Feature Extraction
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
English
bert
sentence-similarity
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use adamezzaim/MNLP_M3_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use adamezzaim/MNLP_M3_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("adamezzaim/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 adamezzaim/MNLP_M3_document_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="adamezzaim/MNLP_M3_document_encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("adamezzaim/MNLP_M3_document_encoder") model = AutoModel.from_pretrained("adamezzaim/MNLP_M3_document_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download onnx/model.onnx from adamezzaim/MNLP_M3_document_encoder: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/adamezzaim/MNLP_M3_document_encoder/resolve/main/onnx/model.onnx
- Command line
-
hf download hf://adamezzaim/MNLP_M3_document_encoder/onnx/model.onnx
-
curl -L -o model.onnx https://huggingface.co/adamezzaim/MNLP_M3_document_encoder/resolve/main/onnx/model.onnx
1.34 GB
- Xet hash:
- 5f82c6c252ca6bb93ff648e232cec7f72c455d26b89ed58f4abe12bb622e5cee
- Size of remote file:
- 1.34 GB
- SHA256:
- 69ed3f810d3b6d13f70dff9ca89966f39c0a0e877fb88211be7bcc070df2a2ce
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