Spaces:
Sleeping
Sleeping
AjithKSenthil
commited on
Commit
•
840f6e0
1
Parent(s):
8b16ee5
Upload ChatAttachmentAnalysis.py
Browse files- ChatAttachmentAnalysis.py +16 -1
ChatAttachmentAnalysis.py
CHANGED
@@ -15,7 +15,8 @@ df['embedding'] = df['embedding'].apply(lambda x: [float(num) for num in x.strip
|
|
15 |
|
16 |
# Split the data into features (X) and labels (y)
|
17 |
X = list(df.embedding.values)
|
18 |
-
y = ['avoide', 'avoida', 'avoidb', 'avoidc', 'avoidd', 'anxietye', 'anxietya', 'anxietyb', 'anxietyc', 'anxietyd']
|
|
|
19 |
|
20 |
# Split data into training and testing sets
|
21 |
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
|
@@ -34,3 +35,17 @@ mae = mean_absolute_error(y_test, preds)
|
|
34 |
print(f"Chat transcript embeddings performance: mse={mse:.2f}, mae={mae:.2f}")
|
35 |
|
36 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
15 |
|
16 |
# Split the data into features (X) and labels (y)
|
17 |
X = list(df.embedding.values)
|
18 |
+
y = df[['avoide', 'avoida', 'avoidb', 'avoidc', 'avoidd', 'anxietye', 'anxietya', 'anxietyb', 'anxietyc', 'anxietyd']].values
|
19 |
+
|
20 |
|
21 |
# Split data into training and testing sets
|
22 |
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
|
|
|
35 |
print(f"Chat transcript embeddings performance: mse={mse:.2f}, mae={mae:.2f}")
|
36 |
|
37 |
|
38 |
+
# Mean Squared Error (MSE) is a measure of how close a fitted line is to data points.
|
39 |
+
# In the context of this task, a lower MSE means that our model's predicted attachment scores are closer to the true scores.
|
40 |
+
# An MSE of 1.32 suggests that the average squared difference between the predicted and actual scores is 1.32.
|
41 |
+
# Since our scores are normalized between 0 and 1, this error could be considered relatively high,
|
42 |
+
# meaning the model's predictions are somewhat off from the true values.
|
43 |
+
|
44 |
+
# Mean Absolute Error (MAE) is another measure of error in our predictions.
|
45 |
+
# It's the average absolute difference between the predicted and actual scores.
|
46 |
+
# An MAE of 0.96 suggests that, on average, our predicted attachment scores are off by 0.96 from the true scores.
|
47 |
+
# Considering that our scores are normalized between 0 and 1, this error is also quite high, indicating that
|
48 |
+
# the model's predictions are not very accurate.
|
49 |
+
|
50 |
+
# Both MSE and MAE are loss functions that we want to minimize. Lower values for both indicate better model performance.
|
51 |
+
# In general, the lower these values, the better the model's predictions are.
|