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electra-large-classifier-sentiment

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electra-large-classifier-sentiment / README.md
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 ---
license: mit
tags:
- sentiment-analysis
- text-classification
- electra
- pytorch
- transformers
---
# ELECTRA Large Classifier for Sentiment Analysis
This is an [ELECTRA large discriminator](https://huggingface.co/google/electra-large-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.
## Updates
- **2025-Mar-25**: Uploaded a better performing model fine-tuned with a different random seed (123 vs. 42) and from an earlier training checkpoint (epoch 10 vs. 13).
## 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-large-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: **83.16** (was 82.36)
- Accuracy: **83.71** (was 82.96)
- **DynaSent R1**
- Macro Average F1: **86.53** (was 85.91)
- Accuracy: **86.44** (was 85.83)
- **DynaSent R2**
- Macro Average F1: **78.36** (was 76.29)
- Accuracy: **78.61** (was 76.53)
- **SST-3**
- Macro Average F1: **72.63** (was 70.90)
- Accuracy: **80.91** (was 80.36)
## Model Architecture
- **Base Model**: ELECTRA large discriminator (`google/electra-large-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, 1024, padding_idx=0)
(position_embeddings): Embedding(512, 1024)
(token_type_embeddings): Embedding(2, 1024)
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(encoder): ElectraEncoder(
(layer): ModuleList(
(0-23): 24 x ElectraLayer(
(attention): ElectraAttention(
(self): ElectraSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): ElectraSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): ElectraIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): ElectraOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
)
)
(custom_pooling): PoolingLayer()
(classifier): Classifier(
(layers): Sequential(
(0): Linear(in_features=1024, 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`: 1024
- `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').
## Updated Performance by Dataset
### Merged Dataset
```
Merged Dataset Classification Report
precision recall f1-score support
negative 0.874178 0.847789 0.860781 2352
neutral 0.741715 0.770913 0.756032 1829
positive 0.878194 0.877820 0.878007 2349
accuracy 0.837060 6530
macro avg 0.831362 0.832174 0.831607 6530
weighted avg 0.838521 0.837060 0.837639 6530
ROC AUC: 0.947808
Predicted negative neutral positive
Actual
negative 1994 268 90
neutral 223 1410 196
positive 64 223 2062
Macro F1 Score: 0.83
```
### DynaSent Round 1
```
DynaSent Round 1 Classification Report
precision recall f1-score support
negative 0.925512 0.828333 0.874230 1200
neutral 0.781536 0.924167 0.846888 1200
positive 0.911472 0.840833 0.874729 1200
accuracy 0.864444 3600
macro avg 0.872840 0.864444 0.865283 3600
weighted avg 0.872840 0.864444 0.865283 3600
ROC AUC: 0.962647
Predicted negative neutral positive
Actual
negative 994 159 47
neutral 40 1109 51
positive 40 151 1009
Macro F1 Score: 0.87
```
### DynaSent Round 2
```
DynaSent Round 2 Classification Report
precision recall f1-score support
negative 0.791339 0.837500 0.813765 240
neutral 0.803030 0.662500 0.726027 240
positive 0.768657 0.858333 0.811024 240
accuracy 0.786111 720
macro avg 0.787675 0.786111 0.783605 720
weighted avg 0.787675 0.786111 0.783605 720
ROC AUC: 0.932089
Predicted negative neutral positive
Actual
negative 201 18 21
neutral 40 159 41
positive 13 21 206
Macro F1 Score: 0.78
```
### Stanford Sentiment Treebank (SST-3)
```
SST-3 Classification Report
precision recall f1-score support
negative 0.838405 0.876096 0.856836 912
neutral 0.500000 0.365039 0.421991 389
positive 0.870504 0.931793 0.900106 909
accuracy 0.809050 2210
macro avg 0.736303 0.724309 0.726311 2210
weighted avg 0.792042 0.809050 0.798093 2210
ROC AUC: 0.905255
Predicted negative neutral positive
Actual
negative 799 91 22
neutral 143 142 104
positive 11 51 847
Macro F1 Score: 0.73
```
## Old Performance by Dataset
### Merged Dataset
```
Merged Dataset Classification Report
precision recall f1-score support
negative 0.858503 0.843537 0.850954 2352
neutral 0.747684 0.750137 0.748908 1829
positive 0.864513 0.877395 0.870906 2349
accuracy 0.829556 6530
macro avg 0.823567 0.823690 0.823590 6530
weighted avg 0.829626 0.829556 0.829549 6530
ROC AUC: 0.947247
Predicted negative neutral positive
Actual
negative 1984 256 112
neutral 246 1372 211
positive 81 207 2061
Macro F1 Score: 0.82
```
### DynaSent Round 1
```
DynaSent Round 1 Classification Report
precision recall f1-score support
negative 0.913204 0.824167 0.866404 1200
neutral 0.779433 0.915833 0.842146 1200
positive 0.905149 0.835000 0.868661 1200
accuracy 0.858333 3600
macro avg 0.865929 0.858333 0.859070 3600
weighted avg 0.865929 0.858333 0.859070 3600
ROC AUC: 0.963133
Predicted negative neutral positive
Actual
negative 989 156 55
neutral 51 1099 50
positive 43 155 1002
Macro F1 Score: 0.86
```
### DynaSent Round 2
```
DynaSent Round 2 Classification Report
precision recall f1-score support
negative 0.764706 0.812500 0.787879 240
neutral 0.814815 0.641667 0.717949 240
positive 0.731884 0.841667 0.782946 240
accuracy 0.765278 720
macro avg 0.770468 0.765278 0.762924 720
weighted avg 0.770468 0.765278 0.762924 720
ROC AUC: 0.927688
Predicted negative neutral positive
Actual
negative 195 19 26
neutral 38 154 48
positive 22 16 202
Macro F1 Score: 0.76
```
### Stanford Sentiment Treebank (SST-3)
```
SST-3 Classification Report
precision recall f1-score support
negative 0.822199 0.877193 0.848806 912
neutral 0.504237 0.305913 0.380800 389
positive 0.856144 0.942794 0.897382 909
accuracy 0.803620 2210
macro avg 0.727527 0.708633 0.708996 2210
weighted avg 0.780194 0.803620 0.786409 2210
ROC AUC: 0.904787
Predicted negative neutral positive
Actual
negative 800 81 31
neutral 157 119 113
positive 16 36 857
Macro F1 Score: 0.71
```
## 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)