razhan
/

bart-kurd-spell-base-05

Text2Text Generation

Transformers

PyTorch

bart

Generated from Trainer

Inference Endpoints

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razhan commited on Sep 8, 2023

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1 Parent(s): 96a4911

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README.md +29 -6
pytorch_model.bin +1 -1

README.md CHANGED Viewed

@@ -17,13 +17,13 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 4.2736
 - Rouge1: 0.0
 - Rouge2: 0.0
 - Rougel: 0.0
 - Rougelsum: 0.0
-- Cer: 10.7576
-- Gen Len: 16.9735
 ## Model description
@@ -43,15 +43,38 @@ More information needed
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
-- train_batch_size: 224
-- eval_batch_size: 128
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 3.0
 ### Training results
 ### Framework versions

 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.6353
 - Rouge1: 0.0
 - Rouge2: 0.0
 - Rougel: 0.0
 - Rougelsum: 0.0
+- Cer: 4.7349
+- Gen Len: 13.2035
 ## Model description
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
+- train_batch_size: 320
+- eval_batch_size: 256
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
+- num_epochs: 5.0
 ### Training results
+| Training Loss | Epoch | Step  | Cer     | Gen Len | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
+|:-------------:|:-----:|:-----:|:-------:|:-------:|:---------------:|:------:|:------:|:------:|:---------:|
+| 3.5765        | 0.11  | 500   | 10.3648 | 16.358  | 3.4338          | 0.0    | 0.0    | 0.0    | 0.0       |
+| 3.1749        | 0.22  | 1000  | 10.955  | 15.828  | 3.0110          | 0.0    | 0.0    | 0.0    | 0.0       |
+| 2.8696        | 0.33  | 1500  | 11.2579 | 15.358  | 2.6943          | 0.0    | 0.0    | 0.0    | 0.0       |
+| 2.6279        | 0.43  | 2000  | 11.6734 | 14.8565 | 2.4300          | 0.0    | 0.0    | 0.0    | 0.0       |
+| 2.4166        | 0.54  | 2500  | 11.028  | 14.523  | 2.2248          | 0.0    | 0.0    | 0.0    | 0.0       |
+| 2.2444        | 0.65  | 3000  | 10.4379 | 14.4185 | 2.0490          | 0.0    | 0.0    | 0.0    | 0.0       |
+| 2.0902        | 0.76  | 3500  | 10.0905 | 14.242  | 1.8756          | 0.0    | 0.0    | 0.0    | 0.0       |
+| 1.9565        | 0.87  | 4000  | 9.7629  | 14.042  | 1.7377          | 0.0    | 0.0    | 0.0    | 0.0       |
+| 1.8319        | 0.98  | 4500  | 9.4737  | 13.877  | 1.6244          | 0.0    | 0.0    | 0.0    | 0.0       |
+| 1.7104        | 1.09  | 5000  | 9.153   | 13.825  | 1.5353          | 0.0    | 0.0    | 0.0    | 0.0       |
+| 1.6148        | 1.2   | 5500  | 8.8125  | 13.726  | 1.4422          | 0.0    | 0.0    | 0.0    | 0.0       |
+| 1.5459        | 1.3   | 6000  | 8.5589  | 13.681  | 1.3755          | 0.0    | 0.0    | 0.0    | 0.0       |
+| 1.4837        | 1.41  | 6500  | 8.2717  | 13.6225 | 1.3035          | 0.0    | 0.0    | 0.0    | 0.0       |
+| 1.4219        | 1.52  | 7000  | 8.0684  | 13.549  | 1.2407          | 0.0    | 0.0    | 0.0    | 0.0       |
+| 1.3743        | 1.63  | 7500  | 7.7684  | 13.502  | 1.1865          | 0.0    | 0.0    | 0.0    | 0.0       |
+| 1.3318        | 1.74  | 8000  | 7.5247  | 13.509  | 1.1503          | 0.0    | 0.0    | 0.0    | 0.0       |
+| 1.2893        | 1.85  | 8500  | 7.3826  | 13.456  | 1.1085          | 0.0    | 0.0    | 0.0    | 0.0       |
+| 1.2228        | 2.0   | 9198  | 1.0506  | 0.0     | 0.0             | 0.0    | 0.0    | 7.0411 | 13.3935   |
+| 0.9343        | 3.0   | 13797 | 0.7769  | 0.0     | 0.0             | 0.0    | 0.0    | 5.5303 | 13.2935   |
+| 0.7915        | 4.0   | 18396 | 0.6663  | 0.0     | 0.0             | 0.0    | 0.0    | 4.8928 | 13.209    |
+| 0.7436        | 5.0   | 22995 | 0.6353  | 0.0     | 0.0             | 0.0    | 0.0    | 4.7349 | 13.2035   |
 ### Framework versions

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