README.md

---
language:
- en
license: openrail
tags:
- code
datasets:
- multi_news
metrics:
- rouge
pipeline_tag: summarization
model-index:
- name: TinaLiHF/fined-tuned-T5small
  results:
  - task:
      type: summarization
      name: summarization
    dataset:
      name: multi_news
      type: multi_news
      split: validation
    metrics:
    - type: rouge
      value: 15.28
      name: ROUGE-1
    - type: rouge2
      value: 15.07
      name: ROUGE-2
    - type: rougel
      value: 1.68
      name: ROUGE-L
    - type: rougelsum
      value: 13.46
      name: ROUGE-LSUM
---
---
license: openrail
datasets:
- multi_news
language:
- en


# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->


## Model Details
This is developed for the TLDR project of ANLP.

This is fine-tuned T5 small model with the Multi_news dataset, with adam optimiser.

Aim to summarise long articles into shorten summaries 

### Model Description

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



- **Developed by:** Li, T
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** https://huggingface.co/t5-small

### 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.

[More Information Needed]

## 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]

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

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## 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:** NVIDIA GeForce RTX 3060 Laptop GPU
- **Hours used:** 3:06:45 Hr 
- **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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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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