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fine-tuned-DatasetQAS-Squad-ID-with-indobert-large-p2-without-ITTL-without-freeze-LR-1e-05

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afaji commited on Mar 19, 2023

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@@ -18,11 +18,11 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.7845
-- Exact Match: 45.6041
-- F1: 62.3356
-- Precision: 63.8471
-- Recall: 67.9879
 ## Model description
@@ -55,15 +55,15 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss | Exact Match | F1      | Precision | Recall  |
 |:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|:---------:|:-------:|
-| 1.7432        | 0.5   | 1024 | 1.8021          | 39.5047     | 56.5465 | 57.6988   | 63.9291 |
-| 1.5898        | 1.0   | 2048 | 1.6635          | 42.4464     | 59.4761 | 60.6303   | 66.7865 |
-| 1.3894        | 1.5   | 3072 | 1.6423          | 43.9588     | 61.1011 | 62.8012   | 67.0315 |
-| 1.3911        | 2.0   | 4096 | 1.6038          | 45.0224     | 62.0873 | 63.6704   | 67.9330 |
-| 1.1414        | 2.5   | 5120 | 1.6397          | 44.6069     | 61.3475 | 62.4666   | 68.0443 |
-| 1.1714        | 3.0   | 6144 | 1.6298          | 45.3881     | 62.8970 | 63.9346   | 69.0708 |
-| 1.0337        | 3.5   | 7168 | 1.7190          | 45.8368     | 62.3645 | 63.5357   | 68.6232 |
-| 0.9957        | 4.0   | 8192 | 1.6851          | 45.8617     | 62.5615 | 64.1871   | 68.2415 |
-| 0.9075        | 4.5   | 9216 | 1.7845          | 45.6041     | 62.3356 | 63.8471   | 67.9879 |
 ### Framework versions

 This model is a fine-tuned version of [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) on the None dataset.
 It achieves the following results on the evaluation set:
+- Loss: 1.7843
+- Exact Match: 45.4462
+- F1: 62.3862
+- Precision: 63.7620
+- Recall: 67.9874
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss | Exact Match | F1      | Precision | Recall  |
 |:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|:---------:|:-------:|
+| 1.7808        | 0.5   | 1024 | 1.8192          | 39.1225     | 56.2210 | 57.3004   | 64.1431 |
+| 1.598         | 1.0   | 2048 | 1.6753          | 42.0808     | 59.2251 | 60.4591   | 66.8518 |
+| 1.4195        | 1.5   | 3072 | 1.6611          | 43.5599     | 60.7862 | 62.5640   | 66.8640 |
+| 1.4262        | 2.0   | 4096 | 1.6420          | 45.0474     | 62.2214 | 63.9056   | 67.8747 |
+| 1.1907        | 2.5   | 5120 | 1.6686          | 44.6319     | 61.3804 | 62.6910   | 68.0916 |
+| 1.2017        | 3.0   | 6144 | 1.6597          | 45.4130     | 62.6561 | 63.9677   | 68.6391 |
+| 1.0831        | 3.5   | 7168 | 1.7486          | 45.3714     | 62.2018 | 63.5154   | 68.2643 |
+| 1.0256        | 4.0   | 8192 | 1.6899          | 45.6955     | 62.5503 | 64.2337   | 67.9408 |
+| 0.9378        | 4.5   | 9216 | 1.7843          | 45.4462     | 62.3862 | 63.7620   | 67.9874 |
 ### Framework versions