doc: update README.md

- add ROUGE scores
- add qualitative subjective impressions of the summary quality

README.md

@@ -5,6 +5,13 @@ tags:
 model-index:
 - name: pegasus-samsum-nlp-with-transformers-ch06
   results: []
+datasets:
+- samsum
+language:
+- en
+metrics:
+- rouge
+pipeline_tag: summarization
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -12,21 +19,30 @@ should probably proofread and complete it, then remove this comment. -->
 
 # pegasus-samsum-nlp-with-transformers-ch06
 
-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 the [SAMSum](https://huggingface.co/datasets/samsum) dataset.
+
 It achieves the following results on the evaluation set:
 - Loss: 1.4839
 
+It achieves the following ROUGE scores on the test set:
+- rouge1: 0.555556
+- rouge2: 0.230769
+- rougeL: 0.518519
+- rougeLsum: 0.518519
+
+**Quick human evaluation of summarization quality:** the results are generally good, after visual inspection of the summaries generated on test set conversations. However it seems some entities/attributions are incorrect (saw an example where model confuses peoples' roles in multi-person chat)
+
 ## Model description
 
-More information needed
+PEGASUS doc can be found here: [https://huggingface.co/docs/transformers/model_doc/pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)
 
 ## Intended uses & limitations
 
-More information needed
+This model was trained while studying the NLP With Transformers book; it is not intended to be used for any real applications.
 
 ## Training and evaluation data
 
-More information needed
+The finetuning data is the SAMSum dataset only.
 
 ## Training procedure
 
@@ -56,4 +72,4 @@ The following hyperparameters were used during training:
 - Transformers 4.39.3
 - Pytorch 2.1.2
 - Datasets 2.18.0
-- Tokenizers 0.15.2
+- Tokenizers 0.15.2