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:
- 9d1e27fe667062f3b5419b647568aeb1851cd1bf1fdab2a451a3b47231f623f5
- Size of remote file:
- 3.06 kB
- SHA256:
- 25c73720a9a4e208ae9e614a44c2e1a133427689348104387fa2f94334e52584
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