Sentence Similarity
Transformers
PyTorch
ONNX
Safetensors
xlm-roberta
feature-extraction
language
granite
embeddings
multilingual
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use ibm-granite/granite-embedding-107m-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibm-granite/granite-embedding-107m-multilingual with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-embedding-107m-multilingual") model = AutoModel.from_pretrained("ibm-granite/granite-embedding-107m-multilingual", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from ibm-granite/granite-embedding-107m-multilingual: direct link, hf CLI and curl.
- Browser
- Download file 191 Bytes
-
https://huggingface.co/ibm-granite/granite-embedding-107m-multilingual/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://ibm-granite/granite-embedding-107m-multilingual/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/ibm-granite/granite-embedding-107m-multilingual/resolve/main/1_Pooling/config.json
191 Bytes
| { | |
| "word_embedding_dimension": 384, | |
| "pooling_mode_cls_token": true, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false | |
| } | |