Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Manirathinam21/bert_emo_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Manirathinam21/bert_emo_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Manirathinam21/bert_emo_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Manirathinam21/bert_emo_classifier") model = AutoModelForSequenceClassification.from_pretrained("Manirathinam21/bert_emo_classifier") - Notebooks
- Google Colab
- Kaggle
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README.md
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It achieves the following results on the evaluation set:
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- Loss: 0.3768
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## Model description
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More information needed
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It achieves the following results on the evaluation set:
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- Loss: 0.3768
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## Target Labels
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label: a classification label, with possible values including
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sadness : 0
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joy : 1
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love : 2
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anger : 3
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fear : 4
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surprise : 5
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## Model description
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More information needed
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