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
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:361fec8d3c6923765a0cfd763d5e0f45f0a7a8f19171399e0fe980f32671edda
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size 440204932
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