Instructions to use vashuag/HindiEmotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vashuag/HindiEmotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vashuag/HindiEmotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vashuag/HindiEmotion") model = AutoModelForSequenceClassification.from_pretrained("vashuag/HindiEmotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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- en
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datasets:
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- utkarsharora100/google_go_emotions_hindi_translated
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metrics:
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- accuracy: 0.8085
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- precision: 0.7996
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- en
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datasets:
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- utkarsharora100/google_go_emotions_hindi_translated
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widget:
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- text: "बिजली जल्दी आ गई, बहुत शुक्रिया"
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output:
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- label: anger
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score: 0.0046975212171673775
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- label: disgust
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score: 0.0031991226132959127
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- label: fear
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score: 0.0033638915047049522
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- label: joy
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score: 0.7621134519577026
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- label: neutral
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score: 0.21544277667999268
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- label: sadness
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score: 0.21544277667999268
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- label: surprise
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score: 0.008416484110057354
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metrics:
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- accuracy: 0.8085
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- precision: 0.7996
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