Update README.md

README.md

@@ -16,5 +16,5 @@ tags:
 - lucas
 - speech-emotion-recognition
 ---
-The model is a recreation of [3loi/SER-Odyssey-Baseline-WavLM-Multi-Attributes](https://huggingface.co/3loi/SER-Odyssey-Baseline-WavLM-Multi-Attributes) for direct implementation in torch, with class definition and feed forward method. This model was recreated with the hopes of greater flexibilty of control, training/fine-tuning of model. The model was trained on the same [MSP-Podcast](https://ecs.utdallas.edu/research/researchlabs/msp-lab/MSP-Podcast.html) dataset as the original, but a different smaller subset was used. Thesubset is evenly distributed across gender and emotion category. 
-This model is therefore a multi-attributed based model which predict arousal, dominance and valence. However, unlike the original model, I just kept the original attribute score range of  0...7 (the range the dataset follows). I will provide the
+The model is a recreation of [3loi/SER-Odyssey-Baseline-WavLM-Multi-Attributes](https://huggingface.co/3loi/SER-Odyssey-Baseline-WavLM-Multi-Attributes) for direct implementation in torch, with class definition and feed forward method. This model was recreated with the hopes of greater flexibilty of control, training/fine-tuning of model. The model was trained on the same [MSP-Podcast](https://ecs.utdallas.edu/research/researchlabs/msp-lab/MSP-Podcast.html) dataset as the original, but a different smaller subset was used. The subset is evenly distributed across gender and emotion category with hopes that training would improve accuracy of valence and arousal predictions. 
+This model is therefore a multi-attributed based model which predict arousal, dominance and valence. However, unlike the original model, I just kept the original attribute score range of  0...7 (the range the dataset follows). I will provide the evaluations later on. For now I decided to make this repo so that other people could test out my model and see what they think of the inference accuracy themselves. My best trained weights s of now are provided in this repo. The class definition for the model is can be found in my [github](https://github.com/PhilipAmadasun/SER-Model-for-dimensional-attribute-prediction#).