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:
- ea61e30a7e1daed30441ff1505e62100822e0df07a55b20a18daf87b36767269
- Size of remote file:
- 3.26 kB
- SHA256:
- 27344619ee48342231ed2dca3b179bc77e84a1718ed0c993f730728f3d8f88c8
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