Update README.md

README.md

@@ -2,9 +2,20 @@
 license: apache-2.0
 tags:
 - generated_from_keras_callback
+- named entity recognition
+- bert-base finetuned
+- umair akram
 model-index:
 - name: MUmairAB/bert-ner
   results: []
+datasets:
+- conll2003
+language:
+- en
+metrics:
+- seqeval
+library_name: keras
+pipeline_tag: token-classification
 ---
 
 <!-- This model card has been generated automatically according to the information Keras had access to. You should
@@ -12,23 +23,65 @@ probably proofread and complete it, then remove this comment. -->
 
 # MUmairAB/bert-ner
 
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
+The model training notebook is available on my [GitHub Repo](https://github.com/MUmairAB/BERT-based-NER-using-HuggingFace-Transformers/tree/main).
+
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on [Cnoll2003](https://huggingface.co/datasets/conll2003) dataset.
 It achieves the following results on the evaluation set:
 - Train Loss: 0.0003
 - Validation Loss: 0.0880
 - Epoch: 19
 
+
 ## Model description
 
-More information needed
+Model: "tf_bert_for_token_classification"
+_________________________________________________________________
+ Layer (type)                Output Shape              Param #   
+=================================================================
+ bert (TFBertMainLayer)      multiple                  107719680 
+                                                                 
+ dropout_37 (Dropout)        multiple                  0         
+                                                                 
+ classifier (Dense)          multiple                  6921      
+                                                                 
+=================================================================
+Total params: 107,726,601
+Trainable params: 107,726,601
+Non-trainable params: 0
+_________________________________________________________________
 
 ## Intended uses & limitations
 
-More information needed
+This model can be used for named entity recognition tasks. It is trained on [Conll2003](https://huggingface.co/datasets/conll2003) dataset. The model can classify four types of named entities:
+1. persons,
+2. locations,
+3. organizations, and
+4. names of miscellaneous entities that do not belong to the previous three groups.
 
 ## Training and evaluation data
 
-More information needed
+The model is evaluated on [seqeval](https://github.com/chakki-works/seqeval) metric and the result is as follows:
+
+{'LOC': {'precision': 0.9655361050328227,
+  'recall': 0.9608056614044638,
+  'f1': 0.9631650750341064,
+  'number': 1837},
+ 'MISC': {'precision': 0.8789144050104384,
+  'recall': 0.913232104121475,
+  'f1': 0.8957446808510638,
+  'number': 922},
+ 'ORG': {'precision': 0.9075144508670521,
+  'recall': 0.9366144668158091,
+  'f1': 0.9218348623853211,
+  'number': 1341},
+ 'PER': {'precision': 0.962011771000535,
+  'recall': 0.9761129207383279,
+  'f1': 0.9690110482349771,
+  'number': 1842},
+ 'overall_precision': 0.9374068554396423,
+ 'overall_recall': 0.9527095254123191,
+ 'overall_f1': 0.944996244053084,
+ 'overall_accuracy': 0.9864013657502796}
 
 ## Training procedure
 
@@ -69,4 +122,4 @@ The following hyperparameters were used during training:
 - Transformers 4.30.2
 - TensorFlow 2.12.0
 - Datasets 2.13.1
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3