Text Classification
Transformers
PyTorch
TensorBoard
roberta
biology
chemistry
text-embeddings-inference
Instructions to use bmiles/chem-clin-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bmiles/chem-clin-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bmiles/chem-clin-2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bmiles/chem-clin-2") model = AutoModelForSequenceClassification.from_pretrained("bmiles/chem-clin-2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51583c472ed9986bb9863acfcb6ed0db5dea745c1f284ab96711f5b392a92ae4
- Size of remote file:
- 13.7 MB
- SHA256:
- e07c6076fc0f85d86463cc0188b9673a59c23e23186a306954ac2bf7ff3bf1e5
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