README.md

metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - imdb_urdu_reviews
model-index:
  - name: UrduClassification
    results: []
widget:
  - text: >-
      میں نے یہ فلم دیکھنے کے لئے بہت احتیاط کی تھی، لیکن اس کی کہانی اور
      اداکاری نے میری توقعات کو پورا کیا۔ بالکل شاندار فلم!
    example_title: Positive Example 1
  - text: >-
      اس فلم کی کہانی بہت بے معنی اور بے چارہ ہے۔ میں نے اپنا وقت اور پیسہ برباد
      کر دیا۔ براہ کرم اس سے بچیں!
    example_title: Negative Example 1
  - text: >-
      یہ ناقابل فہم فلم ہے۔ کوئی بھی اسے دیکھ کر توڑ دل ہو جائے گا۔ بلکل بے
      فائدہ!
    example_title: Negative Example 2
  - text: >-
      میں نے ہمیشہ کی طرح اس فلم کو بھی بہت مزہ دیا۔ اداکاری، کہانی، اور
      ڈائریکشن سب بہترین تھی۔ دل کھول کر تصویر دیکھنے کا موقع!
    example_title: Positive Example 2
  - text: >-
      اس فلم میں اتنی بے وقوفی دکھائی گئی ہے کہ آپ بھی اپنے دماغ کو چیک کریں گے۔
      بلکل بکواس!
    example_title: Negative Example 3

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

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

2. 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.

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

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

5. 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.

6. 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.

7. 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