Update README.md

README.md

@@ -1,10 +1,16 @@
 ---
 base_model: google/pegasus-cnn_dailymail
-tags:
-- generated_from_trainer
 model-index:
 - name: pegasus-samsum
   results: []
+datasets:
+- Samsung/samsum
+language:
+- en
+metrics:
+- rouge
+pipeline_tag: summarization
+library_name: transformers
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -12,25 +18,21 @@ should probably proofread and complete it, then remove this comment. -->
 
 # pegasus-samsum
 
-This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on an unknown dataset.
+This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on 
+[SAMSum](https://huggingface.co/datasets/Samsung/samsum) dataset.
 It achieves the following results on the evaluation set:
 - Loss: 1.3839
 
-## Model description
+# Intended uses & limitations
+## Intended uses:
+ * Dialogue summarization (e.g., chat logs, meetings)
+ * Text summarization for conversational datasets
 
-More information needed
+## Limitations:
+ * May struggle with very long conversations or non-dialogue text.
 
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
+# Training procedure
+## Training hyperparameters
 
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
@@ -44,7 +46,7 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_steps: 500
 - num_epochs: 2
 
-### Training results
+## Training results
 
 | Training Loss | Epoch  | Step | Validation Loss |
 |:-------------:|:------:|:----:|:---------------:|
@@ -52,10 +54,51 @@ The following hyperparameters were used during training:
 | 1.4737        | 1.0861 | 1000 | 1.4040          |
 | 1.4735        | 1.6292 | 1500 | 1.3839          |
 
+### Test results
 
+| rouge1 | rouge2  | rougeL | rougeLsum Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 0.427614       | 0.200571 | 0.340648  | 0.340738     |
+
+## How to use
+You can use this model with the transformers library for dialogue summarization. Here's an example in Python:
+
+```python
+from transformers import pipeline
+import torch
+
+device = 0 if torch.cuda.is_available() else -1
+pipe = pipeline("summarization",
+                model="seddiktrk/pegasus-samsum",
+                device=device)
+
+custom_dialogue = """\
+Seddik: Hey, have you tried using PEGASUS for summarization?
+John: Yeah, I just started experimenting with it last week!
+Seddik: It's pretty powerful, especially for abstractive summaries.
+John: I agree! The results are really impressive.
+Seddik: I was thinking of using it for my next project. Want to collaborate?
+John: Absolutely! We could make some awesome improvements together.
+Seddik: Perfect, let's brainstorm ideas this weekend.
+John: Sounds like a plan!
+"""
+
+# Summarize dialogue
+gen_kwargs = {"length_penalty": 0.8, "num_beams":8, "max_length": 128}
+print(pipe(custom_dialogue, **gen_kwargs)[0]["summary_text"])
+```
+Example Output
+```
+John started using PEG for summarization last week. Seddik is thinking of using it for his next project.
+John and Seddik will brainstorm ideas this weekend.
+
+```
+
+
+			
 ### Framework versions
 
 - Transformers 4.44.0
 - Pytorch 2.4.0
 - Datasets 2.21.0
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1