Instructions to use nllg/clf-bert-e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nllg/clf-bert-e with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nllg/clf-bert-e")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nllg/clf-bert-e") model = AutoModelForSequenceClassification.from_pretrained("nllg/clf-bert-e", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d654c54fa31a4ccb3260810cdd4d64086625d5b5b2b5957e1b90d8327e00d2ca
- Size of remote file:
- 3.38 kB
- SHA256:
- 62342e06b363c8742b5c640518700efc62d50f49e26424473dd2d81eb177e530
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