Instructions to use Jeevesh8/roberta_base_qqp_ft_12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_12 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_12")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_12") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_12") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 629420e2682486217dc841a92fedb509a57bf75a41b8f62a02686f01a517e735
- Size of remote file:
- 499 MB
- SHA256:
- 2697208505f0cf2f4e58d9b338849fa87d5fc623d7f863363acc64cbc2a2c9f0
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