Update README.md

README.md

@@ -64,12 +64,6 @@ This BERT-based multilabel multiclass classifier is designed to predict the genr
 
 ## How to Get Started with the Model
 
-Here's the "How to Get Started with the Model" section for your model card:
-
----
-
-## How to Get Started with the Model
-
 Use the code below to get started with the model.
 
 ```python
@@ -108,100 +102,76 @@ print(f"Predicted genres: {predicted_genres}")
 
 
 
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
+Here's the "Evaluation" section for your model card:
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
-[More Information Needed]
+## Evaluation
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+The evaluation metrics used for this model include precision, recall, and F1-score. These metrics were chosen because they provide a comprehensive view of the model's performance, particularly in a multilabel classification setting where it is important to understand not only how many correct predictions were made but also the balance between precision (accuracy of the positive predictions) and recall (the ability to find all positive instances).
 
 ### Results
 
-[More Information Needed]
-
 #### Summary
 
+Below are the classification reports for the train, validation, and test splits of the dataset.
 
+**Classification Report for Train Split:**
+```
+              precision    recall  f1-score   support
+
+     Action       1.00      1.00      1.00      1655
+      Drama       1.00      1.00      1.00      4109
+     Comedy       1.00      1.00      1.00      2094
+  Animation       1.00      1.00      1.00       669
+      Crime       1.00      1.00      1.00      1284
+
+  micro avg       1.00      1.00      1.00      9811
+  macro avg       1.00      1.00      1.00      9811
+weighted avg       1.00      1.00      1.00      9811
+samples avg       1.00      1.00      1.00      9811
+```
 
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **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]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## 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:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
+**Classification Report for Val Split:**
+```
+              precision    recall  f1-score   support
+
+     Action       0.70      0.73      0.71       220
+      Drama       0.77      0.84      0.80       507
+     Comedy       0.69      0.54      0.61       260
+  Animation       0.59      0.44      0.50        80
+      Crime       0.72      0.66      0.69       165
+
+  micro avg       0.73      0.71      0.72      1232
+  macro avg       0.70      0.64      0.66      1232
+weighted avg       0.72      0.71      0.71      1232
+samples avg       0.75      0.74      0.71      1232
+```
 
-## More Information [optional]
+**Classification Report for Test Split:**
+```
+              precision    recall  f1-score   support
+
+     Action       0.62      0.66      0.64       191
+      Drama       0.80      0.85      0.82       520
+     Comedy       0.69      0.58      0.63       260
+  Animation       0.60      0.49      0.54        78
+      Crime       0.65      0.67      0.66       154
+
+  micro avg       0.72      0.71      0.72      1203
+  macro avg       0.67      0.65      0.66      1203
+weighted avg       0.72      0.71      0.71      1203
+samples avg       0.75      0.75      0.72      1203
+```
 
-[More Information Needed]
+The results indicate that the model performs well on the training data with high precision, recall, and F1-scores across all genres. However, there is a drop in performance on the validation and test splits, highlighting areas where the model could be further improved to generalize better to unseen data.
 
-## Model Card Authors [optional]
+## Model Card Authors 
 
-[More Information Needed]
+Sina Namazi
 
 ## Model Card Contact
 
-[More Information Needed]
+- **Github:** github.com/Sinanmz
+- **Hugging Face:** huggingface.co/Sinanmz