Instructions to use Gnider/hackat_ver2_30ep_bert_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gnider/hackat_ver2_30ep_bert_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Gnider/hackat_ver2_30ep_bert_base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Gnider/hackat_ver2_30ep_bert_base") model = AutoModelForSequenceClassification.from_pretrained("Gnider/hackat_ver2_30ep_bert_base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 34f94d2ca610a2c4d271f84cbef217986d39b55f3f893188881f66cb1b0ed6c2
- Size of remote file:
- 876 MB
- SHA256:
- b97e2818beaca29cf180862c0d16e34dcc979c128b342dd14b8589918fcce882
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