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emotions-BETO-uncased
UMUTeam/emotions-BETO-uncased
emotions-BETO-uncased is a machine learning model from UMUTeam. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as unknown.
- Python 3.10.15 - Transformers 4.50.3 - Torch 2.6.0
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At a glance
- License
- unknown
- Model type
- bert
- Access
- Public
- Created
- Oct 22, 2025
- Updated
- Oct 22, 2025
- SHA
- 0e0b0267
Model card
From the Hugging Face model README
Spanish Emotion Classification
Usage
from transformers import pipeline
pipe = pipeline("text-classification",
model = "camilum/emotions-BETO-uncased",
padding = True,
truncation = "longest_first",
top_k = None
)
print(list(pipe("estoy triste")))
print(list(pipe("estoy triste" * 1024)))
Output
[[{'label': 'sadness', 'score': 0.9985587000846863}, {'label': 'fear', 'score': 0.000408734631491825}, {'label': 'joy', 'score': 0.0002496271627023816}, {'label': 'anger', 'score': 0.00022718278341926634}, {'label': 'neutral', 'score': 0.00017562056018505245}, {'label': 'others', 'score': 0.00015616531891282648}, {'label': 'surprise', 'score': 0.00012429524213075638}, {'label': 'disgust', 'score': 9.977085574064404e-05}]]
[[{'label': 'sadness', 'score': 0.9396132826805115}, {'label': 'joy', 'score': 0.023490000516176224}, {'label': 'neutral', 'score': 0.015399268828332424}, {'label': 'fear', 'score': 0.008135460317134857}, {'label': 'others', 'score': 0.00671452097594738}, {'label': 'disgust', 'score': 0.004311822820454836}, {'label': 'anger', 'score': 0.0016200717072933912}, {'label': 'surprise', 'score': 0.0007155040511861444}]]
Dependencies
- Python 3.10.15
- Transformers 4.50.3
- Torch 2.6.0
requirements.txt:
transformers==4.50.3
torch==2.6.0
- Type
- bert
- License
- unknown
- Languages
- es
- Created
- Oct 22, 2025
- Updated
- Oct 22, 2025