Text Classification
Transformers
PyTorch
Safetensors
Enawené-Nawé
roberta
text-regression
anger
emotion
emotion intensity
text-embeddings-inference
Instructions to use garrettbaber/twitter-roberta-base-anger-intensity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use garrettbaber/twitter-roberta-base-anger-intensity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="garrettbaber/twitter-roberta-base-anger-intensity")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("garrettbaber/twitter-roberta-base-anger-intensity") model = AutoModelForSequenceClassification.from_pretrained("garrettbaber/twitter-roberta-base-anger-intensity", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from garrettbaber/twitter-roberta-base-anger-intensity: direct link, hf CLI and curl.
- Browser
- Download file 2.11 MB
-
https://huggingface.co/garrettbaber/twitter-roberta-base-anger-intensity/resolve/main/tokenizer.json
- Command line
-
hf download hf://garrettbaber/twitter-roberta-base-anger-intensity/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/garrettbaber/twitter-roberta-base-anger-intensity/resolve/main/tokenizer.json
2.11 MB
- Xet hash:
- 7f47f6d21ef3326ed7d6a1d6d6fa3c1e82f33ce7f8060b473b78e2022b58c8e7
- Size of remote file:
- 2.11 MB
- SHA256:
- b374924beb422033e02af444719785d778d2511bc7aaa4e9741cb9d580d6567e
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