Instructions to use NazzX1/Bert-section-classification-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NazzX1/Bert-section-classification-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NazzX1/Bert-section-classification-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NazzX1/Bert-section-classification-v2") model = AutoModelForSequenceClassification.from_pretrained("NazzX1/Bert-section-classification-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from NazzX1/Bert-section-classification-v2: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/NazzX1/Bert-section-classification-v2/resolve/main/training_args.bin
- Command line
-
hf download hf://NazzX1/Bert-section-classification-v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/NazzX1/Bert-section-classification-v2/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 67a180a32b921fa93325d7be71b29cb8ed58a51d17f00efbd7c59bb7e676d9ab
- Size of remote file:
- 5.3 kB
- SHA256:
- 4ed32422372dacf5dc61d875288208c4370c61a996b9802def2cd83dcfe64384
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