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: albert-base-v2
+- name: albert-base-v2-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: 0.9252213840603477
-    - name: recall
+    - name: Recall
       type: recall
       value: 0.9329732113328189
-    - name: f1
+    - name: F1
       type: f1
       value: 0.9290811285541773
-    - name: accuracy
+    - name: Accuracy
       type: accuracy
       value: 0.9848205157332728
 ---
@@ -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. -->
 
-# albert-base-v2
+# albert-base-v2-finetuned-ner
 
 This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the conll2003 dataset.
 It achieves the following results on the evaluation set:
-- precision: 0.9252
-- recall: 0.9330
-- f1: 0.9291
-- accuracy: 0.9848
+- Loss: 0.0626
+- Precision: 0.9252
+- Recall: 0.9330
+- F1: 0.9291
+- Accuracy: 0.9848
 
 ## Model description
 
@@ -63,12 +64,24 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- num_train_epochs: 5
-- train_batch_size: 16
 - learning_rate: 2e-05
-- weight_decay_rate: 0.01
-- num_warmup_steps: 0
-- fp16: True
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 220  | 0.0863          | 0.8827    | 0.8969 | 0.8898 | 0.9773   |
+| No log        | 2.0   | 440  | 0.0652          | 0.8951    | 0.9199 | 0.9073 | 0.9809   |
+| 0.1243        | 3.0   | 660  | 0.0626          | 0.9191    | 0.9208 | 0.9200 | 0.9827   |
+| 0.1243        | 4.0   | 880  | 0.0585          | 0.9227    | 0.9281 | 0.9254 | 0.9843   |
+| 0.0299        | 5.0   | 1100 | 0.0626          | 0.9252    | 0.9330 | 0.9291 | 0.9848   |
+
 
 ### Framework versions