Model Card for t5_small Summarization Model

Model Details

This model is a fine-tuned version of t5_small for abstractive summarization tasks.

Training Data

The model was trained on the CNN/Daily mail dataset.

Training Procedure

Epochs- : 1
Batch Size : 4
Learning Rate : 2e-5
Warmup Steps : 500
Weight Decay : 0.01

How to Use

from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('google-t5/t5-small')
model = AutoModel.from_pretrained('google-t5/t5-small')

input_text =     "(CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories.
    The formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based.
    The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East Jerusalem, since June 13, 2014."
inputs = tokenizer.encode(input_text, return_tensors='pt')

max_chunk_length = 512
for i in range(0, len(inputs), max_chunk_length):
    chunk = inputs[:, i:i+max_chunk_length]
    chunks.append(chunk)

summary = ""
for chunk in chunks:
    chunk_summary = model(tokenizer.decode(chunk[0]),
                                max_new_tokens=150,
                                min_length=10,
                                num_beams=3,
                                do_sample=True,
                                top_p=0.8)[0]['summary_text']
    summary += chunk_summary + " "
print(summary)

Evaluation

Rouge1: 0.33
Rouge2: 0.30
RougeL: 0.33
BLEU1: 60.00
BLEU2: 55.56
BLEU4: 42.86

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.