Update README.md

README.md

@@ -11,6 +11,8 @@ model-index:
   results: []
 library_name: adapter-transformers
 pipeline_tag: text-classification
+datasets:
+- proj-persona/PersonaHub
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -58,21 +60,17 @@ The evaluation was performed on a separate validation set derived from the IMDB
 
 ### Training Procedure
 
-The IMDB dataset was first loaded and preprocessed using Pandas and Scikit-learn. The dataset, which originally contained columns for movie reviews and sentiments, was transformed to include a numerical label for sentiment, with positive and negative sentiments encoded as 1 and 0 respectively. This processed dataset was then split into three subsets: training (60%), validation (20%), and test (20%), ensuring that the indices were reset for proper formatting and usage.
-
-Next, the datasets were converted into the `DatasetDict` format, which is compatible with the Hugging Face Transformers library. This step facilitated the seamless integration of the data with the tokenization and model training pipeline.
-
-The tokenization process was handled by the `AutoTokenizer` from the Hugging Face library, specifically using the DistilBERT tokenizer. A preprocessing function was defined to tokenize the text data, truncating where necessary to fit the model's input requirements. This function was applied to the entire dataset, transforming the text into tokenized sequences ready for model ingestion.
+### Training Procedure
 
-To handle the variability in sequence lengths, a `DataCollatorWithPadding` was employed. This ensured that during batching, the input sequences were dynamically padded to the same length, making the training process more efficient and standardized.
+The IMDB dataset was loaded and preprocessed to include numerical labels for sentiments (positive and negative). It was then split into training (60%), validation (20%), and test (20%) sets, and the indices were reset for proper formatting.
 
-For the model setup, the `AutoModelForSequenceClassification` was used, initializing it with DistilBERT as the base model. This model was configured for binary classification, with labels mapped to sentiment classes—positive and negative. This setup provided the structural foundation for the sentiment analysis task.
+The data was converted into the `DatasetDict` format compatible with the Hugging Face Transformers library. The `AutoTokenizer` for DistilBERT was used to tokenize the text data, truncating where necessary. A preprocessing function applied tokenization to the entire dataset, preparing it for model training.
 
-Training arguments were meticulously defined, including critical parameters such as learning rate, batch size, number of training epochs, evaluation strategy, and logging steps. These parameters were tailored to optimize the training process, ensuring that the model learned effectively from the data while also being regularly evaluated on the validation set.
+A `DataCollatorWithPadding` was used to handle the variability in sequence lengths during batching, ensuring efficiency and standardization. The `AutoModelForSequenceClassification` with DistilBERT as the base model was set up for binary classification, mapping labels to sentiment classes (positive and negative).
 
-Evaluation metrics were established to assess the model's performance. The metrics included accuracy and F1 score, which are crucial for evaluating classification models. A dedicated function was implemented to compute these metrics, comparing the predicted labels with the true labels to provide a quantitative measure of model performance.
+Training arguments included learning rate, batch size, number of epochs, evaluation strategy, and logging steps, optimized for effective training. Evaluation metrics, including accuracy and F1 score, were defined to assess model performance, with a function to compute these metrics by comparing predictions with true labels.
 
-Finally, the training was conducted using the `Trainer` class from the Transformers library. This class orchestrated the training process, integrating the model, training arguments, tokenized datasets, data collator, and evaluation metrics. The training process was conducted over three epochs, with the model being evaluated at the end of each epoch to track its performance and adjust accordingly. This comprehensive training procedure ensured that the model was fine-tuned effectively for sentiment analysis on the IMDB dataset, achieving high accuracy and F1 scores on the evaluation set.
+The `Trainer` class from the Transformers library was used to conduct the training over three epochs, integrating the model, training arguments, tokenized datasets, data collator, and evaluation metrics. This comprehensive approach ensured effective fine-tuning for sentiment analysis on the IMDB dataset, achieving high accuracy and F1 scores.
 
 ### Training hyperparameters
 
@@ -98,4 +96,4 @@ The following hyperparameters were used during training:
 - Transformers 4.42.4
 - Pytorch 2.3.0+cu121
 - Datasets 2.20.0
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1