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--- |
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language: |
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- vi |
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base_model: |
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- ZycckZ/Simple_VieQA |
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pipeline_tag: text-classification |
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datasets: |
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- ura-hcmut/UIT-VSFC |
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- anotherpolarbear/vietnamese-sentiment-analysis |
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license: mit |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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library_name: transformers |
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tags: |
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- sentiment-analysis |
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- fine-tuned |
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- bert |
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- vietnamese |
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- text-classification |
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--- |
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# Simple_VieQA Sentiment (ZycckZ) |
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Fine-tuned version of [Simple_VieQA](https://huggingface.co/ZycckZ/Simple_VieQA) |
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for Vietnamese sentiment analysis on `UIT-VSFC` dataset. |
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## 🧠 Performance |
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| Metric | Score | |
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|--------|--------| |
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| Accuracy | 0.936 | |
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| F1-weighted | 0.930 | |
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## 🚀 How to use |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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model_name = "ZycckZ/Simple_VieQA-sentiment" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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text = "Sản phẩm rất tốt, pin trâu" |
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inputs = tokenizer(text, return_tensors="pt") |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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pred = torch.argmax(logits, dim=-1).item() |
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print(pred) |
