malexandersalazar

This model is a fine-tuned version of `xlm-roberta-large` designed for binary toxicity classification in multilingual contexts. The model was optimized through over 50 hyperparameter experiments and rigorously benchmarked against strong public baselines. It supports multilingual input, making it ideal for real-world, globally-distributed moderation tasks. Base Model: `FacebookAI/xlm-roberta-large` Task: Sequence classification (Binary) Loss: Cross-entropy with class weights Datasets: Combination of multiple multilingual and toxicity datasets (details below) Training Epochs: 10 (with early stopping) Eval Metric: Best model selected based on weighted precision Optimized Hyperparameters: Learning rate, warmup ratio, weight decay, batch size & gradient accumulation Thanks for the clarification! Here's an updated version that accurately describes your full tuning process across multiple grids and your staged sampling strategy: More than 50 experiments were conducted using an iterative grid refinement strategy. Instead of relying on a single hyperparameter grid, multiple evolving grids were explored over time. The grid shown below represents only the final stage of tuning: Initially, \~10% of the combinations from early-stage grids were sampled. Based on the best and worst performers, both the grid ranges and model parameters were dynamically adjusted. This process continued iteratively until reaching the final grid above, from which a larger sample (around 50% of combinations) was evaluated in-depth. This adaptive tuning process allowed for efficient convergence toward high-performing configurations while reducing computational waste on suboptimal regions of the search space. The following subsets of public datasets were merged for model evaluation: | Dataset | Purpose | Subset Details | | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------ | ------------------------------------------------------------------------------------------------------------- | | ToxiGen - Annotated | Toxic / Non-toxic labels | Used the `'annotated'` subset. Only included samples where `toxicityhuman ≥ 4` (toxic) or `≤ 2` (non-toxic). | | TextDetox Multilingual Toxicity Dataset | Toxic / Non-toxic labels | Included only the `en`, `es`, `de`, and `hi` language splits. | | Depression Detection | Additional non-toxic | Used the `test` split, labeled entirely as non-toxic. | | Toxicity Multilingual Binary Classification Dataset | Real-world distribution | Used the `test` split only, with original binary labels. | | Model | Accuracy | Precision | Recall | F1 | | ------------------------------------------ | ---------- | ---------- | ---------- | ---------- | | `tomh/toxigenroberta` | 0.7982 | 0.4485 | 0.3318 | 0.3815 | | `textdetox/xlmr-large-toxicity-classifier` | 0.7876 | 0.4582 | 0.7260 | 0.5618 | | `This model` | 0.9043 | 0.6656 | 0.9837 | 0.7940 | This benchmark uses only the `test` split of the Toxicity Multilingual Binary Classification Dataset, offering a focused evaluation under multilingual, real-world conditions. | Model | Accuracy | Precision | Recall | F1 | | ------------------------------------------ | ---------- | ---------- | ---------- | ---------- | | `tomh/toxigenroberta` | 0.7075 | 0.9741 | 0.1990 | 0.3305 | | `textdetox/xlmr-large-toxicity-classifier` | 0.8061 | 0.9129 | 0.5148 | 0.6583 | | `This model` | 0.9825 | 0.9778 | 0.9739 | 0.9758 | > 🏆 This model consistently outperformed all benchmarks across accuracy, precision, recall, and F1-score. > 💡 Apply a threshold of 0.85 on the positive class probability for high-precision binary classification. Social media moderation Online community health analysis Real-time chatbot toxicity filtering Research on multilingual hate speech Hugging Face 🤗 for providing the base models and datasets. Researchers behind ToxiGen, and TextDetox. MLflow for experiment tracking. If you use this model in your research or product, please consider citing: