Instructions to use dwsunimannheim/TaSeR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dwsunimannheim/TaSeR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dwsunimannheim/TaSeR")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dwsunimannheim/TaSeR") model = AutoModelForSequenceClassification.from_pretrained("dwsunimannheim/TaSeR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6cc4a5196b02810c6d0e8018573ba6faaf9fc39367d882da7f3c9a4f82fe8dbf
- Size of remote file:
- 2.67 kB
- SHA256:
- ce658ff62cb17c05def41794d62aaf99b2ccd8d5dadfe7113fc00d96dff52ce6
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