|
--- |
|
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 |
|
--- |
|
# 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). |
|
- Warning: Hosted inference API produces inaccurate values |
|
|
|
# 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} |
|
} |
|
--- |
