Instructions to use min9805/bert-base-finetuned-ynat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use min9805/bert-base-finetuned-ynat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="min9805/bert-base-finetuned-ynat")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("min9805/bert-base-finetuned-ynat") model = AutoModelForSequenceClassification.from_pretrained("min9805/bert-base-finetuned-ynat", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from min9805/bert-base-finetuned-ynat: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/min9805/bert-base-finetuned-ynat/resolve/38983a5b9b86c5c99712db3b4f4e24ed512844f5/training_args.bin
- Command line
-
hf download hf://min9805/bert-base-finetuned-ynat@38983a5b9b86c5c99712db3b4f4e24ed512844f5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/min9805/bert-base-finetuned-ynat/resolve/38983a5b9b86c5c99712db3b4f4e24ed512844f5/training_args.bin
4.03 kB
- Xet hash:
- 1a013a66cc24ac53100ed805e44830de7b8080c2b374b4486dc1240f8a5f542c
- Size of remote file:
- 4.03 kB
- SHA256:
- 877b6ff396467bb3788c90dd6167286486e7eeb53b8677ccf1c5cc058ef0306f
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