Instructions to use ChrisUPM/BioBERT_Re_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChrisUPM/BioBERT_Re_trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ChrisUPM/BioBERT_Re_trained")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ChrisUPM/BioBERT_Re_trained") model = AutoModelForSequenceClassification.from_pretrained("ChrisUPM/BioBERT_Re_trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 32c5520f91ae704603e9bcacb2d7b7a740f68cf186f9463cc3d128030d85b96d
- Size of remote file:
- 1.39 kB
- SHA256:
- 504f30f74783a152c8a6061aaa6286a75ee98c3cf7f46d68a7474e40f0c2b1ba
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