Update README.md

README.md

@@ -1,29 +1,42 @@
 ---
 pipeline_tag: summarization
 datasets:
-- samsum
+  - samsum
 language:
-- en
+  - en
 metrics:
-- rouge
+  - rouge
 library_name: transformers
 widget:
-- text: | 
-    John: Hey! I've been thinking about getting a PlayStation 5. Do you think it is worth it? 
-    Dan: Idk man. R u sure ur going to have enough free time to play it? 
-    John: Yeah, that's why I'm not sure if I should buy one or not. I've been working so much lately idk if I'm gonna be able to play it as much as I'd like.
-- text: | 
-    Sarah: Do you think it's a good idea to invest in Bitcoin?
-    Emily: I'm skeptical. The market is very volatile, and you could lose money.
-    Sarah: True. But there's also a high upside, right?
-- text: | 
-    Madison: Hello Lawrence are you through with the article?
-    Lawrence: Not yet sir.
-    Lawrence: But i will be in a few.
-    Madison: Okay. But make it quick.
-    Madison: The piece is needed by today
-    Lawrence: Sure thing
-    Lawrence: I will get back to you once i am through."
+  - text: |
+      Rita: I'm so bloody tired. Falling asleep at work. :-(
+      Tina: I know what you mean.
+      Tina: I keep on nodding off at my keyboard hoping that the boss doesn't notice..
+      Rita: The time just keeps on dragging on and on and on.... 
+      Rita: I keep on looking at the clock and there's still 4 hours of this drudgery to go.
+      Tina: Times like these I really hate my work.
+      Rita: I'm really not cut out for this level of boredom.
+      Tina: Neither am I.
+  - text: |
+      Beatrice: I am in town, shopping. They have nice scarfs in the shop next to the church. Do you want one?
+      Leo: No, thanks
+      Beatrice: But you don't have a scarf.
+      Leo: Because I don't need it.
+      Beatrice: Last winter you had a cold all the time. A scarf could help.
+      Leo: I don't like them.
+      Beatrice: Actually, I don't care. You will get a scarf.
+      Leo: How understanding of you!
+      Beatrice: You were complaining the whole winter that you're going to die. I've had enough.
+      Leo: Eh.
+  - text: |
+      Jack: Cocktails later?
+      May: YES!!!
+      May: You read my mind...
+      Jack: Possibly a little tightly strung today?
+      May: Sigh... without question.
+      Jack: Thought so.
+      May: A little drink will help!
+      Jack: Maybe two!
 
 model-index:
   - name: bart-finetuned-samsum
@@ -37,52 +50,47 @@ model-index:
         metrics:
           - name: Validation ROUGE-1
             type: rouge-1
-            value: 53.8804
+            value: 53.6163
           - name: Validation ROUGE-2
             type: rouge-2
-            value: 29.2329
+            value: 28.914
           - name: Validation ROUGE-L
             type: rougeL
-            value: 44.774
+            value: 44.1443
           - name: Validation ROUGE-L Sum
             type: rougeLsum
-            value: 49.8255
-          - name: Test ROUGE-1
-            type: rouge-1
-            value: 52.8156
-          - name: Test ROUGE-2
-            type: rouge-2
-            value: 28.1259
-          - name: Test ROUGE-L
-            type: rougeL
-            value: 43.7147
-          - name: Test ROUGE-L Sum
-            type: rougeLsum
-            value: 48.5712
+            value: 49.2995
 ---
 
 # Description
 
-This model is a specialized adaptation of the <b>facebook/bart-large-xsum</b>, fine-tuned for enhanced performance on dialogue summarization using the <b>SamSum</b> dataset.
+This model was trained by fine-tuning the [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) model using [these parameters](#training-parameters) and the [samsum dataset](https://huggingface.co/datasets/samsum).
 
 ## Development
-- Kaggle Notebook: [Text Summarization with Large Language Models](https://www.kaggle.com/code/lusfernandotorres/text-summarization-with-large-language-models)
+
+- Jupyter Notebook: [Text Summarization With BART](https://github.com/adedamola26/text-summarization/blob/main/Text_Summarization_with_BART.ipynb)
 
 ## Usage
 
 ```python
 from transformers import pipeline
 
-model = pipeline("summarization", model="luisotorres/bart-finetuned-samsum")
+model = pipeline("summarization", model="adedamolade26/bart-finetuned-samsum")
 
-conversation = '''Sarah: Do you think it's a good idea to invest in Bitcoin?
-    Emily: I'm skeptical. The market is very volatile, and you could lose money.
-    Sarah: True. But there's also a high upside, right?                                     
+conversation = '''Jack: Cocktails later?
+May: YES!!!
+May: You read my mind...
+Jack: Possibly a little tightly strung today?
+May: Sigh... without question.
+Jack: Thought so.
+May: A little drink will help!
+Jack: Maybe two!
 '''
 model(conversation)
 ```
 
 ## Training Parameters
+
 ```python
 evaluation_strategy = "epoch",
 save_strategy = 'epoch',
@@ -101,7 +109,6 @@ fp16=True,
 report_to="none"
 ```
 
-## Reference
-This model is based on the original <b>BART</b> architecture, as detailed in:
+## References
 
-Lewis et al. (2019). BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. [arXiv:1910.13461](https://arxiv.org/abs/1910.13461)
+Model Training process was adapted from Luis Fernando Torres's [Kaggle Notebook](https://www.kaggle.com/code/lusfernandotorres/text-summarization-with-large-language-models): 📝 Text Summarization with Large Language Models