Update README.md

README.md

@@ -1,3 +1,44 @@
----
-license: apache-2.0
----
+---
+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 ...')
+```