Instructions to use opticalmaterials/opticalpurebert_abstract_classification_uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use opticalmaterials/opticalpurebert_abstract_classification_uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="opticalmaterials/opticalpurebert_abstract_classification_uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("opticalmaterials/opticalpurebert_abstract_classification_uncased") model = AutoModelForSequenceClassification.from_pretrained("opticalmaterials/opticalpurebert_abstract_classification_uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from opticalmaterials/opticalpurebert_abstract_classification_uncased: direct link, hf CLI and curl.
- Browser
- Download file 703 kB
-
https://huggingface.co/opticalmaterials/opticalpurebert_abstract_classification_uncased/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
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hf download hf://opticalmaterials/opticalpurebert_abstract_classification_uncased@refs/pr/1/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/opticalmaterials/opticalpurebert_abstract_classification_uncased/resolve/refs%2Fpr%2F1/tokenizer.json
703 kB
File too large to display, you can check the raw version instead.