Feature Extraction
sentence-transformers
Safetensors
Transformers
xlm-roberta
sentence-similarity
granite
embeddings
multilingual
text-embeddings-inference
Instructions to use RikoteMaster/MNLP_M3_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RikoteMaster/MNLP_M3_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RikoteMaster/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 RikoteMaster/MNLP_M3_document_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RikoteMaster/MNLP_M3_document_encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RikoteMaster/MNLP_M3_document_encoder") model = AutoModel.from_pretrained("RikoteMaster/MNLP_M3_document_encoder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff652dcae81b53c562149ba26970e2e120931767da2cb12dcfbf4d5e59e55a4c
- Size of remote file:
- 17.1 MB
- SHA256:
- 14917dd757b81bc44d4af6b028367351702656670c1954e055dabdfcf21593cf
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