license: mit
tags:
- generated_from_trainer
datasets:
- imdb_urdu_reviews
model-index:
- name: UrduClassification
results: []
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:
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.
Model Architecture: The "urduhack/roberta-urdu-small" pre-trained model was loaded as the base model for sequence classification using the RobertaForSequenceClassification class.
Training Arguments: The training arguments were set, including the number of training epochs, batch size, learning rate, evaluation strategy, logging strategy, and more.
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.
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.
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
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4078 | 1.0 | 2500 | 0.3954 |
| 0.2633 | 2.0 | 5000 | 0.4007 |
| 0.1205 | 3.0 | 7500 | 0.4703 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3