Instructions to use KalaiselvanD/model_bert_check_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_bert_check_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_bert_check_1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_bert_check_1") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_bert_check_1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 75f7c05f3a0ff0f1ffaca93d2961b83f4ce973387b9b9eb7a5ddc2a7b432e2ad
- Size of remote file:
- 46.7 MB
- SHA256:
- d0330bb495e316d38a73c1676080c30523cfd521c2db397822e25f73955c8020
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