TusharJoshi89/title-generator

Model Card for Model ID

This model is used to automatically generate title from paragraph.

Model Details

Model Description

This is a text generative model to summarize long abstract text jourals into one liners. These one liners can be used as titles in the journal.

• Developed by: Tushar Joshi
• Shared by [optional]: Tushar Joshi
• Model type: t5-small
• Language(s) (NLP): English
• License: Apache 2.0
• Finetuned from model [optional]: t5-small baseline

Model Sources [optional]

• Repository: https://huggingface.co/t5-small

Uses

• As a text summarizer to create titles.
• As a tunable language model for downstream tasks.

Direct Use

• As a text summarizer for paragraphs.

Out-of-Scope Use

Should not be used as a text summarizer for very long paragraphs.

Bias, Risks, and Limitations

• Max input token size of 1024
• Max output token size of 24

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import pipeline
text = """Text that needs to be summarized"""

summarizer = pipeline("summarization", model="path-to-model")
summary = summarizer(text)[0]["summary_text"]

print (summary)

Training Details

Training Data

The training data is internally curated and canot be exposed.

Training Procedure

None

Preprocessing [optional]

None

Training Hyperparameters

• Training regime: [More Information Needed]
• None

Speeds, Sizes, Times [optional]

The training was done using GPU T4x 2. The task took 4:09:47 to complete. The dataset size of 10,000 examples was used for training the generative model.

Evaluation

The quality of summarization was tested on 5000 research journals created over last 20 years.

Testing Data, Factors & Metrics

Test Data Size: 5000 examples

Testing Data

The testing data is internally generated and curated.

Factors

[More Information Needed]

Metrics

The model was evaluated on Rouge Metrics below are the baseline results achieved

Results

Epoch	Training Loss	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
18	2.442800	2.375408	0.313700	0.134600	0.285400	0.285400	16.414100
19	2.454800	2.372553	0.312900	0.134100	0.284900	0.285000	16.445100
20	2.438900	2.372551	0.312300	0.134000	0.284500	0.284600	16.435500

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: GPU T4 x 2
• Hours used: 4.5
• Cloud Provider: GCP
• Compute Region: Ireland
• Carbon Emitted: Unknown

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

Tushar Joshi

Model Card Contact

Tushar Joshi LinkedIn - https://www.linkedin.com/in/tushar-joshi-816133100/