Update README.md

README.md

@@ -32,13 +32,6 @@ The model recreates the work of this [Greek emotion extractor](https://huggingfa
 - **Language(s) (NLP):** English
 - **Finetuned from model [optional]:** [wav2vec2](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english)
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## How to Get Started with the Model
 
@@ -52,88 +45,41 @@ Use the code below to get started with the model.
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-[More Information Needed]
+The RAVDESS dataset was split into training, validation and test sets with 60, 20 and 20 splits, respectively. 
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+The fine-tuning process was centred on four hyper-parameters: 
+- the number of batches (4, 8),
+- gradient accumulation steps (GAS) (2, 4, 6, 8),
+- number of epochs (10, 20) and
+- the learning rate (1e-3, 1e-4, 1e-5).
 
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+Each experiment was repeated 10 times.
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
+The set of hyper-parameters resulting in the best performance is: 4 batches, 4 GAS, 10 epochs and 1e-4 learning rate
 
-#### Testing Data
+## Testing
 
-<!-- This should link to a Dataset Card if possible. -->
+The model was retrained on the combined train and validation sets using the best hyper-parameter set. The performance on the test set has an average Accuracy and F1 scores of 84.84% (SD 2 and 2.08, respectively)
 
-[More Information Needed]
-
-#### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+## Results
 
 [More Information Needed]
 
-#### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-### Compute Infrastructure
-Used Google Colab for training.
-
-#### Software
-
-[More Information Needed]
 
-## Citation [optional]
+<!-- ## Citation [optional] -->
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
-**BibTeX:**
+<!-- **BibTeX:**
 
 [More Information Needed]
 
 **APA:**
 
-[More Information Needed]
+[More Information Needed] -->