update README

README.md

@@ -10,27 +10,24 @@ model-index:
   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. -->
-
 # movie-review-classifier
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.2743
-- F1: 0.9327
+This model classifies (text) movie reviews as either a 1 (*i.e.,* thumbs-up) or a 0 (*i.e.,* a thumbs-down).
 
 ## Model description
 
-More information needed
+This model is a version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) that was fine-tuned on the [IMDB movie-review dataset](https://huggingface.co/datasets/stanfordnlp/imdb).
+It achieves the following results on the evaluation set:
+- Loss: 0.2743
+- F1: 0.9327
 
 ## Intended uses & limitations
 
-More information needed
+Training this model was completed as part of a project from a data science bootcamp. It is intended to be used perhaps by students and/or hobbyists.
 
 ## Training and evaluation data
 
-More information needed
+This model was trained on the [IMDB movie-review dataset](https://huggingface.co/datasets/stanfordnlp/imdb), a set of highly polarized (*i.e.,* clearly positive or negative) movie reviews. The dataset contains 25k labelled train samples, 25k labelled test samples, and 50k unlabelled samples. 
 
 ## Training procedure
 
@@ -44,6 +41,7 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - num_epochs: 3
+- weight_decay: 0.1
 
 ### Training results