Instructions to use KalaiselvanD/model_bert_7000_32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_bert_7000_32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_bert_7000_32")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_bert_7000_32") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_bert_7000_32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24e12bbd0cb892c57273cc270653cf9426e0f210bb2a40bdfd8c947ab8f7880b
- Size of remote file:
- 46.7 MB
- SHA256:
- 5418b7e77c908d05ecd4166a3e4f4547e1b095b64994436186f5e7aaa2524289
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