---
tags:
- text-regression
- anger
- emotion
- emotion intensity
language:
- unk
widget:
- text: I am furious
datasets:
- SemEval-2018-Task-1-Text-Regression-Task
co2_eq_emissions:
  emissions: 0.030118000944741423
---
# twitter-roberta-base-anger-intensity
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).

    Warning: Hosted inference API produces inaccurate values

# Model Trained Using AutoTrain

- Problem type: Single Column Regression
- Model ID: 72775139028
- CO2 Emissions (in grams): 0.0301

## Validation Metrics

- Loss: 0.011
- MSE: 0.011
- MAE: 0.085
- R2: 0.641
- RMSE: 0.103
- Explained Variance: 0.641

## Usage

You can use cURL to access this model:

```
$ 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
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("garrettbaber/twitter-roberta-base-anger-intensity")

tokenizer = AutoTokenizer.from_pretrained("garrettbaber/twitter-roberta-base-anger-intensity")

inputs = tokenizer("I am furious", return_tensors="pt")

outputs = model(**inputs)
```