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SauerkrautLM-Reason-EuroColBERT

Sentence Similarity
Safetensors
sentence-transformers
PyLate
eurobert
ColBERT
feature-extraction
multilingual
late-interaction
retrieval
bright
loss:Distillation
custom_code
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  4. README.md +406 -0
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  6. config_sentence_transformers.json +49 -0
  7. configuration_eurobert.py +216 -0
  8. model.safetensors +3 -0
  9. modules.json +14 -0
  10. sentence_bert_config.json +4 -0
  11. special_tokens_map.json +24 -0
  12. tokenizer.json +3 -0
  13. tokenizer_config.json +2087 -0
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+ ---
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+ tags:
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+ - ColBERT
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+ - PyLate
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+ - sentence-transformers
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+ - sentence-similarity
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+ - feature-extraction
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+ - multilingual
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+ - late-interaction
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+ - retrieval
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+ - bright
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+ - loss:Distillation
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+ pipeline_tag: sentence-similarity
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+ library_name: PyLate
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+ license: apache-2.0
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+ base_model:
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+ - DavidGF/SauerkrautLM-EuroColBERT
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+ ---
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+ <img src="https://vago-solutions.ai/wp-content/uploads/2025/08/SauerkrautLM-REASON-EUROCO-BERT.png" width="500" height="auto">
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+
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+ # SauerkrautLM-Reason-EuroColBERT
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+
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+ This model is a powerful Late Interaction retriever that leverages:
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+
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+ **Knowledge Distillation** from strong synthetic data (200k samples generated with Qwen/Qwen3-32B-AWQ and scored by a high-performing reranker).
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+ **Robust 210M parameter architecture** optimized for multilingual reasoning-focused retrieval without compression trade-offs.
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+
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+ ### 🎯 Core Features and Innovations:
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+
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+ - **Next-Generation Knowledge Distillation**: By utilizing 200,000 synthetically generated, high-quality training examples (created with `Qwen/Qwen3-32B-AWQ` and scored by a state-of-the-art reranker), our model learns complex reasoning patterns from models **54× its size**.
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+
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+ - **Optimized Architecture**: Full 210M parameters preserve maximum capacity for complex reasoning patterns
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+
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+ ### 💪 David vs. Goliath: Small but Mighty
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+
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+ With **210 million parameters** – that's **less than 1/33rd the size** of some competing models – SauerkrautLM-Reason-EuroColBERT achieves or exceeds the performance of:
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+ - Models with **over 7 billion parameters** (33× larger than ours)
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+ - Proprietary API-based solutions from major tech companies
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+ - Specialized reasoning models like ReasonIR-8B (38× larger)
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+
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+ This balanced architecture provides exceptional performance while remaining deployable on standard infrastructure.
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+
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+
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+
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+ ## Model Overview
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+
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+ **Model:** `VAGOsolutions/SauerkrautLM-Reason-EuroColBERT`\
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+ **Base:** Fine-tuned from [VAGOsolutions/SauerkrautLM-EuroColBERT](https://huggingface.co/VAGOsolutions/SauerkrautLM-EuroColBERT) using knowledge distillation\
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+ **Architecture:** PyLate / ColBERT (Late Interaction)\
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+ **Languages:** Multilingual (optimized for 7 European languages: German, English, Spanish, French, Italian, Dutch, Portuguese)\
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+ **License:** Apache 2.0\
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+ **Model Size:** 210M parameters
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+ **Efficiency Ratio:** Up to **38× smaller** than comparable performing models
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+
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+ ### Model Description
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+ - **Model Type:** PyLate model with innovative Late Interaction architecture
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+ - **Document Length:** 8192 tokens (32× longer than traditional BERT models)
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+ - **Query Length:** 256 tokens (optimized for complex, multi-part queries)
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+ - **Output Dimensionality:** 128 tokens (efficient vector representation)
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+ - **Similarity Function:** MaxSim (enables precise token-level matching)
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+ - **Training Loss:** Knowledge Distillation (PyLate)
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+
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+ ### Architecture
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+
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+ ```
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+ ColBERT(
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+ (0): Transformer(ModernBertModel)
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+ (1): Dense(768 -> 128 dim, no bias)
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+ )
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+ ```
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+
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+ ## 🔬 Technical Innovations in Detail
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+
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+ ### Knowledge Distillation: The Student Surpassing the Master
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+
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+ Our 210M parameter model leverages state-of-the-art knowledge distillation:
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+
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+ 1. **Synthetic Data Generation**: 200,000 high-quality query-document pairs generated using the `Qwen/Qwen3-32B-AWQ` model (32 billion parameters) based on the [ReasonIR approach](https://huggingface.co/datasets/reasonir/reasonir-data)
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+ 2. **Quality Assurance**: Each pair evaluated and filtered by a state-of-the-art reranker
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+ 3. **Distillation Process**: The EuroColBERT model learns to replicate the ranking patterns of large models while maintaining its multilingual strengths
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+
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+ ### Architectural Advantages
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+
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+ SauerkrautLM-Reason-EuroColBERT leverages its full 210M parameters to deliver:
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+
86
+ - **Superior multilingual performance**: Native optimization for 7 European languages
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+ - **No compression trade-offs**: Full parameter capacity ensures maximum reasoning capability
88
+ - **Balanced efficiency**: 33-38× smaller than large models while maintaining competitive performance
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+
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+ This architecture combines the advantages of Late Interaction Retrieval (precise token-level matching) with robust multilingual capabilities.
91
+
92
+ ---
93
+
94
+ ## 🔬 Benchmarks: David vs. Goliath Performance
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+
96
+ Our comprehensive evaluation demonstrates that model size is not destiny. Despite being **33-38× smaller** than competing models, SauerkrautLM-Reason-EuroColBERT consistently delivers superior or comparable performance across challenging reasoning and multilingual retrieval tasks.
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+
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+ ### BRIGHT Benchmark (English, reasoning‑focused retrieval)
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+
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+ The [BRIGHT benchmark](https://huggingface.co/datasets/xlangai/BRIGHT) is designed to evaluate **reasoning‑intensive retrieval**. All scores are nDCG\@10. SauerkrautLM-Reason-EuroColBERT (210M parameters) is compared with dense and proprietary baselines as well as other SauerkrautLM variants.
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+
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+ | Model / Metric | Biology | Earth | Economics | Psychology | Robotics | Stackoverflow | Sustainable | Leetcode | Pony | AoPS | Theorem‑Q | Theorem‑T | Mean StackEx | Mean coding | Mean theorem | Full Mean |
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+ | ---------------------------------------- | --------- | --------- | --------- | ---------- | -------- | ------------- | ----------- | --------- | --------- | --------- | --------- | --------- | ------------ | ----------- | ------------ | --------- |
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+ | **BM25** | 18.90 | 27.20 | 14.90 | 12.50 | 13.60 | 18.40 | 15.00 | 24.40 | 7.90 | 6.20 | 10.40 | 4.90 | 17.21 | 16.15 | 7.17 | 14.53 |
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+ | **< 1 B OS** | | | | | | | | | | | | | | | | |
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+ | BGE | 11.70 | 24.60 | 16.60 | 17.50 | 11.70 | 10.80 | 13.30 | 26.70 | 5.70 | 6.00 | 13.00 | 6.90 | 15.17 | 16.20 | 8.63 | 13.71 |
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+ | Inst‑L | 15.20 | 21.20 | 14.70 | 22.30 | 11.40 | 13.30 | 13.50 | 19.50 | 1.30 | 8.10 | 20.90 | 9.10 | 15.94 | 10.40 | 12.70 | 14.21 |
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+ | SBERT | 15.10 | 20.40 | 16.60 | 22.70 | 8.20 | 11.00 | 15.30 | 26.40 | 7.00 | 5.30 | 20.00 | 10.80 | 15.61 | 16.70 | 12.03 | 14.90 |
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+ | **> 1 B OS** | | | | | | | | | | | | | | | | |
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+ | E5 | 18.60 | 26.00 | 15.50 | 15.80 | 16.30 | 11.20 | 18.10 | 28.70 | 4.90 | 7.10 | 26.10 | 26.80 | 17.36 | 16.80 | 20.00 | 17.93 |
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+ | SFR | 19.10 | 26.70 | 17.80 | 19.00 | 16.30 | 14.40 | 19.20 | 27.40 | 2.00 | 7.40 | 24.30 | 26.00 | 18.93 | 14.70 | 19.23 | 18.30 |
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+ | Inst‑XL | 21.60 | 34.30 | 22.40 | 27.40 | 18.20 | 21.20 | 19.10 | 27.50 | 5.00 | 8.50 | 15.60 | 5.90 | 23.46 | 16.25 | 10.00 | 18.89 |
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+ | GritLM | 24.80 | 32.30 | 18.90 | 19.80 | 17.10 | 13.60 | 17.80 | 29.90 | 22.00 | 8.80 | 25.20 | 21.20 | 20.61 | 25.95 | 18.40 | 20.95 |
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+ | Qwen | 30.60 | 36.40 | 17.80 | 24.60 | 13.20 | 22.20 | 14.80 | 25.50 | 9.90 | 14.40 | 27.80 | 32.90 | 22.80 | 17.70 | 25.03 | **22.51** |
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+ | **Proprietary** | | | | | | | | | | | | | | | | |
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+ | Cohere | 18.70 | 28.40 | 20.40 | 21.60 | 16.30 | 18.30 | 17.60 | 26.80 | 1.90 | 6.30 | 15.70 | 7.20 | 20.19 | 14.35 | 9.73 | 16.60 |
117
+ | OpenAI | 23.30 | 26.70 | 19.50 | 27.60 | 12.80 | 14.30 | 20.50 | 23.60 | 2.40 | 8.50 | 23.50 | 11.70 | 20.67 | 13.00 | 14.57 | 17.87 |
118
+ | Voyage | 23.10 | 25.40 | 19.90 | 24.90 | 10.80 | 16.80 | 15.40 | 30.60 | 1.50 | 7.50 | 27.40 | 11.60 | 19.47 | 16.05 | 15.50 | 17.91 |
119
+ | Google | 22.70 | 34.80 | 19.60 | 27.80 | 15.70 | 20.10 | 17.10 | 29.60 | 3.60 | 9.30 | 23.80 | 15.90 | 22.54 | 16.60 | 16.33 | 20.00 |
120
+ | **ReasonIR data** | | | | | | | | | | | | | | | | |
121
+ | ReasonIR‑8B | 26.20 | 31.40 | 23.30 | 30.00 | 18.00 | 23.90 | 20.50 | 35.00 | 10.50 | 14.70 | 31.90 | 27.20 | 24.76 | 22.75 | 24.60 | **24.38** |
122
+ | Reason‑ModernColBERT (149 M) reported | 33.25 | 41.02 | 24.93 | 30.73 | 21.12 | 20.62 | 20.31 | 31.07 | 8.51 | 9.17 | 19.51 | 11.24 | 27.43 | 19.79 | 15.38 | **22.62** |
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+ | Reason‑ModernColBERT (149 M) our eval\*\* | 34.28 | 41.53 | 19.96 | 27.02 | 21.15 | 23.62 | 17.21 | 26.61 | 1.32 | 7.30 | 19.79 | 9.70 | 27.93 | 13.97 | 12.26 | 20.79 |
124
+ | **SauerkrautLM Reasoning data** | | | | | | | | | | | | | | | | |
125
+ | SauerkrautLM-Multi-Reason-ModernColBERT (149 M) | 36.92 | **45.53** | **19.47** | **27.04** | **19.35** | **25.31** | **20.78** | **29.74** | 12.54 | 10.52 | **14.62** | **7.65** | **28.94** | 21.14 | **10.93** | **22.45** |
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+ | **SauerkrautLM‑Reason‑EuroColBERT (210 M)** | **38.16** | 39.43 | 16.99 | 24.49 | 17.50 | 17.60 | 20.72 | 29.10 | **13.57** | **12.04** | 10.43 | 4.95 | 25.70 | **21.33** | 9.14 | 20.42 |
127
+ | SauerkrautLM‑Reason‑Multi‑ColBERT (15 M) | 23.33 | 23.78 | 10.53 | 9.03 | 10.28 | 10.88 | 13.13 | 18.10 | 15.86 | 1.75 | 4.29 | 0.81 | 14.64 | 16.98 | 2.28 | 11.81 |
128
+
129
+ **Evaluation note:** our re‑evaluation of Reason‑ModernColBERT uses the **same query‑length settings** from the original Lighton repo; the instructions for the originally reported scores are not public.
130
+
131
+
132
+ #### ⚖️ Relative Efficiency
133
+
134
+ With **210M parameters**, SauerkrautLM-Reason-EuroColBERT demonstrates that balanced architecture design can surpass several ≥7B dense and proprietary retrievers on reasoning‑centric tasks while maintaining excellent multilingual performance.
135
+
136
+ ### BRIGHT Benchmark (German, reasoning‑focused retrieval)
137
+
138
+ All scores are nDCG\@10.
139
+
140
+ | Model / Metric | Biology | Earth | Economics | Psychology | Robotics | Stackoverflow | Sustainable | Leetcode | Pony | AoPS | Theorem‑Q | Theorem‑T | Mean StackEx | Mean coding | Mean theorem | Full Mean |
141
+ | --------------------------------------------------- | --------- | --------- | --------- | ---------- | --------- | ------------- | ----------- | --------- | --------- | -------- | --------- | --------- | ------------ | ----------- | ------------ | --------- |
142
+ | SauerkrautLM‑Multi‑Reason‑ModernColBERT (149 M) | 28.00 | **34.71** | **12.90** | 17.98 | **13.67** | **19.64** | 17.70 | 11.66 | **15.49** | 7.27 | 6.76 | 1.32 | **21.15** | 13.57 | 5.11 | 15.59 |
143
+ | **SauerkrautLM‑Reason‑EuroColBERT (210 M)** | **31.09** | 31.48 | 11.95 | **18.39** | 11.25 | 14.43 | **20.26** | **25.67** | 12.15 | **9.58** | **8.15** | **2.76** | 19.76 | **18.91** | **6.83** | **16.43** |
144
+ | SauerkrautLM‑Reason‑Multi‑ColBERT (15 M) | 15.37 | 20.11 | 7.36 | 7.07 | 4.24 | 4.71 | 7.67 | 0.77 | 6.31 | 3.81 | 0.76 | 0.00 | 9.81 | 3.54 | 1.52 | 6.51 |
145
+
146
+ > **Observation:** The 210M EuroColBERT model secures the **highest Full‑Mean (16.43)** across German benchmarks, with particularly strong performance on coding (18.91) and theorem proving (6.83) tasks, demonstrating its superior multilingual reasoning capabilities.
147
+
148
+ ---
149
+
150
+ ### NanoBEIR Europe (multilingual retrieval)
151
+
152
+ Average nDCG\@10 across the seven languages we evaluated:
153
+
154
+ | Language | nDCG@10 |
155
+ | -------- | -------- |
156
+ | de | 47.71 |
157
+ | en | 58.72 |
158
+ | es | 52.15 |
159
+ | fr | 50.46 |
160
+ | it | 49.85 |
161
+ | nl | 48.47 |
162
+ | pt | 50.72 |
163
+
164
+
165
+ ---
166
+
167
+ ### Why SauerkrautLM Matters for Production
168
+
169
+ - **Outperforms proprietary APIs**: beats Cohere, OpenAI, Voyage and Google on BRIGHT Full Mean while remaining fully open‑source under a permissive **Apache 2.0** license.
170
+ - **Superior multilingual performance** with the highest German BRIGHT Full-Mean (16.43) — demonstrating exceptional cross-lingual reasoning capabilities.
171
+ - **Full parameter range**: from the tiny **15 M** Multi‑ColBERT (competitive with SBERT‑scale encoders) to the robust 210 M EuroColBERT variant.
172
+ - **Matches or exceeds** models 33–38× larger (e.g. ReasonIR‑8B, GritLM-7B, Qwen-7B).
173
+ - **Strong multilingual coverage** across seven European languages without language‑specific fine‑tuning.
174
+
175
+ We translated both **BRIGHT** and **NanoBEIR** into seven European languages to rigorously evaluate multilingual retrieval capabilities.
176
+
177
+ Below is a **scatter plot** that visualises model size (millions of parameters) against BRIGHT Full‑Mean nDCG\@10. SauerkrautLM models occupy the best trade‑off region—smallest models with top‑tier reasoning performance.
178
+ <img src="https://vago-solutions.ai/wp-content/uploads/2025/08/Image-graph-2.jpeg">
179
+
180
+
181
+ ### Real-World Impact
182
+
183
+ The efficiency gains translate to tangible benefits:
184
+
185
+ 1. **Democratized AI**: Run state-of-the-art retrieval on consumer hardware
186
+ 2. **Edge Deployment**: Enable on-device search for privacy-sensitive applications
187
+ 3. **Massive Scale**: Index billions of documents at a fraction of traditional costs
188
+
189
+ ## 📈 Summary: Balanced Excellence in Multilingual Retrieval
190
+
191
+ SauerkrautLM-Reason-EuroColBERT represents the optimal balance between model size and performance. By combining cutting-edge knowledge distillation with a robust 210M parameter architecture, we've created a model that:
192
+
193
+ - **Achieves the highest German BRIGHT Full-Mean (16.43)** among all SauerkrautLM variants
194
+ - **Excels at multilingual reasoning** with particularly strong performance on coding and theorem-proving tasks
195
+ - **Outperforms models 33-38× larger** while maintaining manageable infrastructure requirements
196
+ - **Delivers superior multilingual coverage** across 7 European languages
197
+ - **Provides production-ready performance** without the extreme compression trade-offs
198
+
199
+ This model demonstrates that the EuroBERT architecture design at 210M parameters can deliver exceptional multilingual reasoning capabilities while remaining practical for real-world deployment.
200
+
201
+ ---
202
+
203
+ # PyLate
204
+
205
+ This is a [PyLate](https://github.com/lightonai/pylate) model trained. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
206
+
207
+
208
+ ## Usage
209
+ First install the PyLate library:
210
+
211
+ ```bash
212
+ pip install -U pylate
213
+ ```
214
+
215
+ ### Retrieval
216
+
217
+ PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.
218
+
219
+ #### Indexing documents
220
+
221
+ First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:
222
+
223
+ ```python
224
+ from pylate import indexes, models, retrieve
225
+
226
+ # Step 1: Load the ColBERT model
227
+ model = models.ColBERT(
228
+ model_name_or_path="VAGOsolutions/SauerkrautLM-Reason-EuroColBERT",
229
+ )
230
+
231
+ # Step 2: Initialize the Voyager index
232
+ index = indexes.Voyager(
233
+ index_folder="pylate-index",
234
+ index_name="index",
235
+ override=True, # This overwrites the existing index if any
236
+ )
237
+
238
+ # Step 3: Encode the documents
239
+ documents_ids = ["1", "2", "3"]
240
+ documents = ["document 1 text", "document 2 text", "document 3 text"]
241
+
242
+ documents_embeddings = model.encode(
243
+ documents,
244
+ batch_size=32,
245
+ is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
246
+ show_progress_bar=True,
247
+ )
248
+
249
+ # Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
250
+ index.add_documents(
251
+ documents_ids=documents_ids,
252
+ documents_embeddings=documents_embeddings,
253
+ )
254
+ ```
255
+
256
+ Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
257
+
258
+ ```python
259
+ # To load an index, simply instantiate it with the correct folder/name and without overriding it
260
+ index = indexes.Voyager(
261
+ index_folder="pylate-index",
262
+ index_name="index",
263
+ )
264
+ ```
265
+
266
+ #### Retrieving top-k documents for queries
267
+
268
+ Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
269
+ To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
270
+
271
+ ```python
272
+ # Step 1: Initialize the ColBERT retriever
273
+ retriever = retrieve.ColBERT(index=index)
274
+
275
+ # Step 2: Encode the queries
276
+ queries_embeddings = model.encode(
277
+ ["query for document 3", "query for document 1"],
278
+ batch_size=32,
279
+ is_query=True, # # Ensure that it is set to False to indicate that these are queries
280
+ show_progress_bar=True,
281
+ )
282
+
283
+ # Step 3: Retrieve top-k documents
284
+ scores = retriever.retrieve(
285
+ queries_embeddings=queries_embeddings,
286
+ k=10, # Retrieve the top 10 matches for each query
287
+ )
288
+ ```
289
+
290
+ ### Reranking
291
+ If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
292
+
293
+ ```python
294
+ from pylate import rank, models
295
+
296
+ queries = [
297
+ "query A",
298
+ "query B",
299
+ ]
300
+
301
+ documents = [
302
+ ["document A", "document B"],
303
+ ["document 1", "document C", "document B"],
304
+ ]
305
+
306
+ documents_ids = [
307
+ [1, 2],
308
+ [1, 3, 2],
309
+ ]
310
+
311
+ model = models.ColBERT(
312
+ model_name_or_path="VAGOsolutions/SauerkrautLM-Reason-EuroColBERT",
313
+ )
314
+
315
+ queries_embeddings = model.encode(
316
+ queries,
317
+ is_query=True,
318
+ )
319
+
320
+ documents_embeddings = model.encode(
321
+ documents,
322
+ is_query=False,
323
+ )
324
+
325
+ reranked_documents = rank.rerank(
326
+ documents_ids=documents_ids,
327
+ queries_embeddings=queries_embeddings,
328
+ documents_embeddings=documents_embeddings,
329
+ )
330
+ ```
331
+ ## Citation
332
+
333
+ ### BibTeX
334
+
335
+ #### SauerkrautLM‑Reason‑EuroColBERT
336
+
337
+ ```bibtex
338
+ @misc{SauerkrautLM-Reason-EuroColBERT,
339
+ title={SauerkrautLM-Reason-EuroColBERT},
340
+ author={David Golchinfar},
341
+ url={https://huggingface.co/VAGOsolutions/SauerkrautLM-Reason-EuroColBERT},
342
+ year={2025}
343
+ }
344
+ ```
345
+
346
+ #### EuroBERT-210m
347
+
348
+ ```bibtex
349
+ @misc{boizard2025eurobertscalingmultilingualencoders,
350
+ title={EuroBERT: Scaling Multilingual Encoders for European Languages},
351
+ author={Nicolas Boizard and Hippolyte Gisserot-Boukhlef and Duarte M. Alves and André Martins and Ayoub Hammal and Caio Corro and Céline Hudelot and Emmanuel Malherbe and Etienne Malaboeuf and Fanny Jourdan and Gabriel Hautreux and João Alves and Kevin El-Haddad and Manuel Faysse and Maxime Peyrard and Nuno M. Guerreiro and Patrick Fernandes and Ricardo Rei and Pierre Colombo},
352
+ year={2025},
353
+ eprint={2503.05500},
354
+ archivePrefix={arXiv},
355
+ primaryClass={cs.CL},
356
+ url={https://arxiv.org/abs/2503.05500},
357
+ }
358
+ }
359
+ ```
360
+
361
+ #### Sentence Transformers
362
+
363
+ ```bibtex
364
+ @inproceedings{reimers-2019-sentence-bert,
365
+ title = {Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
366
+ author = {Reimers, Nils and Gurevych, Iryna},
367
+ booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing},
368
+ month = {11},
369
+ year = {2019},
370
+ publisher = {Association for Computational Linguistics},
371
+ url = {https://arxiv.org/abs/1908.10084}
372
+ }
373
+ ```
374
+
375
+ #### PyLate
376
+
377
+ ```bibtex
378
+ @misc{PyLate,
379
+ title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
380
+ author={Chaffin, Antoine and Sourty, Raphaël},
381
+ url={https://github.com/lightonai/pylate},
382
+ year={2024}
383
+ }
384
+ ```
385
+
386
+
387
+ ## Acknowledgements
388
+ We thank Antoine Chaffin (LightOn AI) for helpful discussions and for clarifying evaluation settings for Reason‑ModernColBERT, and the PyLate team for providing the training framework that made this work possible.
389
+
390
+ <!--
391
+ ## Glossary
392
+
393
+ *Clearly define terms in order to be accessible across audiences.*
394
+ -->
395
+
396
+ <!--
397
+ ## Model Card Authors
398
+
399
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
400
+ -->
401
+
402
+ <!--
403
+ ## Model Card Contact
404
+
405
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
406
+ -->
config.json ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
2
+ "architectures": [
3
+ "EuroBertModel"
4
+ ],
5
+ "attention_bias": false,
6
+ "attention_dropout": 0.0,
7
+ "auto_map": {
8
+ "AutoConfig": "configuration_eurobert.EuroBertConfig",
9
+ "AutoModel": "EuroBERT/EuroBERT-210m--modeling_eurobert.EuroBertModel",
10
+ "AutoModelForMaskedLM": "EuroBERT/EuroBERT-210m--modeling_eurobert.EuroBertForMaskedLM",
11
+ "AutoModelForPreTraining": "EuroBERT/EuroBERT-210m--modeling_eurobert.EuroBertPreTrainedModel",
12
+ "AutoModelForSequenceClassification": "EuroBERT/EuroBERT-210m--modeling_eurobert.EuroBertForSequenceClassification",
13
+ "AutoModelForTokenClassification": "EuroBERT/EuroBERT-210m--modeling_eurobert.EuroBertForTokenClassification"
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,
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+ "head_dim": 64,
21
+ "hidden_act": "silu",
22
+ "hidden_dropout": 0.0,
23
+ "hidden_size": 768,
24
+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
26
+ "mask_token": "<|mask|>",
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+ "mask_token_id": 128002,
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+ "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": "4.51.0",
43
+ "use_cache": false,
44
+ "vocab_size": 128258
45
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,49 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
2
+ "__version__": {
3
+ "sentence_transformers": "4.0.2",
4
+ "transformers": "4.51.0",
5
+ "pytorch": "2.7.0+cu126"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null,
9
+ "similarity_fn_name": "MaxSim",
10
+ "query_prefix": "[Q] ",
11
+ "document_prefix": "[D] ",
12
+ "query_length": 256,
13
+ "document_length": 2048,
14
+ "attend_to_expansion_tokens": false,
15
+ "skiplist_words": [
16
+ "!",
17
+ "\"",
18
+ "#",
19
+ "$",
20
+ "%",
21
+ "&",
22
+ "'",
23
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24
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25
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26
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27
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28
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29
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30
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31
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32
+ ";",
33
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34
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35
+ ">",
36
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37
+ "@",
38
+ "[",
39
+ "\\",
40
+ "]",
41
+ "^",
42
+ "_",
43
+ "`",
44
+ "{",
45
+ "|",
46
+ "}",
47
+ "~"
48
+ ]
49
+ }
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
+ num_key_value_heads = num_attention_heads
186
+
187
+ super().__init__(
188
+ vocab_size=vocab_size,
189
+ hidden_size=hidden_size,
190
+ intermediate_size=intermediate_size,
191
+ num_hidden_layers=num_hidden_layers,
192
+ num_attention_heads=num_attention_heads,
193
+ num_key_value_heads=num_key_value_heads,
194
+ hidden_act=hidden_act,
195
+ max_position_embeddings=max_position_embeddings,
196
+ initializer_range=initializer_range,
197
+ rms_norm_eps=rms_norm_eps,
198
+ use_cache=False,
199
+ bos_token_id=bos_token_id,
200
+ eos_token_id=eos_token_id,
201
+ pad_token_id=pad_token_id,
202
+ pretraining_tp=pretraining_tp,
203
+ tie_word_embeddings=tie_word_embeddings,
204
+ rope_theta=rope_theta,
205
+ rope_scaling=rope_scaling,
206
+ attention_bias=attention_bias,
207
+ attention_dropout=attention_dropout,
208
+ mlp_bias=mlp_bias,
209
+ head_dim=head_dim,
210
+ **kwargs,
211
+ )
212
+ self.mask_token_id = mask_token_id
213
+ self.clf_pooling = classifier_pooling
214
+
215
+
216
+ __all__ = ["EuroBertConfig"]
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modules.json ADDED
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+ "path": "1_Dense",
12
+ "type": "pylate.models.Dense.Dense"
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+ }
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+ ]
sentence_bert_config.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
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+ {
2
+ "max_seq_length": 255,
3
+ "do_lower_case": false
4
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "lstrip": false,
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+ "eos_token": {
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "mask_token": {
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+ "content": "<|mask|>",
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+ "lstrip": true,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ },
23
+ "pad_token": "<|mask|>"
24
+ }
tokenizer.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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