Summarisation Model

🚀 Model Card for Model ID

This is a fine - tuned T5 model for text summarization, offering efficient summarization solutions for dialogues and conversations.

🚀 Quick Start

Use the code below to start using 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)

✨ Features

• 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.
• It can be used for text summarization tasks, especially for summarizing dialogues and conversations.
• The model can be further fine - tuned on other summarization datasets or used in larger NLP applications requiring summarization capabilities.

📚 Documentation

Model Details

Model Description

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]

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

Use Cases

Direct Use

This model can be used for text summarization tasks, particularly for summarizing dialogues and conversations.

Downstream Use

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

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.