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:
- 9c9efef9bb40c90a7ab26a283022971387d609ca69c5d200a824d8ca7ab2ee33
- Size of remote file:
- 268 MB
- SHA256:
- 7b42d7d0483f7c009060dc2659c54ed53009f0662e764ae3f5ce3f8fec1fb164
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