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:
- c2ad3e02b79ebe0c34768036a10aa8a7ca3ba863b8b74bc58c8c561b0871e1a3
- Size of remote file:
- 3.26 kB
- SHA256:
- a2803bbcd70b50762f43f9b321ab98822e0b87ba72cc8bafe52cfe1bf2c2b431
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