---
library_name: peft
base_model: google/mt5-base
license: apache-2.0
language:
- ar
pipeline_tag: summarization
tags:
- summarization
- mt5
- pytorch
- transformers
---

# Mojiz

Mojiz is a finetuned MT5 model for Arabic summarization.


## Model Description

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


## Usage

```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForSeq2SeqLM

config = PeftConfig.from_pretrained("ahmedabdelwahed/sft-base-12-epochs")
model = AutoModelForSeq2SeqLM.from_pretrained("google/mt5-base")
model = PeftModel.from_pretrained(model, "ahmedabdelwahed/sft-base-12-epochs")
```
### Direct Use

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

## 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. -->

[More Information Needed]

### 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 [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 -->


[More Information Needed]

## Evaluation

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


#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]



## Model Examination [optional]

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


## 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. -->


### Framework versions

- PEFT 0.7.1