Instructions to use KalaiselvanD/kala-_bert_model_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/kala-_bert_model_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/kala-_bert_model_test")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/kala-_bert_model_test") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/kala-_bert_model_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a5ef2d1290ab3d3115d4438f5b15aa7b53f25267865f6d5333c5b6ab3871fcf
- Size of remote file:
- 46.7 MB
- SHA256:
- 7ede8bcea4a715dbc1a21a889e854381c5c5cca21e0da0948f121566dc9c7e8d
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