Text Classification
Transformers
PyTorch
Safetensors
English
roberta
argument-mining
opinion-mining
information-extraction
inference-extraction
Twitter
Instructions to use TomatenMarc/WRAP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TomatenMarc/WRAP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TomatenMarc/WRAP")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TomatenMarc/WRAP") model = AutoModelForSequenceClassification.from_pretrained("TomatenMarc/WRAP") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0578e8fb9088da4243f302465c26c5c61326a1c8ce54d0ff25c206e90bd1f9c5
- Size of remote file:
- 540 MB
- SHA256:
- 7dd2b637c9e39effd7913d261cccbff28bc84289ad16e632b4b86ee439c3efc2
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