Update README.md

README.md

@@ -3,34 +3,47 @@ library_name: transformers
 base_model: google/pegasus-xsum
 tags:
 - generated_from_trainer
+- summarization
+- transformers
+- fine-tuned
+- google-pegasus-xsum
+- ccdv/govreport-summarization
+
 model-index:
 - name: a-text-summarizer
   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. -->
-
 # a-text-summarizer
 
-This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on an unknown dataset.
+This model is a fine-tuned version of the google/pegasus-xsum model (https://huggingface.co/google/pegasus-xsum). 
+It has been trained to generate summaries for governmental reports based on the GovReport summarization dataset (https://huggingface.co/datasets/ccdv/govreport-summarization).
 It achieves the following results on the evaluation set:
 - Loss: 2.3989
 
 ## Model description
 
-More information needed
+This is a summarization model fine-tuned on the ccdv/govreport-summarization dataset.
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended for generating concise summaries of governmental reports or similar long-form documents in an official or formal American English register.
+
+The model's performance is limited by the data it was trained on (GovReport summarization dataset). It may not generalize well to other domains or types of text.
+Summarization models can sometimes hallucinate information or produce summaries that are not entirely accurate.
+Potential biases present in the training data may be reflected in the generated summaries. Further analysis is needed to identify and mitigate potential biases.
 
 ## Training and evaluation data
 
-More information needed
+The model was fine-tuned on a subset of the ccdv/govreport-summarization dataset.
+Specifically, a subset of 5000 training examples and 500 validation examples were used for fine-tuning.
+
+The GovReport dataset contains governmental reports and their corresponding summaries.
 
 ## Training procedure
 
+The model was fine-tuned using the Hugging Face transformers library and Trainer API.
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training: