Uploading CrossEncoder model.
Browse files- .gitattributes +1 -0
- 1_CausalScoreHead/config.json +4 -0
- README.md +138 -0
- added_tokens.json +24 -0
- chat_template.jinja +9 -0
- config.json +59 -0
- config_sentence_transformers.json +11 -0
- generation_config.json +14 -0
- merges.txt +0 -0
- model.safetensors +3 -0
- modules.json +14 -0
- sentence_bert_config.json +14 -0
- special_tokens_map.json +31 -0
- tokenizer.json +3 -0
- tokenizer_config.json +208 -0
- vocab.json +0 -0
.gitattributes
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*.zip 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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*.zip 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
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1_CausalScoreHead/config.json
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{
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"true_token_id": 16,
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"false_token_id": 15
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}
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README.md
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| 1 |
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---
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| 2 |
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tags:
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- sentence-transformers
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- cross-encoder
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- reranker
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base_model: mixedbread-ai/mxbai-rerank-large-v2
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pipeline_tag: text-ranking
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library_name: sentence-transformers
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---
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# CrossEncoder based on mixedbread-ai/mxbai-rerank-large-v2
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This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [mixedbread-ai/mxbai-rerank-large-v2](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
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## Model Details
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### Model Description
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- **Model Type:** Cross Encoder
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- **Base model:** [mixedbread-ai/mxbai-rerank-large-v2](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2) <!-- at revision 763adb305c5b6af647d386e8dd5d511a45cca766 -->
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- **Maximum Sequence Length:** 32768 tokens
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- **Number of Output Labels:** 1 label
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<!-- - **Training Dataset:** Unknown -->
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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### Model Sources
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
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| 30 |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
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| 31 |
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- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
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## Usage
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| 34 |
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### Direct Usage (Sentence Transformers)
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First install the Sentence Transformers library:
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| 38 |
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| 39 |
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```bash
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| 40 |
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pip install -U sentence-transformers
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```
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| 42 |
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Then you can load this model and run inference.
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```python
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| 45 |
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from sentence_transformers import CrossEncoder
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# Download from the 🤗 Hub
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model = CrossEncoder("cross-encoder-testing/mxbai-rerank-large-v2-v6")
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| 49 |
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# Get scores for pairs of texts
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| 50 |
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pairs = [
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['How many calories in an egg', 'There are on average between 55 and 80 calories in an egg depending on its size.'],
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| 52 |
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['How many calories in an egg', 'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.'],
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['How many calories in an egg', 'Most of the calories in an egg come from the yellow yolk in the center.'],
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]
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scores = model.predict(pairs)
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print(scores.shape)
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# (3,)
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# Or rank different texts based on similarity to a single text
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ranks = model.rank(
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'How many calories in an egg',
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[
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| 63 |
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'There are on average between 55 and 80 calories in an egg depending on its size.',
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| 64 |
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'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.',
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'Most of the calories in an egg come from the yellow yolk in the center.',
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]
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)
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# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
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```
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<!--
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### Direct Usage (Transformers)
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<details><summary>Click to see the direct usage in Transformers</summary>
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</details>
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-->
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<!--
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### Downstream Usage (Sentence Transformers)
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You can finetune this model on your own dataset.
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<details><summary>Click to expand</summary>
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</details>
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-->
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<!--
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### Out-of-Scope Use
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| 91 |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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-->
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<!--
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## Bias, Risks and Limitations
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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-->
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| 100 |
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<!--
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### Recommendations
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-->
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## Training Details
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| 108 |
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### Framework Versions
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- Python: 3.11.6
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- Sentence Transformers: 5.3.0.dev0
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- Transformers: 4.57.3
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- PyTorch: 2.9.1+cu126
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- Accelerate: 1.6.0
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- Datasets: 4.2.0
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- Tokenizers: 0.22.1
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| 117 |
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## Citation
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### BibTeX
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<!--
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## Glossary
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*Clearly define terms in order to be accessible across audiences.*
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-->
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| 127 |
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<!--
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| 129 |
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## Model Card Authors
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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| 132 |
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-->
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| 133 |
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<!--
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## Model Card Contact
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| 136 |
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| 137 |
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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| 138 |
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-->
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added_tokens.json
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{
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"</tool_call>": 151658,
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"<tool_call>": 151657,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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| 17 |
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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| 22 |
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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chat_template.jinja
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<|im_start|>system
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You are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>
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| 3 |
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<|im_start|>user
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| 4 |
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query: {{ messages[1]["content"] }}
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| 5 |
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document: {{ messages[2]["content"] }}
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| 6 |
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You are a search relevance expert who evaluates how well documents match search queries. For each query-document pair, carefully analyze the semantic relationship between them, then provide your binary relevance judgment (0 for not relevant, 1 for relevant).
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| 7 |
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Relevance:<|im_end|>
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| 8 |
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<|im_start|>assistant
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config.json
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{
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| 2 |
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"architectures": [
|
| 3 |
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"Qwen2ForCausalLM"
|
| 4 |
+
],
|
| 5 |
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"attention_dropout": 0.0,
|
| 6 |
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"bos_token_id": 151643,
|
| 7 |
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"dtype": "bfloat16",
|
| 8 |
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"eos_token_id": 151645,
|
| 9 |
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"hidden_act": "silu",
|
| 10 |
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"hidden_size": 1536,
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| 11 |
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"initializer_range": 0.02,
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| 12 |
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"intermediate_size": 8960,
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| 13 |
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"layer_types": [
|
| 14 |
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"full_attention",
|
| 15 |
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"full_attention",
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| 16 |
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"full_attention",
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| 17 |
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"full_attention",
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| 18 |
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"full_attention",
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"full_attention",
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| 20 |
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"full_attention",
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| 21 |
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"full_attention",
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| 22 |
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"full_attention",
|
| 23 |
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"full_attention",
|
| 24 |
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"full_attention",
|
| 25 |
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"full_attention",
|
| 26 |
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"full_attention",
|
| 27 |
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"full_attention",
|
| 28 |
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"full_attention",
|
| 29 |
+
"full_attention",
|
| 30 |
+
"full_attention",
|
| 31 |
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"full_attention",
|
| 32 |
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"full_attention",
|
| 33 |
+
"full_attention",
|
| 34 |
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"full_attention",
|
| 35 |
+
"full_attention",
|
| 36 |
+
"full_attention",
|
| 37 |
+
"full_attention",
|
| 38 |
+
"full_attention",
|
| 39 |
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"full_attention",
|
| 40 |
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"full_attention",
|
| 41 |
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"full_attention"
|
| 42 |
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],
|
| 43 |
+
"max_position_embeddings": 32768,
|
| 44 |
+
"max_window_layers": 21,
|
| 45 |
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"model_type": "qwen2",
|
| 46 |
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"num_attention_heads": 12,
|
| 47 |
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"num_hidden_layers": 28,
|
| 48 |
+
"num_key_value_heads": 2,
|
| 49 |
+
"pad_token_id": 151645,
|
| 50 |
+
"rms_norm_eps": 1e-06,
|
| 51 |
+
"rope_scaling": null,
|
| 52 |
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"rope_theta": 1000000.0,
|
| 53 |
+
"sliding_window": null,
|
| 54 |
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"tie_word_embeddings": true,
|
| 55 |
+
"transformers_version": "4.57.3",
|
| 56 |
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"use_cache": true,
|
| 57 |
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"use_sliding_window": false,
|
| 58 |
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"vocab_size": 151936
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| 59 |
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}
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config_sentence_transformers.json
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{
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"model_type": "CrossEncoder",
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| 3 |
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"__version__": {
|
| 4 |
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"sentence_transformers": "5.3.0.dev0",
|
| 5 |
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"transformers": "4.57.3",
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| 6 |
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"pytorch": "2.9.1+cu126"
|
| 7 |
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},
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| 8 |
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"prompts": {},
|
| 9 |
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"default_prompt_name": null,
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| 10 |
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"activation_fn": "torch.nn.modules.linear.Identity"
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}
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generation_config.json
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{
|
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| 4 |
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| 14 |
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merges.txt
ADDED
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model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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size 3087467144
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modules.json
ADDED
|
@@ -0,0 +1,14 @@
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| 1 |
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[
|
| 2 |
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|
| 3 |
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| 4 |
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|
| 5 |
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|
| 6 |
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|
| 7 |
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|
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
+
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|
| 14 |
+
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|
sentence_bert_config.json
ADDED
|
@@ -0,0 +1,14 @@
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| 1 |
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{
|
| 2 |
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|
| 3 |
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"modality_config": {
|
| 4 |
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"text": {
|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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"message": {
|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
+
},
|
| 13 |
+
"module_output_name": "causal_logits"
|
| 14 |
+
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|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,31 @@
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| 1 |
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| 2 |
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|
| 3 |
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|
| 4 |
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|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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| 10 |
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|
| 11 |
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| 12 |
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| 13 |
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| 14 |
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| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 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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tokenizer.json
ADDED
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@@ -0,0 +1,3 @@
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size 11422163
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tokenizer_config.json
ADDED
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| 1 |
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| 4 |
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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 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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| 37 |
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| 38 |
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| 42 |
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| 44 |
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| 60 |
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| 68 |
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| 70 |
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| 72 |
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| 74 |
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| 76 |
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| 92 |
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| 100 |
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| 108 |
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| 114 |
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| 115 |
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| 116 |
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| 123 |
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| 126 |
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| 204 |
+
"padding_side": "left",
|
| 205 |
+
"split_special_tokens": false,
|
| 206 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 207 |
+
"unk_token": null
|
| 208 |
+
}
|
vocab.json
ADDED
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|
|