Instructions to use avacaondata/maria-exist22-task1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avacaondata/maria-exist22-task1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="avacaondata/maria-exist22-task1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("avacaondata/maria-exist22-task1") model = AutoModelForSequenceClassification.from_pretrained("avacaondata/maria-exist22-task1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8be0c678acbadf694dec98768e4f844d3ba069778cfa65627e5f4f21fb5b9988
- Size of remote file:
- 499 MB
- SHA256:
- 2215bea62f9eaadfdf1d71e80ab436a7f96e30bccf60c42cbb729812b5e522e1
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