---
license: apache-2.0
language:
- en
metrics:
- Rouge
pipeline_tag: summarization
tags:
- t5
- t5-small
- summarization
- medical-research
---
# 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

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

| 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]

<!-- 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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **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/