Sentence Similarity
Transformers
PyTorch
bert
feature-extraction
text2vec
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use barisaydin/text2vec-base-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use barisaydin/text2vec-base-multilingual with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("barisaydin/text2vec-base-multilingual") model = AutoModel.from_pretrained("barisaydin/text2vec-base-multilingual", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download modules.json from barisaydin/text2vec-base-multilingual: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/barisaydin/text2vec-base-multilingual/resolve/refs%2Fpr%2F1/modules.json
- Command line
-
hf download hf://barisaydin/text2vec-base-multilingual@refs/pr/1/modules.json
-
curl -L -o modules.json https://huggingface.co/barisaydin/text2vec-base-multilingual/resolve/refs%2Fpr%2F1/modules.json
229 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |