vad – Wilkin Jones

!pip uninstall -y sentence-transformers transformers huggingface-hub
!pip install sentence-transformers==2.2.2 transformers==4.30.2 huggingface-hub==0.16.4
!pip install sentence-transformers scikit-learn pandas numpy tqdm

Found existing installation: sentence-transformers 5.1.2

Uninstalling sentence-transformers-5.1.2:

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Found existing installation: transformers 4.57.1

Uninstalling transformers-4.57.1:

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Found existing installation: huggingface-hub 0.36.0

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Collecting sentence-transformers==2.2.2

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Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers==4.30.2)

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Building wheels for collected packages: sentence-transformers, tokenizers

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ERROR: ERROR: Failed to build installable wheels for some pyproject.toml based projects (tokenizers)

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Installing collected packages: huggingface-hub, transformers, sentence-transformers

Successfully installed huggingface-hub-0.36.0 sentence-transformers-5.1.2 transformers-4.57.1

# SETUP

import pandas as pd
import numpy as np
from tqdm import tqdm
from sentence_transformers import SentenceTransformer
from sklearn.linear_model import RidgeCV
from sklearn.multioutput import MultiOutputRegressor
from sklearn.metrics import mean_absolute_error, r2_score
from sklearn.model_selection import train_test_split
import pickle

# Load Data
df = pd.read_csv("VAD.csv", encoding="utf-8", engine="python", on_bad_lines="skip")

# Standardize column names
df.columns = [c.strip().lower() for c in df.columns]

# Expecting: text, v, a, d
expected_cols = {"text", "v", "a", "d"}
missing = expected_cols - set(df.columns)
if missing:
    raise ValueError(f"Your VAD.csv is missing columns: {missing}")

# Rename into valence / arousal / dominance for clarity
df = df.rename(columns={
    "v": "valence",
    "a": "arousal",
    "d": "dominance"
})

# Drop rows missing text or scores
df = df.dropna(subset=["text", "valence", "arousal", "dominance"])

# Ensure VAD scores are floats
df["valence"] = df["valence"].astype(float)
df["arousal"] = df["arousal"].astype(float)
df["dominance"] = df["dominance"].astype(float)

print(f"Loaded {len(df):,} VAD samples.")
print(df.head(), "\n")

# --- Quick summary ---
print("Dataset Summary")
print(df.describe())

Loaded 310,538 VAD samples.
             id  split emotion_label  valence  arousal  dominance  \
0  a0f805e3-e15    dev       fearful     2.39     6.78       2.54   
1  a415e639-4db   test       neutral     4.09     4.84       5.37   
2  ebf2507e-847  train         angry     2.81     8.63       5.95   
3  360d8c31-6c9  train       disgust     2.68     6.94       3.52   
4  fc3fd738-176   test      surprise     6.95     5.83       5.89   

                                                text  
0  I felt pressured by my own anxiety., and I was...  
1  I simply experienced it and continued., and it...  
2  I reacted harshly because I felt overwhelmed.,...  
3  I felt violated by how disturbing it was., in ...  
4  I tried to make sense of the sudden turn., tha...   

Dataset Summary
             valence        arousal      dominance
count  310538.000000  310538.000000  310538.000000
mean        4.428955       5.556738       5.096671
std         2.304871       1.791122       1.861135
min         1.000000       1.500000       1.500000
25%         2.540000       4.230000       3.540000
50%         3.580000       5.410000       5.230000
75%         6.520000       6.830000       6.560000
max         9.500000       9.500000       9.200000



# Embedding

model_name = "sentence-transformers/all-MiniLM-L6-v2"
embedder = SentenceTransformer(model_name)
data = df
# Encode sentences to fixed-length embeddings

tqdm.pandas()
X = np.vstack(data["text"].progress_apply(lambda x: embedder.encode(str(x), show_progress_bar=False)))
y = data[["valence", "arousal", "dominance"]].values

# Training Split 85/15

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=42)

# Training

reg = MultiOutputRegressor(RidgeCV(alphas=(0.1, 1.0, 10.0)))
reg.fit(X_train, y_train)

# Evaluation

preds = reg.predict(X_test)
mae = mean_absolute_error(y_test, preds, multioutput='raw_values')
r2 = r2_score(y_test, preds, multioutput='raw_values')

print("\nEvaluation Metrics")
print(f"MAE - Valence: {mae[0]:.3f}, Arousal: {mae[1]:.3f}, Dominance: {mae[2]:.3f}")
print(f"R²  - Valence: {r2[0]:.3f}, Arousal: {r2[1]:.3f}, Dominance: {r2[2]:.3f}")

#  SAVE MODEL

with open("vad_regression_model.pkl", "wb") as f:
    pickle.dump({"embedder": embedder, "model": reg}, f)

print("\n Model saved as vad_regression_model.pkl")

#  SAMPLE PREDICTION

sample = "I feel nervous but excited for tomorrow."
vec = embedder.encode(sample)
pred = reg.predict([vec])[0]
print(f"\nUser input: {sample}")
print(f"Predicted VAD → Valence: {pred[0]:.2f}, Arousal: {pred[1]:.2f}, Dominance: {pred[2]:.2f}")

100%|██████████| 310538/310538 [30:28<00:00, 169.85it/s]


Evaluation Metrics
MAE - Valence: 0.868, Arousal: 0.851, Dominance: 0.890
R²  - Valence: 0.773, Arousal: 0.655, Dominance: 0.648

 Model saved as vad_regression_model.pkl

User input: I feel nervous but excited for tomorrow.
Predicted VAD → Valence: 6.42, Arousal: 6.35, Dominance: 5.94

# INTERACTIVE VAD PREDICTOR

import pickle
from sentence_transformers import SentenceTransformer

# Load the trained model (if not already in memory)

with open("vad_regression_model.pkl", "rb") as f:
    bundle = pickle.load(f)

embedder = bundle["embedder"]
model = bundle["model"]

print("VAD model loaded and ready.")

VAD model loaded and ready.

# Create a text input box (Colab magic)

from IPython.display import display
import ipywidgets as widgets
import numpy as np

prompt_box = widgets.Textarea(
    placeholder='Type something to analyze emotion (e.g., "I’m feeling hopeful today.")',
    description='Input:',
    layout=widgets.Layout(width='100%', height='80px')
)
output_box = widgets.Output()

button = widgets.Button(description="Predict VAD", button_style='info')

def cap_scale(pred, low=1.0, high=10.0):
    """Clip model predictions into the desired VAD range."""
    return np.clip(pred, low, high)

def on_button_click(b):
    text = prompt_box.value.strip()
    if not text:
        with output_box:
            output_box.clear_output()
            print("Please enter some text.")
        return

    # Encode and predict
    emb = embedder.encode(text)
    raw_pred = model.predict([emb])[0]

    pred = cap_scale(raw_pred, 1.0, 10.0)

    # Display results
    with output_box:
        output_box.clear_output()
        print(f"Input: {text}\n")
        print(f"🔹 Valence (Pleasantness): {pred[0]:.3f}")
        print(f"🔹 Arousal (Energy):       {pred[1]:.3f}")
        print(f"🔹 Dominance (Control):    {pred[2]:.3f}")

button.on_click(on_button_click)

display(prompt_box, button, output_box)