mahwizzzz / UrduClassification

UrduClassification

This model is a fine-tuned version of urduhack/roberta-urdu-small on the imdb_urdu_reviews dataset. It achieves the following results on the evaluation set:

• Loss: 0.4703

Model Details

• Model Name: Urdu Sentiment Classification
• Model Architecture: RobertaForSequenceClassification
• Base Model: urduhack/roberta-urdu-small
• Dataset: IMDB Urdu Reviews
• Task: Sentiment Classification (Positive/Negative)

Training Procedure

The model was fine-tuned using the transformers library and the Trainer class from Hugging Face. The training process involved the following steps:

1. Tokenization: The input Urdu text was tokenized using the RobertaTokenizerFast from the "urduhack/roberta-urdu-small" pre-trained model. The texts were padded and truncated to a maximum length of 256 tokens.

2. Model Architecture: The "urduhack/roberta-urdu-small" pre-trained model was loaded as the base model for sequence classification using the RobertaForSequenceClassification class.

3. Training Arguments: The training arguments were set, including the number of training epochs, batch size, learning rate, evaluation strategy, logging strategy, and more.

4. Training: The model was trained on the training dataset using the Trainer class. The training process was performed with gradient-based optimization techniques to minimize the cross-entropy loss between predicted and actual sentiment labels.

5. Evaluation: After each epoch, the model was evaluated on the validation dataset to monitor its performance. The evaluation results, including training loss and validation loss, were logged for analysis.

6. Fine-Tuning: The model parameters were fine-tuned during the training process to optimize its performance on the IMDb Urdu movie reviews sentiment analysis task.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 3

Training results

Evaluation Results

The model was evaluated on an undisclosed dataset using a language modeling task. The evaluation results after 3 epochs of fine-tuning are as follows:

• Evaluation Loss: 0.3954
• Evaluation Runtime: 51.60 seconds
• Average Samples per Second: 96.89
• Average Steps per Second: 6.06
• Epoch: 3.0

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.0
• Datasets 2.1.0
• Tokenizers 0.13.3