---
license: apache-2.0
language:
- fr
- en
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
library_name: sentence-transformers
---

# Takeda Section Classifier
Pretrained model (finetuned version of [BERT Multilingual Uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased)) on french and english documents using supervised training for sections classification.
This work has been made by Digital Innovation Team from Belgium 🇧🇪 (LE).

## Model Description
The model aims at classifying text in classes representing part of reports:
* Description
* Immediate Correction
* Root Cause
* Action Plan
* Impacted Elements

## Intended uses & limitations
The model can be use for Takeda documentation, the team do not guarantee results for out of the scope documentation.

## How to Use
You can use this model directly with a pipeline for text classification:

```python
from transformers import (
    TextClassificationPipeline,
    AutoTokenizer,
    AutoModelForSequenceClassification,
)
tokenizer = AutoTokenizer.from_pretrained("TakedaAIML/section_classifier")

model = AutoModelForSequenceClassification.from_pretrained(
    "TakedaAIML/section_classifier"
)

pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer)
prediction = pipe('this is a piece of text representing the Description section. An event occur on june 24 and ...')
```