---
library_name: transformers
tags: [summarization, transformers, t5, fine-tuning, custom-dataset, text-generation]
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is a fine-tuned T5 model for text summarization using the SAMSum dataset. The model has been trained using 🤗 Transformers and Hugging Face Trainer with mixed precision (fp16) to optimize memory efficiency.

- **Developed by:** Saravanan K
- **Finetuned from model [optional]:** t5-base

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/SARAVANANVIJAY123/DL-Assessment/blob/main/DL-L%26D%20CODE.ipynb


### Use Cases

### Direct Use

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
This model can be used for text summarization tasks, particularly for summarizing dialogues and conversations.

### Downstream Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
The model can be fine-tuned further on other summarization datasets or used in larger NLP applications requiring summarization capabilities.

## Out Of Scope Use

The model may not perform well on non-dialogue-based text or non-English languages.



## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

Biases: Since it is trained on the SAMSum dataset, it may have biases related to conversational English data.
Limitations: Performance may degrade on texts that are significantly different from the training dataset.



## How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Define model name (same as uploaded one)
model_name = "Saravanankumaran/summarisation_model"

# Load model and tokenizer from Hugging Face Hub
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

print("Model loaded successfully! ✅")

# Below is the example text to use the model

text = """
Laxmi Kant: what work you planning to give Tom?
Juli: i was hoping to send him on a business trip first.
Laxmi Kant: cool. is there any suitable work for him?
Juli: he did excellent in last quarter. i will assign new project, once he is back.
"""
inputs = tokenizer(text, return_tensors="pt")

output = model.generate(**inputs)
summary = tokenizer.decode(output[0], skip_special_tokens=True)

print("Generated Output:", summary)