update model card README.md

README.md

@@ -2,8 +2,6 @@
 license: apache-2.0
 tags:
 - generated_from_trainer
-datasets:
-- conll2003
 metrics:
 - precision
 - recall
@@ -11,27 +9,7 @@ metrics:
 - accuracy
 model-index:
 - name: bert-base-uncased-finetuned-ner
-  results:
-  - task:
-      name: Token Classification
-      type: token-classification
-    dataset:
-      name: conll2003
-      type: conll2003
-      args: conll2003
-    metrics:
-    - name: Precision
-      type: precision
-      value: 0.9412415624654199
-    - name: Recall
-      type: recall
-      value: 0.9515605772457769
-    - name: F1
-      type: f1
-      value: 0.9463729417000445
-    - name: Accuracy
-      type: accuracy
-      value: 0.9869572815225507
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -39,13 +17,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bert-base-uncased-finetuned-ner
 
-This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0605
-- Precision: 0.9412
-- Recall: 0.9516
-- F1: 0.9464
-- Accuracy: 0.9870
+- Loss: 0.0905
+- Precision: 0.9068
+- Recall: 0.9200
+- F1: 0.9133
+- Accuracy: 0.9787
 
 ## Model description
 
@@ -65,8 +43,8 @@ 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
@@ -76,14 +54,14 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0786        | 1.0   | 1756 | 0.0573          | 0.9274    | 0.9341 | 0.9307 | 0.9843   |
-| 0.039         | 2.0   | 3512 | 0.0566          | 0.9370    | 0.9472 | 0.9421 | 0.9863   |
-| 0.0217        | 3.0   | 5268 | 0.0605          | 0.9412    | 0.9516 | 0.9464 | 0.9870   |
+| 0.1266        | 1.0   | 1123 | 0.0952          | 0.8939    | 0.8869 | 0.8904 | 0.9742   |
+| 0.0741        | 2.0   | 2246 | 0.0866          | 0.8936    | 0.9247 | 0.9089 | 0.9774   |
+| 0.0496        | 3.0   | 3369 | 0.0905          | 0.9068    | 0.9200 | 0.9133 | 0.9787   |
 
 
 ### Framework versions
 
-- Transformers 4.14.1
-- Pytorch 1.10.1
-- Datasets 1.17.0
-- Tokenizers 0.10.3
+- Transformers 4.16.2
+- Pytorch 1.10.0+cu111
+- Datasets 1.18.3
+- Tokenizers 0.11.0