Instructions to use CWrecker/Clinical-Longformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CWrecker/Clinical-Longformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CWrecker/Clinical-Longformer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CWrecker/Clinical-Longformer") model = AutoModelForSequenceClassification.from_pretrained("CWrecker/Clinical-Longformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4162f428d544c501d3589e908c9af2d18658661341b6d6aa9a2e408907e183ff
- Size of remote file:
- 595 MB
- SHA256:
- 5f045a4b00daf7dbfe7067c7a662012855563ecac586dbc7547028b043810ef5
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