End of training

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9337843833185449
+      value: 0.8794544654641443
     - name: Recall
       type: recall
-      value: 0.9418279449602864
+      value: 0.8945072155722117
     - name: F1
       type: f1
-      value: 0.9377889167362852
+      value: 0.8869169763185625
     - name: Accuracy
       type: accuracy
-      value: 0.9845584380510588
+      value: 0.9731996759178356
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0955
-- Precision: 0.9338
-- Recall: 0.9418
-- F1: 0.9378
-- Accuracy: 0.9846
+- Loss: 0.1902
+- Precision: 0.8795
+- Recall: 0.8945
+- F1: 0.8869
+- Accuracy: 0.9732
 
 ## Model description
 
@@ -67,26 +67,22 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 3e-05
+- learning_rate: 2e-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: 8
+- num_epochs: 4
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 439  | 0.0978          | 0.9270    | 0.9286 | 0.9278 | 0.9825   |
-| 0.0068        | 2.0   | 878  | 0.0887          | 0.9255    | 0.9409 | 0.9332 | 0.9838   |
-| 0.0058        | 3.0   | 1317 | 0.0874          | 0.9348    | 0.9401 | 0.9375 | 0.9844   |
-| 0.0045        | 4.0   | 1756 | 0.0927          | 0.9341    | 0.9399 | 0.9370 | 0.9842   |
-| 0.003         | 5.0   | 2195 | 0.0911          | 0.9346    | 0.9417 | 0.9381 | 0.9845   |
-| 0.0021        | 6.0   | 2634 | 0.0927          | 0.9336    | 0.9431 | 0.9383 | 0.9847   |
-| 0.0013        | 7.0   | 3073 | 0.0939          | 0.9321    | 0.9432 | 0.9376 | 0.9845   |
-| 0.0011        | 8.0   | 3512 | 0.0955          | 0.9338    | 0.9418 | 0.9378 | 0.9846   |
+| No log        | 1.0   | 439  | 0.1942          | 0.8818    | 0.8931 | 0.8874 | 0.9732   |
+| 0.0009        | 2.0   | 878  | 0.1902          | 0.8817    | 0.8933 | 0.8875 | 0.9729   |
+| 0.001         | 3.0   | 1317 | 0.1894          | 0.8794    | 0.8952 | 0.8872 | 0.9733   |
+| 0.0009        | 4.0   | 1756 | 0.1902          | 0.8795    | 0.8945 | 0.8869 | 0.9732   |
 
 
 ### Framework versions

model.safetensors

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