Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. --> This model is used to automatically generate title from paragraph.

Model Details

Model Description

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

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

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

Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

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

Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

• As a text summarizer for paragraphs.

Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

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

Bias, Risks, and Limitations

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

Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> The training data is internally curated and canot be exposed.

Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> None

Preprocessing [optional]

None

Training Hyperparameters

• Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
• None

Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

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

<!-- This section describes the evaluation protocols and provides the results. --> 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

<!-- This should link to a Data Card if possible. --> The testing data is internally generated and curated.

Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. --> The model was evaluated on Rouge Metrics below are the baseline results achieved

Results

Summary

Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[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/