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AraEuroBert-210M

Feature Extraction
sentence-transformers
Safetensors
Arabic
eurobert
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
EuroBert
Arabic
custom_code
Eval Results
Inference Endpoints
Model card Files Community
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  1. .gitattributes +1 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +558 -0
  4. config.json +45 -0
  5. config_sentence_transformers.json +10 -0
  6. configuration_eurobert.py +216 -0
  7. model.safetensors +3 -0
  8. modules.json +14 -0
  9. optimizer.pt +3 -0
  10. rng_state.pth +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 +591 -0
  17. training_args.bin +3 -0
.gitattributes CHANGED
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
5
+ "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 ADDED
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+ ---
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+ tags:
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+ - sentence-transformers
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+ - sentence-similarity
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+ - feature-extraction
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+ - generated_from_trainer
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+ - dataset_size:1000000
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+ - loss:MatryoshkaLoss
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+ - loss:MultipleNegativesRankingLoss
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+ base_model: EuroBERT/EuroBERT-210m
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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-210m
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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 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: 0.8111988062913815
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8100586279907306
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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: 0.8092891955563192
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8087644228771842
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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 256
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+ type: sts-dev-256
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.8076510620939634
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8080588277305082
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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 128
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+ type: sts-dev-128
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.8028710019029521
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8054855987917489
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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 64
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+ type: sts-dev-64
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.7923252906438638
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.7975941111911333
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+ name: Spearman Cosine
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+ ---
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+
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+ # SentenceTransformer based on EuroBERT/EuroBERT-210m
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [EuroBERT/EuroBERT-210m](https://huggingface.co/EuroBERT/EuroBERT-210m). 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:** [EuroBERT/EuroBERT-210m](https://huggingface.co/EuroBERT/EuroBERT-210m) <!-- at revision 15568905cfed68a254242872f0bdbc81e2327b92 -->
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+ - **Maximum Sequence Length:** 8192 tokens
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+ - **Output Dimensionality:** 768 dimensions
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+ - **Similarity Function:** Cosine Similarity
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+ <!-- - **Training Dataset:** Unknown -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
131
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
132
+ - **HF中国镜像站:** [Sentence Transformers on HF中国镜像站](https://huggingface.co/models?library=sentence-transformers)
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+
134
+ ### Full Model Architecture
135
+
136
+ ```
137
+ SentenceTransformer(
138
+ (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: EuroBertModel
139
+ (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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+
143
+ ## Usage
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+
145
+ ### Direct Usage (Sentence Transformers)
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+
147
+ First install the Sentence Transformers library:
148
+
149
+ ```bash
150
+ pip install -U sentence-transformers
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+ ```
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+
153
+ Then you can load this model and run inference.
154
+ ```python
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+ from sentence_transformers import SentenceTransformer
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+
157
+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("sentence_transformers_model_id")
159
+ # Run inference
160
+ sentences = [
161
+ 'لاعبة كرة ناعمة ترمي الكرة إلى زميلتها في الفريق',
162
+ 'شخصان يلعبان كرة البيسبول',
163
+ 'لاعبين لكرة البيسبول يجلسان على مقعد',
164
+ ]
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+ embeddings = model.encode(sentences)
166
+ print(embeddings.shape)
167
+ # [3, 768]
168
+
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+ # Get the similarity scores for the embeddings
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+ similarities = model.similarity(embeddings, embeddings)
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+ print(similarities.shape)
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+ # [3, 3]
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+ ```
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+
175
+ <!--
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+ ### Direct Usage (Transformers)
177
+
178
+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
180
+ </details>
181
+ -->
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+
183
+ <!--
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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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+
193
+ <!--
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+ ### Out-of-Scope Use
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+
196
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
198
+
199
+ ## Evaluation
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+
201
+ ### Metrics
202
+
203
+ #### Semantic Similarity
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+
205
+ * Datasets: `sts-dev-768`, `sts-dev-512`, `sts-dev-256`, `sts-dev-128` and `sts-dev-64`
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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-768 | sts-dev-512 | sts-dev-256 | sts-dev-128 | sts-dev-64 |
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+ |:--------------------|:------------|:------------|:------------|:------------|:-----------|
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+ | pearson_cosine | 0.8112 | 0.8093 | 0.8077 | 0.8029 | 0.7923 |
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+ | **spearman_cosine** | **0.8101** | **0.8088** | **0.8081** | **0.8055** | **0.7976** |
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+
213
+ <!--
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+ ## Bias, Risks and Limitations
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+
216
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
218
+
219
+ <!--
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+ ### Recommendations
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+
222
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
225
+ ## Training Details
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+
227
+ ### Training Dataset
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+
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+ #### Unnamed Dataset
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+
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+
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+ * Size: 1,000,000 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: 4 tokens</li><li>mean: 23.56 tokens</li><li>max: 89 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.02 tokens</li><li>max: 123 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.16 tokens</li><li>max: 54 tokens</li></ul> |
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+ * Samples:
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+ | anchor | positive | negative |
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+ |:--------------------------------|:--------------------------------------------------------|:----------------------------------------------------------------------|
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+ | <code>هناك رجل في الشارع</code> | <code>رجل يحمل مالاً يقف أمام فرقة موسيقية ومتجر</code> | <code>رجلين و صبي صغير في سترة أرجوانية يمسكون منشورات ترويجية</code> |
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+ | <code>الكلب يلعب بالجلب.</code> | <code>هناك كلب سمراء في منتصف الحقل يجلب كرة تنس</code> | <code>هناك كلب على العشب يهز نفسه حتى يجف.</code> |
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+ | <code>شخصان يسيران.</code> | <code>شخصان يضحكان</code> | <code>رجل وامرأة يركبان دراجة مزدوجة معاً</code> |
245
+ * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
246
+ ```json
247
+ {
248
+ "loss": "MultipleNegativesRankingLoss",
249
+ "matryoshka_dims": [
250
+ 768,
251
+ 512,
252
+ 256,
253
+ 128,
254
+ 64
255
+ ],
256
+ "matryoshka_weights": [
257
+ 1,
258
+ 1,
259
+ 1,
260
+ 1,
261
+ 1
262
+ ],
263
+ "n_dims_per_step": -1
264
+ }
265
+ ```
266
+
267
+ ### Evaluation Dataset
268
+
269
+ #### Unnamed Dataset
270
+
271
+
272
+ * Size: 6,609 evaluation samples
273
+ * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
274
+ * Approximate statistics based on the first 1000 samples:
275
+ | | anchor | positive | negative |
276
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
277
+ | type | string | string | string |
278
+ | 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> |
279
+ * Samples:
280
+ | anchor | positive | negative |
281
+ |:----------------------------------------------------------------------------------------------|:-----------------------------------|:-------------------------------------------|
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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> |
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+ | <code>امرأة ترتدي حجاب أخضر، وقميص أزرق وابتسامة كبيرة</code> | <code>المرأة سعيدة جداً</code> | <code>لقد تم إطلاق النار على المرأة</code> |
284
+ | <code>رجل عجوز يحمل طردًا يتصور أمام إعلان.</code> | <code>رجل يتصور أمام إعلان.</code> | <code>رجل يمشي بجانب إعلان</code> |
285
+ * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
286
+ ```json
287
+ {
288
+ "loss": "MultipleNegativesRankingLoss",
289
+ "matryoshka_dims": [
290
+ 768,
291
+ 512,
292
+ 256,
293
+ 128,
294
+ 64
295
+ ],
296
+ "matryoshka_weights": [
297
+ 1,
298
+ 1,
299
+ 1,
300
+ 1,
301
+ 1
302
+ ],
303
+ "n_dims_per_step": -1
304
+ }
305
+ ```
306
+
307
+ ### Training Hyperparameters
308
+ #### Non-Default Hyperparameters
309
+
310
+ - `eval_strategy`: steps
311
+ - `per_device_train_batch_size`: 32
312
+ - `per_device_eval_batch_size`: 32
313
+ - `num_train_epochs`: 1
314
+ - `warmup_ratio`: 0.1
315
+ - `fp16`: True
316
+ - `load_best_model_at_end`: True
317
+ - `batch_sampler`: no_duplicates
318
+
319
+ #### All Hyperparameters
320
+ <details><summary>Click to expand</summary>
321
+
322
+ - `overwrite_output_dir`: False
323
+ - `do_predict`: False
324
+ - `eval_strategy`: steps
325
+ - `prediction_loss_only`: True
326
+ - `per_device_train_batch_size`: 32
327
+ - `per_device_eval_batch_size`: 32
328
+ - `per_gpu_train_batch_size`: None
329
+ - `per_gpu_eval_batch_size`: None
330
+ - `gradient_accumulation_steps`: 1
331
+ - `eval_accumulation_steps`: None
332
+ - `torch_empty_cache_steps`: None
333
+ - `learning_rate`: 5e-05
334
+ - `weight_decay`: 0.0
335
+ - `adam_beta1`: 0.9
336
+ - `adam_beta2`: 0.999
337
+ - `adam_epsilon`: 1e-08
338
+ - `max_grad_norm`: 1.0
339
+ - `num_train_epochs`: 1
340
+ - `max_steps`: -1
341
+ - `lr_scheduler_type`: linear
342
+ - `lr_scheduler_kwargs`: {}
343
+ - `warmup_ratio`: 0.1
344
+ - `warmup_steps`: 0
345
+ - `log_level`: passive
346
+ - `log_level_replica`: warning
347
+ - `log_on_each_node`: True
348
+ - `logging_nan_inf_filter`: True
349
+ - `save_safetensors`: True
350
+ - `save_on_each_node`: False
351
+ - `save_only_model`: False
352
+ - `restore_callback_states_from_checkpoint`: False
353
+ - `no_cuda`: False
354
+ - `use_cpu`: False
355
+ - `use_mps_device`: False
356
+ - `seed`: 42
357
+ - `data_seed`: None
358
+ - `jit_mode_eval`: False
359
+ - `use_ipex`: False
360
+ - `bf16`: False
361
+ - `fp16`: True
362
+ - `fp16_opt_level`: O1
363
+ - `half_precision_backend`: auto
364
+ - `bf16_full_eval`: False
365
+ - `fp16_full_eval`: False
366
+ - `tf32`: None
367
+ - `local_rank`: 0
368
+ - `ddp_backend`: None
369
+ - `tpu_num_cores`: None
370
+ - `tpu_metrics_debug`: False
371
+ - `debug`: []
372
+ - `dataloader_drop_last`: False
373
+ - `dataloader_num_workers`: 0
374
+ - `dataloader_prefetch_factor`: None
375
+ - `past_index`: -1
376
+ - `disable_tqdm`: False
377
+ - `remove_unused_columns`: True
378
+ - `label_names`: None
379
+ - `load_best_model_at_end`: True
380
+ - `ignore_data_skip`: False
381
+ - `fsdp`: []
382
+ - `fsdp_min_num_params`: 0
383
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
384
+ - `fsdp_transformer_layer_cls_to_wrap`: None
385
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
386
+ - `deepspeed`: None
387
+ - `label_smoothing_factor`: 0.0
388
+ - `optim`: adamw_torch
389
+ - `optim_args`: None
390
+ - `adafactor`: False
391
+ - `group_by_length`: False
392
+ - `length_column_name`: length
393
+ - `ddp_find_unused_parameters`: None
394
+ - `ddp_bucket_cap_mb`: None
395
+ - `ddp_broadcast_buffers`: False
396
+ - `dataloader_pin_memory`: True
397
+ - `dataloader_persistent_workers`: False
398
+ - `skip_memory_metrics`: True
399
+ - `use_legacy_prediction_loop`: False
400
+ - `push_to_hub`: False
401
+ - `resume_from_checkpoint`: None
402
+ - `hub_model_id`: None
403
+ - `hub_strategy`: every_save
404
+ - `hub_private_repo`: None
405
+ - `hub_always_push`: False
406
+ - `gradient_checkpointing`: False
407
+ - `gradient_checkpointing_kwargs`: None
408
+ - `include_inputs_for_metrics`: False
409
+ - `include_for_metrics`: []
410
+ - `eval_do_concat_batches`: True
411
+ - `fp16_backend`: auto
412
+ - `push_to_hub_model_id`: None
413
+ - `push_to_hub_organization`: None
414
+ - `mp_parameters`:
415
+ - `auto_find_batch_size`: False
416
+ - `full_determinism`: False
417
+ - `torchdynamo`: None
418
+ - `ray_scope`: last
419
+ - `ddp_timeout`: 1800
420
+ - `torch_compile`: False
421
+ - `torch_compile_backend`: None
422
+ - `torch_compile_mode`: None
423
+ - `dispatch_batches`: None
424
+ - `split_batches`: None
425
+ - `include_tokens_per_second`: False
426
+ - `include_num_input_tokens_seen`: False
427
+ - `neftune_noise_alpha`: None
428
+ - `optim_target_modules`: None
429
+ - `batch_eval_metrics`: False
430
+ - `eval_on_start`: False
431
+ - `use_liger_kernel`: False
432
+ - `eval_use_gather_object`: False
433
+ - `average_tokens_across_devices`: False
434
+ - `prompts`: None
435
+ - `batch_sampler`: no_duplicates
436
+ - `multi_dataset_batch_sampler`: proportional
437
+
438
+ </details>
439
+
440
+ ### Training Logs
441
+ | Epoch | Step | Training Loss | Validation Loss | sts-dev-768_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-128_spearman_cosine | sts-dev-64_spearman_cosine |
442
+ |:------:|:----:|:-------------:|:---------------:|:---------------------------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|
443
+ | 0.0256 | 200 | 8.8816 | - | - | - | - | - | - |
444
+ | 0.0512 | 400 | 5.1404 | - | - | - | - | - | - |
445
+ | 0.0640 | 500 | - | 6.5304 | 0.7855 | 0.7818 | 0.7766 | 0.7712 | 0.7635 |
446
+ | 0.0768 | 600 | 4.7789 | - | - | - | - | - | - |
447
+ | 0.1024 | 800 | 4.6845 | - | - | - | - | - | - |
448
+ | 0.1280 | 1000 | 4.6628 | 6.3298 | 0.7900 | 0.7888 | 0.7855 | 0.7829 | 0.7757 |
449
+ | 0.1536 | 1200 | 4.2947 | - | - | - | - | - | - |
450
+ | 0.1792 | 1400 | 4.0669 | - | - | - | - | - | - |
451
+ | 0.1920 | 1500 | - | 6.1192 | 0.7762 | 0.7717 | 0.7688 | 0.7651 | 0.7546 |
452
+ | 0.2048 | 1600 | 3.7798 | - | - | - | - | - | - |
453
+ | 0.2304 | 1800 | 3.6295 | - | - | - | - | - | - |
454
+ | 0.2560 | 2000 | 3.4326 | 5.5251 | 0.7968 | 0.7941 | 0.7926 | 0.7905 | 0.7822 |
455
+ | 0.2816 | 2200 | 3.5024 | - | - | - | - | - | - |
456
+ | 0.3072 | 2400 | 3.2039 | - | - | - | - | - | - |
457
+ | 0.3200 | 2500 | - | 5.4173 | 0.7985 | 0.7957 | 0.7946 | 0.7904 | 0.7806 |
458
+ | 0.3328 | 2600 | 3.1517 | - | - | - | - | - | - |
459
+ | 0.3584 | 2800 | 3.0409 | - | - | - | - | - | - |
460
+ | 0.3840 | 3000 | 2.9611 | 5.0394 | 0.7923 | 0.7894 | 0.7871 | 0.7848 | 0.7789 |
461
+ | 0.4096 | 3200 | 2.8913 | - | - | - | - | - | - |
462
+ | 0.4352 | 3400 | 2.6737 | - | - | - | - | - | - |
463
+ | 0.4480 | 3500 | - | 4.8450 | 0.8124 | 0.8111 | 0.8075 | 0.8076 | 0.7968 |
464
+ | 0.4608 | 3600 | 2.6488 | - | - | - | - | - | - |
465
+ | 0.4864 | 3800 | 2.6208 | - | - | - | - | - | - |
466
+ | 0.5120 | 4000 | 2.4823 | 4.5711 | 0.8111 | 0.8102 | 0.8082 | 0.8075 | 0.8015 |
467
+ | 0.5376 | 4200 | 2.5081 | - | - | - | - | - | - |
468
+ | 0.5632 | 4400 | 2.3827 | - | - | - | - | - | - |
469
+ | 0.5760 | 4500 | - | 4.5276 | 0.8237 | 0.8227 | 0.8205 | 0.8200 | 0.8117 |
470
+ | 0.5888 | 4600 | 2.2867 | - | - | - | - | - | - |
471
+ | 0.6144 | 4800 | 2.2608 | - | - | - | - | - | - |
472
+ | 0.6400 | 5000 | 2.6285 | 2.6928 | 0.8124 | 0.8113 | 0.8099 | 0.8087 | 0.8023 |
473
+ | 0.6656 | 5200 | 3.2569 | - | - | - | - | - | - |
474
+ | 0.6912 | 5400 | 2.7108 | - | - | - | - | - | - |
475
+ | 0.7040 | 5500 | - | 3.4081 | 0.8112 | 0.8100 | 0.8080 | 0.8060 | 0.7994 |
476
+ | 0.7168 | 5600 | 2.2756 | - | - | - | - | - | - |
477
+ | 0.7424 | 5800 | 1.9964 | - | - | - | - | - | - |
478
+ | 0.7680 | 6000 | 1.8278 | 3.6261 | 0.8116 | 0.8101 | 0.8088 | 0.8071 | 0.8013 |
479
+ | 0.7935 | 6200 | 1.7105 | - | - | - | - | - | - |
480
+ | 0.8191 | 6400 | 1.5719 | - | - | - | - | - | - |
481
+ | 0.8319 | 6500 | - | 3.7826 | 0.8097 | 0.8085 | 0.8072 | 0.8040 | 0.7966 |
482
+ | 0.8447 | 6600 | 1.4569 | - | - | - | - | - | - |
483
+ | 0.8703 | 6800 | 1.3572 | - | - | - | - | - | - |
484
+ | 0.8959 | 7000 | 1.2607 | 3.7323 | 0.8114 | 0.8102 | 0.8093 | 0.8070 | 0.8005 |
485
+ | 0.9215 | 7200 | 1.1676 | - | - | - | - | - | - |
486
+ | 0.9471 | 7400 | 1.1663 | - | - | - | - | - | - |
487
+ | 0.9599 | 7500 | - | 3.8307 | 0.8101 | 0.8088 | 0.8081 | 0.8055 | 0.7976 |
488
+ | 0.9727 | 7600 | 1.1079 | - | - | - | - | - | - |
489
+ | 0.9983 | 7800 | 1.0827 | - | - | - | - | - | - |
490
+
491
+
492
+ ### Framework Versions
493
+ - Python: 3.10.12
494
+ - Sentence Transformers: 3.3.1
495
+ - Transformers: 4.48.0
496
+ - PyTorch: 2.5.1+cu124
497
+ - Accelerate: 1.2.1
498
+ - Datasets: 2.21.0
499
+ - Tokenizers: 0.21.0
500
+
501
+ ## Citation
502
+
503
+ ### BibTeX
504
+
505
+ #### Sentence Transformers
506
+ ```bibtex
507
+ @inproceedings{reimers-2019-sentence-bert,
508
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
509
+ author = "Reimers, Nils and Gurevych, Iryna",
510
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
511
+ month = "11",
512
+ year = "2019",
513
+ publisher = "Association for Computational Linguistics",
514
+ url = "https://arxiv.org/abs/1908.10084",
515
+ }
516
+ ```
517
+
518
+ #### MatryoshkaLoss
519
+ ```bibtex
520
+ @misc{kusupati2024matryoshka,
521
+ title={Matryoshka Representation Learning},
522
+ 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},
523
+ year={2024},
524
+ eprint={2205.13147},
525
+ archivePrefix={arXiv},
526
+ primaryClass={cs.LG}
527
+ }
528
+ ```
529
+
530
+ #### MultipleNegativesRankingLoss
531
+ ```bibtex
532
+ @misc{henderson2017efficient,
533
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
534
+ 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},
535
+ year={2017},
536
+ eprint={1705.00652},
537
+ archivePrefix={arXiv},
538
+ primaryClass={cs.CL}
539
+ }
540
+ ```
541
+
542
+ <!--
543
+ ## Glossary
544
+
545
+ *Clearly define terms in order to be accessible across audiences.*
546
+ -->
547
+
548
+ <!--
549
+ ## Model Card Authors
550
+
551
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
552
+ -->
553
+
554
+ <!--
555
+ ## Model Card Contact
556
+
557
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
558
+ -->
config.json ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "EuroBERT/EuroBERT-210m",
3
+ "architectures": [
4
+ "EuroBertModel"
5
+ ],
6
+ "attention_bias": false,
7
+ "attention_dropout": 0.0,
8
+ "auto_map": {
9
+ "AutoConfig": "configuration_eurobert.EuroBertConfig",
10
+ "AutoModel": "EuroBERT/EuroBERT-210m--modeling_eurobert.EuroBertModel",
11
+ "AutoModelForMaskedLM": "EuroBERT/EuroBERT-210m--modeling_eurobert.EuroBertForMaskedLM",
12
+ "AutoModelForPreTraining": "EuroBERT/EuroBERT-210m--modeling_eurobert.EuroBertPreTrainedModel",
13
+ "AutoModelForSequenceClassification": "EuroBERT/EuroBERT-210m--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": 768,
24
+ "initializer_range": 0.02,
25
+ "intermediate_size": 3072,
26
+ "mask_token": "<|mask|>",
27
+ "mask_token_id": 128002,
28
+ "max_position_embeddings": 8192,
29
+ "mlp_bias": false,
30
+ "model_type": "eurobert",
31
+ "num_attention_heads": 12,
32
+ "num_hidden_layers": 12,
33
+ "num_key_value_heads": 12,
34
+ "pad_token": "<|end_of_text|>",
35
+ "pad_token_id": 128001,
36
+ "pretraining_tp": 1,
37
+ "rms_norm_eps": 1e-05,
38
+ "rope_scaling": null,
39
+ "rope_theta": 250000,
40
+ "tie_word_embeddings": false,
41
+ "torch_dtype": "float32",
42
+ "transformers_version": "4.48.0",
43
+ "use_cache": false,
44
+ "vocab_size": 128256
45
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "3.3.1",
4
+ "transformers": "4.48.0",
5
+ "pytorch": "2.5.1+cu124"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null,
9
+ "similarity_fn_name": "cosine"
10
+ }
configuration_eurobert.py ADDED
@@ -0,0 +1,216 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
2
+ # This file was automatically generated from src/transformers/models/eurobert/modular_eurobert.py.
3
+ # Do NOT edit this file manually as any edits will be overwritten by the generation of
4
+ # the file from the modular. If any change should be done, please apply the change to the
5
+ # modular_eurobert.py file directly. One of our CI enforces this.
6
+ # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
7
+ # coding=utf-8
8
+ # Copyright 2025 Nicolas Boizard, Duarte M. Alves, Hippolyte Gisserot-Boukhlef and the EuroBert team. All rights reserved.
9
+ #
10
+ #
11
+ # Licensed under the Apache License, Version 2.0 (the "License");
12
+ # you may not use this file except in compliance with the License.
13
+ # You may obtain a copy of the License at
14
+ #
15
+ # http://www.apache.org/licenses/LICENSE-2.0
16
+ #
17
+ # Unless required by applicable law or agreed to in writing, software
18
+ # distributed under the License is distributed on an "AS IS" BASIS,
19
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
20
+ # See the License for the specific language governing permissions and
21
+ # limitations under the License.
22
+
23
+ from transformers.utils import logging
24
+ from transformers.models.llama import LlamaConfig
25
+
26
+
27
+ logger = logging.get_logger(__name__)
28
+
29
+
30
+ class EuroBertConfig(LlamaConfig):
31
+ r"""
32
+ This is the configuration class to store the configuration of a [`EuroBertModel`]. It is used to instantiate an EuroBert
33
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
34
+ defaults will yield a similar configuration to that of the EuroBERT-210m.
35
+
36
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
37
+ documentation from [`PretrainedConfig`] for more information.
38
+
39
+
40
+ Args:
41
+ vocab_size (`int`, *optional*, defaults to 128256):
42
+ Vocabulary size of the EuroBert model. Defines the number of different tokens that can be represented by the
43
+ `inputs_ids` passed when calling [`EuroBertModel`]
44
+ hidden_size (`int`, *optional*, defaults to 768):
45
+ Dimensionality of the encoder layers and the pooler layer.
46
+ intermediate_size (`int`, *optional*, defaults to 3072):
47
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
48
+ num_hidden_layers (`int`, *optional*, defaults to 12):
49
+ Number of hidden layers in the Transformer encoder.
50
+ num_attention_heads (`int`, *optional*, defaults to 12):
51
+ Number of attention heads for each attention layer in the Transformer encoder.
52
+ num_key_value_heads (`int`, *optional*):
53
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
54
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
55
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
56
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
57
+ by meanpooling all the original heads within that group. For more details checkout [this
58
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
59
+ `num_attention_heads`.
60
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
61
+ The non-linear activation function (function or string) in the encoder and pooler.
62
+ max_position_embeddings (`int`, *optional*, defaults to 8192):
63
+ The maximum sequence length that this model might ever be used with. EuroBert supports up to 8192 tokens,
64
+ EuroBert-pretrained up to 2048.
65
+ initializer_range (`float`, *optional*, defaults to 0.02):
66
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
67
+ rms_norm_eps (`float`, *optional*, defaults to 1e-05):
68
+ The epsilon used by the rms normalization layers.
69
+ bos_token_id (`int`, *optional*, defaults to 128000):
70
+ Beginning of stream token id.
71
+ eos_token_id (`int`, *optional*, defaults to 128001):
72
+ End of stream token id.
73
+ pad_token_id (`int`, *optional*, defaults to 128001):
74
+ Padding token id.
75
+ mask_token_id (`int`, *optional*, defaults to 128002):
76
+ Mask token id.
77
+ pretraining_tp (`int`, *optional*, defaults to 1):
78
+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
79
+ document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to
80
+ understand more about it. This value is necessary to ensure exact reproducibility of the pretraining
81
+ results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).
82
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
83
+ Whether to tie weight embeddings
84
+ rope_theta (`float`, *optional*, defaults to 250000.0):
85
+ The base period of the RoPE embeddings. EuroBert used base period of 250000.0,
86
+ EuroBert-pretrained 10000.0.
87
+ rope_scaling (`Dict`, *optional*):
88
+ Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
89
+ and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
90
+ accordingly.
91
+ Expected contents:
92
+ `rope_type` (`str`):
93
+ The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
94
+ 'eurobert3'], with 'default' being the original RoPE implementation.
95
+ `factor` (`float`, *optional*):
96
+ Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
97
+ most scaling types, a `factor` of x will enable the model to handle sequences of length x *
98
+ original maximum pre-trained length.
99
+ `original_max_position_embeddings` (`int`, *optional*):
100
+ Used with 'dynamic', 'longrope' and 'eurobert3'. The original max position embeddings used during
101
+ pretraining.
102
+ `attention_factor` (`float`, *optional*):
103
+ Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
104
+ computation. If unspecified, it defaults to value recommended by the implementation, using the
105
+ `factor` field to infer the suggested value.
106
+ `beta_fast` (`float`, *optional*):
107
+ Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
108
+ ramp function. If unspecified, it defaults to 32.
109
+ `beta_slow` (`float`, *optional*):
110
+ Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
111
+ ramp function. If unspecified, it defaults to 1.
112
+ `short_factor` (`List[float]`, *optional*):
113
+ Only used with 'longrope'. The scaling factor to be applied to short contexts (<
114
+ `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
115
+ size divided by the number of attention heads divided by 2
116
+ `long_factor` (`List[float]`, *optional*):
117
+ Only used with 'longrope'. The scaling factor to be applied to long contexts (<
118
+ `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
119
+ size divided by the number of attention heads divided by 2
120
+ `low_freq_factor` (`float`, *optional*):
121
+ Only used with 'eurobert3'. Scaling factor applied to low frequency components of the RoPE
122
+ `high_freq_factor` (`float`, *optional*):
123
+ Only used with 'eurobert3'. Scaling factor applied to high frequency components of the RoPE
124
+ attention_bias (`bool`, *optional*, defaults to `False`):
125
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
126
+ attention_dropout (`float`, *optional*, defaults to 0.0):
127
+ The dropout ratio for the attention probabilities.
128
+ mlp_bias (`bool`, *optional*, defaults to `False`):
129
+ Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
130
+ head_dim (`int`, *optional*):
131
+ The attention head dimension. If None, it will default to hidden_size // num_attention_heads
132
+ classifier_pooling (`str`, *optional*, defaults to `"late"`):
133
+ The pooling strategy to use for the classifier. Can be one of ['bos', 'mean', 'late'].
134
+
135
+ ```python
136
+ >>> from transformers import EuroBertModel, EuroBertConfig
137
+
138
+ >>> # Initializing a EuroBert eurobert-base style configuration
139
+ >>> configuration = EuroBertConfig()
140
+
141
+ >>> # Initializing a model from the eurobert-base style configuration
142
+ >>> model = EuroBertModel(configuration)
143
+
144
+ >>> # Accessing the model configuration
145
+ >>> configuration = model.config
146
+ ```"""
147
+
148
+ model_type = "eurobert"
149
+
150
+ def __init__(
151
+ self,
152
+ vocab_size=128256,
153
+ hidden_size=768,
154
+ intermediate_size=3072,
155
+ num_hidden_layers=12,
156
+ num_attention_heads=12,
157
+ num_key_value_heads=None,
158
+ hidden_act="silu",
159
+ max_position_embeddings=8192,
160
+ initializer_range=0.02,
161
+ rms_norm_eps=1e-05,
162
+ bos_token_id=128000,
163
+ eos_token_id=128001,
164
+ pad_token_id=128001,
165
+ mask_token_id=128002,
166
+ pretraining_tp=1,
167
+ tie_word_embeddings=False,
168
+ rope_theta=250000.0,
169
+ rope_scaling=None,
170
+ attention_bias=False,
171
+ attention_dropout=0.0,
172
+ mlp_bias=False,
173
+ head_dim=None,
174
+ classifier_pooling="late",
175
+ **kwargs,
176
+ ):
177
+ # use_cache is specific to decoder models and should be set to False for encoder models
178
+ use_cache = kwargs.pop("use_cache", None)
179
+ if use_cache:
180
+ logger.warning_once(
181
+ "The `use_cache` argument to EuroBertConfig is set to `False`, as caching is never used for encoder models."
182
+ )
183
+
184
+ if num_key_value_heads is None:
185
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+ "bos_token": "<|begin_of_text|>",
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+ "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": "PreTrainedTokenizerFast"
2069
+ }
trainer_state.json ADDED
@@ -0,0 +1,591 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_metric": 0.8237398652434835,
3
+ "best_model_checkpoint": "output/eurobert_simce_EuroBERT-EuroBERT-210m_32_bs_1_e/checkpoint-4500",
4
+ "epoch": 1.0,
5
+ "eval_steps": 500,
6
+ "global_step": 7813,
7
+ "is_hyper_param_search": false,
8
+ "is_local_process_zero": true,
9
+ "is_world_process_zero": true,
10
+ "log_history": [
11
+ {
12
+ "epoch": 0.02559836170485089,
13
+ "grad_norm": 21741072.0,
14
+ "learning_rate": 1.2787723785166241e-05,
15
+ "loss": 8.8816,
16
+ "step": 200
17
+ },
18
+ {
19
+ "epoch": 0.05119672340970178,
20
+ "grad_norm": 11201852.0,
21
+ "learning_rate": 2.5575447570332482e-05,
22
+ "loss": 5.1404,
23
+ "step": 400
24
+ },
25
+ {
26
+ "epoch": 0.06399590426212723,
27
+ "eval_loss": 6.53036642074585,
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+ "eval_runtime": 185.969,
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