Instructions to use david-inf/bert-sci-am with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use david-inf/bert-sci-am with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="david-inf/bert-sci-am")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("david-inf/bert-sci-am") model = AutoModelForSequenceClassification.from_pretrained("david-inf/bert-sci-am", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 97412bc0f12f7846b83027c06a89f4e26076f97981595828015193e18c4df542
- Size of remote file:
- 438 MB
- SHA256:
- 2edc2353007f0139d581fbd174c126a09fa9615996ede9a9fdbe32a9b1adffb7
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