Instructions to use michamcs/bert-law-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michamcs/bert-law-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="michamcs/bert-law-text-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("michamcs/bert-law-text-classification") model = AutoModelForSequenceClassification.from_pretrained("michamcs/bert-law-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a5e2646df01d1206fc38889098d118dbaa15d3e8cd8adaeded72848611f32d4
- Size of remote file:
- 4.6 kB
- SHA256:
- eb4afd732156af84b0c509f4f5da88463f7b729d830933b376ae0ad02e5e47f1
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