metadata
language: en
license: apache-2.0
tags:
- text-classification
- multi-label
- bert
- go_emotions
- emotion-classification
datasets:
- google-research-datasets/go_emotions
metrics:
- f1
- precision
- recall
widget:
- text: I’m just chilling today.
example_title: Neutral Example
- text: Thank you for saving my life!
example_title: Gratitude Example
- text: I’m nervous about my exam tomorrow.
example_title: Nervousness Example
inference:
parameters:
type: text-classification
return_type: list
script: inference.py
GoEmotions BERT Classifier
This is a fine-tuned BERT-base-uncased model for multi-label emotion classification on the GoEmotions dataset, predicting 28 emotions (e.g., admiration, anger, joy, neutral).
Model Details
- Architecture: BERT-base-uncased (110M parameters)
- Training Data: GoEmotions (58k Reddit comments, 28 emotions)
- Loss Function: Focal Loss (gamma=2)
- Optimizer: AdamW (lr=2e-5, weight_decay=0.01)
- Epochs: 5
- Hardware: Kaggle T4 x2 GPUs
Performance
- Micro F1: 0.6025 (optimized thresholds)
- Macro F1: 0.5266
- Precision: 0.5425
- Recall: 0.6775
- Hamming Loss: 0.0372
- Avg Positive Predictions: 1.4564
Class-wise Performance (selected):
- Gratitude: F1 0.9120
- Love: F1 0.8032
- Neutral: F1 0.6827
- Nervousness: F1 0.2564
- Relief: F1 0.2857
Usage
The model uses optimized thresholds stored in thresholds.json for predictions. Example in Python:
from transformers import BertForSequenceClassification, BertTokenizer
import torch
import json
import requests
# Load model and tokenizer
repo_id = "logasanjeev/goemotions-bert"
model = BertForSequenceClassification.from_pretrained(repo_id)
tokenizer = BertTokenizer.from_pretrained(repo_id)
# Load thresholds
thresholds_url = f"https://huggingface.co/{repo_id}/raw/main/thresholds.json"
thresholds_data = json.loads(requests.get(thresholds_url).text)
emotion_labels = thresholds_data["emotion_labels"]
thresholds = thresholds_data["thresholds"]
# Predict
text = "I’m just chilling today."
encodings = tokenizer(text, padding='max_length', truncation=True, max_length=128, return_tensors='pt')
with torch.no_grad():
logits = torch.sigmoid(model(**encodings).logits).numpy()[0]
predictions = [(emotion_labels[i], logit) for i, (logit, thresh) in enumerate(zip(logits, thresholds)) if logit >= thresh]
print(sorted(predictions, key=lambda x: x[1], reverse=True))
# Output: [('neutral', 0.8147)]