Text Classification
Transformers
PyTorch
Safetensors
English
deberta
text-regression
joy
emotion
emotion intensity
Instructions to use garrettbaber/twitter-roberta-base-joy-intensity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use garrettbaber/twitter-roberta-base-joy-intensity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="garrettbaber/twitter-roberta-base-joy-intensity")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("garrettbaber/twitter-roberta-base-joy-intensity") model = AutoModelForSequenceClassification.from_pretrained("garrettbaber/twitter-roberta-base-joy-intensity") - Notebooks
- Google Colab
- Kaggle
twitter-roberta-base-joy-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: 72771139026
- CO2 Emissions (in grams): 0.0399
Validation Metrics
- Loss: 0.013
- MSE: 0.013
- MAE: 0.088
- R2: 0.707
- RMSE: 0.116
- Explained Variance: 0.709
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 elated!"}' https://api-inference.huggingface.co/models/garrettbaber/twitter-roberta-base-joy-intensity
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("garrettbaber/twitter-roberta-base-joy-intensity")
tokenizer = AutoTokenizer.from_pretrained("garrettbaber/twitter-roberta-base-joy-intensity")
inputs = tokenizer("I am elated!", return_tensors="pt")
outputs = model(**inputs)
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