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 pytorch_model.bin from opticalmaterials/opticalpurebert_abstract_classification_uncased: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/opticalmaterials/opticalpurebert_abstract_classification_uncased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://opticalmaterials/opticalpurebert_abstract_classification_uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/opticalmaterials/opticalpurebert_abstract_classification_uncased/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 434ab1fd88320e6b63adb44eccdc2716b1f4c80e12dae582e40dd2a62f05cb35
- Size of remote file:
- 438 MB
- SHA256:
- 38fb2786775cf6940c0634f5666b2d4ff5423948a51584fba5db17388e3220cb
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