Update README.md

README.md

@@ -1,12 +1,22 @@
 ---
 license: apache-2.0
+datasets:
+- mediabiasgroup/BABE
+language:
+- en
+pipeline_tag: text-classification
 ---
 
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-The model is fine-tuned from valurank/distilroberta-bias model for research purpose. 
+This model is designed to detect bias in text data.
+It analyzes text inputs to identify and classify types of biases,
+aiding in the development of more inclusive and fair AI systems.
+The model is fine-tuned from valurank/distilroberta-bias model for research purpose. The model is able to detect bias in formal language since the 
+training corpus is news titles. 
+
 ## Model Details
 
 ### Model Description
@@ -23,73 +33,67 @@ The model is capable of classifying any text into Biased or Non_biased. Max leng
 - **Model type:** DistillRoBERTa transformer
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
-- **Finetuned from model [optional]:** valurank/distilroberta-bias
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** To be uploaded
+- **Finetuned from model:** valurank/distilroberta-bias
+- **Repository:** ***To be uploaded***
 
 ### The following sections are under construction...
 
 
-### Recommendations
+<!--### Recommendations
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+<!--Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
+More information needed for further recommendations. -->
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
+***Link to the github demo page to be included***
+
 [More Information Needed]
 
 ## Training Details
 
-### Training Data
+******
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+Size of the Dataset: 1700 entries
 
-[More Information Needed]
+Preprocessing Steps: Tokenization using a pre-specified tokenizer, padding, and truncation to convert text to numerical features. Classes are encoded numerically.
 
-### Training Procedure
+Data Splitting Strategy: 80% training, 20% validation split, with a random state for reproducibility.
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+Optimization Algorithm: AdamW
 
-#### Preprocessing [optional]
+Loss Function: CrossEntropyLoss, weighted by class frequencies to address class imbalance.
 
-[More Information Needed]
+Learning Rate: 1e-5
 
+Number of Epochs: 3
 
-#### Training Hyperparameters
+Batch Size: 16
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+Regularization Techniques: Gradient clipping is applied with a max norm of 1.0.
 
-#### Speeds, Sizes, Times [optional]
+Model-Specific Hyperparameters: Scheduler with step size of 3 and gamma of 0.1 for learning rate decay.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+Training time: around 150 iterations/s under CUDA pytorch, less than 10 minutes for training.
 
-[More Information Needed]
-
-## Evaluation
+Monitoring Strategies: Training and validation losses and validation accuracy are monitored.
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+Details on the Validation Dataset: Generated from the same DataFrame df using a train-test split.
 
-### Testing Data, Factors & Metrics
+Techniques Used for Fine-tuning: Learning rate scheduler for adjusting the learning rate.
 
-#### Testing Data
+## Challenges and Solutions
 
-<!-- This should link to a Dataset Card if possible. -->
+**Challenges Faced During Training**: Class imbalance is addressed through weighted CrossEntropyLoss.
 
-[More Information Needed]
+**Solutions and Techniques Applied**: Calculation of class weights from the training data and applying gradient clipping.
 
-#### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
-[More Information Needed]
 
 #### Metrics
 
@@ -103,3 +107,6 @@ Use the code below to get started with the model.
 
 #### Summary
 
+
+
+### Model Update Log