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