Update README.md

README.md

@@ -15,8 +15,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 # BARTkrame-abstract
 
-This model is a fine-tuned version of [krm/BARTkrame-abstract](https://huggingface.co/krm/BARTkrame-abstract) on the None dataset.
-It achieves the following results on the evaluation set:
+This model is a fine-tuned version of [krm/BARTkrame-abstract](https://huggingface.co/krm/BARTkrame-abstract) on the [krm/for-ULPGL-Dissertation](https://huggingface.co/datasets/krm/for-ULPGL-Dissertation) dataset.
+It achieves (15/10/2022) the following results on the evaluation set:
 - Loss: 2.4196
 - Rouge1: 0.2703
 - Rouge2: 0.1334
@@ -25,15 +25,18 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This model is primarly a finetuned version of [moussaKam/mbarthez](https://huggingface.co/moussaKam/mbarthez).
 
 ## Intended uses & limitations
 
 More information needed
 
 ## Training and evaluation data
+We have used the [krm/for-ULPGL-Dissertation](https://huggingface.co/datasets/krm/for-ULPGL-Dissertation) dataset reduced to :
 
-More information needed
+> **Training data :** **5000** samples taken at random with *seed=42*.
+
+> **Validation data :** **100** samples taken at random with *seed=42*.
 
 ## Training procedure
 
@@ -44,18 +47,18 @@ The following hyperparameters were used during training:
 - train_batch_size: 4
 - eval_batch_size: 4
 - seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 4
+- num_epochs: 12
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
 |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
-| 0.1316        | 1.0   | 1250 | 2.3251          | 0.2505 | 0.1158 | 0.2150 | 0.2184    |
-| 0.0894        | 2.0   | 2500 | 2.3467          | 0.2526 | 0.1073 | 0.2067 | 0.2124    |
-| 0.045         | 3.0   | 3750 | 2.3742          | 0.2593 | 0.1211 | 0.2281 | 0.2308    |
-| 0.0242        | 4.0   | 5000 | 2.4196          | 0.2703 | 0.1334 | 0.2392 | 0.2419    |
+| 0.1316        | 9.0   | 1250 | 2.3251          | 0.2505 | 0.1158 | 0.2150 | 0.2184    |
+| 0.0894        | 10.0   | 2500 | 2.3467          | 0.2526 | 0.1073 | 0.2067 | 0.2124    |
+| 0.045         | 11.0   | 3750 | 2.3742          | 0.2593 | 0.1211 | 0.2281 | 0.2308    |
+| 0.0242        | 12.0   | 5000 | 2.4196          | 0.2703 | 0.1334 | 0.2392 | 0.2419    |
 
 
 ### Framework versions