Feature Extraction
sentence-transformers
Safetensors
Transformers
English
bert
sentence-similarity
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use igzi/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use igzi/MNLP_M2_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("igzi/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 igzi/MNLP_M2_document_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="igzi/MNLP_M2_document_encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("igzi/MNLP_M2_document_encoder") model = AutoModel.from_pretrained("igzi/MNLP_M2_document_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 205 Bytes
d1645cf | 1 2 3 4 5 6 7 8 9 10 | {
"__version__": {
"sentence_transformers": "3.4.1",
"transformers": "4.51.3",
"pytorch": "2.5.1+cu124"
},
"prompts": {},
"default_prompt_name": null,
"similarity_fn_name": "cosine"
} |