Text Classification
Transformers
PyTorch
ONNX
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use AdamCodd/tinybert-emotion-balanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AdamCodd/tinybert-emotion-balanced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AdamCodd/tinybert-emotion-balanced")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AdamCodd/tinybert-emotion-balanced") model = AutoModelForSequenceClassification.from_pretrained("AdamCodd/tinybert-emotion-balanced") - Inference
- Notebooks
- Google Colab
- Kaggle
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# tinybert-emotion
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This model is a fine-tuned version of [bert-tiny](prajjwal1/bert-tiny) on the [emotion balanced dataset](https://huggingface.co/datasets/AdamCodd/emotion-balanced).
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It achieves the following results on the evaluation set:
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- Loss: 0.1809
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- Accuracy: 0.9354
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# tinybert-emotion
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This model is a fine-tuned version of [bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the [emotion balanced dataset](https://huggingface.co/datasets/AdamCodd/emotion-balanced).
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It achieves the following results on the evaluation set:
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- Loss: 0.1809
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- Accuracy: 0.9354
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