File size: 4,582 Bytes
2efd830 6de4027 d3f45f9 6de4027 d3f45f9 2cfa0cf d3f45f9 2cfa0cf d3f45f9 6de4027 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 | ---
language: en
license: apache-2.0
base_model: microsoft/deberta-v3-base
tags:
- text-classification
- deberta-v3
datasets:
- ealvaradob/phishing-dataset
- ucberkeley-dlab/measuring-hate-speech
- cardiffnlp/tweet_eval
- lmsys/toxic-chat
- tasksource/jigsaw_toxicity
- KoalaAI/Text-Moderation-Multilingual
---
# Constellation One
An experimental text classification model fine-tuned from Microsoft/DeBERTa-V3 base for [Cockatoo](https://cockatoo.dev/)
This model is licensed under the `Apache-2.0` license.
**Available Labels:**
```json:
"id2label": {
"0": "scam",
"1": "violence",
"2": "harassment",
"3": "hate_speech",
"4": "toxicity",
"5": "obscenity"
}
```
## Performance
Constellation One achieves a near-SOTA levels of performance within its weight class, specifically excelling in detecting scams and harassment.
By default, the model has very high recall values (~0.9) in all categories. After tuning threshold values, recall values will drop to ~0.81, but F1 will increase to ~0.74.
### Evaluation (Untuned Thresholds):
**Thresholds:**
```python
LABEL_THRESHOLDS = {
'scam': 0.5,
'violence': 0.5,
'harassment': 0.5,
'hate_speech': 0.5,
'toxicity': 0.5,
'obscenity': 0.5
}
```
---
### Evaluation (Tuned Thresholds):
**Thresholds:**
```python
LABEL_THRESHOLDS = {
'scam': 0.60,
'violence': 0.73,
'harassment': 0.70,
'hate_speech': 0.80,
'toxicity': 0.75,
'obscenity': 0.85
}
```
---
## Resources:
Training/Inferencing server: https://github.com/DominicTWHV/Cockatoo_ML_Training/
Training Metrics: https://cockatoo.dev/ml-training.html
## Datasets Used | Citations
| Dataset | License | Link |
| --- | --- | --- |
| **Phishing Dataset** | MIT | [Hugging Face](https://huggingface.co/datasets/ealvaradob/phishing-dataset) |
| **Measuring Hate Speech** | CC-BY-4.0 | [Hugging Face](https://huggingface.co/datasets/ucberkeley-dlab/measuring-hate-speech) |
| **Tweet Eval (SemEval-2019)** | [See Citation]* | [Hugging Face](https://huggingface.co/datasets/cardiffnlp/tweet_eval) |
| **Toxic Chat** | CC-BY-NC-4.0 | [Hugging Face](https://huggingface.co/datasets/lmsys/toxic-chat) |
| **Jigsaw Toxicity** | Apache-2.0 | [Hugging Face](https://huggingface.co/datasets/tasksource/jigsaw_toxicity) |
| **Text Moderation Multilingual** | Apache-2.0 | [Hugging Face](https://huggingface.co/datasets/KoalaAI/Text-Moderation-Multilingual) |
---
### Citation: ucberkeley-dlab/measuring-hate-speech
```bibtex
@article{kennedy2020constructing,
title={Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application},
author={Kennedy, Chris J and Bacon, Geoff and Sahn, Alexander and von Vacano, Claudia},
journal={arXiv preprint arXiv:2009.10277},
year={2020}
}
```
### Citation: cardiffnlp/tweet_eval
```bibtex
@inproceedings{basile-etal-2019-semeval,
title = "{S}em{E}val-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter",
author = "Basile, Valerio and Bosco, Cristina and Fersini, Elisabetta and Nozza, Debora and Patti, Viviana and Rangel Pardo, Francisco Manuel and Rosso, Paolo and Sanguinetti, Manuela",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/S19-2007",
doi = "10.18653/v1/S19-2007",
pages = "54--63"
}
```
### Citation: lmsys/toxic-chat
```bibtex
@misc{lin2023toxicchat,
title={ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation},
author={Zi Lin and Zihan Wang and Yongqi Tong and Yangkun Wang and Yuxin Guo and Yujia Wang and Jingbo Shang},
year={2023},
eprint={2310.17389},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Citation: KoalaAI/Text-Moderation-Multilingual
```bibtex
@misc{text-moderation-large,
title={Text-Moderation-Multilingual: A Multilingual Text Moderation Dataset},
author={[KoalaAI]},
year={2025},
note={Aggregated from ifmain's and OpenAI's moderation datasets}
}
``` |