Text Classification
Transformers
PyTorch
Spanish
roberta
emotion-recognition
speech-emotion-recognition
spanish
affective-computing
umuteam
Eval Results (legacy)
text-embeddings-inference
Instructions to use UMUTeam/MarIA-emotion-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UMUTeam/MarIA-emotion-es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UMUTeam/MarIA-emotion-es")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("UMUTeam/MarIA-emotion-es") model = AutoModelForSequenceClassification.from_pretrained("UMUTeam/MarIA-emotion-es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
e63a921
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Parent(s): 1208e2c
Update README.md (#3)
Browse files- Update README.md (9eb7f05d551d9beb4ff2d9732d82183501fb9c82)
Co-authored-by: Tomás Bernal Beltrán <tomasBernal@users.noreply.huggingface.co>
README.md
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metrics:
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- type: accuracy
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value: 77.0204
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- type: weighted-f1
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value: 76.8367
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- type: macro-f1
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value: 69.3886
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---
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# UMUTeam/MarIA-emotion-es
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metrics:
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- type: accuracy
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value: 77.0204
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name: Accuracy
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- type: weighted-f1
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value: 76.8367
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name: Weighted F1
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- type: macro-f1
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value: 69.3886
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name: Macro F1
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---
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# UMUTeam/MarIA-emotion-es
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