Instructions to use VarshitaChauhan/1k-samples-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VarshitaChauhan/1k-samples-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VarshitaChauhan/1k-samples-classifier", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VarshitaChauhan/1k-samples-classifier") model = AutoModelForSequenceClassification.from_pretrained("VarshitaChauhan/1k-samples-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 946bcb0d26515bf262bd4db97b38d5666eb957412c920cd0b237947d3fa3ed03
- Size of remote file:
- 5.2 kB
- SHA256:
- aded72843bcd707a082100f08df6c9dfcfa956b3091abae356ea0e976497fb42
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