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:
- ef0bd49aaedf3e54174d1557e50e5b4d9c5301bd936668c35e7ee1c6e033f3fd
- Size of remote file:
- 31.7 MB
- SHA256:
- 5f84f8d815484ad61b099db424bcb751cb8b5027deff809f0b55fa2a17682363
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