Instructions to use bdpc/SciBERT_20K_steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bdpc/SciBERT_20K_steps with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bdpc/SciBERT_20K_steps")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bdpc/SciBERT_20K_steps") model = AutoModelForSequenceClassification.from_pretrained("bdpc/SciBERT_20K_steps") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b0c5a4641245da4419ff80ecf8d3af5f8159bfc970fc61ea0596740b5b5fbf9a
- Size of remote file:
- 440 MB
- SHA256:
- 65327ff52cf3e5daab9c36672ca45968d221dece51f07a67c494e984a1da8e84
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