Feature Extraction
sentence-transformers
Safetensors
Transformers
English
bert
sentence-similarity
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use anlanwang925/MNLP_M3_document_encoder_test5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use anlanwang925/MNLP_M3_document_encoder_test5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("anlanwang925/MNLP_M3_document_encoder_test5") 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 anlanwang925/MNLP_M3_document_encoder_test5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="anlanwang925/MNLP_M3_document_encoder_test5")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("anlanwang925/MNLP_M3_document_encoder_test5") model = AutoModel.from_pretrained("anlanwang925/MNLP_M3_document_encoder_test5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8859a9afa24289c35cff82970969c6ecc1e20b5e9418af0bb7f339795d04c32b
- Size of remote file:
- 1.34 GB
- SHA256:
- 8a91d2ecbd94ef8de116bb9996a6611661e573d1a2427bbfc0603174ee75fa12
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