Instructions to use TFLai/Bert-Multilingual-NLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TFLai/Bert-Multilingual-NLI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TFLai/Bert-Multilingual-NLI")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TFLai/Bert-Multilingual-NLI") model = AutoModelForSequenceClassification.from_pretrained("TFLai/Bert-Multilingual-NLI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from TFLai/Bert-Multilingual-NLI: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/TFLai/Bert-Multilingual-NLI/resolve/main/optimizer.pt
- Command line
-
hf download hf://TFLai/Bert-Multilingual-NLI/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/TFLai/Bert-Multilingual-NLI/resolve/main/optimizer.pt
1.34 GB
- Xet hash:
- 4e82e4119d8f41f6cf91e160206838f6e5d2698be76127b7e1cee87f1f068574
- Size of remote file:
- 1.34 GB
- SHA256:
- c0575e182ac3e58c7abb07aca03ead501e340f0bb70d3337c2da0d898b2505ca
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