---
library_name: transformers
datasets:
- jeanflop/post_ocr_correction-512
language:
- fr
- en
base_model:
- google/flan-t5-large
license: apache-2.0
pipeline_tag: text-generation
---

## **Ocr Correcteur v1**

![image/jpeg](https://huggingface.co/jeanflop/ocr_correcteur-v1/resolve/main/an-illustration-of-a-superhero-with-a-smiley-face--R5MceaXPSHOnN5fLITavoQ-zUPTdsBuQKSJyWZmx7sdXw.jpeg)      

This model lora weight has been finetune on french OCR dataset. The architecture used is Flan T large. On a sample of 1000. More stong model is under cooks.

* **Install dependencies**

```bash
!pip install -q transformers accelerate peft diffusers
!pip install -U bitsandbytes
```
* **Load and merge adaptaters in 8Bit** (recommanded)

```python
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer,BitsAndBytesConfig

# Load peft config for pre-trained checkpoint etc.
peft_model_id = "jeanflop/ocr_correcteur-v1"
config = PeftConfig.from_pretrained(peft_model_id)

# load base LLM model and tokenizer
peft_model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path,  load_in_8bit=True,  device_map={"":1})
peft_tokenizer = AutoTokenizer.from_pretrained('google/flan-t5-large')

# Load the Lora model
peft_model = PeftModel.from_pretrained(peft_model, peft_model_id, device_map={"":1})
# model.eval()

print("Peft model loaded")
```
* **Run inference** (recommanded)

Add your text

```python
inputs=f"""
Fix text : {text}"""
```
Run
```python
peft_model.config.max_length=512
peft_tokenizer.model_max_length=512
inputs = peft_tokenizer(inputs, return_tensors="pt")
outputs = peft_model.generate(**inputs,max_length=512)
answer = peft_tokenizer.decode(outputs[0])
from textwrap import fill

print(fill(answer, width=80))
```