update model card README.md

README.md

@@ -3,7 +3,7 @@ license: apache-2.0
 tags:
 - generated_from_trainer
 datasets:
-- amazon_reviews_multi
+- pubmed-summarization
 metrics:
 - rouge
 model-index:
@@ -13,15 +13,15 @@ model-index:
       name: Sequence-to-sequence Language Modeling
       type: text2text-generation
     dataset:
-      name: amazon_reviews_multi
-      type: amazon_reviews_multi
-      config: en
+      name: pubmed-summarization
+      type: pubmed-summarization
+      config: section
       split: validation
-      args: en
+      args: section
     metrics:
     - name: Rouge1
       type: rouge
-      value: 19.7803
+      value: 14.1074
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -29,13 +29,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # mt5-small-finetuned-amazon-en-es
 
-This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the amazon_reviews_multi dataset.
+This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the pubmed-summarization dataset.
 It achieves the following results on the evaluation set:
-- Loss: 3.3776
-- Rouge1: 19.7803
-- Rouge2: 11.1015
-- Rougel: 19.2549
-- Rougelsum: 19.0799
+- Loss: 2.3381
+- Rouge1: 14.1074
+- Rouge2: 5.3407
+- Rougel: 11.9593
+- Rougelsum: 12.9286
 
 ## Model description
 
@@ -55,33 +55,23 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 5.6e-05
-- train_batch_size: 12
-- eval_batch_size: 12
+- train_batch_size: 4
+- eval_batch_size: 4
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 16
+- num_epochs: 6
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
-|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
-| 2.7004        | 1.0   | 514  | 3.2936          | 17.2056 | 9.3595  | 17.2163 | 16.8991   |
-| 2.6472        | 2.0   | 1028 | 3.2995          | 17.4081 | 9.9623  | 17.3287 | 16.9701   |
-| 2.6173        | 3.0   | 1542 | 3.3039          | 17.9581 | 9.5537  | 17.7729 | 17.6022   |
-| 2.5812        | 4.0   | 2056 | 3.3057          | 17.438  | 8.8331  | 17.1341 | 17.0436   |
-| 2.5446        | 5.0   | 2570 | 3.3316          | 18.6351 | 10.4851 | 18.5086 | 18.2872   |
-| 2.5133        | 6.0   | 3084 | 3.3255          | 19.1331 | 10.3396 | 18.8557 | 18.6551   |
-| 2.4885        | 7.0   | 3598 | 3.3522          | 18.998  | 10.1323 | 18.709  | 18.5662   |
-| 2.464         | 8.0   | 4112 | 3.3314          | 18.8978 | 10.1726 | 18.5683 | 18.3049   |
-| 2.4431        | 9.0   | 4626 | 3.3508          | 18.777  | 10.3018 | 18.5103 | 18.2069   |
-| 2.4092        | 10.0  | 5140 | 3.3520          | 19.3359 | 11.2355 | 19.0265 | 18.7083   |
-| 2.4082        | 11.0  | 5654 | 3.3534          | 19.3633 | 11.2181 | 19.0465 | 18.7726   |
-| 2.3815        | 12.0  | 6168 | 3.3687          | 18.7702 | 10.395  | 18.5383 | 18.2271   |
-| 2.3706        | 13.0  | 6682 | 3.3716          | 19.0868 | 10.6534 | 18.791  | 18.5447   |
-| 2.3628        | 14.0  | 7196 | 3.3756          | 19.1222 | 10.9791 | 18.6601 | 18.5131   |
-| 2.3518        | 15.0  | 7710 | 3.3831          | 19.9227 | 11.0903 | 19.4883 | 19.2418   |
-| 2.3497        | 16.0  | 8224 | 3.3776          | 19.7803 | 11.1015 | 19.2549 | 19.0799   |
+| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum |
+|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|
+| 3.0498        | 1.0   | 2500  | 2.4883          | 12.7167 | 5.1639 | 10.969  | 11.902    |
+| 2.8737        | 2.0   | 5000  | 2.4022          | 13.812  | 5.1042 | 11.7056 | 12.6907   |
+| 2.7603        | 3.0   | 7500  | 2.3895          | 13.6588 | 5.1146 | 11.6214 | 12.5331   |
+| 2.6946        | 4.0   | 10000 | 2.3523          | 13.7167 | 5.2024 | 11.669  | 12.5419   |
+| 2.6527        | 5.0   | 12500 | 2.3383          | 14.082  | 5.2787 | 11.9031 | 12.875    |
+| 2.6303        | 6.0   | 15000 | 2.3381          | 14.1074 | 5.3407 | 11.9593 | 12.9286   |
 
 
 ### Framework versions