Update README.md

README.md

@@ -1,58 +1,141 @@
----
-tags:
-- generated_from_keras_callback
-model-index:
-- name: bias_identificaiton45
-  results: []
----
+---
+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:
 
-# race color - 0, 
-# socioeconomic - 1,
-# gender - 2,
-# disability - 3,
-# nationality - 4,
-# sexualorientation - 5,
-# physical-appearance - 6,
-# religion - 7,
-# age - 8.
-# Proffesion - 9.
+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
+<!-- # bias_identificaiton45
 
-This model was trained from scratch on an unknown dataset.
-It achieves the following results on the evaluation set:
+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
 
-More information needed
+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
 
-More information needed
+### 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.
+
 
-## Training and evaluation data
+## 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`
 
-More information needed
 
 ## Training procedure
 
-### Training hyperparameters
+- **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
 
-The following hyperparameters were used during training:
-- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
-- training_precision: float32
+## Training hyperparameters
 
-### Training results
+- **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 -->
 
-### Framework versions
+## Training Results
 
-- Transformers 4.39.3
-- TensorFlow 2.15.0
-- Datasets 2.18.0
-- Tokenizers 0.15.2
+- **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)