Feature Extraction
sentence-transformers
Safetensors
Transformers
English
bert
sentence-similarity
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use igzi/MNLP_M3_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use igzi/MNLP_M3_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("igzi/MNLP_M3_document_encoder") 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 igzi/MNLP_M3_document_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="igzi/MNLP_M3_document_encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("igzi/MNLP_M3_document_encoder") model = AutoModel.from_pretrained("igzi/MNLP_M3_document_encoder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b2c672a0e50a8b3666791d22ec3a71e182a658255aa4b09421943e99f6f57956
- Size of remote file:
- 133 MB
- SHA256:
- ea1d11a3f23d14fe09fc1826fc7944e89c09a634d2217d57a21dd136805ee3e8
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