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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README.md
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## Model description
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TinyBERT is 7.5 times smaller and 9.4 times faster on inference compared to its teacher BERT model (while DistilBERT is 40% smaller and 1.6 times faster than BERT).
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## Intended uses & limitations
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## Model description
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TinyBERT is 7.5 times smaller and 9.4 times faster on inference compared to its teacher BERT model (while DistilBERT is 40% smaller and 1.6 times faster than BERT).
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The model has been trained on 89_754 examples split into train, validation and test. Each label was perfectly balanced in each split.
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## Intended uses & limitations
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