Fine-tunined the t5-small model

This is a text summarization fine-tuned model based on t5-small architecture with cnn_dailymail dataset.

Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("JayasakthiBalaji/Text_Summarization_2e-5")
model = AutoModelForSeq2SeqLM.from_pretrained("JayasakthiBalaji/Text_Summarization_2e-5")

text = "Type your long story for summarization...."
inputs = tokenizer("summarize: " + text, return_tensors="pt", max_length=512, truncation=True)
outputs = model.generate(inputs.input_ids, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
summary = tokenizer.decode(outputs, skip_special_tokens=True)

print(summary)
Downloads last month
93
Safetensors
Model size
60.5M params
Tensor type
F32
·
Inference Providers NEW
Examples

Model tree for JayasakthiBalaji/Text_Summarization_2e-5

Base model

google-t5/t5-small
Finetuned
(1763)
this model

Dataset used to train JayasakthiBalaji/Text_Summarization_2e-5