
# 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:


```python
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.
```bash
{'label': 'impatient', 'score': 0.924211859703064} I can't wait any longer 
```
{"language": ["en"], "license": "mit", "tags": ["emotion", "20 classes", "code", "emotions"], "pipeline_tag": "text-classification", "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"}]}
transformers pytorch safetensors roberta text-classification emotion 20 classes code emotions en license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #emotion #20 classes #code #emotions #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

Emotion classification from 20 classes
======================================


20 Emotion labels
-----------------



How to use
----------


Here is how to use this model to get the emotion label of a given text:


The above code outputs the following line.



TAGS
#transformers #pytorch #safetensors #roberta #text-classification #emotion #20 classes #code #emotions #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

TAGS
#transformers #pytorch #safetensors #roberta #text-classification #emotion #20 classes #code #emotions #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
