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README.md

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----
-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](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
-```
-
-## Original Model
-This model is based on [ibm-granite/granite-embedding-107m-multilingual](https://huggingface.co/ibm-granite/granite-embedding-107m-multilingual) by IBM.