https://github.com/YJiangcm/SST-2-sentiment-analysis
Use BiLSTM_attention, BERT, ALBERT, RoBERTa, XLNet model to classify the SST-2 data set based on pytorch
https://github.com/YJiangcm/SST-2-sentiment-analysis
albert bert bilstm-attention google-colab pytorch roberta xlnet
Last synced: 7 months ago
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Use BiLSTM_attention, BERT, ALBERT, RoBERTa, XLNet model to classify the SST-2 data set based on pytorch
- Host: GitHub
- URL: https://github.com/YJiangcm/SST-2-sentiment-analysis
- Owner: YJiangcm
- Created: 2020-11-17T08:16:27.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-12-09T19:40:00.000Z (over 5 years ago)
- Last Synced: 2023-10-20T23:56:20.371Z (over 2 years ago)
- Topics: albert, bert, bilstm-attention, google-colab, pytorch, roberta, xlnet
- Language: Jupyter Notebook
- Homepage:
- Size: 528 KB
- Stars: 67
- Watchers: 3
- Forks: 16
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-Mixup - SST-2
README
# SST-2-sentiment-analysis
Use BiLSTM_attention, BERT, RoBERTa, XLNet and ALBERT models to classify the SST-2 data set based on pytorch.
These codes are recommended to run in **Google Colab**, where you may use free GPU resources.
## 1. Experiment results of BiLSTM_attention models on test set:
The **BiLSTM_attention model** can let us know which words in a sentence do contributions to the sentiment of this sentence. The code is avalibale in "bilstm_attention.ipynb", where **two types of self-attention mechanism** have been achieved. You can run it in Google Colab for practice. The visualization result is shown below:
## 2. Experiment results of BERT models on test set:
For specific BERT models, you can find them from https://huggingface.co/models and then do modify in "models.py".
### 2.1 base model
Model | Accuracy | Precision | Recall | F1
---- | ----- |----- |----- |-----
BERT (base-uncased) | 91.8 | 91.8 | 91.8 | 91.8
RoBERTa (base-uncased) | **93.4** | **93.5** | **93.4** | **93.3**
XLNet (base-uncased) | 92.5 | 92.5 | 92.5 | 92.5
ALBERT (base-v2-uncased) | 91.4 | 91.4 | 91.4 | 91.4
### 2.2 large model
Model | Accuracy | Precision | Recall | F1
---- | ----- |----- |----- |-----
BERT (large-uncased) | 93.1 | 93.2 | 93.1 | 93.1
RoBERTa (large-uncased) | 94.9 | 95.0 | 95.0 | 94.9
XLNet (large-uncased) | 94.6 | 94.7 | 94.6 | 94.6
ALBERT (large-v2-uncased) | 92.2 | 92.3 | 92.2 | 92.2
ALBERT (xlarge-v2-uncased) | 93.8 | 93.8 | 93.9 | 93.8
ALBERT (xxlarge-v2-uncased) | **95.9** | **95.9** | **95.9** | **95.9**
### 2.3 base model + text attack
Model | Accuracy | Precision | Recall | F1
---- | ----- |----- |----- |-----
BERT (base-uncased) + textattack | 92.4 |92.8 |92.4 |92.4
RoBERTa (base-uncased) + textattack |**94.3** |**94.3** |**94.3** |**94.3**
XLNet (base-uncased) + textattack |93.7 |93.8 |93.7 |93.7
ALBERT (base-uncased) + textattack |92.0 |92.0| 92.0 |92.0
## LICENSE
Please refer to [MIT License Copyright (c) 2020 YJiangcm](https://github.com/YJiangcm/Movielens1M-Movie-Recommendation-System/blob/main/LICENSE)