README.md

---
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](https://huggingface.co/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
```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer('RikoteMaster/MNLP_M3_document_encoder')
embeddings = model.encode(['Your text here'])
```

### With Transformers
```python
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](https://huggingface.co/ibm-granite/granite-embedding-107m-multilingual) by IBM.