Upload toxicthesis-vicuna-distillseq-classification-2 model

README.md

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+---
+license: mit
+tags:
+- toxicity-detection
+- distillseq
+- vicuna
+pipeline_tag: text-classification
+language:
+- en
+library_name: pytorch
+---
+
+# DISTILLSEQ - VICUNA - Classification (2 classes)
+
+Toxicity prediction model trained on the VICUNA dataset.
+
+| Property | Value |
+|----------|-------|
+| Model | DISTILLSEQ |
+| Task | Classification (2 classes) |
+| Dataset | vicuna |
+| Framework | PyTorch / PyTorch Lightning |
+
+
+## Model Information
+
+See the ToxicThesis repository for model class documentation.
+
+
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+
+checkpoint_path = hf_hub_download(
+    repo_id="simocorbo/toxicthesis-vicuna-distillseq-classification-2",
+    filename="checkpoints/best.pt"
+)
+
+checkpoint = torch.load(checkpoint_path, map_location='cpu', weights_only=False)
+print("Checkpoint keys:", checkpoint.keys())
+
+# See ToxicThesis repository for model implementation
+# git clone https://github.com/simo-corbo/ToxicThesis
+```
+
+
+
+## Score Interpretation
+
+| Output | Range | Meaning |
+|--------|-------|---------|
+| `probability` | [0, 1] | Probability of being toxic (class 1). |
+| `class` | 0 or 1 | 0 = non-toxic, 1 = toxic. |
+
+**Decision boundary**: Class 1 if `probability >= 0.5`.
+
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `checkpoints/best.pt` | Model checkpoint (best validation loss) |
+| `hparams.yaml` | Hyperparameters used for training |
+| `train.csv` | Training metrics per epoch |
+| `val.csv` | Validation metrics per epoch |
+| `vocab_stanza_hybrid.pkl` | Vocabulary (for tree-based models) |
+
+## Installation
+
+```bash
+# Clone ToxicThesis for full model implementations
+git clone https://github.com/simo-corbo/ToxicThesis
+cd ToxicThesis
+pip install -r requirements.txt
+
+# Or install dependencies directly
+pip install torch transformers huggingface_hub fasttext-wheel stanza
+```
+
+## Citation
+
+```bibtex
+@software{toxicthesis2025,
+  title={ToxicThesis},
+  author={Corbo, Simone},
+  year={2025},
+  url={https://github.com/simo-corbo/ToxicThesis}
+}
+```

checkpoints/best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a894f94c37d26e7f4a9e4b1cc68b89742159c03ce6843bffe6eb8c6e1ddcfe60
+size 1488879905

hparams.json

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+{
+  "experiment_name": "distillseq_vicuna_classes2_auto_20260618_195550",
+  "model_type": "distillseq",
+  "sut": "vicuna",
+  "num_classes": 2,
+  "device": "cuda",
+  "training_config": {
+    "max_epochs": 10,
+    "early_stopping_patience": 50,
+    "batch_size": 128,
+    "num_workers": 4,
+    "device": "auto",
+    "gradient_clip": 1.0,
+    "checkpoint_dir": "output/checkpoints",
+    "log_dir": "output/logs",
+    "gradient_accumulation_steps": 32,
+    "use_amp": true
+  },
+  "model_config": {
+    "model_name": "roberta-base",
+    "lr": 4e-05,
+    "weight_decay": 0.01,
+    "warmup_steps": 0,
+    "max_length": 800,
+    "gradient_checkpointing": true,
+    "freeze_layers": 0,
+    "dropout": 0.1,
+    "batch_size": 128,
+    "gradient_accumulation_steps": 32,
+    "use_amp": true,
+    "binary_threshold": 0.7,
+    "num_classes": 2,
+    "loss_fn": "bce"
+  }
+}

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test.csv

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train.csv

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val.csv

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