Instructions to use Jeevesh8/std_0pnt2_bert_ft_cola-70 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/std_0pnt2_bert_ft_cola-70 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/std_0pnt2_bert_ft_cola-70")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/std_0pnt2_bert_ft_cola-70") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/std_0pnt2_bert_ft_cola-70") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8510b8d9d3f42b1fa769e35aeadae14a9ec7fecf48d5ca7557810f3e96aab542
- Size of remote file:
- 438 MB
- SHA256:
- 33da80ddb5f76bd9a91ba4eb2e287f618ca4842c9eefdfadb529785fb4c07f67
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