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electra-base-classifier-sentiment

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electra
sentiment-analysis
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 ---
license: mit
tags:
- sentiment-analysis
- text-classification
- electra
- pytorch
- transformers
---
# ELECTRA Base Classifier for Sentiment Analysis
This is an [ELECTRA base discriminator](https://huggingface.co/google/electra-base-discriminator) fine-tuned for sentiment analysis of reviews. It has a mean pooling layer and a classifier head (2 layers of 1024 dimension) with SwishGLU activation and dropout (0.3). It classifies text into three sentiment categories: 'negative' (0), 'neutral' (1), and 'positive' (2). It was fine-tuned on the [Sentiment Merged](https://huggingface.co/datasets/jbeno/sentiment_merged) dataset, which is a merge of Stanford Sentiment Treebank (SST-3), and DynaSent Rounds 1 and 2.
## Labels
The model predicts the following labels:
- `0`: negative
- `1`: neutral
- `2`: positive
## How to Use
### Install package
This model requires the classes in `electra_classifier.py`. You can download the file, or you can install the package from PyPI.
```bash
pip install electra-classifier
```
### Load classes and model
```python
# Install the package in a notebook
import sys
!{sys.executable} -m pip install electra-classifier
# Import libraries
import torch
from transformers import AutoTokenizer
from electra_classifier import ElectraClassifier
# Load tokenizer and model
model_name = "jbeno/electra-base-classifier-sentiment"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = ElectraClassifier.from_pretrained(model_name)
# Set model to evaluation mode
model.eval()
# Run inference
text = "I love this restaurant!"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs)
predicted_class_id = torch.argmax(logits, dim=1).item()
predicted_label = model.config.id2label[predicted_class_id]
print(f"Predicted label: {predicted_label}")
```
## Requirements
- Python 3.7+
- PyTorch
- Transformers
- [electra-classifier](https://pypi.org/project/electra-classifier/) - Install with pip, or download electra_classifier.py
## Training Details
### Dataset
The model was trained on the [Sentiment Merged](https://huggingface.co/datasets/jbeno/sentiment_merged) dataset, which is a mix of Stanford Sentiment Treebank (SST-3), DynaSent Round 1, and DynaSent Round 2.
### Code
The code used to train the model can be found on GitHub:
- [jbeno/sentiment](https://github.com/jbeno/sentiment)
- [jbeno/electra-classifier](https://github.com/jbeno/electra-classifier)
### Research Paper
The research paper can be found here: [ELECTRA and GPT-4o: Cost-Effective Partners for Sentiment Analysis](http://arxiv.org/abs/2501.00062) (arXiv:2501.00062)
### Performance Summary
- **Merged Dataset**
- Macro Average F1: **79.29**
- Accuracy: **79.69**
- **DynaSent R1**
- Macro Average F1: **82.10**
- Accuracy: **82.14**
- **DynaSent R2**
- Macro Average F1: **71.83**
- Accuracy: **71.94**
- **SST-3**
- Macro Average F1: **69.95**
- Accuracy: **78.24**
## Model Architecture
- **Base Model**: ELECTRA base discriminator (`google/electra-base-discriminator`)
- **Pooling Layer**: Custom pooling layer supporting 'cls', 'mean', and 'max' pooling types.
- **Classifier**: Custom classifier with configurable hidden dimensions, number of layers, and dropout rate.
- **Activation Function**: Custom SwishGLU activation function.
```
ElectraClassifier(
(electra): ElectraModel(
(embeddings): ElectraEmbeddings(
(word_embeddings): Embedding(30522, 768, padding_idx=0)
(position_embeddings): Embedding(512, 768)
(token_type_embeddings): Embedding(2, 768)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(encoder): ElectraEncoder(
(layer): ModuleList(
(0-11): 12 x ElectraLayer(
(attention): ElectraAttention(
(self): ElectraSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): ElectraSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): ElectraIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): ElectraOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
)
)
(pooling): PoolingLayer()
(classifier): Classifier(
(layers): Sequential(
(0): Linear(in_features=768, out_features=1024, bias=True)
(1): SwishGLU(
(projection): Linear(in_features=1024, out_features=2048, bias=True)
(activation): SiLU()
)
(2): Dropout(p=0.3, inplace=False)
(3): Linear(in_features=1024, out_features=1024, bias=True)
(4): SwishGLU(
(projection): Linear(in_features=1024, out_features=2048, bias=True)
(activation): SiLU()
)
(5): Dropout(p=0.3, inplace=False)
(6): Linear(in_features=1024, out_features=3, bias=True)
)
)
)
```
## Custom Model Components
### SwishGLU Activation Function
The SwishGLU activation function combines the Swish activation with a Gated Linear Unit (GLU). It enhances the model's ability to capture complex patterns in the data.
```python
class SwishGLU(nn.Module):
def __init__(self, input_dim: int, output_dim: int):
super(SwishGLU, self).__init__()
self.projection = nn.Linear(input_dim, 2 * output_dim)
self.activation = nn.SiLU()
def forward(self, x):
x_proj_gate = self.projection(x)
projected, gate = x_proj_gate.tensor_split(2, dim=-1)
return projected * self.activation(gate)
```
### PoolingLayer
The PoolingLayer class allows you to choose between different pooling strategies:
- `cls`: Uses the representation of the \[CLS\] token.
- `mean`: Calculates the mean of the token embeddings.
- `max`: Takes the maximum value across token embeddings.
**'mean'** pooling was used in the fine-tuned model.
```python
class PoolingLayer(nn.Module):
def __init__(self, pooling_type='cls'):
super().__init__()
self.pooling_type = pooling_type
def forward(self, last_hidden_state, attention_mask):
if self.pooling_type == 'cls':
return last_hidden_state[:, 0, :]
elif self.pooling_type == 'mean':
return (last_hidden_state * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1)
elif self.pooling_type == 'max':
return torch.max(last_hidden_state * attention_mask.unsqueeze(-1), dim=1)[0]
else:
raise ValueError(f"Unknown pooling method: {self.pooling_type}")
```
### Classifier
The Classifier class is a customizable feed-forward neural network used for the final classification.
The fine-tuned model had:
- `input_dim`: 768
- `num_layers`: 2
- `hidden_dim`: 1024
- `hidden_activation`: SwishGLU
- `dropout_rate`: 0.3
- `n_classes`: 3
```python
class Classifier(nn.Module):
def __init__(self, input_dim, hidden_dim, hidden_activation, num_layers, n_classes, dropout_rate=0.0):
super().__init__()
layers = []
layers.append(nn.Linear(input_dim, hidden_dim))
layers.append(hidden_activation)
if dropout_rate > 0:
layers.append(nn.Dropout(dropout_rate))
for _ in range(num_layers - 1):
layers.append(nn.Linear(hidden_dim, hidden_dim))
layers.append(hidden_activation)
if dropout_rate > 0:
layers.append(nn.Dropout(dropout_rate))
layers.append(nn.Linear(hidden_dim, n_classes))
self.layers = nn.Sequential(*layers)
```
## Model Configuration
The model's configuration (config.json) includes custom parameters:
- `hidden_dim`: Size of the hidden layers in the classifier.
- `hidden_activation`: Activation function used in the classifier ('SwishGLU').
- `num_layers`: Number of layers in the classifier.
- `dropout_rate`: Dropout rate used in the classifier.
- `pooling`: Pooling strategy used ('mean').
## Performance by Dataset
### Merged Dataset
```
Merged Dataset Classification Report
precision recall f1-score support
negative 0.847081 0.777211 0.810643 2352
neutral 0.704453 0.761072 0.731669 1829
positive 0.828047 0.844615 0.836249 2349
accuracy 0.796937 6530
macro avg 0.793194 0.794299 0.792854 6530
weighted avg 0.800285 0.796937 0.797734 6530
ROC AUC: 0.926344
Predicted negative neutral positive
Actual
negative 1828 331 193
neutral 218 1392 219
positive 112 253 1984
Macro F1 Score: 0.79
```
### DynaSent Round 1
```
DynaSent Round 1 Classification Report
precision recall f1-score support
negative 0.901222 0.737500 0.811182 1200
neutral 0.745957 0.922500 0.824888 1200
positive 0.850970 0.804167 0.826907 1200
accuracy 0.821389 3600
macro avg 0.832716 0.821389 0.820992 3600
weighted avg 0.832716 0.821389 0.820992 3600
ROC AUC: 0.945131
Predicted negative neutral positive
Actual
negative 885 201 114
neutral 38 1107 55
positive 59 176 965
Macro F1 Score: 0.82
```
### DynaSent Round 2
```
DynaSent Round 2 Classification Report
precision recall f1-score support
negative 0.696154 0.754167 0.724000 240
neutral 0.770408 0.629167 0.692661 240
positive 0.704545 0.775000 0.738095 240
accuracy 0.719444 720
macro avg 0.723702 0.719444 0.718252 720
weighted avg 0.723702 0.719444 0.718252 720
ROC AUC: 0.88842
Predicted negative neutral positive
Actual
negative 181 26 33
neutral 44 151 45
positive 35 19 186
Macro F1 Score: 0.72
```
### Stanford Sentiment Treebank (SST-3)
```
SST-3 Classification Report
precision recall f1-score support
negative 0.831878 0.835526 0.833698 912
neutral 0.452703 0.344473 0.391241 389
positive 0.834669 0.916392 0.873623 909
accuracy 0.782353 2210
macro avg 0.706417 0.698797 0.699521 2210
weighted avg 0.766284 0.782353 0.772239 2210
ROC AUC: 0.885009
Predicted negative neutral positive
Actual
negative 762 104 46
neutral 136 134 119
positive 18 58 833
Macro F1 Score: 0.70
```
## License
This model is licensed under the MIT License.
## Citation
If you use this model in your work, please cite:
```bibtex
@article{beno-2024-electragpt,
title={ELECTRA and GPT-4o: Cost-Effective Partners for Sentiment Analysis},
author={James P. Beno},
journal={arXiv preprint arXiv:2501.00062},
year={2024},
eprint={2501.00062},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2501.00062},
}
```
## Contact
For questions or comments, please open an issue on the repository or contact [Jim Beno](https://huggingface.co/jbeno).
## Acknowledgments
- The [Hugging Face Transformers library](https://github.com/huggingface/transformers) for providing powerful tools for model development.
- The creators of the [ELECTRA model](https://arxiv.org/abs/2003.10555) for their foundational work.
- The authors of the datasets used: [Stanford Sentiment Treebank](https://huggingface.co/datasets/stanfordnlp/sst), [DynaSent](https://huggingface.co/datasets/dynabench/dynasent).
- [Stanford Engineering CGOE](https://cgoe.stanford.edu), [Chris Potts](https://stanford.edu/~cgpotts/), and the Course Facilitators of [XCS224U](https://online.stanford.edu/courses/xcs224u-natural-language-understanding)