Feature Extraction
sentence-transformers
PyTorch
Transformers
English
Chinese
xlm-roberta
sentence-similarity
text-embeddings-inference
Instructions to use maidalun1020/bce-embedding-base_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use maidalun1020/bce-embedding-base_v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("maidalun1020/bce-embedding-base_v1") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use maidalun1020/bce-embedding-base_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="maidalun1020/bce-embedding-base_v1")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("maidalun1020/bce-embedding-base_v1") model = AutoModel.from_pretrained("maidalun1020/bce-embedding-base_v1") - Inference
- Notebooks
- Google Colab
- Kaggle
Update tokenizer_config.json
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
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"cls_token": "<s>",
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"eos_token": "</s>",
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"mask_token": "<mask>",
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"model_max_length":
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"sp_model_kwargs": {},
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"cls_token": "<s>",
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"eos_token": "</s>",
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"mask_token": "<mask>",
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"model_max_length": 512,
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"sp_model_kwargs": {},
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