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