update model card README.md

README.md

@@ -1,7 +1,7 @@
 ---
-language:
-- en
 license: apache-2.0
+tags:
+- generated_from_trainer
 datasets:
 - conll2003
 metrics:
@@ -10,7 +10,7 @@ metrics:
 - f1
 - accuracy
 model-index:
-- name: bert-large-uncased
+- name: bert-large-uncased-finetuned-ner
   results:
   - task:
       name: Token Classification
@@ -18,18 +18,18 @@ model-index:
     dataset:
       name: conll2003
       type: conll2003
-      args: default
+      args: conll2003
     metrics:
-    - name: precision
+    - name: Precision
       type: precision
       value: 0.9504719600222099
-    - name: recall
+    - name: Recall
       type: recall
       value: 0.9574896520863632
-    - name: f1
+    - name: F1
       type: f1
       value: 0.9539679001337494
-    - name: accuracy
+    - name: Accuracy
       type: accuracy
       value: 0.9885618059637473
 ---
@@ -37,14 +37,15 @@ model-index:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# bert-large-uncased
+# bert-large-uncased-finetuned-ner
 
 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the conll2003 dataset.
 It achieves the following results on the evaluation set:
-- precision: 0.9505
-- recall: 0.9575
-- f1: 0.9540
-- accuracy: 0.9886
+- Loss: 0.0778
+- Precision: 0.9505
+- Recall: 0.9575
+- F1: 0.9540
+- Accuracy: 0.9886
 
 ## Model description
 
@@ -63,12 +64,29 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- num_train_epochs: 10
-- train_batch_size: 4
 - learning_rate: 2e-05
-- weight_decay_rate: 0.01
-- num_warmup_steps: 0
-- fp16: True
+- train_batch_size: 16
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.1997        | 1.0   | 878  | 0.0576          | 0.9316    | 0.9257 | 0.9286 | 0.9837   |
+| 0.04          | 2.0   | 1756 | 0.0490          | 0.9400    | 0.9513 | 0.9456 | 0.9870   |
+| 0.0199        | 3.0   | 2634 | 0.0557          | 0.9436    | 0.9540 | 0.9488 | 0.9879   |
+| 0.0112        | 4.0   | 3512 | 0.0602          | 0.9443    | 0.9569 | 0.9506 | 0.9881   |
+| 0.0068        | 5.0   | 4390 | 0.0631          | 0.9451    | 0.9589 | 0.9520 | 0.9882   |
+| 0.0044        | 6.0   | 5268 | 0.0638          | 0.9510    | 0.9567 | 0.9538 | 0.9885   |
+| 0.003         | 7.0   | 6146 | 0.0722          | 0.9495    | 0.9560 | 0.9527 | 0.9885   |
+| 0.0016        | 8.0   | 7024 | 0.0762          | 0.9491    | 0.9595 | 0.9543 | 0.9887   |
+| 0.0018        | 9.0   | 7902 | 0.0769          | 0.9496    | 0.9542 | 0.9519 | 0.9883   |
+| 0.0009        | 10.0  | 8780 | 0.0778          | 0.9505    | 0.9575 | 0.9540 | 0.9886   |
+
 
 ### Framework versions