Update README.md

README.md

@@ -48,15 +48,19 @@ model-index:
 
 # flan-t5-large-stacked-samsum-1024
 
+ <a href="https://colab.research.google.com/gist/pszemraj/a4bf61f593ebda9a8db6dc58839d9de4/brief-demo-flan-t5-stacked-samsum.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
+
 This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the `stacked-summaries/stacked-samsum-1024` dataset.
 
 It achieves the following results on the evaluation set:
-- Loss: 2.1311
-- Rouge1: 58.1114
-- Rouge2: 29.339
-- Rougel: 44.7611
-- Rougelsum: 54.2823
-- Gen Len: 122.364
+- Loss: 2.1846
+- Rouge1: 57.9637
+- Rouge2: 28.7446
+- Rougel: 44.3826
+- Rougelsum: 54.0399
+- Gen Len: 122.77
 
 ## Model description
 
@@ -65,37 +69,38 @@ More information needed
 ## Intended uses & limitations
 
 - max input/output is 1024 tokens
-- this is mostly a test because `samsum` is not exactly the best dataset for general purpose summarization
+- this is mostly a test because `samsum` is not exactly the best dataset for general-purpose summarization
 
 ## Training and evaluation data
 
-More information needed
+See the dataset card linked on this page for info
 
 ## Training procedure
 
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 0.0006
-- train_batch_size: 4
-- eval_batch_size: 2
-- seed: 2760
+- learning_rate: 0.0001
+- train_batch_size: 8
+- eval_batch_size: 4
+- seed: 24915
 - distributed_type: multi-GPU
-- num_devices: 2
 - gradient_accumulation_steps: 32
 - total_train_batch_size: 256
-- total_eval_batch_size: 4
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: cosine
 - lr_scheduler_warmup_ratio: 0.02
-- num_epochs: 2.0
+- num_epochs: 1.0
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
 |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
-| 0.1734        | 1.0   | 115  | 1.8751          | 57.9286 | 29.2743 | 44.7181 | 54.2295   | 122.123 |
-| 0.1098        | 2.0   | 230  | 2.1311          | 58.1114 | 29.339  | 44.7611 | 54.2823   | 122.364 |
+| 0.1195        | 0.17  | 20   | 2.0635          | 57.8829 | 28.7887 | 44.4256 | 54.1299   | 121.8   |
+| 0.1084        | 0.35  | 40   | 2.1178          | 58.0416 | 28.6487 | 44.3905 | 54.1557   | 122.893 |
+| 0.1019        | 0.52  | 60   | 2.1576          | 57.816  | 28.7069 | 44.4242 | 53.9598   | 120.524 |
+| 0.0975        | 0.7   | 80   | 2.1821          | 57.9597 | 28.8178 | 44.4854 | 54.068    | 121.793 |
+| 0.0947        | 0.87  | 100  | 2.1846          | 57.9637 | 28.7446 | 44.3826 | 54.0399   | 122.77  |
 
 
 ### Framework versions
@@ -103,4 +108,4 @@ The following hyperparameters were used during training:
 - Transformers 4.26.0.dev0
 - Pytorch 1.13.0+cu117
 - Datasets 2.6.1
-- Tokenizers 0.13.1
+- Tokenizers 0.13.1