Upload folder using huggingface_hub
Browse files- 1_Dense/config.json +1 -0
- 1_Dense/model.safetensors +3 -0
- README.md +144 -0
- config.json +26 -0
- config_sentence_transformers.json +49 -0
- model.safetensors +3 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +31 -0
- tokenizer.json +0 -0
- tokenizer_config.json +71 -0
- vocab.txt +0 -0
1_Dense/config.json
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{"in_features": 768, "out_features": 128, "bias": false, "activation_function": "torch.nn.modules.linear.Identity"}
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1_Dense/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:cb3d683617f336df4ee6033b8afa40648ee2f9030408704db63a8fe531489400
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size 393304
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README.md
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---
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base_model: colbert-ir/colbertv2.0
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datasets: []
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language: []
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library_name: sentence-transformers
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pipeline_tag: sentence-similarity
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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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widget: []
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---
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# SentenceTransformer based on colbert-ir/colbertv2.0
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0). It maps sentences & paragraphs to a 128-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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## Model Details
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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0) <!-- at revision c1e84128e85ef755c096a95bdb06b47793b13acf -->
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- **Maximum Sequence Length:** 512 tokens
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- **Output Dimensionality:** 128 tokens
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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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### Model Sources
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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### Full Model Architecture
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```
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ColBERT(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
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(1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
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)
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```
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## Usage
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### Direct Usage (Sentence Transformers)
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First install the Sentence Transformers library:
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```bash
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pip install -U sentence-transformers
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```
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Then you can load this model and run inference.
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```python
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from sentence_transformers import SentenceTransformer
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# Download from the 🤗 Hub
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model = SentenceTransformer("sentence_transformers_model_id")
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# Run inference
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sentences = [
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'The weather is lovely today.',
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"It's so sunny outside!",
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'He drove to the stadium.',
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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# [3, 128]
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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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<!--
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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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*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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<!--
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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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### Framework Versions
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- Python: 3.12.4
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- Sentence Transformers: 3.0.1
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- Transformers: 4.45.0.dev0
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- PyTorch: 2.4.0+cu121
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- Accelerate: 0.33.0
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- Datasets: 2.20.0
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- Tokenizers: 0.19.1
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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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<!--
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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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-->
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<!--
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## Model Card Contact
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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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-->
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config.json
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{
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"_name_or_path": "colbert-ir/colbertv2.0",
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.45.0.dev0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "3.0.1",
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"transformers": "4.45.0.dev0",
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"pytorch": "2.4.0+cu121"
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},
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"prompts": {},
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"default_prompt_name": null,
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"similarity_fn_name": null,
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"query_prefix": "[unused0]",
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"document_prefix": "[unused1]",
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"query_length": 32,
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"document_length": 300,
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"attend_to_expansion_tokens": false,
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"skiplist_words": [
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"!",
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"\"",
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"#",
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"$",
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"%",
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"&",
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"'",
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"(",
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")",
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"*",
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"+",
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",",
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"-",
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".",
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"/",
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":",
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";",
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"<",
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"=",
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">",
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"?",
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"@",
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"[",
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"\\",
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"]",
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"^",
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"_",
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"`",
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"{",
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"|",
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"}",
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"~"
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]
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:dc0663e33ba672f20177adba1fb0b4a471ca8d208436a8f37b0474795402164d
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size 435588776
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modules.json
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[
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{
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.models.Transformer"
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},
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{
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"idx": 1,
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"name": "1",
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"path": "1_Dense",
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"type": "pylate.models.Dense"
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}
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]
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sentence_bert_config.json
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{
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"max_seq_length": 512,
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"do_lower_case": false
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}
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special_tokens_map.json
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{
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"cls_token": {
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"content": "[CLS]",
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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": 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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"pad_token": "[MASK]",
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"sep_token": {
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"content": "[SEP]",
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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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"unk_token": {
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"content": "[UNK]",
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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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}
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tokenizer.json
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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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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"special": true
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},
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"1": {
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"content": "[unused0]",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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},
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"2": {
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"content": "[unused1]",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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},
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"100": {
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"content": "[UNK]",
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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,
|
33 |
+
"special": true
|
34 |
+
},
|
35 |
+
"101": {
|
36 |
+
"content": "[CLS]",
|
37 |
+
"lstrip": false,
|
38 |
+
"normalized": false,
|
39 |
+
"rstrip": false,
|
40 |
+
"single_word": false,
|
41 |
+
"special": true
|
42 |
+
},
|
43 |
+
"102": {
|
44 |
+
"content": "[SEP]",
|
45 |
+
"lstrip": false,
|
46 |
+
"normalized": false,
|
47 |
+
"rstrip": false,
|
48 |
+
"single_word": false,
|
49 |
+
"special": true
|
50 |
+
},
|
51 |
+
"103": {
|
52 |
+
"content": "[MASK]",
|
53 |
+
"lstrip": false,
|
54 |
+
"normalized": false,
|
55 |
+
"rstrip": false,
|
56 |
+
"single_word": false,
|
57 |
+
"special": true
|
58 |
+
}
|
59 |
+
},
|
60 |
+
"clean_up_tokenization_spaces": true,
|
61 |
+
"cls_token": "[CLS]",
|
62 |
+
"do_lower_case": true,
|
63 |
+
"mask_token": "[MASK]",
|
64 |
+
"model_max_length": 512,
|
65 |
+
"pad_token": "[MASK]",
|
66 |
+
"sep_token": "[SEP]",
|
67 |
+
"strip_accents": null,
|
68 |
+
"tokenize_chinese_chars": true,
|
69 |
+
"tokenizer_class": "BertTokenizer",
|
70 |
+
"unk_token": "[UNK]"
|
71 |
+
}
|
vocab.txt
ADDED
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|