metadata
license: mit
language:
- en
pipeline_tag: text-classification
tags:
- code
widget:
- text: I'm so angry right now. I can't believe he did that to me.
example_title: anger
- text: I'm feeling disgusted by the smell of this food.
example_title: disgust
- text: I'm feeling very afraid of what might happen next.
example_title: fear
- text: I'm so joyful right now! This is the best day of my life.
example_title: joy
- text: >-
I'm feeling neutral about this situation. I don't really care one way or
another.
example_title: neutral
- text: I'm feeling really sad today after my dog passed away."
example_title: sadness
- text: I'm so surprised by what just happened! I never saw that coming.
example_title: surprise
- text: I'm feeling cheeky today. I'm going to play a little prank on my friend.
example_title: cheeky
- text: I'm feeling confused about what to do next. I need some guidance.
example_title: confuse
- text: I'm feeling curious about the world around me. There's so much to learn!
example_title: curious
- text: >-
I'm feeling empathetic towards my friend who is going through a tough
time.
example_title: empathetic
- text: I'm feeling grumpy today. Everything is annoying me!
example_title: grumpy
- text: I'm feeling guilty about what I did. I wish I could take it back.
example_title: guilty
- text: I'm feeling very energetic today. I'm ready to take on the world!
example_title: energetic
- text: I'm feeling impatient waiting for this movie to start.
example_title: impatient
- text: >-
I'm feeling so much love for my family right now. They mean everything to
me.
example_title: love
- text: I'm thinking about my future and what I want to achieve.
example_title: think
- text: >-
I'm feeling serious about this issue. It's important and needs to be
addressed.
example_title: serious
- text: >-
I'm feeling suspicious of what he's telling me. I think he's hiding
something.
example_title: suspicious
- text: I'm feeling whiny today. Everything is bothering me!
example_title: whiny
- text: I love football so much
example_title: love 2
- text: I'm reflecting on my experiences to gain insights
example_title: think 2
- text: >-
I borrowed money from a friend and haven't paid it back yet. Now I feel
ashamed.
example_title: guilty 2
- text: I'm starting to think that he's up to something.
example_title: suspicious 2
- text: We need to approach this matter with a sense of purpose
example_title: serious 2
Emotion classification from 20 classes
20 Emotion labels
| id | label |
|---|---|
| 0 | anger |
| 1 | cheeky |
| 2 | confuse |
| 3 | curious |
| 4 | disgust |
| 5 | empathetic |
| 6 | energetic |
| 7 | fear |
| 8 | grumpy |
| 9 | guilty |
| 10 | impatient |
| 11 | joy |
| 12 | love |
| 13 | neutral |
| 14 | sadness |
| 15 | serious |
| 16 | surprise |
| 17 | suspicious |
| 18 | think |
| 19 | whiny |
How to use
Here is how to use this model to get the emotion label of a given text:
from transformers import AutoModelForSequenceClassification, pipeline
model_name = 'jitesh/emotion-english'
model = AutoModelForSequenceClassification.from_pretrained(model_name)
classifier = pipeline("text-classification", model=model, tokenizer=model_name)
text = "I can't wait any longer "
prediction = classifier(text)
print(prediction[0], text)
The above code outputs the following line.
{'label': 'impatient', 'score': 0.924211859703064} I can't wait any longer