Instructions to use KalaiselvanD/bigbert_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/bigbert_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/bigbert_1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/bigbert_1") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/bigbert_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 32818d222e6f6e9912dd592e9871666cb9e9e10f997c2745fd89086694c3caef
- Size of remote file:
- 512 MB
- SHA256:
- 1baf23702c9fcedbece374437347c1133f213981326699f44ccc221f667351e9
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