Update README.md

README.md

@@ -36,7 +36,7 @@ To use the model, you need to load it with the Hugging Face Transformers library
 from transformers import pipeline
 
 # Load the model
-nlp = pipeline("ner", model="your-username/ner-darija-darner")
+nlp = pipeline("ner", model="mohannad-tazi/ner-darija-darner")
 
 # Use the model
 text = "محمد كان في الرباط."
@@ -56,6 +56,11 @@ The model is trained on a specific corpus and may not generalize well to all Mor
 Performance may vary depending on text quality and tagging consistency in the dataset.
 
 ---
+model-index:
+  - name: NER_Darija_MAR_FSBM
+    results:
+      - task: 
+          type: ner
 library_name: transformers
 base_model: aubmindlab/bert-base-arabertv02
 datasets:
@@ -70,56 +75,6 @@ pipeline_tag: token-classification
 license: apache-2.0
 ---
 
-# NER Model for Moroccan Dialect (Darija)
-
-This model is fine-tuned for Named Entity Recognition (NER) in Moroccan Arabic (Darija). It recognizes entities such as locations, organizations, and person names in text written in Darija.
-
-## Base Model
-
-This model is fine-tuned from the [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) model, which is optimized for Arabic NLP tasks.
-
-## Dataset
-
-The model is trained on the **DarNERcorp** dataset, a corpus designed for Named Entity Recognition in the Moroccan Arabic dialect.
-
-## Task
-
-The model is designed for the **token-classification** task, specifically Named Entity Recognition (NER).
-
-### NER Tags
-The model recognizes the following tags:
-- **PER**: Person names
-- **LOC**: Locations
-- **ORG**: Organizations
-- **MISC**: Miscellaneous entities
-
-## Evaluation Results
-
-The model achieves the following results on the evaluation dataset:
-- **Precision**: 74.04%
-- **Recall**: 85.16%
-- **F1 Score**: 78.61%
-
-## Intended Use
-
-This model is intended for extracting named entities from Moroccan Arabic (Darija) text. It can be applied to:
-- Social media content
-- News articles
-- Other informal or formal texts in Darija
-
-## How to Use
-
-You can use this model with the Hugging Face Transformers library:
-
-```python
-from transformers import pipeline
-
-# Load the model
-nlp = pipeline("ner", model="ymohannad-tazi/ner-darija-darner")
 
-# Use the model
-text = "محمد كان في الرباط."
-result = nlp(text)
-print(result)