Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use Volowan/MNLP_M3_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Volowan/MNLP_M3_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Volowan/MNLP_M3_document_encoder") 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 Volowan/MNLP_M3_document_encoder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Volowan/MNLP_M3_document_encoder") model = AutoModel.from_pretrained("Volowan/MNLP_M3_document_encoder") - Notebooks
- Google Colab
- Kaggle
Initial fork of sentence-transformers/all-MiniLM-L6-v2 tokenizer
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
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"extra_special_tokens": {},
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"mask_token": "[MASK]",
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"max_length": 128,
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"model_max_length":
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"never_split": null,
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"pad_to_multiple_of": null,
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"pad_token": "[PAD]",
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"extra_special_tokens": {},
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"mask_token": "[MASK]",
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"max_length": 128,
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"model_max_length": 512,
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"never_split": null,
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"pad_to_multiple_of": null,
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"pad_token": "[PAD]",
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