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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 371181abdc1fd8a1f6d8548093efefa39a956afaf27e9667687a6cd3390bc32b
- Size of remote file:
- 4.09 kB
- SHA256:
- 70dde213249e3ce0464bf46a108806e85ed783638238609eff688142fecd5d15
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