Update README.md

README.md

@@ -12,11 +12,11 @@ metrics:
 model-index:
 - name: wav2vec2-base-is_vinyl_scratched_or_not
   results: []
+language:
+- en
+pipeline_tag: audio-classification
 ---
 
-<!-- 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. -->
-
 # wav2vec2-base-is_vinyl_scratched_or_not
 
 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset.
@@ -29,15 +29,17 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This is a binary classifier that predicts whether or not the vinyl record played in the audio sample is scratched.
+
+For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Audio-Projects/Classification/Vinyl%20Scratched%20or%20Not.ipynb
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended to demonstrate my ability to solve a complex problem using technology.
 
 ## Training and evaluation data
 
-More information needed
+Dataset Source: https://www.kaggle.com/datasets/seandaly/detecting-scratch-noise-in-vinyl-playback
 
 ## Training procedure
 
@@ -70,10 +72,9 @@ The following hyperparameters were used during training:
 | 0.0807        | 8.98  | 189  | 0.1088          | 0.9679   | 0.9536 | 0.9576 | 0.9496    |
 | 0.0744        | 9.98  | 210  | 0.1041          | 0.9752   | 0.9638 | 0.9576 | 0.9700    |
 
-
 ### Framework versions
 
 - Transformers 4.26.0
 - Pytorch 1.12.1
 - Datasets 2.8.0
-- Tokenizers 0.12.1
+- Tokenizers 0.12.1