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:
- 9299e06b1df76dba0b695caca59ab0d9792692b400a614a011a2ed5d9ceacccf
- Size of remote file:
- 440 MB
- SHA256:
- 79ecfcee826908c74dae5ff529f55be9c9a30324e2d8e005bc2f8a0f9fca39e7
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