Text Classification
Transformers
ONNX
Safetensors
bert
Eval Results (legacy)
text-embeddings-inference
Instructions to use AdamCodd/tinybert-sentiment-amazon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AdamCodd/tinybert-sentiment-amazon with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AdamCodd/tinybert-sentiment-amazon")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AdamCodd/tinybert-sentiment-amazon") model = AutoModelForSequenceClassification.from_pretrained("AdamCodd/tinybert-sentiment-amazon") - Notebooks
- Google Colab
- Kaggle
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# tinybert-sentiment-amazon
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This model is a fine-tuned version of [bert-tiny](prajjwal1/bert-tiny) on [amazon-polarity dataset](https://huggingface.co/datasets/amazon_polarity). It achieves the following results on the evaluation set:
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* Loss: 0.153
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* Accuracy: 0.942
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* F1_score: 0.940
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# tinybert-sentiment-amazon
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This model is a fine-tuned version of [bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on [amazon-polarity dataset](https://huggingface.co/datasets/amazon_polarity). It achieves the following results on the evaluation set:
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* Loss: 0.153
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* Accuracy: 0.942
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* F1_score: 0.940
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