You can deploy this model and start earning money today!

ax3lrod/
praktikum-modul-6-roberta-base

pytorch

roberta

custom_code

Views

No views yet

Multi-Label Emotion Classification Model

This model was trained for multi-label emotion classification with 14 emotion categories.

Model Details

Base model: roberta-base
Maximum sequence length: 90
Best validation F1 score: 0.5065
Training epochs: 3

Emotion Labels

['amusement', 'anger', 'annoyance', 'caring', 'confusion', 'disappointment', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'joy', 'love', 'sadness']

Usage

python

from transformers import AutoTokenizer, AutoModel
import torch
import json

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("ax3lrod/praktikum-modul-6-roberta-base")
model = AutoModel.from_pretrained("ax3lrod/praktikum-modul-6-roberta-base", trust_remote_code=True)

# Load thresholds
with open("best_thresholds.json", "r") as f:
    thresholds = json.load(f)

# Inference
text = "Your text here"
inputs = tokenizer(text, return_tensors="pt", max_length=90, truncation=True, padding=True)
outputs = model(**inputs)
probabilities = torch.sigmoid(outputs.logits)

# Apply thresholds
predictions = []
for i, prob in enumerate(probabilities[0]):
    if prob > thresholds[i]:
        predictions.append(i)