Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use AShi846/MNLP_M3_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AShi846/MNLP_M3_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AShi846/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 AShi846/MNLP_M3_document_encoder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("AShi846/MNLP_M3_document_encoder") model = AutoModel.from_pretrained("AShi846/MNLP_M3_document_encoder") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- config.json +1 -1
- model.safetensors +1 -1
config.json
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"transformers_version": "4.
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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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"position_embedding_type": "absolute",
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"transformers_version": "4.51.3",
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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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model.safetensors
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