End of training

README.md

@@ -5,36 +5,9 @@ tags:
 - generated_from_trainer
 datasets:
 - ner
-metrics:
-- precision
-- recall
-- f1
-- accuracy
 model-index:
 - name: Bert-NER
-  results:
-  - task:
-      name: Token Classification
-      type: token-classification
-    dataset:
-      name: ner
-      type: ner
-      config: indian_names
-      split: train
-      args: indian_names
-    metrics:
-    - name: Precision
-      type: precision
-      value: 0.9952566491614433
-    - name: Recall
-      type: recall
-      value: 0.9982668388499966
-    - name: F1
-      type: f1
-      value: 0.9967594713357425
-    - name: Accuracy
-      type: accuracy
-      value: 0.9982867445455388
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,12 +16,6 @@ should probably proofread and complete it, then remove this comment. -->
 # Bert-NER
 
 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ner dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0041
-- Precision: 0.9953
-- Recall: 0.9983
-- F1: 0.9968
-- Accuracy: 0.9983
 
 ## Model description
 
@@ -67,28 +34,19 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 5e-05
+- learning_rate: 1e-05
 - train_batch_size: 32
 - eval_batch_size: 32
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 10
+- num_epochs: 1
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 486  | 0.0354          | 0.9822    | 0.9680 | 0.9750 | 0.9875   |
-| 0.04          | 2.0   | 972  | 0.0328          | 0.9846    | 0.9700 | 0.9772 | 0.9886   |
-| 0.0362        | 3.0   | 1458 | 0.0241          | 0.9957    | 0.9719 | 0.9837 | 0.9918   |
-| 0.0302        | 4.0   | 1944 | 0.0163          | 0.9970    | 0.9787 | 0.9877 | 0.9937   |
-| 0.0249        | 5.0   | 2430 | 0.0134          | 0.9914    | 0.9888 | 0.9901 | 0.9949   |
-| 0.0188        | 6.0   | 2916 | 0.0092          | 0.9929    | 0.9934 | 0.9932 | 0.9965   |
-| 0.0145        | 7.0   | 3402 | 0.0070          | 0.9934    | 0.9952 | 0.9943 | 0.9971   |
-| 0.0115        | 8.0   | 3888 | 0.0060          | 0.9941    | 0.9966 | 0.9954 | 0.9976   |
-| 0.009         | 9.0   | 4374 | 0.0048          | 0.9955    | 0.9973 | 0.9964 | 0.9981   |
-| 0.0072        | 10.0  | 4860 | 0.0041          | 0.9953    | 0.9983 | 0.9968 | 0.9983   |
+| No log        | 1.0   | 486  | 0.0032          | 0.9972    | 0.9978 | 0.9975 | 0.9987   |
 
 
 ### Framework versions