Text Classification
Transformers
Safetensors
English
roberta
goemotions
emotion-classification
multi-label-classification
roberta-large
focal-loss
threshold-optimization
nlp
Eval Results (legacy)
text-embeddings-inference
Instructions to use AliceYin/goemotions-roberta-large-focal-sota with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AliceYin/goemotions-roberta-large-focal-sota with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AliceYin/goemotions-roberta-large-focal-sota")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AliceYin/goemotions-roberta-large-focal-sota") model = AutoModelForSequenceClassification.from_pretrained("AliceYin/goemotions-roberta-large-focal-sota") - Notebooks
- Google Colab
- Kaggle
Use script wording in model card reproducibility note
Browse files
README.md
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## Reproducibility
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The Kaggle artifact includes `metrics.json`, `thresholds.json`, `labels.json`,
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the tokenizer, the model weights, and the Kaggle run log. The training
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and experiment notes record the exact settings used for the reported metrics.
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## Reproducibility
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The Kaggle artifact includes `metrics.json`, `thresholds.json`, `labels.json`,
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the tokenizer, the model weights, and the Kaggle run log. The training script
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and experiment notes record the exact settings used for the reported metrics.
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