Update README.md

README.md

@@ -6,14 +6,17 @@ datasets:
 - audiofolder
 metrics:
 - accuracy
+- f1
+- recall
+- precision
 model-index:
 - name: wav2vec2-base-Toronto_emotional_speech_set
   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-Toronto_emotional_speech_set
 
 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 model classifies the emotion when someone speaks in audio sample.
+
+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/Emotion%20Detection/Toronto%20Emotional%20Speech%20Set%20(TESS)/Toronto%20Emotional%20Speech%20Set%20(TESS).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/ejlok1/toronto-emotional-speech-set-tess
 
 ## Training procedure
 
@@ -84,4 +89,4 @@ The following hyperparameters were used during training:
 - Transformers 4.27.4
 - Pytorch 2.0.0
 - Datasets 2.11.0
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3