File size: 3,958 Bytes
7356415 e79d18b 7356415 e79d18b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | ---
language: en
license: cc-by-nc-4.0
library_name: transformers
tags:
- moderation
- toxicity
- content-moderation
- safety
- quark
- multi-label-classification
- jigsaw
- hate-speech
- italian-ai
pipeline_tag: text-classification
metrics:
- f1
- macro-f1
base_model: ThingAI/Quark-135m
model_name: Quark-Mod-v0.1
pretty_name: Quark-Mod-v0.1
size_categories: 135M
task_categories:
- text-classification
---
# Quark-Mod-v0.1
<div align="center">
**A 135M parameter content moderation model fine-tuned from Quark-135M**
[](https://things-ai.org)
[](LICENSE)
[](https://python.org)
[](https://huggingface.co/docs/transformers)
</div>
---
## 📋 Model Overview
| Property | Value |
|----------|-------|
| **Full name** | Quark-Mod-v0.1 |
| **Base model** | [Quark-135M](https://huggingface.co/ThingAI/Quark-135m) (pretrained from scratch) |
| **Architecture** | Decoder-only, GQA (9:3), SwiGLU, RoPE, RMSNorm |
| **Parameters** | 135M |
| **Context length** | 2048 tokens |
| **Task** | Multi-label content moderation (9 classes) |
| **Language** | English (v0.1) |
---
## 🎯 Intended Use
This model is designed to **classify toxic and harmful content** across 9 categories. It is intended for:
- ✅ Social media moderation
- ✅ Comment filtering systems
- ✅ Content safety pipelines
- ✅ Research on efficient moderation models
### Limitations
- ⚠️ English only (v0.1)
- ⚠️ May struggle with subtle sarcasm or highly contextual toxicity
- ⚠️ Lower performance on rare classes due to dataset imbalance
- ⚠️ Not recommended for high-stakes decisions without human review
---
## 🏷️ Labels (9 classes)
| Label | Description | Training examples |
|-------|-------------|-------------------|
| `toxic` | General toxic content | 32,263 (19.4%) |
| `severe_toxic` | Severe toxicity | 1,423 (0.9%) |
| `obscene` | Obscene/profane language | 7,567 (4.6%) |
| `threat` | Direct threats | 445 (0.3%) |
| `insult` | Insulting content | 7,065 (4.3%) |
| `identity_hate` | Hate targeting identity | 1,263 (0.8%) |
| `hate_speech` | Explicit hate speech | 1,265 (0.8%) |
| `offensive` | Offensive language | 17,274 (10.4%) |
**Note:** Multi-label classification — multiple classes can be active simultaneously.
---
## 📊 Evaluation Results
**Validation set:** 18,436 examples
| Class | F1 Score |
|-------|----------|
| `toxic` | **0.909** |
| `offensive` | **0.938** |
| `obscene` | **0.796** |
| `insult` | **0.721** |
| `severe_toxic` | **0.498** |
| `identity_hate` | **0.415** |
| `hate_speech` | **0.319** |
| `threat` | **0.372** |
| Metric | Score |
|--------|-------|
| **Macro F1** | **0.552** |
| **Validation Loss** | **0.037** |
---
## 🚀 Usage Example
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# Load model and tokenizer
model_name = "ThingsAI/Quark-Mod-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Labels
labels = ["toxic", "severe_toxic", "obscene", "threat", "insult",
"identity_hate", "hate_speech", "offensive"]
# Predict
def moderate(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=2048)
with torch.no_grad():
outputs = model(**inputs)
predictions = (outputs.logits > 0).int()[0]
detected = [labels[i] for i, v in enumerate(predictions) if v == 1]
return detected if detected else ["clean"]
# Test
print(moderate("I love this community!")) # ['clean']
print(moderate("You are an idiot and should die")) # ['toxic', 'insult']
print(moderate("Nice post, thanks for sharing")) # ['clean'] |