Update README.md

README.md

@@ -14,23 +14,23 @@ pipeline_tag: text2text-generation
 
 # 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.
+This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base).
 It achieves the following results on the evaluation set:
 - Loss: 0.1603
 
 ## Model description
 
-Using the dataset from the following link, I trained a text summarization model.
-
-https://www.kaggle.com/datasets/hadasu92/cnn-articles-after-basic-cleaning
+For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Text%20Summarization/CNN%20News%20Text%20Summarization/CNN%20News%20Text%20Summarization.ipynb
 
 ## Intended uses & limitations
 
-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.
+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. 
+
+Please 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
+Dataset Source: https://www.kaggle.com/datasets/hadasu92/cnn-articles-after-basic-cleaning
 
 ## 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.
@@ -54,7 +54,7 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss |  rouge1  |  rouge2  |   rougeL   | rougeLsum  |
+| 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  |