---
tags:
- text-regression
- emotion
- sentiment
- emotion intensity
language:
- unk
widget:
- text: I'm scared
datasets:
- SemEval-2018-Task-1-Text-Regression-Task
co2_eq_emissions:
  emissions: 0.17201402406362057
pipeline_tag: text-classification
inference: false
---
# twitter-roberta-base-fear-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).

Try it using the Spaces UI: 

https://huggingface.co/spaces/garrettbaber/garrettbaber-twitter-roberta-base-fear-intensity

# Model Trained Using AutoTrain

- Problem type: Single Column Regression
- Model ID: 68748137460
- CO2 Emissions (in grams): 0.1720

## Validation Metrics

- Loss: 0.011
- MSE: 0.011
- MAE: 0.083
- R2: 0.712
- RMSE: 0.107
- Explained Variance: 0.743

## 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'm scared"}' https://api-inference.huggingface.co/models/garrettbaber/twitter-roberta-base-fear-intensity
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

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

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

inputs = tokenizer("I'm scared", return_tensors="pt")

outputs = model(**inputs)
```

---
citation: |
  @misc{garrettbaber/twitter-roberta-base-fear-intensity,
    title={Twitter RoBERTa Base Fear Intensity},
    author={Garrett Baber},
    year={2023}
  }
---