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 training_args.bin from opticalmaterials/opticalpurebert_abstract_classification_uncased: direct link, hf CLI and curl.
- Browser
- Download file 3.25 kB
-
https://huggingface.co/opticalmaterials/opticalpurebert_abstract_classification_uncased/resolve/main/training_args.bin
- Command line
-
hf download hf://opticalmaterials/opticalpurebert_abstract_classification_uncased/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/opticalmaterials/opticalpurebert_abstract_classification_uncased/resolve/main/training_args.bin
3.25 kB
- Xet hash:
- b506a22b4022553f2602e78149e2b60ecb2e4109d10a5b6395c264c5bf556a23
- Size of remote file:
- 3.25 kB
- SHA256:
- 56da3f56b5257e09c4ef4c62191ebda9bbd4a84c86027539f1b329bd757de4e9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.