
313
+ - `dataloader_num_workers`: 0
314
+ - `dataloader_prefetch_factor`: None
315
+ - `past_index`: -1
316
+ - `disable_tqdm`: False
317
+ - `remove_unused_columns`: True
318
+ - `label_names`: None
319
+ - `load_best_model_at_end`: False
320
+ - `ignore_data_skip`: False
321
+ - `fsdp`: []
322
+ - `fsdp_min_num_params`: 0
323
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
324
+ - `fsdp_transformer_layer_cls_to_wrap`: None
325
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
326
+ - `deepspeed`: None
327
+ - `label_smoothing_factor`: 0.0
328
+ - `optim`: adamw_torch
329
+ - `optim_args`: None
330
+ - `adafactor`: False
331
+ - `group_by_length`: False
332
+ - `length_column_name`: length
333
+ - `ddp_find_unused_parameters`: None
334
+ - `ddp_bucket_cap_mb`: None
335
+ - `ddp_broadcast_buffers`: False
336
+ - `dataloader_pin_memory`: True
337
+ - `dataloader_persistent_workers`: False
338
+ - `skip_memory_metrics`: True
339
+ - `use_legacy_prediction_loop`: False
340
+ - `push_to_hub`: False
341
+ - `resume_from_checkpoint`: None
342
+ - `hub_model_id`: None
343
+ - `hub_strategy`: every_save
344
+ - `hub_private_repo`: False
345
+ - `hub_always_push`: False
346
+ - `gradient_checkpointing`: False
347
+ - `gradient_checkpointing_kwargs`: None
348
+ - `include_inputs_for_metrics`: False
349
+ - `include_for_metrics`: []
350
+ - `eval_do_concat_batches`: True
351
+ - `fp16_backend`: auto
352
+ - `push_to_hub_model_id`: None
353
+ - `push_to_hub_organization`: None
354
+ - `mp_parameters`:
355
+ - `auto_find_batch_size`: False
356
+ - `full_determinism`: False
357
+ - `torchdynamo`: None
358
+ - `ray_scope`: last
359
+ - `ddp_timeout`: 1800
360
+ - `torch_compile`: False
361
+ - `torch_compile_backend`: None
362
+ - `torch_compile_mode`: None
363
+ - `dispatch_batches`: None
364
+ - `split_batches`: None
365
+ - `include_tokens_per_second`: False
366
+ - `include_num_input_tokens_seen`: False
367
+ - `neftune_noise_alpha`: None
368
+ - `optim_target_modules`: None
369
+ - `batch_eval_metrics`: False
370
+ - `eval_on_start`: False
371
+ - `use_liger_kernel`: False
372
+ - `eval_use_gather_object`: False
373
+ - `average_tokens_across_devices`: False
374
+ - `prompts`: None
375
+ - `batch_sampler`: batch_sampler
376
+ - `multi_dataset_batch_sampler`: proportional
377
+
378
+ </details>
379
+
380
+ ### Training Logs
381
+ <details><summary>Click to expand</summary>
382
+
383
+ | Epoch | Step | Training Loss | en-sa loss | en-sa_negative_mse | en-sa_mean_accuracy |
384
+ |:------:|:-----:|:-------------:|:----------:|:------------------:|:-------------------:|
385
+ | 0.0124 | 100 | 0.6774 | - | - | - |
386
+ | 0.0248 | 200 | 0.6328 | - | - | - |
387
+ | 0.0372 | 300 | 0.5541 | - | - | - |
388
+ | 0.0496 | 400 | 0.4007 | - | - | - |
389
+ | 0.0620 | 500 | 0.3031 | - | - | - |
390
+ | 0.0745 | 600 | 0.2789 | - | - | - |
391
+ | 0.0869 | 700 | 0.2674 | - | - | - |
392
+ | 0.0993 | 800 | 0.2603 | - | - | - |
393
+ | 0.1117 | 900 | 0.2564 | - | - | - |
394
+ | 0.1241 | 1000 | 0.254 | - | - | - |
395
+ | 0.1365 | 1100 | 0.2496 | - | - | - |
396
+ | 0.1489 | 1200 | 0.2486 | - | - | - |
397
+ | 0.1613 | 1300 | 0.2476 | - | - | - |
398
+ | 0.1737 | 1400 | 0.2487 | - | - | - |
399
+ | 0.1861 | 1500 | 0.2439 | - | - | - |
400
+ | 0.1985 | 1600 | 0.2441 | - | - | - |
401
+ | 0.2109 | 1700 | 0.2427 | - | - | - |
402
+ | 0.2234 | 1800 | 0.2414 | - | - | - |
403
+ | 0.2358 | 1900 | 0.2395 | - | - | - |
404
+ | 0.2482 | 2000 | 0.2395 | - | - | - |
405
+ | 0.2606 | 2100 | 0.2383 | - | - | - |
406
+ | 0.2730 | 2200 | 0.2363 | - | - | - |
407
+ | 0.2854 | 2300 | 0.2348 | - | - | - |
408
+ | 0.2978 | 2400 | 0.2316 | - | - | - |
409
+ | 0.3102 | 2500 | 0.235 | - | - | - |
410
+ | 0.3226 | 2600 | 0.2328 | - | - | - |
411
+ | 0.3350 | 2700 | 0.2307 | - | - | - |
412
+ | 0.3474 | 2800 | 0.2295 | - | - | - |
413
+ | 0.3598 | 2900 | 0.2267 | - | - | - |
414
+ | 0.3723 | 3000 | 0.2246 | - | - | - |
415
+ | 0.3847 | 3100 | 0.225 | - | - | - |
416
+ | 0.3971 | 3200 | 0.2239 | - | - | - |
417
+ | 0.4095 | 3300 | 0.2201 | - | - | - |
418
+ | 0.4219 | 3400 | 0.2149 | - | - | - |
419
+ | 0.4343 | 3500 | 0.2161 | - | - | - |
420
+ | 0.4467 | 3600 | 0.2168 | - | - | - |
421
+ | 0.4591 | 3700 | 0.212 | - | - | - |
422
+ | 0.4715 | 3800 | 0.2135 | - | - | - |
423
+ | 0.4839 | 3900 | 0.2087 | - | - | - |
424
+ | 0.4963 | 4000 | 0.2083 | - | - | - |
425
+ | 0.5087 | 4100 | 0.2061 | - | - | - |
426
+ | 0.5212 | 4200 | 0.2084 | - | - | - |
427
+ | 0.5336 | 4300 | 0.2011 | - | - | - |
428
+ | 0.5460 | 4400 | 0.2023 | - | - | - |
429
+ | 0.5584 | 4500 | 0.2 | - | - | - |
430
+ | 0.5708 | 4600 | 0.2006 | - | - | - |
431
+ | 0.5832 | 4700 | 0.1987 | - | - | - |
432
+ | 0.5956 | 4800 | 0.1946 | - | - | - |
433
+ | 0.6080 | 4900 | 0.197 | - | - | - |
434
+ | 0.6204 | 5000 | 0.1962 | - | - | - |
435
+ | 0.6328 | 5100 | 0.192 | - | - | - |
436
+ | 0.6452 | 5200 | 0.1931 | - | - | - |
437
+ | 0.6576 | 5300 | 0.1928 | - | - | - |
438
+ | 0.6701 | 5400 | 0.1896 | - | - | - |
439
+ | 0.6825 | 5500 | 0.1906 | - | - | - |
440
+ | 0.6949 | 5600 | 0.1882 | - | - | - |
441
+ | 0.7073 | 5700 | 0.1867 | - | - | - |
442
+ | 0.7197 | 5800 | 0.1867 | - | - | - |
443
+ | 0.7321 | 5900 | 0.1847 | - | - | - |
444
+ | 0.7445 | 6000 | 0.186 | - | - | - |
445
+ | 0.7569 | 6100 | 0.1843 | - | - | - |
446
+ | 0.7693 | 6200 | 0.1806 | - | - | - |
447
+ | 0.7817 | 6300 | 0.1812 | - | - | - |
448
+ | 0.7941 | 6400 | 0.1779 | - | - | - |
449
+ | 0.8066 | 6500 | 0.178 | - | - | - |
450
+ | 0.8190 | 6600 | 0.1778 | - | - | - |
451
+ | 0.8314 | 6700 | 0.1769 | - | - | - |
452
+ | 0.8438 | 6800 | 0.1768 | - | - | - |
453
+ | 0.8562 | 6900 | 0.1753 | - | - | - |
454
+ | 0.8686 | 7000 | 0.1749 | - | - | - |
455
+ | 0.8810 | 7100 | 0.1722 | - | - | - |
456
+ | 0.8934 | 7200 | 0.1727 | - | - | - |
457
+ | 0.9058 | 7300 | 0.1736 | - | - | - |
458
+ | 0.9182 | 7400 | 0.1717 | - | - | - |
459
+ | 0.9306 | 7500 | 0.1691 | - | - | - |
460
+ | 0.9430 | 7600 | 0.1678 | - | - | - |
461
+ | 0.9555 | 7700 | 0.1709 | - | - | - |
462
+ | 0.9679 | 7800 | 0.168 | - | - | - |
463
+ | 0.9803 | 7900 | 0.167 | - | - | - |
464
+ | 0.9927 | 8000 | 0.1647 | - | - | - |
465
+ | 1.0051 | 8100 | 0.1658 | - | - | - |
466
+ | 1.0175 | 8200 | 0.1661 | - | - | - |
467
+ | 1.0299 | 8300 | 0.1629 | - | - | - |
468
+ | 1.0423 | 8400 | 0.1646 | - | - | - |
469
+ | 1.0547 | 8500 | 0.1631 | - | - | - |
470
+ | 1.0671 | 8600 | 0.1603 | - | - | - |
471
+ | 1.0795 | 8700 | 0.1608 | - | - | - |
472
+ | 1.0919 | 8800 | 0.1605 | - | - | - |
473
+ | 1.1044 | 8900 | 0.1593 | - | - | - |
474
+ | 1.1168 | 9000 | 0.1598 | - | - | - |
475
+ | 1.1292 | 9100 | 0.158 | - | - | - |
476
+ | 1.1416 | 9200 | 0.1561 | - | - | - |
477
+ | 1.1540 | 9300 | 0.1562 | - | - | - |
478
+ | 1.1664 | 9400 | 0.1563 | - | - | - |
479
+ | 1.1788 | 9500 | 0.1545 | - | - | - |
480
+ | 1.1912 | 9600 | 0.1525 | - | - | - |
481
+ | 1.2036 | 9700 | 0.1531 | - | - | - |
482
+ | 1.2160 | 9800 | 0.1534 | - | - | - |
483
+ | 1.2284 | 9900 | 0.1525 | - | - | - |
484
+ | 1.2408 | 10000 | 0.1515 | 0.1755 | -19.4347 | 0.7575 |
485
+ | 1.2533 | 10100 | 0.152 | - | - | - |
486
+ | 1.2657 | 10200 | 0.1507 | - | - | - |
487
+ | 1.2781 | 10300 | 0.1492 | - | - | - |
488
+ | 1.2905 | 10400 | 0.1485 | - | - | - |
489
+ | 1.3029 | 10500 | 0.1488 | - | - | - |
490
+ | 1.3153 | 10600 | 0.1496 | - | - | - |
491
+ | 1.3277 | 10700 | 0.1495 | - | - | - |
492
+ | 1.3401 | 10800 | 0.1475 | - | - | - |
493
+ | 1.3525 | 10900 | 0.1484 | - | - | - |
494
+ | 1.3649 | 11000 | 0.1465 | - | - | - |
495
+ | 1.3773 | 11100 | 0.1481 | - | - | - |
496
+ | 1.3898 | 11200 | 0.1477 | - | - | - |
497
+ | 1.4022 | 11300 | 0.148 | - | - | - |
498
+ | 1.4146 | 11400 | 0.1445 | - | - | - |
499
+ | 1.4270 | 11500 | 0.1429 | - | - | - |
500
+ | 1.4394 | 11600 | 0.1443 | - | - | - |
501
+ | 1.4518 | 11700 | 0.144 | - | - | - |
502
+ | 1.4642 | 11800 | 0.1455 | - | - | - |
503
+ | 1.4766 | 11900 | 0.1438 | - | - | - |
504
+ | 1.4890 | 12000 | 0.1425 | - | - | - |
505
+ | 1.5014 | 12100 | 0.1427 | - | - | - |
506
+ | 1.5138 | 12200 | 0.1426 | - | - | - |
507
+ | 1.5262 | 12300 | 0.1422 | - | - | - |
508
+ | 1.5387 | 12400 | 0.1395 | - | - | - |
509
+ | 1.5511 | 12500 | 0.1403 | - | - | - |
510
+ | 1.5635 | 12600 | 0.1414 | - | - | - |
511
+ | 1.5759 | 12700 | 0.1404 | - | - | - |
512
+ | 1.5883 | 12800 | 0.1391 | - | - | - |
513
+ | 1.6007 | 12900 | 0.1377 | - | - | - |
514
+ | 1.6131 | 13000 | 0.1408 | - | - | - |
515
+ | 1.6255 | 13100 | 0.1378 | - | - | - |
516
+ | 1.6379 | 13200 | 0.1387 | - | - | - |
517
+ | 1.6503 | 13300 | 0.1383 | - | - | - |
518
+ | 1.6627 | 13400 | 0.1393 | - | - | - |
519
+ | 1.6751 | 13500 | 0.137 | - | - | - |
520
+ | 1.6876 | 13600 | 0.1386 | - | - | - |
521
+ | 1.7000 | 13700 | 0.1366 | - | - | - |
522
+ | 1.7124 | 13800 | 0.137 | - | - | - |
523
+ | 1.7248 | 13900 | 0.1365 | - | - | - |
524
+ | 1.7372 | 14000 | 0.1367 | - | - | - |
525
+ | 1.7496 | 14100 | 0.1379 | - | - | - |
526
+ | 1.7620 | 14200 | 0.1355 | - | - | - |
527
+ | 1.7744 | 14300 | 0.1349 | - | - | - |
528
+ | 1.7868 | 14400 | 0.134 | - | - | - |
529
+ | 1.7992 | 14500 | 0.133 | - | - | - |
530
+ | 1.8116 | 14600 | 0.1337 | - | - | - |
531
+ | 1.8240 | 14700 | 0.1332 | - | - | - |
532
+ | 1.8365 | 14800 | 0.1335 | - | - | - |
533
+ | 1.8489 | 14900 | 0.1334 | - | - | - |
534
+ | 1.8613 | 15000 | 0.1333 | - | - | - |
535
+ | 1.8737 | 15100 | 0.1329 | - | - | - |
536
+ | 1.8861 | 15200 | 0.132 | - | - | - |
537
+ | 1.8985 | 15300 | 0.1322 | - | - | - |
538
+ | 1.9109 | 15400 | 0.1334 | - | - | - |
539
+ | 1.9233 | 15500 | 0.1308 | - | - | - |
540
+ | 1.9357 | 15600 | 0.1302 | - | - | - |
541
+ | 1.9481 | 15700 | 0.1313 | - | - | - |
542
+ | 1.9605 | 15800 | 0.1319 | - | - | - |
543
+ | 1.9729 | 15900 | 0.1305 | - | - | - |
544
+ | 1.9854 | 16000 | 0.1299 | - | - | - |
545
+ | 1.9978 | 16100 | 0.1288 | - | - | - |
546
+ | 2.0102 | 16200 | 0.1313 | - | - | - |
547
+ | 2.0226 | 16300 | 0.1299 | - | - | - |
548
+ | 2.0350 | 16400 | 0.1304 | - | - | - |
549
+ | 2.0474 | 16500 | 0.1304 | - | - | - |
550
+ | 2.0598 | 16600 | 0.1292 | - | - | - |
551
+ | 2.0722 | 16700 | 0.1276 | - | - | - |
552
+ | 2.0846 | 16800 | 0.1283 | - | - | - |
553
+ | 2.0970 | 16900 | 0.129 | - | - | - |
554
+ | 2.1094 | 17000 | 0.1294 | - | - | - |
555
+ | 2.1219 | 17100 | 0.1281 | - | - | - |
556
+ | 2.1343 | 17200 | 0.1276 | - | - | - |
557
+ | 2.1467 | 17300 | 0.1266 | - | - | - |
558
+ | 2.1591 | 17400 | 0.1263 | - | - | - |
559
+ | 2.1715 | 17500 | 0.1273 | - | - | - |
560
+ | 2.1839 | 17600 | 0.1263 | - | - | - |
561
+ | 2.1963 | 17700 | 0.1257 | - | - | - |
562
+ | 2.2087 | 17800 | 0.1256 | - | - | - |
563
+ | 2.2211 | 17900 | 0.1269 | - | - | - |
564
+ | 2.2335 | 18000 | 0.1256 | - | - | - |
565
+ | 2.2459 | 18100 | 0.1255 | - | - | - |
566
+ | 2.2583 | 18200 | 0.126 | - | - | - |
567
+ | 2.2708 | 18300 | 0.1243 | - | - | - |
568
+ | 2.2832 | 18400 | 0.125 | - | - | - |
569
+ | 2.2956 | 18500 | 0.1242 | - | - | - |
570
+ | 2.3080 | 18600 | 0.1249 | - | - | - |
571
+ | 2.3204 | 18700 | 0.1248 | - | - | - |
572
+ | 2.3328 | 18800 | 0.1248 | - | - | - |
573
+ | 2.3452 | 18900 | 0.1245 | - | - | - |
574
+ | 2.3576 | 19000 | 0.124 | - | - | - |
575
+ | 2.3700 | 19100 | 0.1246 | - | - | - |
576
+ | 2.3824 | 19200 | 0.125 | - | - | - |
577
+ | 2.3948 | 19300 | 0.1251 | - | - | - |
578
+ | 2.4072 | 19400 | 0.1243 | - | - | - |
579
+ | 2.4197 | 19500 | 0.1218 | - | - | - |
580
+ | 2.4321 | 19600 | 0.1217 | - | - | - |
581
+ | 2.4445 | 19700 | 0.1239 | - | - | - |
582
+ | 2.4569 | 19800 | 0.1219 | - | - | - |
583
+ | 2.4693 | 19900 | 0.1241 | - | - | - |
584
+ | 2.4817 | 20000 | 0.1222 | 0.1380 | -16.1712 | 0.864 |
585
+ | 2.4941 | 20100 | 0.1223 | - | - | - |
586
+ | 2.5065 | 20200 | 0.1216 | - | - | - |
587
+ | 2.5189 | 20300 | 0.1231 | - | - | - |
588
+ | 2.5313 | 20400 | 0.1208 | - | - | - |
589
+ | 2.5437 | 20500 | 0.1208 | - | - | - |
590
+ | 2.5561 | 20600 | 0.1202 | - | - | - |
591
+ | 2.5686 | 20700 | 0.1225 | - | - | - |
592
+ | 2.5810 | 20800 | 0.1209 | - | - | - |
593
+ | 2.5934 | 20900 | 0.1201 | - | - | - |
594
+ | 2.6058 | 21000 | 0.1203 | - | - | - |
595
+ | 2.6182 | 21100 | 0.1212 | - | - | - |
596
+ | 2.6306 | 21200 | 0.1199 | - | - | - |
597
+ | 2.6430 | 21300 | 0.1198 | - | - | - |
598
+ | 2.6554 | 21400 | 0.1212 | - | - | - |
599
+ | 2.6678 | 21500 | 0.1207 | - | - | - |
600
+ | 2.6802 | 21600 | 0.1199 | - | - | - |
601
+ | 2.6926 | 21700 | 0.1198 | - | - | - |
602
+ | 2.7051 | 21800 | 0.1196 | - | - | - |
603
+ | 2.7175 | 21900 | 0.1196 | - | - | - |
604
+ | 2.7299 | 22000 | 0.119 | - | - | - |
605
+ | 2.7423 | 22100 | 0.1197 | - | - | - |
606
+ | 2.7547 | 22200 | 0.1201 | - | - | - |
607
+ | 2.7671 | 22300 | 0.1187 | - | - | - |
608
+ | 2.7795 | 22400 | 0.1184 | - | - | - |
609
+ | 2.7919 | 22500 | 0.1177 | - | - | - |
610
+ | 2.8043 | 22600 | 0.1167 | - | - | - |
611
+ | 2.8167 | 22700 | 0.1187 | - | - | - |
612
+ | 2.8291 | 22800 | 0.1168 | - | - | - |
613
+ | 2.8415 | 22900 | 0.1174 | - | - | - |
614
+ | 2.8540 | 23000 | 0.1181 | - | - | - |
615
+ | 2.8664 | 23100 | 0.1185 | - | - | - |
616
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617
+ | 2.8912 | 23300 | 0.1169 | - | - | - |
618
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619
+ | 2.9160 | 23500 | 0.1179 | - | - | - |
620
+ | 2.9284 | 23600 | 0.116 | - | - | - |
621
+ | 2.9408 | 23700 | 0.1148 | - | - | - |
622
+ | 2.9532 | 23800 | 0.1183 | - | - | - |
623
+ | 2.9656 | 23900 | 0.1162 | - | - | - |
624
+ | 2.9780 | 24000 | 0.1165 | - | - | - |
625
+ | 2.9904 | 24100 | 0.115 | - | - | - |
626
+ | 3.0029 | 24200 | 0.1155 | - | - | - |
627
+ | 3.0153 | 24300 | 0.1177 | - | - | - |
628
+ | 3.0277 | 24400 | 0.1145 | - | - | - |
629
+ | 3.0401 | 24500 | 0.1175 | - | - | - |
630
+ | 3.0525 | 24600 | 0.1159 | - | - | - |
631
+ | 3.0649 | 24700 | 0.1149 | - | - | - |
632
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633
+ | 3.0897 | 24900 | 0.1152 | - | - | - |
634
+ | 3.1021 | 25000 | 0.1157 | - | - | - |
635
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636
+ | 3.1269 | 25200 | 0.1145 | - | - | - |
637
+ | 3.1393 | 25300 | 0.1139 | - | - | - |
638
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639
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640
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641
+ | 3.1890 | 25700 | 0.113 | - | - | - |
642
+ | 3.2014 | 25800 | 0.1133 | - | - | - |
643
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644
+ | 3.2262 | 26000 | 0.1138 | - | - | - |
645
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646
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647
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648
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649
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650
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651
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652
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653
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654
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655
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656
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657
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658
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659
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660
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661
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662
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663
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664
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665
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666
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667
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668
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669
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670
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671
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672
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673
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674
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675
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676
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677
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678
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679
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680
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681
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682
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683
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684
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685
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686
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687
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688
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689
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690
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691
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692
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693
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694
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695
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696
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697
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698
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699
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700
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701
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702
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703
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704
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705
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706
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707
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708
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709
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710
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711
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712
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713
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714
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715
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716
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717
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718
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719
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720
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721
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722
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723
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724
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725
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726
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727
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728
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729
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730
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731
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732
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733
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734
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735
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736
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737
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738
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739
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740
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741
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742
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743
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744
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745
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746
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747
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748
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749
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750
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751
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752
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753
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754
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755
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756
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757
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758
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759
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760
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761
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762
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763
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764
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765
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766
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767
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768
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769
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770
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771
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772
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773
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774
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775
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776
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777
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778
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779
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780
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781
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782
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783
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784
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785
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786
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787
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788
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789
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790
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791
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792
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793
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794
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795
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796
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797
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798
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799
+ | 5.1495 | 41500 | 0.1005 | - | - | - |
800
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801
+ | 5.1743 | 41700 | 0.101 | - | - | - |
802
+ | 5.1867 | 41800 | 0.1004 | - | - | - |
803
+ | 5.1992 | 41900 | 0.1006 | - | - | - |
804
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805
+ | 5.2240 | 42100 | 0.1004 | - | - | - |
806
+ | 5.2364 | 42200 | 0.1006 | - | - | - |
807
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808
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809
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810
+ | 5.2860 | 42600 | 0.1004 | - | - | - |
811
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812
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813
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814
+ | 5.3356 | 43000 | 0.1001 | - | - | - |
815
+ | 5.3481 | 43100 | 0.1007 | - | - | - |
816
+ | 5.3605 | 43200 | 0.1005 | - | - | - |
817
+ | 5.3729 | 43300 | 0.1007 | - | - | - |
818
+ | 5.3853 | 43400 | 0.1019 | - | - | - |
819
+ | 5.3977 | 43500 | 0.1016 | - | - | - |
820
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821
+ | 5.4225 | 43700 | 0.0987 | - | - | - |
822
+ | 5.4349 | 43800 | 0.1001 | - | - | - |
823
+ | 5.4473 | 43900 | 0.1003 | - | - | - |
824
+ | 5.4597 | 44000 | 0.0996 | - | - | - |
825
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826
+ | 5.4846 | 44200 | 0.0994 | - | - | - |
827
+ | 5.4970 | 44300 | 0.1002 | - | - | - |
828
+ | 5.5094 | 44400 | 0.0996 | - | - | - |
829
+ | 5.5218 | 44500 | 0.1012 | - | - | - |
830
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831
+ | 5.5466 | 44700 | 0.0992 | - | - | - |
832
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833
+ | 5.5714 | 44900 | 0.1005 | - | - | - |
834
+ | 5.5838 | 45000 | 0.0996 | - | - | - |
835
+ | 5.5962 | 45100 | 0.0986 | - | - | - |
836
+ | 5.6086 | 45200 | 0.0995 | - | - | - |
837
+ | 5.6210 | 45300 | 0.0999 | - | - | - |
838
+ | 5.6335 | 45400 | 0.0984 | - | - | - |
839
+ | 5.6459 | 45500 | 0.1001 | - | - | - |
840
+ | 5.6583 | 45600 | 0.1006 | - | - | - |
841
+ | 5.6707 | 45700 | 0.0994 | - | - | - |
842
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843
+ | 5.6955 | 45900 | 0.0988 | - | - | - |
844
+ | 5.7079 | 46000 | 0.0985 | - | - | - |
845
+ | 5.7203 | 46100 | 0.0991 | - | - | - |
846
+ | 5.7327 | 46200 | 0.0996 | - | - | - |
847
+ | 5.7451 | 46300 | 0.0991 | - | - | - |
848
+ | 5.7575 | 46400 | 0.0997 | - | - | - |
849
+ | 5.7699 | 46500 | 0.0984 | - | - | - |
850
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851
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852
+ | 5.8072 | 46800 | 0.0984 | - | - | - |
853
+ | 5.8196 | 46900 | 0.0977 | - | - | - |
854
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855
+ | 5.8444 | 47100 | 0.0983 | - | - | - |
856
+ | 5.8568 | 47200 | 0.0985 | - | - | - |
857
+ | 5.8692 | 47300 | 0.0993 | - | - | - |
858
+ | 5.8816 | 47400 | 0.0974 | - | - | - |
859
+ | 5.8940 | 47500 | 0.0978 | - | - | - |
860
+ | 5.9064 | 47600 | 0.0996 | - | - | - |
861
+ | 5.9188 | 47700 | 0.0981 | - | - | - |
862
+ | 5.9313 | 47800 | 0.0981 | - | - | - |
863
+ | 5.9437 | 47900 | 0.0969 | - | - | - |
864
+ | 5.9561 | 48000 | 0.0997 | - | - | - |
865
+ | 5.9685 | 48100 | 0.098 | - | - | - |
866
+ | 5.9809 | 48200 | 0.0981 | - | - | - |
867
+ | 5.9933 | 48300 | 0.0969 | - | - | - |
868
+ | 6.0057 | 48400 | 0.0982 | - | - | - |
869
+ | 6.0181 | 48500 | 0.0983 | - | - | - |
870
+ | 6.0305 | 48600 | 0.0974 | - | - | - |
871
+ | 6.0429 | 48700 | 0.0991 | - | - | - |
872
+ | 6.0553 | 48800 | 0.0978 | - | - | - |
873
+ | 6.0678 | 48900 | 0.0973 | - | - | - |
874
+ | 6.0802 | 49000 | 0.0976 | - | - | - |
875
+ | 6.0926 | 49100 | 0.0978 | - | - | - |
876
+ | 6.1050 | 49200 | 0.0976 | - | - | - |
877
+ | 6.1174 | 49300 | 0.0981 | - | - | - |
878
+ | 6.1298 | 49400 | 0.0974 | - | - | - |
879
+ | 6.1422 | 49500 | 0.0967 | - | - | - |
880
+ | 6.1546 | 49600 | 0.0966 | - | - | - |
881
+ | 6.1670 | 49700 | 0.098 | - | - | - |
882
+ | 6.1794 | 49800 | 0.0967 | - | - | - |
883
+ | 6.1918 | 49900 | 0.0964 | - | - | - |
884
+ | 6.2042 | 50000 | 0.0966 | 0.1101 | -13.5564 | 0.9045 |
885
+ | 6.2167 | 50100 | 0.0975 | - | - | - |
886
+ | 6.2291 | 50200 | 0.0968 | - | - | - |
887
+ | 6.2415 | 50300 | 0.0972 | - | - | - |
888
+ | 6.2539 | 50400 | 0.0967 | - | - | - |
889
+ | 6.2663 | 50500 | 0.0971 | - | - | - |
890
+ | 6.2787 | 50600 | 0.0961 | - | - | - |
891
+ | 6.2911 | 50700 | 0.0967 | - | - | - |
892
+ | 6.3035 | 50800 | 0.0969 | - | - | - |
893
+ | 6.3159 | 50900 | 0.0965 | - | - | - |
894
+ | 6.3283 | 51000 | 0.0972 | - | - | - |
895
+ | 6.3407 | 51100 | 0.0967 | - | - | - |
896
+ | 6.3531 | 51200 | 0.0972 | - | - | - |
897
+ | 6.3656 | 51300 | 0.0965 | - | - | - |
898
+ | 6.3780 | 51400 | 0.0978 | - | - | - |
899
+ | 6.3904 | 51500 | 0.0976 | - | - | - |
900
+ | 6.4028 | 51600 | 0.0986 | - | - | - |
901
+ | 6.4152 | 51700 | 0.0957 | - | - | - |
902
+ | 6.4276 | 51800 | 0.0957 | - | - | - |
903
+ | 6.4400 | 51900 | 0.0966 | - | - | - |
904
+ | 6.4524 | 52000 | 0.096 | - | - | - |
905
+ | 6.4648 | 52100 | 0.097 | - | - | - |
906
+ | 6.4772 | 52200 | 0.0971 | - | - | - |
907
+ | 6.4896 | 52300 | 0.0959 | - | - | - |
908
+ | 6.5020 | 52400 | 0.0967 | - | - | - |
909
+ | 6.5145 | 52500 | 0.0967 | - | - | - |
910
+ | 6.5269 | 52600 | 0.0964 | - | - | - |
911
+ | 6.5393 | 52700 | 0.0954 | - | - | - |
912
+ | 6.5517 | 52800 | 0.096 | - | - | - |
913
+ | 6.5641 | 52900 | 0.0963 | - | - | - |
914
+ | 6.5765 | 53000 | 0.0963 | - | - | - |
915
+ | 6.5889 | 53100 | 0.0958 | - | - | - |
916
+ | 6.6013 | 53200 | 0.0951 | - | - | - |
917
+ | 6.6137 | 53300 | 0.0973 | - | - | - |
918
+ | 6.6261 | 53400 | 0.0955 | - | - | - |
919
+ | 6.6385 | 53500 | 0.0958 | - | - | - |
920
+ | 6.6509 | 53600 | 0.0967 | - | - | - |
921
+ | 6.6634 | 53700 | 0.0971 | - | - | - |
922
+ | 6.6758 | 53800 | 0.0957 | - | - | - |
923
+ | 6.6882 | 53900 | 0.0968 | - | - | - |
924
+ | 6.7006 | 54000 | 0.0951 | - | - | - |
925
+ | 6.7130 | 54100 | 0.0957 | - | - | - |
926
+ | 6.7254 | 54200 | 0.0958 | - | - | - |
927
+ | 6.7378 | 54300 | 0.0962 | - | - | - |
928
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1018
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1019
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1020
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1021
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1022
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1023
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1024
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1025
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1026
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1027
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1028
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1029
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1030
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1031
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1032
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1033
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1034
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1035
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1036
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1037
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1038
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1039
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1040
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1041
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1042
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1043
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1044
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1045
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1046
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1047
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1048
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1049
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1050
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1051
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1052
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1053
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1054
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1055
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1056
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1057
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1058
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1059
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1060
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1061
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1062
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1063
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1064
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1065
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1066
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1067
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1068
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1069
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1070
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1071
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1072
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1073
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1074
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1075
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1076
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1077
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1078
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1079
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1080
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1081
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1082
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1083
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1084
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1085
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1086
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1087
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1088
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1089
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1090
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1091
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1092
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1093
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1094
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1095
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1096
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1097
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1098
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1099
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1100
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1101
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1102
+ | 8.9093 | 71800 | 0.093 | - | - | - |
1103
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1104
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1105
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1106
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1107
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1108
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1109
+ | 8.9962 | 72500 | 0.0908 | - | - | - |
1110
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1111
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1112
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1113
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1114
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1115
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1116
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1117
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1118
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1119
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1120
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1121
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1122
+ | 9.1575 | 73800 | 0.0912 | - | - | - |
1123
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1124
+ | 9.1823 | 74000 | 0.0906 | - | - | - |
1125
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1126
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1127
+ | 9.2195 | 74300 | 0.0919 | - | - | - |
1128
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1129
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1130
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1131
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1132
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1133
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1134
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1135
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1136
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1137
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1138
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1139
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1140
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1141
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1142
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1143
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1144
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1145
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1146
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1147
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1148
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1149
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1150
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1151
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1152
+ | 9.5297 | 76800 | 0.0902 | - | - | - |
1153
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1154
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1155
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1156
+ | 9.5794 | 77200 | 0.091 | - | - | - |
1157
+ | 9.5918 | 77300 | 0.0906 | - | - | - |
1158
+ | 9.6042 | 77400 | 0.0902 | - | - | - |
1159
+ | 9.6166 | 77500 | 0.0921 | - | - | - |
1160
+ | 9.6290 | 77600 | 0.0907 | - | - | - |
1161
+ | 9.6414 | 77700 | 0.0908 | - | - | - |
1162
+ | 9.6538 | 77800 | 0.0917 | - | - | - |
1163
+ | 9.6662 | 77900 | 0.092 | - | - | - |
1164
+ | 9.6786 | 78000 | 0.091 | - | - | - |
1165
+ | 9.6910 | 78100 | 0.0909 | - | - | - |
1166
+ | 9.7034 | 78200 | 0.0903 | - | - | - |
1167
+ | 9.7158 | 78300 | 0.0914 | - | - | - |
1168
+ | 9.7283 | 78400 | 0.091 | - | - | - |
1169
+ | 9.7407 | 78500 | 0.0909 | - | - | - |
1170
+ | 9.7531 | 78600 | 0.0922 | - | - | - |
1171
+ | 9.7655 | 78700 | 0.0907 | - | - | - |
1172
+ | 9.7779 | 78800 | 0.0909 | - | - | - |
1173
+ | 9.7903 | 78900 | 0.0905 | - | - | - |
1174
+ | 9.8027 | 79000 | 0.0898 | - | - | - |
1175
+ | 9.8151 | 79100 | 0.091 | - | - | - |
1176
+ | 9.8275 | 79200 | 0.09 | - | - | - |
1177
+ | 9.8399 | 79300 | 0.0908 | - | - | - |
1178
+ | 9.8523 | 79400 | 0.0911 | - | - | - |
1179
+ | 9.8647 | 79500 | 0.0913 | - | - | - |
1180
+ | 9.8772 | 79600 | 0.0902 | - | - | - |
1181
+ | 9.8896 | 79700 | 0.0904 | - | - | - |
1182
+ | 9.9020 | 79800 | 0.0908 | - | - | - |
1183
+ | 9.9144 | 79900 | 0.0918 | - | - | - |
1184
+ | 9.9268 | 80000 | 0.0905 | 0.1044 | -13.0248 | 0.915 |
1185
+ | 9.9392 | 80100 | 0.0894 | - | - | - |
1186
+ | 9.9516 | 80200 | 0.0917 | - | - | - |
1187
+ | 9.9640 | 80300 | 0.0908 | - | - | - |
1188
+ | 9.9764 | 80400 | 0.0907 | - | - | - |
1189
+ | 9.9888 | 80500 | 0.0905 | - | - | - |
1190
+
1191
+ </details>
1192
+
1193
+ ### Framework Versions
1194
+ - Python: 3.10.17
1195
+ - Sentence Transformers: 4.1.0
1196
+ - Transformers: 4.46.3
1197
+ - PyTorch: 2.2.0+cu121
1198
+ - Accelerate: 1.1.1
1199
+ - Datasets: 2.18.0
1200
+ - Tokenizers: 0.20.3
1201
+
1202
+ ## Citation
1203
+
1204
+ ### BibTeX
1205
+
1206
+ #### Sentence Transformers
1207
+ ```bibtex
1208
+ @inproceedings{reimers-2019-sentence-bert,
1209
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
1210
+ author = "Reimers, Nils and Gurevych, Iryna",
1211
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
1212
+ month = "11",
1213
+ year = "2019",
1214
+ publisher = "Association for Computational Linguistics",
1215
+ url = "https://arxiv.org/abs/1908.10084",
1216
+ }
1217
+ ```
1218
+
1219
+ #### MSELoss
1220
+ ```bibtex
1221
+ @inproceedings{reimers-2020-multilingual-sentence-bert,
1222
+ title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
1223
+ author = "Reimers, Nils and Gurevych, Iryna",
1224
+ booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
1225
+ month = "11",
1226
+ year = "2020",
1227
+ publisher = "Association for Computational Linguistics",
1228
+ url = "https://arxiv.org/abs/2004.09813",
1229
+ }
1230
+ ```
1231
+
1232
+ <!--
1233
+ ## Glossary
1234
+
1235
+ *Clearly define terms in order to be accessible across audiences.*
1236
+ -->
1237
+
1238
+ <!--
1239
+ ## Model Card Authors
1240
+
1241
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1242
+ -->
1243
+
1244
+ <!--
1245
+ ## Model Card Contact
1246
+
1247
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1248
+ -->
config.json ADDED
@@ -0,0 +1,28 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "_name_or_path": "xlm-roberta-base",
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+ "architectures": [
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+ "XLMRobertaModel"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "bos_token_id": 0,
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+ "classifier_dropout": null,
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+ "eos_token_id": 2,
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