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]
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/
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