---
tags:
- generated_from_keras_callback
model-index:
- name: bias_identificaiton45
  results: []
datasets:
- PriyaPatel/Bias_identification
metrics:
- accuracy
base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
pipeline_tag: text-classification
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
<!-- 
The dataset includes 10 types of biases, each labeled for easy identification. The biases and their corresponding labels are as follows:

1. **Race/Color** - `0`
2. **Socioeconomic Status** - `1`
3. **Gender** - `2`
4. **Disability** - `3`
5. **Nationality** - `4`
6. **Sexual Orientation** - `5`
7. **Physical Appearance** - `6`
8. **Religion** - `7`
9. **Age** - `8`
10. **Profession** - `9`
 -->

<!-- # bias_identificaiton45

This dataset was compiled to analyze various types of stereotypical biases present in language models. It incorporates data from multiple publicly available datasets, each contributing to the identification of specific bias types.

Link of the dataset: [PriyaPatel/Bias_identification](https://huggingface.co/datasets/PriyaPatel/Bias_identification)

The biases are labeled as follows:

1. **Race/Color** - `0`
2. **Socioeconomic Status** - `1`
3. **Gender** - `2`
4. **Disability** - `3`
5. **Nationality** - `4`
6. **Sexual Orientation** - `5`
7. **Physical Appearance** - `6`
8. **Religion** - `7`
9. **Age** - `8`
10. **Profession** - `9` -->



<!-- ### Framework versions

- Transformers 4.39.3
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2 -->


## Model description

This model is a fine-tuned version of the `cardiffnlp/twitter-roberta-base-sentiment-latest` on a custom dataset for bias identification in large language models. It is trained to classify input text into one of 10 bias categories.


## Intended uses & limitations

### Intended Uses:
- **Bias Detection:** Identifying and categorizing bias types in sentences or text fragments.
- **Research:** Analyzing and understanding biases in natural language processing models.

### Limitations:
- **Domain Specificity:** The model's performance is optimized for detecting biases within the domains represented in the training data.
- **Not for General Sentiment Analysis:** This model is not designed for general sentiment analysis or other NLP tasks.


## Dataset Used for Training

This dataset was compiled to analyze various types of stereotypical biases present in language models. It incorporates data from multiple publicly available datasets, each contributing to the identification of specific bias types.

Link of the dataset: [PriyaPatel/Bias_identification](https://huggingface.co/datasets/PriyaPatel/Bias_identification)

The biases are labeled as follows:

1. **Race/Color** - `0`
2. **Socioeconomic Status** - `1`
3. **Gender** - `2`
4. **Disability** - `3`
5. **Nationality** - `4`
6. **Sexual Orientation** - `5`
7. **Physical Appearance** - `6`
8. **Religion** - `7`
9. **Age** - `8`
10. **Profession** - `9`


## Training procedure

- **Base Model:** `cardiffnlp/twitter-roberta-base-sentiment-latest`
- **Optimizer:** Adam with a learning rate of 0.00001
- **Loss Function:** Sparse Categorical Crossentropy
- **Batch Size:** 20
- **Epochs:** 3

## Training hyperparameters

- **Learning Rate:** 0.00001
- **Optimizer:** Adam
- **Loss Function:** Sparse Categorical Crossentropy
- **Batch Size:** 20
- **Epochs:** 3

<!-- It achieves the following results on the validation dataset:
val_loss = 0.0744
val_accuracy = 0.9825

And the results on the testing dataset:
loss = 0.0715
accuracy = 0.9832 -->

## Training Results

- **Validation Loss:** 0.0744
- **Validation Accuracy:** 0.9825
- **Test Loss:** 0.0715
- **Test Accuracy:** 0.9832

## How to Load the Model

You can load the model using the Hugging Face `transformers` library as follows:

```python
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("PriyaPatel/bias_identificaiton45")
model = AutoModelForSequenceClassification.from_pretrained("PriyaPatel/bias_identificaiton45") 

# Example usage
inputs = tokenizer("Your text here", return_tensors="tf")
outputs = model(**inputs)