File size: 2,936 Bytes
b3abb8f
 
 
 
 
 
 
 
 
 
 
 
 
 
72b7ec5
fe5042c
de002d2
fe5042c
de002d2
fe5042c
72b7ec5
fe5042c
72b7ec5
de002d2
 
 
36b2054
de002d2
 
72b7ec5
 
 
 
fe5042c
de002d2
fe5042c
de002d2
fe5042c
de002d2
fe5042c
de002d2
 
 
 
 
 
fe5042c
de002d2
fe5042c
de002d2
fe5042c
72b7ec5
9223eda
325ba48
de002d2
 
7395b10
de002d2
00415df
fe5042c
de002d2
fe5042c
de002d2
fe5042c
de002d2
fe5042c
de002d2
 
fe5042c
de002d2
f750c19
 
fe5042c
de002d2
 
 
 
fe5042c
de002d2
 
 
 
fe5042c
de002d2
 
fe5042c
de002d2
b3abb8f
de002d2
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
---
language:
- fa
metrics:
- bertscore
- rouge
base_model:
- Ahmad/parsT5-base
pipeline_tag: text-generation
tags:
- legal
- Simplification
- text-to-text
---
# Persian Simplification Model (parsT5 Base)

---

## Overview

This model is a fine-tuned ParsT5 (base) version designed explicitly for the Persian Simplification Task. The training data consists of Persian legal texts. The model is trained using supervised fine-tuning and employs the **Unlimiformer Algorithm** to handle large inputs effectively.

- **Architecture**: Ahmad/parsT5-base
- **Language**: Persian
- **Task**: Text Simplification
- **Training Setup**:
  - **Algorithm for reducing computation**: Unlimiformer
  - **Epochs**: 12
  - **Hardware**: NVIDIA GPU 4070
  - **Trainable Blocks**: Last Encoder-Decoder
  - **Optimizer** : AdamW + lr_scheduler
  - **Input max Tokens**: 4096
  - **Output max Tokens**: 512

---

## Readability Scores

The following table summarizes the readability scores for the original texts and the predictions generated by the fine-tuned model:

| Metric         | Original Texts | Predictions |
|----------------|----------------|-------------|
| Gunning Fox    | 14.9676        | **7.5891**  |
| ARI            | 11.8796        | **6.7869**  |
| Dale-Chall     | 2.6473         | **1.2679**  |
| Flesch-Dayani  | 228.2377       | **244.0153**|

---

## Evaluation Results

The fine-tuned model was evaluated using **Rouge** and **BERTScore (mBERT)** metrics. For comparison, the performance of two other Persian LLMs based on LLaMA is also presented:
 

| Prediction Model                              | Rouge1  | Rouge2  | RougeL  | Precision | Recall  | F1      |
|-----------------------------------------------|---------|---------|---------|-----------|---------|---------|
| Fine-Tuned Model                              | **38.08%** | **15.83%** | **19.41%** | **76.75%**  | 71.06% | **73.71%** |
| ViraIntelligentDataMining/PersianLLaMA-13B    | 28.64%  | 9.81%   | 13.67%  | 68.36%    | 73.44%  | 70.80%  |
| MehdiHosseiniMoghadam_AVA_Llama_3_V2          | 30.07%  | 10.33%  | 16.39%  | 68.47%    | **73.47%**  | 70.87%  |

---

## How to Use

You can load and use this model with the Hugging Face library as follows:

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load the model
tokenizer = AutoTokenizer.from_pretrained("Moryjj/FineTuned-parsT5-Simplification")
model = AutoModelForSeq2SeqLM.from_pretrained("Moryjj/FineTuned-parsT5-Simplification")

# Example usage
input_text = "متن پیچیده فارسی"
inputs = tokenizer(input_text, return_tensors="pt", max_length=4096, truncation=True)
outputs = model.generate(**inputs)

# Decode the output
simplified_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(simplified_text)
```

### Contact Information
For inquiries or feedback, please contact:

Author: Mohammadreza Joneidi Jafari

Email: [email protected]