Update README.md

README.md

@@ -7,32 +7,22 @@ base_model:
 pipeline_tag: token-classification
 ---
 
-# NER Model for Legal Texts
+# MEDNER.DE: Medicinal Product Entity Recognition in German-Specific Contexts
 
-Released in January 2024, this is a Turkish BERT language model pretrained from scratch on an **optimized BERT architecture** using a 2 GB Turkish legal corpus. The corpus was sourced from legal-related thesis documents available in the Higher Education Board National Thesis Center (YÖKTEZ). The model has been fine-tuned for Named Entity Recognition (NER) tasks on human-annotated datasets provided by **NewMind**, a legal tech company in Istanbul, Turkey.
+Released in December 2024, this is a German BERT language model further pretrained on `deepset/gbert-base` using a pharmacovigilance-related Case Summary Corpus (GS-Corpus).** The model has been fine-tuned for Named Entity Recognition (NER) tasks on an automatically annotated dataset to recognize medicinal products such as medications and vaccines.  
+In our paper, we outline the steps taken to train this model and demonstrate its superior performance compared to previous approaches
 
-In our paper, we outline the steps taken to train this model and demonstrate its superior performance compared to previous approaches.
 
 ---
 
 ## Overview
-- **Preprint Paper**: [https://arxiv.org/abs/2407.00648](https://arxiv.org/abs/2407.00648)
-- **Architecture**: Optimized BERT Base
-- **Language**: Turkish
-- **Supported Labels**: 
-  - `Person`
-  - `Law`
-  - `Publication`
-  - `Government`
-  - `Corporation`
-  - `Other`
-  - `Project`
-  - `Money`
-  - `Date`
-  - `Location`
-  - `Court`
-
-**Model Name**: LegalLTurk Optimized BERT
+- **Paper**: [https://...
+- **Architecture**: MLM_based BERT Base
+- **Language**: German
+- **Supported Labels**: Medicinal Product
+
+
+**Model Name**: MEDNER.DE
 
 ---
 
@@ -43,10 +33,10 @@ In our paper, we outline the steps taken to train this model and demonstrate its
 from transformers import pipeline
 
 # Load the pipeline
-model = pipeline("ner", model="farnazzeidi/ner-legalturk-bert-model", aggregation_strategy='simple')
+model = pipeline("ner", model="pei-germany/MEDNER-de-fp-gbert", aggregation_strategy='simple')
 
 # Input text
-text = "Burada, Tebligat Kanunu ile VUK düzenlemesi ayrımına dikkat etmek gerekir."
+text="Der Patient bekam den COVID-Impfstoff und nahm danach Aspirin."
 
 # Get predictions
 predictions = model(text)
@@ -61,10 +51,10 @@ import torch
 
 # Load model and tokenizer
 
-tokenizer = AutoTokenizer.from_pretrained("farnazzeidi/ner-legalturk-bert-model")
-model = AutoModelForTokenClassification.from_pretrained("farnazzeidi/ner-legalturk-bert-model")
+tokenizer = AutoTokenizer.from_pretrained("pei-germany/MEDNER-de-fp-gbert")
+model = AutoModelForTokenClassification.from_pretrained("pei-germany/MEDNER-de-fp-gbert")
 
-text = "Burada, Tebligat Kanunu ile VUK düzenlemesi ayrımına dikkat etmek gerekir."
+text="Der Patient bekam den COVID-Impfstoff und nahm danach Aspirin."
 inputs = tokenizer(text, return_tensors="pt")
 outputs = model(**inputs)
 
@@ -82,12 +72,11 @@ print(predictions)
 ```
 ---
 # Authors
-Farnaz Zeidi, Mehmet Fatih Amasyali, Çigdem Erol
+...
+
 
 ---
 
 ## License
-This model is shared under the [CC BY-NC-SA 4.0 License](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en). 
-You are free to use, share, and adapt the model for non-commercial purposes, provided that you give appropriate credit to the authors.
+This model is shared under the [GNU Affero General Public License v3.0 License](https://choosealicense.com/licenses/agpl-3.0/). 
 
-For commercial use, please contact [zeidi.uni@gmail.com].