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:
- 0aefc415686f50f592ac42b2e53eec2247cb4c5700898625f21eb421ac52014b
- Size of remote file:
- 5.18 kB
- SHA256:
- da1c670bfb40badf13df9e560200ae1b90997e57a5e7dea51665d847179d1863
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