---
library_name: transformers
pipeline_tag: summarization
datasets:
- gopalkalpande/bbc-news-summary
language:
- en
metrics:
- rouge
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
The T5_small_lecture_summarization model is a variant of the T5 (Text-to-Text Transfer Transformer) architecture,
which is designed for summarization tasks


## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** [Hyun Lee]
- **Model type:** [Transformers]
- **Language(s) (NLP):** [English]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [Google/T5]

- **Architecture:** The model is based on the T5 architecture, which employs a transformer-based neural network. Transformers have proven effective for various natural language processing (NLP) tasks due to their attention mechanisms and ability to capture contextual information.
- **Task:** The primary purpose of this model is lecture summarization. Given a lecture or a longer text, it aims to generate a concise summary that captures the essential points. This can be valuable for students, researchers, or anyone seeking condensed information.
- **Input Format:** The model expects input in a text-to-text format. Specifically, you provide a prompt (e.g., the lecture content) and specify the desired task (e.g., “summarize”). The model then generates a summary as the output.
- **Fine-Tuning:** The Lucas-Hyun-Lee/T5_small_lecture_summarization model has undergone fine-tuning on bbc-news data. During fine-tuning, it learns to optimize its parameters for summarization by minimizing a loss function.
- **Model Size:** As the name suggests, this is a small-sized variant of T5. Smaller models are computationally efficient and suitable for scenarios where memory or processing power is limited.
- **Performance:** The model’s performance depends on the quality and diversity of the training data, as well as the specific lecture content it encounters during fine-tuning. It should be evaluated based on metrics such as ROUGE (Recall-Oriented Understudy for Gisting Evaluation) scores.

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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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.

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## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### 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]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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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

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**APA:**

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## Glossary [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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