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--- |
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tags: |
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- text-regression |
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- anger |
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- emotion |
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- emotion intensity |
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language: |
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- unk |
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widget: |
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- text: I am furious |
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datasets: |
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- SemEval-2018-Task-1-Text-Regression-Task |
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co2_eq_emissions: |
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emissions: 0.030118000944741423 |
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--- |
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# twitter-roberta-base-anger-intensity |
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This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2022-154m on the SemEval 2018 - Task 1 Affect in Tweets (subtask: El-reg / text regression). |
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Warning: Hosted inference API produces inaccurate values |
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# Model Trained Using AutoTrain |
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- Problem type: Single Column Regression |
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- Model ID: 72775139028 |
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- CO2 Emissions (in grams): 0.0301 |
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## Validation Metrics |
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- Loss: 0.011 |
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- MSE: 0.011 |
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- MAE: 0.085 |
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- R2: 0.641 |
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- RMSE: 0.103 |
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- Explained Variance: 0.641 |
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## Usage |
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You can use cURL to access this model: |
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``` |
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$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I am furious"}' https://api-inference.huggingface.co/models/garrettbaber/twitter-roberta-base-anger-intensity |
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``` |
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Or Python API: |
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``` |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model = AutoModelForSequenceClassification.from_pretrained("garrettbaber/twitter-roberta-base-anger-intensity") |
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tokenizer = AutoTokenizer.from_pretrained("garrettbaber/twitter-roberta-base-anger-intensity") |
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inputs = tokenizer("I am furious", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
