Update README.md

correct the evaluation scores.

README.md

@@ -11,6 +11,8 @@ metrics:
 model-index:
 - name: custom_BERT_NER
   results: []
+datasets:
+- jamie613/custom_NER
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -20,29 +22,25 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2578
-- Perf P: 0.8161
-- Perf R: 0.9861
-- Inst P: 0.8923
-- Inst R: 0.9062
-- Comp P: 0.9537
-- Comp R: 0.8655
-- Precision: 0.8495
-- Recall: 0.8432
-- F1: 0.8463
-- Accuracy: 0.9470
+- Loss: 0.207071
+- Perf P: 0.829268
+- Perf R: 0.944444
+- Inst P: 0.933333
+- Inst R: 0.875000
+- Comp P: 0.962617
+- Comp R: 0.865546
+- Precision: 0.862745
+- Recall: 0.846154
+- F1: 0.854369
+- Accuracy: 0.952260
 
 ## Model description
 
-More information needed
-
-## Intended uses & limitations
-
-More information needed
+This model is for identifying performers, instrumentation and composers of the music played in the concert.
 
 ## Training and evaluation data
 
-More information needed
+This model is trained ane evaluated on a custome dataset.
 
 ## Training procedure
 
@@ -56,6 +54,10 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - num_epochs: 50
+- metric_for_best_model = 'eval_f1'
+- greater_is_better = True
+- load_best_model_at_end = True
+- early_stoping_patience = 3
 
 ### Training results
 
@@ -78,4 +80,4 @@ The following hyperparameters were used during training:
 - Transformers 4.40.0
 - Pytorch 2.2.1+cu121
 - Datasets 2.19.0
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1