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Omartificial-Intelligence-Space
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AraEuroBert-610M

Feature Extraction
sentence-transformers
Safetensors
Arabic
eurobert
sentence-similarity
Generated from Trainer
dataset_size:2280319
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Arabic
EuroBert
Semantic
custom_code
Eval Results
Model card Files Community
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  1. .gitattributes +1 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +674 -3
  4. config.json +45 -0
  5. config_sentence_transformers.json +10 -0
  6. model.safetensors +3 -0
  7. modules.json +14 -0
  8. optimizer.pt +3 -0
  9. rng_state.pth +3 -0
  10. scaler.pt +3 -0
  11. scheduler.pt +3 -0
  12. sentence_bert_config.json +4 -0
  13. special_tokens_map.json +30 -0
  14. tokenizer.json +3 -0
  15. tokenizer_config.json +2069 -0
  16. trainer_state.json +2034 -0
  17. training_args.bin +3 -0
.gitattributes CHANGED
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1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 1152,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md CHANGED
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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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:2280319
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+ - loss:MatryoshkaLoss
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+ - loss:MultipleNegativesRankingLoss
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+ base_model: EuroBERT/EuroBERT-610m
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+ widget:
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+ - source_sentence: امرأة شقراء تطل على مشهد (سياتل سبيس نيدل)
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+ sentences:
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+ - رجل يستمتع بمناظر جسر البوابة الذهبية
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+ - فتاة بالخارج تلعب في الثلج
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+ - شخص ما يأخذ في نظرة إبرة الفضاء.
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+ - source_sentence: سوق الشرق الأوسط
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+ sentences:
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+ - مسرح أمريكي
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+ - متجر في الشرق الأوسط
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+ - البالغون صغار
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+ - source_sentence: رجلين يتنافسان في ملابس فنون الدفاع عن النفس
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+ sentences:
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+ - هناك العديد من الناس الحاضرين.
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+ - الكلب الأبيض على الشاطئ
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+ - هناك شخص واحد فقط موجود.
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+ - source_sentence: مجموعة من الناس تمشي بجانب شاحنة.
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+ sentences:
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+ - الناس يقفون
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+ - بعض الناس بالخارج
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+ - بعض الرجال يقودون على الطريق
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+ - source_sentence: لاعبة كرة ناعمة ترمي الكرة إلى زميلتها في الفريق
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+ sentences:
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+ - شخصان يلعبان كرة البيسبول
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+ - الرجل ينظف
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+ - لاعبين لكرة البيسبول يجلسان على مقعد
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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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+ - pearson_cosine
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+ - spearman_cosine
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+ model-index:
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+ - name: SentenceTransformer based on EuroBERT/EuroBERT-610m
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+ results:
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: sts dev 1152
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+ type: sts-dev-1152
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.8264443610084379
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8306689922163598
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+ name: Spearman Cosine
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: sts dev 960
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+ type: sts-dev-960
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.8258566703064338
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.830247434103489
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+ name: Spearman Cosine
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: sts dev 768
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+ type: sts-dev-768
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.8243518007889306
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8293091429698137
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+ name: Spearman Cosine
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: sts dev 512
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+ type: sts-dev-512
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.8238103920299558
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8293245725151981
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+ name: Spearman Cosine
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+ ---
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+
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+ # SentenceTransformer based on EuroBERT/EuroBERT-610m
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [EuroBERT/EuroBERT-610m](https://huggingface.co/EuroBERT/EuroBERT-610m). It maps sentences & paragraphs to a 1152-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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+
105
+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ - **Base model:** [EuroBERT/EuroBERT-610m](https://huggingface.co/EuroBERT/EuroBERT-610m) <!-- at revision f07f6d12ba7e3e12edb9a1df764462e698cf663f -->
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+ - **Maximum Sequence Length:** 8192 tokens
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+ - **Output Dimensionality:** 1152 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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+
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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)
119
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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+
121
+ ### Full Model Architecture
122
+
123
+ ```
124
+ SentenceTransformer(
125
+ (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: EuroBertModel
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+ (1): Pooling({'word_embedding_dimension': 1152, '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})
127
+ )
128
+ ```
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+
130
+ ## Usage
131
+
132
+ ### Direct Usage (Sentence Transformers)
133
+
134
+ First install the Sentence Transformers library:
135
+
136
+ ```bash
137
+ pip install -U sentence-transformers
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+ ```
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+
140
+ Then you can load this model and run inference.
141
+ ```python
142
+ from sentence_transformers import SentenceTransformer
143
+
144
+ # Download from the 🤗 Hub
145
+ model = SentenceTransformer("sentence_transformers_model_id")
146
+ # Run inference
147
+ sentences = [
148
+ 'لاعبة كرة ناعمة ترمي الكرة إلى زميلتها في الفريق',
149
+ 'شخصان يلعبان كرة البيسبول',
150
+ 'لاعبين لكرة البيسبول يجلسان على مقعد',
151
+ ]
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+ embeddings = model.encode(sentences)
153
+ print(embeddings.shape)
154
+ # [3, 1152]
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+
156
+ # Get the similarity scores for the embeddings
157
+ similarities = model.similarity(embeddings, embeddings)
158
+ print(similarities.shape)
159
+ # [3, 3]
160
+ ```
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+
162
+ <!--
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+ ### Direct Usage (Transformers)
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+
165
+ <details><summary>Click to see the direct usage in Transformers</summary>
166
+
167
+ </details>
168
+ -->
169
+
170
+ <!--
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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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+
175
+ <details><summary>Click to expand</summary>
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+
177
+ </details>
178
+ -->
179
+
180
+ <!--
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+ ### Out-of-Scope Use
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+
183
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
184
+ -->
185
+
186
+ ## Evaluation
187
+
188
+ ### Metrics
189
+
190
+ #### Semantic Similarity
191
+
192
+ * Datasets: `sts-dev-1152`, `sts-dev-960`, `sts-dev-768` and `sts-dev-512`
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+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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+
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+ | Metric | sts-dev-1152 | sts-dev-960 | sts-dev-768 | sts-dev-512 |
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+ |:--------------------|:-------------|:------------|:------------|:------------|
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+ | pearson_cosine | 0.8264 | 0.8259 | 0.8244 | 0.8238 |
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+ | **spearman_cosine** | **0.8307** | **0.8302** | **0.8293** | **0.8293** |
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+
200
+ <!--
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+ ## Bias, Risks and Limitations
202
+
203
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
204
+ -->
205
+
206
+ <!--
207
+ ### Recommendations
208
+
209
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
210
+ -->
211
+
212
+ ## Training Details
213
+
214
+ ### Training Dataset
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+
216
+ #### Unnamed Dataset
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+
218
+
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+ * Size: 2,280,319 training samples
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+ * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | anchor | positive | negative |
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+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
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+ | type | string | string | string |
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+ | details | <ul><li>min: 5 tokens</li><li>mean: 18.35 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.52 tokens</li><li>max: 99 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.06 tokens</li><li>max: 55 tokens</li></ul> |
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+ * Samples:
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+ | anchor | positive | negative |
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+ |:-------------------------------------------------------------------------------|:-----------------------------------------------------|:------------------------------------------------------------------------|
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+ | <code>صبي صغير وفتاة صغيرة يركبان دراجتيهما على الرصيف مع عجلات مساعدة.</code> | <code>فتى وفتاة يتعلمون ركوب الدراجات</code> | <code>الصبي الصغير يصل إلى العصا من الفتاة وهو يدير سباق التتابع</code> |
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+ | <code>كيف أتجنب التفكير في نفسي أكثر من اللازم؟</code> | <code>كيف يمكنني تجنب التفكير أكثر من اللازم؟</code> | <code>كيف أتطوّر قدرة التفكير؟</code> |
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+ | <code>ما هو أفضل كتاب يقرأه مراهق؟</code> | <code>ما هو أفضل كتاب للمراهقين؟</code> | <code>ما هي الكتب التي يمكن للطلاب قراءتها؟</code> |
232
+ * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
233
+ ```json
234
+ {
235
+ "loss": "MultipleNegativesRankingLoss",
236
+ "matryoshka_dims": [
237
+ 1152,
238
+ 960,
239
+ 768,
240
+ 512
241
+ ],
242
+ "matryoshka_weights": [
243
+ 1,
244
+ 1,
245
+ 1,
246
+ 1
247
+ ],
248
+ "n_dims_per_step": -1
249
+ }
250
+ ```
251
+
252
+ ### Evaluation Dataset
253
+
254
+ #### Unnamed Dataset
255
+
256
+
257
+ * Size: 6,609 evaluation samples
258
+ * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
259
+ * Approximate statistics based on the first 1000 samples:
260
+ | | anchor | positive | negative |
261
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
262
+ | type | string | string | string |
263
+ | details | <ul><li>min: 6 tokens</li><li>mean: 25.73 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.99 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.9 tokens</li><li>max: 38 tokens</li></ul> |
264
+ * Samples:
265
+ | anchor | positive | negative |
266
+ |:----------------------------------------------------------------------------------------------|:-----------------------------------|:-------------------------------------------|
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+ | <code>هذه الجوقة الكنيسة تغني للجماهير وهم يغنون الأغاني السعيدة من الكتاب في الكنيسة.</code> | <code>الكنيسة مليئة بالغناء</code> | <code>جوقة تغني في مباراة بيسبول</code> |
268
+ | <code>امرأة ترتدي حجاب أخضر، وقميص أزرق وابتسامة كبيرة</code> | <code>المرأة سعيدة جداً</code> | <code>لقد تم إطلاق النار على المرأة</code> |
269
+ | <code>رجل عجوز يحمل طردًا يتصور أمام إعلان.</code> | <code>رجل يتصور أمام إعلان.</code> | <code>رجل يمشي بجانب إعلان</code> |
270
+ * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
271
+ ```json
272
+ {
273
+ "loss": "MultipleNegativesRankingLoss",
274
+ "matryoshka_dims": [
275
+ 1152,
276
+ 960,
277
+ 768,
278
+ 512
279
+ ],
280
+ "matryoshka_weights": [
281
+ 1,
282
+ 1,
283
+ 1,
284
+ 1
285
+ ],
286
+ "n_dims_per_step": -1
287
+ }
288
+ ```
289
+
290
+ ### Training Hyperparameters
291
+ #### Non-Default Hyperparameters
292
+
293
+ - `eval_strategy`: steps
294
+ - `per_device_train_batch_size`: 16
295
+ - `per_device_eval_batch_size`: 16
296
+ - `weight_decay`: 0.1
297
+ - `adam_beta2`: 0.95
298
+ - `adam_epsilon`: 1e-05
299
+ - `num_train_epochs`: 1
300
+ - `warmup_ratio`: 0.1
301
+ - `fp16`: True
302
+ - `load_best_model_at_end`: True
303
+ - `batch_sampler`: no_duplicates
304
+
305
+ #### All Hyperparameters
306
+ <details><summary>Click to expand</summary>
307
+
308
+ - `overwrite_output_dir`: False
309
+ - `do_predict`: False
310
+ - `eval_strategy`: steps
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+ - `prediction_loss_only`: True
312
+ - `per_device_train_batch_size`: 16
313
+ - `per_device_eval_batch_size`: 16
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+ - `per_gpu_train_batch_size`: None
315
+ - `per_gpu_eval_batch_size`: None
316
+ - `gradient_accumulation_steps`: 1
317
+ - `eval_accumulation_steps`: None
318
+ - `torch_empty_cache_steps`: None
319
+ - `learning_rate`: 5e-05
320
+ - `weight_decay`: 0.1
321
+ - `adam_beta1`: 0.9
322
+ - `adam_beta2`: 0.95
323
+ - `adam_epsilon`: 1e-05
324
+ - `max_grad_norm`: 1.0
325
+ - `num_train_epochs`: 1
326
+ - `max_steps`: -1
327
+ - `lr_scheduler_type`: linear
328
+ - `lr_scheduler_kwargs`: {}
329
+ - `warmup_ratio`: 0.1
330
+ - `warmup_steps`: 0
331
+ - `log_level`: passive
332
+ - `log_level_replica`: warning
333
+ - `log_on_each_node`: True
334
+ - `logging_nan_inf_filter`: True
335
+ - `save_safetensors`: True
336
+ - `save_on_each_node`: False
337
+ - `save_only_model`: False
338
+ - `restore_callback_states_from_checkpoint`: False
339
+ - `no_cuda`: False
340
+ - `use_cpu`: False
341
+ - `use_mps_device`: False
342
+ - `seed`: 42
343
+ - `data_seed`: None
344
+ - `jit_mode_eval`: False
345
+ - `use_ipex`: False
346
+ - `bf16`: False
347
+ - `fp16`: True
348
+ - `fp16_opt_level`: O1
349
+ - `half_precision_backend`: auto
350
+ - `bf16_full_eval`: False
351
+ - `fp16_full_eval`: False
352
+ - `tf32`: None
353
+ - `local_rank`: 0
354
+ - `ddp_backend`: None
355
+ - `tpu_num_cores`: None
356
+ - `tpu_metrics_debug`: False
357
+ - `debug`: []
358
+ - `dataloader_drop_last`: False
359
+ - `dataloader_num_workers`: 0
360
+ - `dataloader_prefetch_factor`: None
361
+ - `past_index`: -1
362
+ - `disable_tqdm`: False
363
+ - `remove_unused_columns`: True
364
+ - `label_names`: None
365
+ - `load_best_model_at_end`: True
366
+ - `ignore_data_skip`: False
367
+ - `fsdp`: []
368
+ - `fsdp_min_num_params`: 0
369
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
370
+ - `fsdp_transformer_layer_cls_to_wrap`: None
371
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
372
+ - `deepspeed`: None
373
+ - `label_smoothing_factor`: 0.0
374
+ - `optim`: adamw_torch
375
+ - `optim_args`: None
376
+ - `adafactor`: False
377
+ - `group_by_length`: False
378
+ - `length_column_name`: length
379
+ - `ddp_find_unused_parameters`: None
380
+ - `ddp_bucket_cap_mb`: None
381
+ - `ddp_broadcast_buffers`: False
382
+ - `dataloader_pin_memory`: True
383
+ - `dataloader_persistent_workers`: False
384
+ - `skip_memory_metrics`: True
385
+ - `use_legacy_prediction_loop`: False
386
+ - `push_to_hub`: False
387
+ - `resume_from_checkpoint`: None
388
+ - `hub_model_id`: None
389
+ - `hub_strategy`: every_save
390
+ - `hub_private_repo`: None
391
+ - `hub_always_push`: False
392
+ - `gradient_checkpointing`: False
393
+ - `gradient_checkpointing_kwargs`: None
394
+ - `include_inputs_for_metrics`: False
395
+ - `include_for_metrics`: []
396
+ - `eval_do_concat_batches`: True
397
+ - `fp16_backend`: auto
398
+ - `push_to_hub_model_id`: None
399
+ - `push_to_hub_organization`: None
400
+ - `mp_parameters`:
401
+ - `auto_find_batch_size`: False
402
+ - `full_determinism`: False
403
+ - `torchdynamo`: None
404
+ - `ray_scope`: last
405
+ - `ddp_timeout`: 1800
406
+ - `torch_compile`: False
407
+ - `torch_compile_backend`: None
408
+ - `torch_compile_mode`: None
409
+ - `dispatch_batches`: None
410
+ - `split_batches`: None
411
+ - `include_tokens_per_second`: False
412
+ - `include_num_input_tokens_seen`: False
413
+ - `neftune_noise_alpha`: None
414
+ - `optim_target_modules`: None
415
+ - `batch_eval_metrics`: False
416
+ - `eval_on_start`: False
417
+ - `use_liger_kernel`: False
418
+ - `eval_use_gather_object`: False
419
+ - `average_tokens_across_devices`: False
420
+ - `prompts`: None
421
+ - `batch_sampler`: no_duplicates
422
+ - `multi_dataset_batch_sampler`: proportional
423
+
424
+ </details>
425
+
426
+ ### Training Logs
427
+ <details><summary>Click to expand</summary>
428
+
429
+ | Epoch | Step | Training Loss | Validation Loss | sts-dev-1152_spearman_cosine | sts-dev-960_spearman_cosine | sts-dev-768_spearman_cosine | sts-dev-512_spearman_cosine |
430
+ |:------:|:-----:|:-------------:|:---------------:|:----------------------------:|:---------------------------:|:---------------------------:|:---------------------------:|
431
+ | 0.0056 | 200 | 7.8472 | - | - | - | - | - |
432
+ | 0.0112 | 400 | 1.8133 | - | - | - | - | - |
433
+ | 0.0140 | 500 | - | 4.3329 | 0.8034 | 0.8024 | 0.8010 | 0.8010 |
434
+ | 0.0168 | 600 | 1.2257 | - | - | - | - | - |
435
+ | 0.0225 | 800 | 1.0662 | - | - | - | - | - |
436
+ | 0.0281 | 1000 | 1.0452 | 4.0554 | 0.8152 | 0.8150 | 0.8130 | 0.8113 |
437
+ | 0.0337 | 1200 | 1.0306 | - | - | - | - | - |
438
+ | 0.0393 | 1400 | 1.0223 | - | - | - | - | - |
439
+ | 0.0421 | 1500 | - | 4.0150 | 0.7964 | 0.7965 | 0.7957 | 0.7939 |
440
+ | 0.0449 | 1600 | 0.9923 | - | - | - | - | - |
441
+ | 0.0505 | 1800 | 1.007 | - | - | - | - | - |
442
+ | 0.0561 | 2000 | 0.9898 | 4.5628 | 0.7838 | 0.7829 | 0.7793 | 0.7779 |
443
+ | 0.0617 | 2200 | 1.033 | - | - | - | - | - |
444
+ | 0.0674 | 2400 | 1.0091 | - | - | - | - | - |
445
+ | 0.0702 | 2500 | - | 4.6133 | 0.7855 | 0.7834 | 0.7801 | 0.7767 |
446
+ | 0.0730 | 2600 | 1.046 | - | - | - | - | - |
447
+ | 0.0786 | 2800 | 1.0212 | - | - | - | - | - |
448
+ | 0.0842 | 3000 | 1.0923 | 5.0382 | 0.7698 | 0.7682 | 0.7656 | 0.7643 |
449
+ | 0.0898 | 3200 | 1.0542 | - | - | - | - | - |
450
+ | 0.0954 | 3400 | 1.025 | - | - | - | - | - |
451
+ | 0.0982 | 3500 | - | 4.9554 | 0.7732 | 0.7712 | 0.7693 | 0.7681 |
452
+ | 0.1010 | 3600 | 1.0056 | - | - | - | - | - |
453
+ | 0.1067 | 3800 | 1.0689 | - | - | - | - | - |
454
+ | 0.1123 | 4000 | 1.0453 | 5.8342 | 0.7656 | 0.7645 | 0.7607 | 0.7607 |
455
+ | 0.1179 | 4200 | 1.0874 | - | - | - | - | - |
456
+ | 0.1235 | 4400 | 1.0051 | - | - | - | - | - |
457
+ | 0.1263 | 4500 | - | 5.0767 | 0.7752 | 0.7728 | 0.7712 | 0.7707 |
458
+ | 0.1291 | 4600 | 1.0007 | - | - | - | - | - |
459
+ | 0.1347 | 4800 | 0.9307 | - | - | - | - | - |
460
+ | 0.1403 | 5000 | 0.9642 | 5.1986 | 0.7683 | 0.7667 | 0.7652 | 0.7618 |
461
+ | 0.1459 | 5200 | 0.9259 | - | - | - | - | - |
462
+ | 0.1516 | 5400 | 0.8908 | - | - | - | - | - |
463
+ | 0.1544 | 5500 | - | 5.2108 | 0.7762 | 0.7741 | 0.7713 | 0.7711 |
464
+ | 0.1572 | 5600 | 0.8812 | - | - | - | - | - |
465
+ | 0.1628 | 5800 | 0.8544 | - | - | - | - | - |
466
+ | 0.1684 | 6000 | 0.8314 | 5.2401 | 0.7732 | 0.7709 | 0.7688 | 0.7660 |
467
+ | 0.1740 | 6200 | 0.8258 | - | - | - | - | - |
468
+ | 0.1796 | 6400 | 0.8083 | - | - | - | - | - |
469
+ | 0.1824 | 6500 | - | 5.2708 | 0.7680 | 0.7656 | 0.7622 | 0.7638 |
470
+ | 0.1852 | 6600 | 0.8373 | - | - | - | - | - |
471
+ | 0.1909 | 6800 | 0.8031 | - | - | - | - | - |
472
+ | 0.1965 | 7000 | 0.7375 | 5.1549 | 0.7763 | 0.7740 | 0.7719 | 0.7738 |
473
+ | 0.2021 | 7200 | 0.743 | - | - | - | - | - |
474
+ | 0.2077 | 7400 | 0.739 | - | - | - | - | - |
475
+ | 0.2105 | 7500 | - | 4.8188 | 0.7868 | 0.7849 | 0.7825 | 0.7835 |
476
+ | 0.2133 | 7600 | 0.7399 | - | - | - | - | - |
477
+ | 0.2189 | 7800 | 0.6723 | - | - | - | - | - |
478
+ | 0.2245 | 8000 | 0.6866 | 5.0776 | 0.7714 | 0.7688 | 0.7663 | 0.7667 |
479
+ | 0.2301 | 8200 | 0.6556 | - | - | - | - | - |
480
+ | 0.2358 | 8400 | 0.6886 | - | - | - | - | - |
481
+ | 0.2386 | 8500 | - | 4.7758 | 0.7845 | 0.7828 | 0.7808 | 0.7811 |
482
+ | 0.2414 | 8600 | 0.685 | - | - | - | - | - |
483
+ | 0.2470 | 8800 | 0.6401 | - | - | - | - | - |
484
+ | 0.2526 | 9000 | 0.6617 | 4.6028 | 0.7821 | 0.7805 | 0.7774 | 0.7777 |
485
+ | 0.2582 | 9200 | 0.6208 | - | - | - | - | - |
486
+ | 0.2638 | 9400 | 0.6307 | - | - | - | - | - |
487
+ | 0.2666 | 9500 | - | 4.5390 | 0.7853 | 0.7844 | 0.7821 | 0.7823 |
488
+ | 0.2694 | 9600 | 0.6557 | - | - | - | - | - |
489
+ | 0.2750 | 9800 | 0.6102 | - | - | - | - | - |
490
+ | 0.2807 | 10000 | 0.5917 | 4.6088 | 0.7826 | 0.7807 | 0.7779 | 0.7770 |
491
+ | 0.2863 | 10200 | 0.5845 | - | - | - | - | - |
492
+ | 0.2919 | 10400 | 0.6018 | - | - | - | - | - |
493
+ | 0.2947 | 10500 | - | 4.5634 | 0.7902 | 0.7886 | 0.7866 | 0.7871 |
494
+ | 0.2975 | 10600 | 0.5859 | - | - | - | - | - |
495
+ | 0.3031 | 10800 | 0.5933 | - | - | - | - | - |
496
+ | 0.3087 | 11000 | 0.5717 | 4.4051 | 0.7930 | 0.7917 | 0.7903 | 0.7902 |
497
+ | 0.3143 | 11200 | 0.5719 | - | - | - | - | - |
498
+ | 0.3200 | 11400 | 0.5422 | - | - | - | - | - |
499
+ | 0.3228 | 11500 | - | 4.4430 | 0.7942 | 0.7932 | 0.7918 | 0.7920 |
500
+ | 0.3256 | 11600 | 0.527 | - | - | - | - | - |
501
+ | 0.3312 | 11800 | 0.5291 | - | - | - | - | - |
502
+ | 0.3368 | 12000 | 0.542 | 4.3001 | 0.7934 | 0.7928 | 0.7906 | 0.7907 |
503
+ | 0.3424 | 12200 | 0.5213 | - | - | - | - | - |
504
+ | 0.3480 | 12400 | 0.5226 | - | - | - | - | - |
505
+ | 0.3508 | 12500 | - | 4.8532 | 0.7724 | 0.7701 | 0.7674 | 0.7687 |
506
+ | 0.3536 | 12600 | 0.5111 | - | - | - | - | - |
507
+ | 0.3592 | 12800 | 0.51 | - | - | - | - | - |
508
+ | 0.3649 | 13000 | 0.5439 | 4.5625 | 0.7871 | 0.7859 | 0.7840 | 0.7859 |
509
+ | 0.3705 | 13200 | 0.4944 | - | - | - | - | - |
510
+ | 0.3761 | 13400 | 0.5055 | - | - | - | - | - |
511
+ | 0.3789 | 13500 | - | 4.6901 | 0.7717 | 0.7704 | 0.7674 | 0.7687 |
512
+ | 0.3817 | 13600 | 0.4914 | - | - | - | - | - |
513
+ | 0.3873 | 13800 | 0.4832 | - | - | - | - | - |
514
+ | 0.3929 | 14000 | 0.4974 | 4.3224 | 0.7833 | 0.7820 | 0.7801 | 0.7802 |
515
+ | 0.3985 | 14200 | 0.4834 | - | - | - | - | - |
516
+ | 0.4042 | 14400 | 0.4526 | - | - | - | - | - |
517
+ | 0.4070 | 14500 | - | 4.6031 | 0.7798 | 0.7789 | 0.7771 | 0.7793 |
518
+ | 0.4098 | 14600 | 0.4621 | - | - | - | - | - |
519
+ | 0.4154 | 14800 | 0.4483 | - | - | - | - | - |
520
+ | 0.4210 | 15000 | 0.4422 | 4.3402 | 0.7853 | 0.7840 | 0.7821 | 0.7836 |
521
+ | 0.4266 | 15200 | 0.4144 | - | - | - | - | - |
522
+ | 0.4322 | 15400 | 0.4099 | - | - | - | - | - |
523
+ | 0.4350 | 15500 | - | 4.4538 | 0.7885 | 0.7874 | 0.7861 | 0.7861 |
524
+ | 0.4378 | 15600 | 0.4196 | - | - | - | - | - |
525
+ | 0.4434 | 15800 | 0.4273 | - | - | - | - | - |
526
+ | 0.4491 | 16000 | 1.9924 | 3.5090 | 0.7927 | 0.7907 | 0.7894 | 0.7897 |
527
+ | 0.4547 | 16200 | 2.0174 | - | - | - | - | - |
528
+ | 0.4603 | 16400 | 1.9566 | - | - | - | - | - |
529
+ | 0.4631 | 16500 | - | 3.3055 | 0.8035 | 0.8026 | 0.8017 | 0.8011 |
530
+ | 0.4659 | 16600 | 1.8733 | - | - | - | - | - |
531
+ | 0.4715 | 16800 | 1.8465 | - | - | - | - | - |
532
+ | 0.4771 | 17000 | 1.8083 | 3.1462 | 0.8045 | 0.8034 | 0.8019 | 0.8015 |
533
+ | 0.4827 | 17200 | 1.7193 | - | - | - | - | - |
534
+ | 0.4884 | 17400 | 1.7423 | - | - | - | - | - |
535
+ | 0.4912 | 17500 | - | 3.0544 | 0.8085 | 0.8073 | 0.8060 | 0.8067 |
536
+ | 0.4940 | 17600 | 1.6114 | - | - | - | - | - |
537
+ | 0.4996 | 17800 | 1.6524 | - | - | - | - | - |
538
+ | 0.5052 | 18000 | 1.568 | 3.0232 | 0.8160 | 0.8148 | 0.8137 | 0.8138 |
539
+ | 0.5108 | 18200 | 1.5263 | - | - | - | - | - |
540
+ | 0.5164 | 18400 | 1.5547 | - | - | - | - | - |
541
+ | 0.5192 | 18500 | - | 2.8708 | 0.8177 | 0.8171 | 0.8162 | 0.8159 |
542
+ | 0.5220 | 18600 | 1.5059 | - | - | - | - | - |
543
+ | 0.5276 | 18800 | 1.4385 | - | - | - | - | - |
544
+ | 0.5333 | 19000 | 1.476 | 2.9468 | 0.8102 | 0.8101 | 0.8092 | 0.8094 |
545
+ | 0.5389 | 19200 | 1.4558 | - | - | - | - | - |
546
+ | 0.5445 | 19400 | 1.4557 | - | - | - | - | - |
547
+ | 0.5473 | 19500 | - | 2.8982 | 0.8094 | 0.8084 | 0.8071 | 0.8071 |
548
+ | 0.5501 | 19600 | 1.4552 | - | - | - | - | - |
549
+ | 0.5557 | 19800 | 1.4342 | - | - | - | - | - |
550
+ | 0.5613 | 20000 | 1.4503 | 2.8073 | 0.8179 | 0.8169 | 0.8156 | 0.8159 |
551
+ | 0.5669 | 20200 | 1.391 | - | - | - | - | - |
552
+ | 0.5726 | 20400 | 1.3529 | - | - | - | - | - |
553
+ | 0.5754 | 20500 | - | 2.6974 | 0.8184 | 0.8175 | 0.8162 | 0.8165 |
554
+ | 0.5782 | 20600 | 1.3428 | - | - | - | - | - |
555
+ | 0.5838 | 20800 | 1.3401 | - | - | - | - | - |
556
+ | 0.5894 | 21000 | 1.3809 | 2.7039 | 0.8188 | 0.8177 | 0.8162 | 0.8171 |
557
+ | 0.5950 | 21200 | 1.3193 | - | - | - | - | - |
558
+ | 0.6006 | 21400 | 1.2531 | - | - | - | - | - |
559
+ | 0.6034 | 21500 | - | 2.6553 | 0.8133 | 0.8124 | 0.8113 | 0.8107 |
560
+ | 0.6062 | 21600 | 1.3294 | - | - | - | - | - |
561
+ | 0.6118 | 21800 | 1.3076 | - | - | - | - | - |
562
+ | 0.6175 | 22000 | 1.2634 | 2.6157 | 0.8135 | 0.8131 | 0.8123 | 0.8123 |
563
+ | 0.6231 | 22200 | 1.242 | - | - | - | - | - |
564
+ | 0.6287 | 22400 | 1.2545 | - | - | - | - | - |
565
+ | 0.6315 | 22500 | - | 2.6083 | 0.8199 | 0.8193 | 0.8179 | 0.8175 |
566
+ | 0.6343 | 22600 | 1.2362 | - | - | - | - | - |
567
+ | 0.6399 | 22800 | 1.1474 | - | - | - | - | - |
568
+ | 0.6455 | 23000 | 1.2125 | 2.5617 | 0.8208 | 0.8200 | 0.8188 | 0.8181 |
569
+ | 0.6511 | 23200 | 1.206 | - | - | - | - | - |
570
+ | 0.6567 | 23400 | 1.1236 | - | - | - | - | - |
571
+ | 0.6596 | 23500 | - | 2.5598 | 0.8251 | 0.8247 | 0.8235 | 0.8232 |
572
+ | 0.6624 | 23600 | 1.1785 | - | - | - | - | - |
573
+ | 0.6680 | 23800 | 1.1376 | - | - | - | - | - |
574
+ | 0.6736 | 24000 | 1.1386 | 2.5229 | 0.8208 | 0.8198 | 0.8186 | 0.8187 |
575
+ | 0.6792 | 24200 | 1.1293 | - | - | - | - | - |
576
+ | 0.6848 | 24400 | 1.101 | - | - | - | - | - |
577
+ | 0.6876 | 24500 | - | 2.5423 | 0.8274 | 0.8263 | 0.8253 | 0.8241 |
578
+ | 0.6904 | 24600 | 1.1306 | - | - | - | - | - |
579
+ | 0.6960 | 24800 | 1.0517 | - | - | - | - | - |
580
+ | 0.7017 | 25000 | 1.0617 | 2.4988 | 0.8319 | 0.8310 | 0.8300 | 0.8296 |
581
+ | 0.7073 | 25200 | 1.0408 | - | - | - | - | - |
582
+ | 0.7129 | 25400 | 1.0741 | - | - | - | - | - |
583
+ | 0.7157 | 25500 | - | 2.4365 | 0.8276 | 0.8267 | 0.8253 | 0.8251 |
584
+ | 0.7185 | 25600 | 1.0373 | - | - | - | - | - |
585
+ | 0.7241 | 25800 | 1.0239 | - | - | - | - | - |
586
+ | 0.7297 | 26000 | 0.9982 | 2.4174 | 0.8253 | 0.8247 | 0.8232 | 0.8223 |
587
+ | 0.7353 | 26200 | 0.9829 | - | - | - | - | - |
588
+ | 0.7409 | 26400 | 0.9758 | - | - | - | - | - |
589
+ | 0.7438 | 26500 | - | 2.4214 | 0.8274 | 0.8269 | 0.8258 | 0.8249 |
590
+ | 0.7466 | 26600 | 1.0123 | - | - | - | - | - |
591
+ | 0.7522 | 26800 | 1.0156 | - | - | - | - | - |
592
+ | 0.7578 | 27000 | 0.9687 | 2.3816 | 0.8222 | 0.8214 | 0.8202 | 0.8191 |
593
+ | 0.7634 | 27200 | 0.9569 | - | - | - | - | - |
594
+ | 0.7690 | 27400 | 0.9543 | - | - | - | - | - |
595
+ | 0.7718 | 27500 | - | 2.3552 | 0.8289 | 0.8283 | 0.8276 | 0.8272 |
596
+ | 0.7746 | 27600 | 0.9453 | - | - | - | - | - |
597
+ | 0.7802 | 27800 | 0.9948 | - | - | - | - | - |
598
+ | 0.7859 | 28000 | 0.9874 | 2.3868 | 0.8280 | 0.8274 | 0.8268 | 0.8264 |
599
+ | 0.7915 | 28200 | 0.8872 | - | - | - | - | - |
600
+ | 0.7971 | 28400 | 0.9327 | - | - | - | - | - |
601
+ | 0.7999 | 28500 | - | 2.3834 | 0.8250 | 0.8246 | 0.8237 | 0.8235 |
602
+ | 0.8027 | 28600 | 0.8715 | - | - | - | - | - |
603
+ | 0.8083 | 28800 | 0.9566 | - | - | - | - | - |
604
+ | 0.8139 | 29000 | 0.9265 | 2.3455 | 0.8307 | 0.8302 | 0.8293 | 0.8293 |
605
+
606
+ </details>
607
+
608
+ ### Framework Versions
609
+ - Python: 3.10.12
610
+ - Sentence Transformers: 3.3.1
611
+ - Transformers: 4.49.0
612
+ - PyTorch: 2.5.1+cu124
613
+ - Accelerate: 1.2.1
614
+ - Datasets: 2.21.0
615
+ - Tokenizers: 0.21.0
616
+
617
+ ## Citation
618
+
619
+ ### BibTeX
620
+
621
+ #### Sentence Transformers
622
+ ```bibtex
623
+ @inproceedings{reimers-2019-sentence-bert,
624
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
625
+ author = "Reimers, Nils and Gurevych, Iryna",
626
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
627
+ month = "11",
628
+ year = "2019",
629
+ publisher = "Association for Computational Linguistics",
630
+ url = "https://arxiv.org/abs/1908.10084",
631
+ }
632
+ ```
633
+
634
+ #### MatryoshkaLoss
635
+ ```bibtex
636
+ @misc{kusupati2024matryoshka,
637
+ title={Matryoshka Representation Learning},
638
+ author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
639
+ year={2024},
640
+ eprint={2205.13147},
641
+ archivePrefix={arXiv},
642
+ primaryClass={cs.LG}
643
+ }
644
+ ```
645
+
646
+ #### MultipleNegativesRankingLoss
647
+ ```bibtex
648
+ @misc{henderson2017efficient,
649
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
650
+ 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},
651
+ year={2017},
652
+ eprint={1705.00652},
653
+ archivePrefix={arXiv},
654
+ primaryClass={cs.CL}
655
+ }
656
+ ```
657
+
658
+ <!--
659
+ ## Glossary
660
+
661
+ *Clearly define terms in order to be accessible across audiences.*
662
+ -->
663
+
664
+ <!--
665
+ ## Model Card Authors
666
+
667
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
668
+ -->
669
+
670
+ <!--
671
+ ## Model Card Contact
672
+
673
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
674
+ -->
config.json ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "EuroBERT/EuroBERT-610M",
3
+ "architectures": [
4
+ "EuroBertModel"
5
+ ],
6
+ "attention_bias": false,
7
+ "attention_dropout": 0.0,
8
+ "auto_map": {
9
+ "AutoConfig": "EuroBERT/EuroBERT-610M--configuration_eurobert.EuroBertConfig",
10
+ "AutoModel": "EuroBERT/EuroBERT-610M--modeling_eurobert.EuroBertModel",
11
+ "AutoModelForMaskedLM": "EuroBERT/EuroBERT-610M--modeling_eurobert.EuroBertForMaskedLM",
12
+ "AutoModelForPreTraining": "EuroBERT/EuroBERT-610M--modeling_eurobert.EuroBertPreTrainedModel",
13
+ "AutoModelForSequenceClassification": "EuroBERT/EuroBERT-610M--modeling_eurobert.EuroBertForSequenceClassification"
14
+ },
15
+ "bos_token": "<|begin_of_text|>",
16
+ "bos_token_id": 128000,
17
+ "clf_pooling": "late",
18
+ "eos_token": "<|end_of_text|>",
19
+ "eos_token_id": 128001,
20
+ "head_dim": 64,
21
+ "hidden_act": "silu",
22
+ "hidden_dropout": 0.0,
23
+ "hidden_size": 1152,
24
+ "initializer_range": 0.02,
25
+ "intermediate_size": 4096,
26
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+ "special": true
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+ }
2051
+ },
2052
+ "bos_token": "<|begin_of_text|>",
2053
+ "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
2054
+ "clean_up_tokenization_spaces": true,
2055
+ "eos_token": "<|end_of_text|>",
2056
+ "extra_special_tokens": {},
2057
+ "mask_token": "<|mask|>",
2058
+ "max_length": 512,
2059
+ "model_input_names": [
2060
+ "input_ids",
2061
+ "attention_mask"
2062
+ ],
2063
+ "model_max_length": 1000000000000000019884624838656,
2064
+ "pad_to_multiple_of": null,
2065
+ "pad_token": "<|end_of_text|>",
2066
+ "pad_token_type_id": 0,
2067
+ "padding_side": "right",
2068
+ "tokenizer_class": "PreTrainedTokenizer"
2069
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