Instructions to use KalaiselvanD/model_bert_check_12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_bert_check_12 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_bert_check_12")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_bert_check_12") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_bert_check_12") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ded86e72af7276f7ae865d807045046dfbf2fa141810627e067015722ad35acb
- Size of remote file:
- 46.7 MB
- SHA256:
- 15ecaebcd70c256bc80c64e1ad7b0f17a69fdaf6f258d00206031cc9b63b24bf
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