---
language: zh
tags:
- sentiment-analysis
- pytorch
widget:
- text: "房间非常非常小，内窗，特别不透气，因为夜里走廊灯光是亮的，内窗对着走廊，窗帘又不能完全拉死，怎么都会有一道光射进来。"
- text: "尽快有洗衣房就好了。"
- text: "很好，干净整洁，交通方便。"
- text: "干净整洁很好"
---

# Note

BERT based sentiment analysis, finetune based on https://huggingface.co/IDEA-CCNL/Erlangshen-Roberta-330M-Sentiment .

The model trained on **hotel human review chinese dataset**.

# Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline

MODEL = "tezign/Erlangshen-Sentiment-FineTune"

tokenizer = AutoTokenizer.from_pretrained(MODEL)

model = AutoModelForSequenceClassification.from_pretrained(MODEL, trust_remote_code=True)

classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)

result = classifier("很好，干净整洁，交通方便。")

print(result)

"""
print result
>> [{'label': 'Positive', 'score': 0.989660382270813}]
"""
```

# Evaluate
We compared and evaluated the performance of **Our finetune model** and the **Original Erlangshen model** on the **hotel human review test dataset**(5429 negative reviews and 1251 positive reviews).

The results showed that our model substantial improved the precision and recall of positive reviews:

```text
Our finetune model:
              precision    recall  f1-score   support

    Negative       0.99      0.98      0.98      5429
    Positive       0.92      0.95      0.93      1251

    accuracy                           0.97      6680
   macro avg       0.95      0.96      0.96      6680
weighted avg       0.97      0.97      0.97      6680

======================================================

Original Erlangshen model:
              precision    recall  f1-score   support

    Negative       0.81      1.00      0.90      5429
    Positive       0.00      0.00      0.00      1251

    accuracy                           0.81      6680
   macro avg       0.41      0.50      0.45      6680
weighted avg       0.66      0.81      0.73      6680
```