|
--- |
|
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 ...') |
|
``` |
