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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ee8f824ed963934eca8eb8dd274e427e001716fcad7cd7df9654c640b522a26
- Size of remote file:
- 471 MB
- SHA256:
- 0ed62ef4c21beacf8f38536f4b7822bb945151ab8dcae0138aec42074790606d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.