Instructions to use KalaiselvanD/model_bert_7000_10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_bert_7000_10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_bert_7000_10")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_bert_7000_10") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_bert_7000_10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8fbe605c8e5f157904796cedf06a5245d113e05c29608b0a0406aaf5d681e488
- Size of remote file:
- 268 MB
- SHA256:
- 59504a8844873e437cb385aa30f88ab29581ba3e043d394a9e608fbe46f34662
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