Takeda Section Classifier
Pretrained model (finetuned version of BERT 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:
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 ...')
- Downloads last month
- 104
Inference API (serverless) does not yet support sentence-transformers models for this pipeline type.
Model tree for TakedaAIML/section_classifier
Base model
google-bert/bert-base-uncased