Instructions to use ncardus/BertSeq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ncardus/BertSeq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ncardus/BertSeq")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ncardus/BertSeq") model = AutoModelForSequenceClassification.from_pretrained("ncardus/BertSeq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ncardus/BertSeq: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/ncardus/BertSeq/resolve/main/training_args.bin
- Command line
-
hf download hf://ncardus/BertSeq/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ncardus/BertSeq/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 76daaab34f59c4fe18023bfffa3ed5341b38eb84db7307f00512976ca5d9cf8e
- Size of remote file:
- 4.54 kB
- SHA256:
- 94ff0fcfea942dc04e6ec0897efe843251d75e10c72c54a546b7dab7f0fe2e80
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