End of training

README.md

@@ -1,7 +1,7 @@
 ---
 library_name: transformers
 license: apache-2.0
-base_model: google-t5/t5-small
+base_model: google-t5/t5-large
 tags:
 - generated_from_trainer
 datasets:
@@ -23,7 +23,7 @@ model-index:
     metrics:
     - name: Rouge1
       type: rouge
-      value: 0.1517
+      value: 0.1434
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,13 +31,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # summarise_cy
 
-This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the generator dataset.
+This model is a fine-tuned version of [google-t5/t5-large](https://huggingface.co/google-t5/t5-large) on the generator dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1101
-- Rouge1: 0.1517
-- Rouge2: 0.1282
-- Rougel: 0.1516
-- Rougelsum: 0.1513
+- Loss: nan
+- Rouge1: 0.1434
+- Rouge2: 0.0535
+- Rougel: 0.1286
+- Rougelsum: 0.1287
 - Gen Len: 20.0
 
 ## Model description
@@ -58,8 +58,8 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
-- train_batch_size: 8
-- eval_batch_size: 8
+- train_batch_size: 1
+- eval_batch_size: 1
 - seed: 42
 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
@@ -70,16 +70,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
 |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
-| No log        | 1.0   | 52   | 1.0993          | 0.1003 | 0.0664 | 0.0959 | 0.0962    | 20.0    |
-| No log        | 2.0   | 104  | 0.4933          | 0.1315 | 0.1058 | 0.1297 | 0.1294    | 20.0    |
-| No log        | 3.0   | 156  | 0.2759          | 0.1467 | 0.1239 | 0.1459 | 0.1458    | 20.0    |
-| No log        | 4.0   | 208  | 0.1963          | 0.1497 | 0.126  | 0.1496 | 0.1498    | 20.0    |
-| No log        | 5.0   | 260  | 0.1583          | 0.1492 | 0.1256 | 0.1491 | 0.1494    | 20.0    |
-| No log        | 6.0   | 312  | 0.1369          | 0.1517 | 0.1282 | 0.1516 | 0.1513    | 20.0    |
-| No log        | 7.0   | 364  | 0.1222          | 0.1517 | 0.1282 | 0.1516 | 0.1513    | 20.0    |
-| No log        | 8.0   | 416  | 0.1150          | 0.1517 | 0.1282 | 0.1516 | 0.1513    | 20.0    |
-| No log        | 9.0   | 468  | 0.1114          | 0.1517 | 0.1282 | 0.1516 | 0.1513    | 20.0    |
-| 0.8187        | 10.0  | 520  | 0.1101          | 0.1517 | 0.1282 | 0.1516 | 0.1513    | 20.0    |
+| No log        | 1.0   | 410  | nan             | 0.1434 | 0.0535 | 0.1286 | 0.1287    | 20.0    |
+| 0.0           | 2.0   | 820  | nan             | 0.1434 | 0.0535 | 0.1286 | 0.1287    | 20.0    |
+| 0.0           | 3.0   | 1230 | nan             | 0.1434 | 0.0535 | 0.1286 | 0.1287    | 20.0    |
+| 0.0           | 4.0   | 1640 | nan             | 0.1434 | 0.0535 | 0.1286 | 0.1287    | 20.0    |
+| 0.0           | 5.0   | 2050 | nan             | 0.1434 | 0.0535 | 0.1286 | 0.1287    | 20.0    |
+| 0.0           | 6.0   | 2460 | nan             | 0.1434 | 0.0535 | 0.1286 | 0.1287    | 20.0    |
+| 0.0           | 7.0   | 2870 | nan             | 0.1434 | 0.0535 | 0.1286 | 0.1287    | 20.0    |
+| 0.0           | 8.0   | 3280 | nan             | 0.1434 | 0.0535 | 0.1286 | 0.1287    | 20.0    |
+| 0.0           | 9.0   | 3690 | nan             | 0.1434 | 0.0535 | 0.1286 | 0.1287    | 20.0    |
+| 0.0           | 10.0  | 4100 | nan             | 0.1434 | 0.0535 | 0.1286 | 0.1287    | 20.0    |
 
 
 ### Framework versions