t5-small-custom

Model Card for t5_small Summarization Model

Model Details

• Model Name:T5-Small Summarization Model
• Model TypeText-to-Text Transformer Model
• Language : English
• License : Apache 2.0

Training Data

• Dataset: The model was fine-tuned on the CNN/DailyMail dataset.
• Dataset Details:
  • Contains over 287,000 news articles paired with human-written summaries.
  • The content ranges from global news, events, and articles covering various topics.
• Preprocessing:
  • Text normalization and tokenization were performed using the T5 tokenizer.
  • Input articles were truncated or padded to a maximum length of 512 tokens.
  • Summaries were truncated or padded to a maximum length of 150 tokens.

Training Procedure

• Hyperparameters:
  • Batch Size: 4
  • Learning Rate: 2e-5
  • Number of Epochs: 5
  • Gradient Accumulation Steps: 4

How to Use

Load the fine-tuned model and tokenizer

tokenizer = T5Tokenizer.from_pretrained('path_to_your_model') model = T5ForConditionalGeneration.from_pretrained('path_to_your_model')

Input text

article = "Your news article text goes here."

Evaluation

Limitations

Ethical Considerations