Updated model card

README.md

@@ -5,6 +5,9 @@ tags:
 model-index:
 - name: praveenseb/product_review_generator
   results: []
+datasets:
+- amazon_us_reviews
+pipeline_tag: text-generation
 ---
 
 <!-- This model card has been generated automatically according to the information Keras had access to. You should
@@ -12,25 +15,24 @@ probably proofread and complete it, then remove this comment. -->
 
 # praveenseb/product_review_generator
 
-This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Train Loss: 0.6720
-- Epoch: 1
+This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on a sample of [amazon_us_reviews](https://huggingface.co/datasets/amazon_us_reviews) dataset. The sample was drawn from 'Apparel_v1_00' subset.
 
 ## Model description
 
-More information needed
+This model can auto generate review text for apparel products on providing product title, review rating (1-5) and review headline as an input prompt.
 
-## Intended uses & limitations
-
-More information needed
+The input prompt should be in the format <|BOS|>product_title<|SEP|>product_rating<|SEP|>review_title<|SEP|>. For example,
+<|BOS|>Columbia Women's Benton Springs Full-Zip Fleece Jacket<|SEP|>5<|SEP|>Awesome jacket!<|SEP|>
 
-## Training and evaluation data
+## Intended uses & limitations
 
-More information needed
+This model is only intended to demonstrate the text generation capabilities of transformer-based models. Do not use it commercially or for any real-life purpose .
+The model is trained specifically on 'Apparel_v1_00' dataset. So, using non-apparel product titles in the input prompt may yield  inconsistent results. 
 
 ## Training procedure
 
+Code used for training can found in my [GitHub repository](https://github.com/praveenseb/product_review_generator).
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
@@ -50,4 +52,4 @@ The following hyperparameters were used during training:
 - Transformers 4.27.3
 - TensorFlow 2.11.0
 - Datasets 2.10.1
-- Tokenizers 0.13.2
+- Tokenizers 0.13.2