---
library_name: transformers
tags: [text-summarization, pegasus, fine-tuned, NLP]
---

# Model Card for Model ID
Model Card for Fine-Tuned Pegasus Summary Generator
<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
This model is a fine-tuned version of the Pegasus model for text summarization, specifically optimized for generating structured summaries from transcripts. The model has been trained to capture key points, remove redundant information, and maintain coherence in summaries.

- **Developed by:** Akshay Choudhary
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** Transformer-based summarization model
- **Language(s) (NLP):** English
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** google/pegasus-large

### Model Sources [optional]

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

- **Repository:** https://huggingface.co/akshay9125/Transcript_Summerizer/
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## 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. -->

### Direct Use

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

The model can be directly used for transcript summarization in various applications, including:

* Meeting and lecture transcript summarization

* Podcast and interview summarization

* Summarization of long-form text data

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

he model can be fine-tuned further for:

* Domain-specific summarization (e.g., medical, legal, educational transcripts)

* Integration into AI-powered note-taking tool

### Out-of-Scope Use

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

* Generating highly creative or fictional content

* Summarizing extremely noisy or low-quality transcripts

* Generating precise legal or medical documentation without expert verification

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The model may exhibit biases based on:

T* he dataset used for fine-tuning

* The quality and clarity of input transcripts

* Potential loss of nuanced context in summarization

### Recommendations

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

* Validate summaries for critical use cases

* Avoid using the model for tasks requiring absolute accuracy without human verification

* Be aware of potential biases in summarization
## How to Get Started with the Model

Use the code below to get started with the model.

from transformers import PegasusForConditionalGeneration, PegasusTokenizer

tokenizer = PegasusTokenizer.from_pretrained("akshay9125/Transcript_Summerizer")
model = PegasusForConditionalGeneration.from_pretrained("akshay9125/Transcript_Summerizer")

def summarize_text(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="longest")
    summary_ids = model.generate(**inputs)
    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)

## Training Details

### Training Data

<!-- This should link to a Dataset 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. -->

* Dataset: Collected and preprocessed transcript datasets

* Preprocessing: Removal of noise, speaker labels, and unnecessary pauses

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
* Preprocessing: Tokenization with Pegasus tokenizer

* Training regime: FP16 mixed precision

#### Preprocessing [optional]

[More Information Needed]


#### 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 -->

#### Speeds, Sizes, Times [optional]

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

* Model size: ~568M parameters

## Evaluation

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

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### 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. -->

[More Information Needed]

### Results

[More Information Needed]

#### 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:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## 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]

**Akshay Choudhary**

## Model Card Contact

[More Information Needed]