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 3.96 kB
-
https://huggingface.co/min9805/bert-base-finetuned-ynat/resolve/4d50baaa8a7149c8baf5423cb2bdaa66162f1eaf/training_args.bin
- Command line
-
hf download hf://min9805/bert-base-finetuned-ynat@4d50baaa8a7149c8baf5423cb2bdaa66162f1eaf/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/min9805/bert-base-finetuned-ynat/resolve/4d50baaa8a7149c8baf5423cb2bdaa66162f1eaf/training_args.bin
3.96 kB
- Xet hash:
- db94087418fb432f855d3caa61e4e9e2a13ed639808a38b34933d5bda8d2a18b
- Size of remote file:
- 3.96 kB
- SHA256:
- e63a7d343a1a886f88cd339ad68136ced9f2ed214f5623482b4015e26b8ac9f6
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