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--- |
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datasets: |
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- nhull/tripadvisor-split-dataset-v2 |
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base_model: |
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- huawei-noah/TinyBERT_General_4L_312D |
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license: apache-2.0 |
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language: |
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- en |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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- confusion_matrix |
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--- |
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# TinyBERT Sentiment Analysis Model |
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This is a fine-tuned TinyBERT model for sentiment analysis on the Tripadvisor dataset. |
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## Model Details |
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- **Base Model**: `huawei-noah/TinyBERT_General_4L_312D` |
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- **Dataset**: `nhull/tripadvisor-split-dataset-v2` |
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- **Task**: Multiclass sentiment analysis (5 classes) |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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# Load the model |
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tokenizer = AutoTokenizer.from_pretrained("elo4/TinyBERT-sentiment-model") |
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model = AutoModelForSequenceClassification.from_pretrained("elo4/TinyBERT-sentiment-model") |
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# Predict sentiment |
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text = "The hotel was amazing and had great service!" |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = model(**inputs) |
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predicted_class = outputs.logits.argmax().item() |
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print(f"Predicted class: {predicted_class}") |
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``` |
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## Testing results |
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- **Evaluation accuracy**: 0.6535 |
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- **Precision**: 0.635 |
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- **Recall**: 0.641 |
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- **F1 score**: 0.636 |
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- **Confusion matrix**: |
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``` |
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| Predicted → | 1 | 2 | 3 | 4 | 5 | |
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|---------------|------|------|------|------|------| |
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| Actual ↓ | | | | | | |
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| 1 (Very Neg.) | 1219 | 318 | 48 | 6 | 9 | |
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| 2 (Negative) | 432 | 826 | 294 | 32 | 16 | |
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| 3 (Neutral) | 51 | 306 | 928 | 275 | 40 | |
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| 4 (Positive) | 3 | 22 | 223 | 833 | 519 | |
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| 5 (Very Pos.) | 9 | 6 | 16 | 247 | 1322 | |
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``` |
