Feature Extraction
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
English
bert
sentence-similarity
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use arnaultsta/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use arnaultsta/MNLP_M2_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("arnaultsta/MNLP_M2_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 arnaultsta/MNLP_M2_document_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="arnaultsta/MNLP_M2_document_encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("arnaultsta/MNLP_M2_document_encoder") model = AutoModel.from_pretrained("arnaultsta/MNLP_M2_document_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f34dad568ecbc8f2452ae7ea84e72884e1bab4f299cec39fd8f978b5fba8d3c9
- Size of remote file:
- 133 MB
- SHA256:
- 3c9f31665447c8911517620762200d2245a2518d6e7208acc78cd9db317e21ad
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