Update README.md

README.md

@@ -7,9 +7,6 @@ model-index:
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # bart-base-News_Summarization_CNN
 
 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
@@ -18,17 +15,23 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+Using the dataset from the following link, I trained a text summarization model.
+
+https://www.kaggle.com/datasets/hadasu92/cnn-articles-after-basic-cleaning
 
 ## Intended uses & limitations
 
-More information needed
+I used this to improve my skillset. I thank all of authors of the different technologies and dataset(s) for their contributions that have this possible. I am not too worried about getting credit for my part, but make sure to properly cite the authors of the different technologies and dataset(s) as they absolutely deserve credit for their contributions.
 
 ## Training and evaluation data
 
 More information needed
 
 ## Training procedure
+CPU trained on all samples where the article length is less than 820 words and the summary length is no more than 52 words in length. Additionally, any sample that was missing a new article or summarization was removed. In all, 24,911 out of the possible 42,025 samples were used for training/testing/evaluation.
+
+Here is the link to the code that was used to train this model:
+https://github.com/DunnBC22/NLP_Projects/blob/main/Text%20Summarization/CNN%20News%20Text%20Summarization.ipynb
 
 ### Training hyperparameters
 
@@ -46,11 +49,10 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss |
-|:-------------:|:-----:|:----:|:---------------:|
-| 0.7491        | 1.0   | 1089 | 0.1618          |
-| 0.1641        | 2.0   | 2178 | 0.1603          |
-
+| Training Loss | Epoch | Step | Validation Loss |  rouge1  |  rouge2  |   rougeL   | rougeLsum  |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:----------:|:----------:|
+| 0.7491        | 1.0   | 1089 | 0.1618          |    N/A   |    N/A   |     N/A    |    N/A     |
+| 0.1641        | 2.0   | 2178 | 0.1603          | 0.834343 | 0.793822 |  0.823824  |	0.823778  |
 
 ### Framework versions