---
library_name: transformers
datasets:
- abisee/cnn_dailymail
language:
- en
base_model:
- google-t5/t5-small
pipeline_tag: summarization
---

# 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)
```