Model Card for Multi‑Label Emotion Classification on Reddit Comments

Repository: https://github.com/amirhossein-yousefi/multi-label-emotion-classification-reddit-comments

Model Details

Model Description

• Developed by: GitHub @amirhossein-yousefi
• Model type: Multi‑label text classification (Transformer encoder)
• Language(s) (NLP): English
• License: No explicit license file was found in the repository; treat as “all rights reserved” unless the author adds a license.
• Finetuned from model : roberta-base

Model Sources

• Repository: https://github.com/amirhossein-yousefi/multi-label-emotion-classification-reddit-comments
• Paper [dataset]: GoEmotions: A Dataset of Fine‑Grained Emotions (Demszky et al., 2020)

Uses

Direct Use

• Tagging short English texts (e.g., social posts, comments) with multiple emotions from the GoEmotions taxonomy (e.g., joy, sadness, anger, admiration, gratitude, etc.).
• Exploratory analytics and visualization of emotion distributions in corpora similar to Reddit.

Downstream Use

• Fine‑tuning or domain adaptation to platforms beyond Reddit (forums, support tickets, app reviews).
• Serving as a baseline component in moderation pipelines or empathetic response systems (with careful human oversight).

Out‑of‑Scope Use

• Medical, psychological, or diagnostic use; mental‑health inference.
• High‑stakes decisions (employment, lending, safety) without rigorous, domain‑specific validation.
• Non‑English or heavily code‑switched text without additional training/testing.

Bias, Risks, and Limitations

• Dataset origin: GoEmotions is built from Reddit comments; models may inherit Reddit‑specific discourse, slang, and toxicity patterns and may underperform on other domains.
• Annotation noise: Third‑party analyses have raised concerns about mislabels in GoEmotions; treat labels as imperfect and consider human review for critical use cases.
• Multi‑label uncertainty: Threshold choice materially affects precision/recall trade‑offs. The repo tunes the threshold on validation data; you should recalibrate for your domain.

Recommendations

• Calibrate thresholds on in‑domain validation data (the repo grid‑searches 0.05–0.95).
• Report per‑label metrics, especially for minority emotions.
• Consider bias audits and human‑in‑the‑loop review before deployment.

How to Get Started with the Model

Environment

• Python ≥ 3.13
• Install dependencies:
  pip install -r requirements.txt

Train

Inference

Training Details

Training Data

• Dataset: GoEmotions (27 emotions + neutral). The default config uses the simplified variant.
• Text column: text
• Labels column: labels
• Max sequence length: 192

Training Procedure

Preprocessing

• Standard Transformer tokenization for roberta-base.
• Multi‑hot label encoding for emotions.

Training Hyperparameters

• Base model: roberta-base
• Batch size: 16 (train), 32 (eval)
• Learning rate: 2e‑5
• Epochs: 5
• Weight decay: 0.01
• Warmup ratio: 0.06
• Gradient accumulation: 1
• Precision: bf16/fp16 if available
• Loss: Binary Cross‑Entropy (optionally focal loss with γ=2.0, α=0.25)
• Threshold tuning: grid 0.05 → 0.95 (step 0.01); best val micro‑F1 ≈ 0.84
• LoRA/PEFT: available in config (default off)

Speeds, Sizes, Times

• See results.txt for an example run’s timing & throughput logs.

Evaluation

Testing Data, Factors & Metrics

• Test split: GoEmotions simplified test.
• Metrics: micro/macro/sample F1, micro/macro Average Precision (AP), micro/macro ROC‑AUC.

Results (example run)

• Threshold (val‑tuned): 0.84
• F1 (micro): 0.5284
• F1 (macro): 0.4995
• F1 (samples): 0.5301
• AP (micro): 0.5352
• AP (macro): 0.5087
• ROC‑AUC (micro): 0.9517
• ROC‑AUC (macro): 0.9310

Model Examination

• Inspect per‑label thresholds and confusion patterns; minority emotions (e.g., grief, pride, nervousness) often suffer lower F1 and need more tuning or class‑balancing strategies.

Environmental Impact

• Not measured. If desired, log GPU type, hours, region, and estimate emissions using the ML CO2 calculator.

Technical Specifications

Model Architecture and Objective

• Transformer encoder (roberta-base) fine‑tuned with a sigmoid multi‑label head and BCE (or focal) loss.

Compute Infrastructure

• Frameworks: transformers, datasets, accelerate, evaluate, scikit-learn, optional peft.
• Hardware/software specifics are user‑dependent.

Citation

Glossary

• AP: Average Precision (area under precision–recall curve).
• AUC: Area under ROC curve.
• Micro/Macro F1: Micro aggregates over all labels; macro averages per‑label F1.

More Information

• The configuration file at configs/base.yaml documents tweakable knobs (loss type, LoRA, precision, etc.).
• Artifacts are saved under outputs/ by default.

Model Card Authors

• Original code: @amirhossein-yousefi
• Model card: generated programmatically for documentation purposes.

Model Card Contact

• Open an issue in the GitHub repository.