Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use ToolBench/ToolBench_IR_bert_based_uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ToolBench/ToolBench_IR_bert_based_uncased with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ToolBench/ToolBench_IR_bert_based_uncased") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use ToolBench/ToolBench_IR_bert_based_uncased with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ToolBench/ToolBench_IR_bert_based_uncased") model = AutoModel.from_pretrained("ToolBench/ToolBench_IR_bert_based_uncased") - Inference
- Notebooks
- Google Colab
- Kaggle
Upload 5 files
Browse files- sentence_bert_config.json +4 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +14 -0
- vocab.txt +0 -0
sentence_bert_config.json
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{
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"max_seq_length": 256,
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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": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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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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"cls_token": "[CLS]",
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 1000000000000000019884624838656,
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"name_or_path": "/home/wanghuadong/zhukunlun/ToolBench/IR/dense_code/SBERT-ndcg/bert-base-uncased",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"special_tokens_map_file": null,
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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vocab.txt
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