Text Classification
Transformers
PyTorch
Safetensors
English
roberta
argument-mining
opinion-mining
information-extraction
inference-extraction
Twitter
Instructions to use TomatenMarc/TACO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TomatenMarc/TACO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TomatenMarc/TACO")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TomatenMarc/TACO") model = AutoModelForSequenceClassification.from_pretrained("TomatenMarc/TACO", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 914a8fbcbfaae991912279f2393ac6175e52b31edd7cbc917a826e256ae4f24a
- Size of remote file:
- 540 MB
- SHA256:
- 4056bc4e6aada20e6f8caafd283078b609ab06cd6bb1fa1cba8fc28b74d600c1
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