Instructions to use Jeevesh8/init_bert_ft_qqp-66 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/init_bert_ft_qqp-66 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/init_bert_ft_qqp-66")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/init_bert_ft_qqp-66") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/init_bert_ft_qqp-66") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7315fed1a1691cf12c9bac05b0da50616822ac07dd300b1d56c6b546363f8c1c
- Size of remote file:
- 438 MB
- SHA256:
- ac217246fabf88acfe1cdf3dea42b9c141d25929217fb95bb9f6dfa74dd99d02
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