Instructions to use htet-98/xml_roberta_oversampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use htet-98/xml_roberta_oversampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="htet-98/xml_roberta_oversampling")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("htet-98/xml_roberta_oversampling") model = AutoModelForSequenceClassification.from_pretrained("htet-98/xml_roberta_oversampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d12fadddda51ad4c88577a5d2709725d438501ea9b08b0ab9a77a6942e52b1dd
- Size of remote file:
- 16.8 MB
- SHA256:
- 4feec7efc736b7e90f34e410f97153d516e3d6e1b443f0222217105ddb1e46ab
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