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:
# 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
- Xet hash:
- 303c2c1342c3411d08077fdbae9b292c26f1d198acc653311731c7b7d9734f38
- Size of remote file:
- 14.8 MB
- SHA256:
- 71b44701d7efd054205115acfa6ef126c5d2f84bd3affe0c59e48163674d19a6
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