metadata
license: apache-2.0
base_model: ibm-granite/granite-embedding-107m-multilingual
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- granite
- embeddings
- multilingual
Granite Embedding 107M Multilingual
This is a copy of the ibm-granite/granite-embedding-107m-multilingual model for document encoding purposes.
Model Summary
Granite-Embedding-107M-Multilingual is a 107M parameter dense biencoder embedding model from the Granite Embeddings suite that can be used to generate high quality text embeddings. This model produces embedding vectors of size 384.
Supported Languages
English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese.
Usage
With Sentence Transformers
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('RikoteMaster/MNLP_M3_document_encoder')
embeddings = model.encode(['Your text here'])
With Transformers
from transformers import AutoModel, AutoTokenizer
import torch
model = AutoModel.from_pretrained('RikoteMaster/MNLP_M3_document_encoder')
tokenizer = AutoTokenizer.from_pretrained('RikoteMaster/MNLP_M3_document_encoder')
inputs = tokenizer(['Your text here'], return_tensors='pt', padding=True, truncation=True)
with torch.no_grad():
outputs = model(**inputs)
embeddings = outputs.last_hidden_state[:, 0] # CLS pooling
Original Model
This model is based on ibm-granite/granite-embedding-107m-multilingual by IBM.