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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imdb_urdu_reviews |
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model-index: |
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- name: UrduClassification |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# UrduClassification |
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This model is a fine-tuned version of [urduhack/roberta-urdu-small](https://huggingface.co/urduhack/roberta-urdu-small) on the imdb_urdu_reviews dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4703 |
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## Model Details |
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- Model Name: Urdu Sentiment Classification |
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- Model Architecture: RobertaForSequenceClassification |
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- Base Model: urduhack/roberta-urdu-small |
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- Dataset: IMDB Urdu Reviews |
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- Task: Sentiment Classification (Positive/Negative) |
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## Training Procedure |
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1. The model was fine-tuned using the transformers library and the Trainer class from Hugging Face. The training process involved the following steps: |
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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. |
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3. Model Architecture: The "urduhack/roberta-urdu-small" pre-trained model was loaded as the base model for sequence classification using the RobertaForSequenceClassification class. |
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4. Training Arguments: The training arguments were set, including the number of training epochs, batch size, learning rate, evaluation strategy, logging strategy, and more. |
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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. |
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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. |
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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. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.4078 | 1.0 | 2500 | 0.3954 | |
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| 0.2633 | 2.0 | 5000 | 0.4007 | |
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| 0.1205 | 3.0 | 7500 | 0.4703 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.3 |
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