Training complete

README.md

@@ -1,6 +1,6 @@
 ---
 license: apache-2.0
-base_model: bert-base-cased
+base_model: bert-base-uncased
 tags:
 - generated_from_trainer
 datasets:
@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9361032941565965
+      value: 0.9271228359439406
     - name: Recall
       type: recall
-      value: 0.9516997643890945
+      value: 0.9463143722652305
     - name: F1
       type: f1
-      value: 0.9438371025619627
+      value: 0.9366203048221869
     - name: Accuracy
       type: accuracy
-      value: 0.986489668570083
+      value: 0.9868937360001271
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -42,13 +42,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bert-finetuned-ner
 
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0618
-- Precision: 0.9361
-- Recall: 0.9517
-- F1: 0.9438
-- Accuracy: 0.9865
+- Loss: 0.0532
+- Precision: 0.9271
+- Recall: 0.9463
+- F1: 0.9366
+- Accuracy: 0.9869
 
 ## Model description
 
@@ -68,8 +68,8 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
-- train_batch_size: 8
-- eval_batch_size: 8
+- train_batch_size: 16
+- eval_batch_size: 16
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
@@ -79,14 +79,14 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0756        | 1.0   | 1756 | 0.0717          | 0.9003    | 0.9318 | 0.9158 | 0.9798   |
-| 0.0351        | 2.0   | 3512 | 0.0689          | 0.9281    | 0.9456 | 0.9368 | 0.9843   |
-| 0.0244        | 3.0   | 5268 | 0.0618          | 0.9361    | 0.9517 | 0.9438 | 0.9865   |
+| 0.2141        | 1.0   | 878  | 0.0595          | 0.9003    | 0.9305 | 0.9152 | 0.9836   |
+| 0.0449        | 2.0   | 1756 | 0.0529          | 0.9236    | 0.9455 | 0.9344 | 0.9861   |
+| 0.0243        | 3.0   | 2634 | 0.0532          | 0.9271    | 0.9463 | 0.9366 | 0.9869   |
 
 
 ### Framework versions
 
 - Transformers 4.41.2
-- Pytorch 2.3.0+cu121
-- Datasets 2.20.0
+- Pytorch 2.1.2
+- Datasets 2.19.2
 - Tokenizers 0.19.1