| # | |
| # Model Card for t5_small Summarization Model | |
| ## Model Details | |
| This model is a t5-small for studing Text Summarization. | |
| ## Training Data | |
| The model was trained on the cnn_dailymail dataset. | |
| ## Training Procedure | |
| - **Learning Rate** : 2e-5 | |
| - **Epochs** : 5 | |
| - **Batch Size ** : 4 | |
| ## How to Use | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| tokenizer = AutoTokenizer.from_pretrained("t5-small") | |
| model = AutoModelForSequenceClassification.from_pretrained("t5-small") | |
| input_text = "The movie was fantastic with a gripping storyline!" | |
| inputs = tokenizer.encode(input_text, return_tensors="pt") | |
| outputs = model(inputs) | |
| print(outputs.logits) | |
| ``` | |
| ## Evaluation | |
| - **Accuracy** : i don't know well. | |
| ## Limitations | |
| The model may generate biased or inappropriate content | |
| due to the nature of the training data. | |
| It is recommended to use the model with caution and apply necessary filters. | |
| ## Ethical Considerations | |
| - **Bias**: The model may inherit biases present in the training data. | |
| - **Misuse**: The model can be misused to generate misleading or harmful content. | |