README.md

metadata

license: apache-2.0
base_model: ibm-granite/granite-embedding-107m-multilingual
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  - granite
  - embeddings
  - multilingual
library_name: sentence-transformers
pipeline_tag: feature-extraction

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
    embeddings = torch.nn.functional.normalize(embeddings, dim=1)

Original Model

This model is based on ibm-granite/granite-embedding-107m-multilingual by IBM.