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---
tags:
- autotrain
- text-classification
widget:
- text: I love AutoTrain
datasets:
- dair-ai/emotion
license: mit
language:
- en
pipeline_tag: text-classification
base_model:
- google-bert/bert-base-uncased
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
# Request Example
```Python
from transformers import pipeline
# Ensure the model and tokenizer are loaded on the GPU by setting device=0
emotion_classifier = pipeline(
"text-classification",
model="XuehangCang/Emotion-Classification",
# device=0 # Use the first GPU device
)
texts = [
"I'm so happy today!",
"This is really sad.",
"I'm a bit nervous about what's going to happen.",
"This news makes me angry."
]
for text in texts:
result = emotion_classifier(text)
print(f"Text: {text}")
print(f"Emotion classification result: {result}\n")
"""
Device set to use cpu
Text: I'm so happy today!
Emotion classification result: [{'label': 'joy', 'score': 0.9994311928749084}]
Text: This is really sad.
Emotion classification result: [{'label': 'sadness', 'score': 0.9989039897918701}]
Text: I'm a bit nervous about what's going to happen.
Emotion classification result: [{'label': 'fear', 'score': 0.998763918876648}]
Text: This news makes me angry.
Emotion classification result: [{'label': 'anger', 'score': 0.9977891445159912}]
"""
```
## Validation Metrics
loss: 0.13341853022575378
f1_macro: 0.9169826832623412
f1_micro: 0.943
f1_weighted: 0.9427985114313238
precision_macro: 0.9227534317185495
precision_micro: 0.943
precision_weighted: 0.9430912986498113
recall_macro: 0.9119580961776227
recall_micro: 0.943
recall_weighted: 0.943
accuracy: 0.943 |