Update README.md

README.md

@@ -6,14 +6,17 @@ datasets:
 - audiofolder
 metrics:
 - accuracy
+- f1
+- precision
+- recall
 model-index:
 - name: wav2vec2-base-Drum_Kit_Sounds
   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-Drum_Kit_Sounds
 
 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset.
@@ -32,15 +35,17 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This is a multiclass classification of sounds to determine which type of drum is hit in the audio sample. The options are: kick, overheads, snare, and toms.
+
+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/Audio-Drum_Kit_Sounds.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/anubhavchhabra/drum-kit-sound-samples
 
 ## Training procedure
 
@@ -79,4 +84,4 @@ The following hyperparameters were used during training:
 - Transformers 4.25.1
 - Pytorch 1.12.1
 - Datasets 2.8.0
-- Tokenizers 0.12.1
+- Tokenizers 0.12.1