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:
- c5f1a833342b22a1ccd80ed0a3f508b28ab94946e3d63428d21f22e3b1b952da
- Size of remote file:
- 559 Bytes
- SHA256:
- 981227375b8c6d2439b9fd2664e9cd784500649faa2e607c97ed38e07fc17be3
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