Instructions to use aloxatel/bert-base-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aloxatel/bert-base-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aloxatel/bert-base-mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aloxatel/bert-base-mnli") model = AutoModelForSequenceClassification.from_pretrained("aloxatel/bert-base-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from aloxatel/bert-base-mnli: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/aloxatel/bert-base-mnli/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://aloxatel/bert-base-mnli/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/aloxatel/bert-base-mnli/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- 80be56cfca921268c5346ff2735f489415d7890023bcc8e699d8c9f19a67533e
- Size of remote file:
- 438 MB
- SHA256:
- ad772e64c2e0cc7eedf23bec4dabdc587ce3dea831d9d002a25652d0bd56e7dd
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