s-nlp
/

mt0-xl-detox-sdm-full

@@ -1,199 +1,133 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- detoxification
+- text_style_transfer
+license: mit
+datasets:
+- s-nlp/synthdetoxm
+language:
+- de
+- es
+- fr
+- ru
+base_model:
+- bigscience/mt0-xl
+pipeline_tag: text2text-generation
 ---
+# mT0-XL (SynthDetoxM Full)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61ade264f602880813dbe10b/V-_UsUgqXy1BStg2G9SfS.png)
+<!-- Provide a quick summary of what the model is/does. -->
+This a fine-tune of [`bigscience/mt0-xl`](https://huggingface.co/bigscience/mt0-xl) model on multilingual text detoxification dataset [SynthDetoxM](https://huggingface.co/datasets/s-nlp/synthdetoxm) from the NAACL 2025 Main Track paper *SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data Annotators* by Daniil Moskovskiy et al.
+## Usage
+The usage is similar to the
+```python
+from transformers import pipeline
+toxic_text = "Your toxic text goes here."
+pipe = pipeline("text2text-generation", model="s-nlp/mt0-xl-detox-sdm-full")
+pipe(f"Detoxify: {toxic_text}")
+```
 ## Training Details
+The model was fine-tuned for 2 epochs on [`s-nlp/synthdetoxm`](https://huggingface.co/datasets/s-nlp/synthdetoxm) dataset with full precision (FP32) using Adafactor optimizer with `1e-4` learning rate and batch size of `4` with gradient checkpointing enabled. The full training configuration is available below:
+```json
+{
+    "do_train": true,
+    "do_eval": true,
+    "per_device_train_batch_size": 4,
+    "per_device_eval_batch_size": 4,
+    "learning_rate": 1e-4,
+    "weight_decay": 0,
+    "num_train_epochs": 2,
+    "gradient_accumulation_steps": 1,
+    "logging_strategy": "steps",
+    "logging_steps": 1,
+    "save_strategy": "epoch",
+    "save_total_limit": 1,
+    "warmup_steps": 1,
+    "report_to": "wandb",
+    "optim": "adafactor",
+    "lr_scheduler_type": "linear",
+    "predict_with_generate": true,
+    "bf16": false,
+    "gradient_checkpointing": true,
+    "output_dir": "/path/",
+    "seed": 42,
+}
+```
 #### Metrics
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
+We use the multilingual detoxification evaluation setup from [TextDetox 2024 Multilingual Text Detoxification Shared Task](https://pan.webis.de/clef24/pan24-web/text-detoxification.html).
+Specifically, we use the following metrics:
+- **Style Transfer Accuracy** (**STA**) is calculated with a [`textdetox/xlmr-large-toxicity-classifier`](https://huggingface.co/textdetox/xlmr-large-toxicity-classifier).
+- **Text Similarity** (**SIM**) is calculated as a similarity of text embeddings given by a [`sentence-transformers/LaBSE`](https://huggingface.co/sentence-transformers/LaBSE) encoder.
+- **Fluency** (**FL**) is calculated as a character n-gram F score - [$\text{ChrF}_1$](https://github.com/m-popovic/chrF).
+These metrics are aggregated in a final **Joint** metric (**J**):
+$$\textbf{J} = \frac{1}{n}\sum\limits_{i=1}^{n}\textbf{STA}(y_i) \cdot \textbf{SIM}(x_i,y_i) \cdot \textbf{FL}(x_i, y_i)$$,
+### Evaluation Results
+This model was evaluated on the test set of [`textdetox/multilingual_paradetox`](https://huggingface.co/datasets/textdetox/multilingual_paradetox) dataset from [TextDetox 2024 Multilingual Text Detoxification Shared Task](https://pan.webis.de/clef24/pan24-web/text-detoxification.html).
+The results of the evaluation are presented below.
+|                | **German** | **Spanish** | **Russian** |
+|----------------|------------|-------------|-------------|
+| **Human References** | 0.733     | 0.709       | 0.732       |
+| **Baselines**  |            |             |             |
+| Duplicate      | 0.287      | 0.090       | 0.048       |
+| Delete         | 0.362      | 0.319       | 0.255       |
+| Backtranslation| 0.233     | 0.275       | 0.223       |
+| **mT0-XL supervised fine-tuning** | | | |
+| [MultiParaDetox](https://huggingface.co/datasets/textdetox/multilingual_paradetox) | 0.446      | 0.344       | 0.472       |
+| [SynthDetoxM](https://huggingface.co/datasets/s-nlp/synthdetoxm) (Subset AVG)   | 0.460      | 0.402       | 0.475       |
+| [SynthDetoxM](https://huggingface.co/datasets/s-nlp/synthdetoxm) (this model)           | **0.482**  | **0.470**   | **0.546**   |
 #### Software
+Code for replicating the results from the paper can be found on [GitHub](https://github.com/s-nlp/synthdetoxm).
+## Citation
 **BibTeX:**
+```latex
+@misc{moskovskiy2025synthdetoxmmodernllmsfewshot,
+      title={SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data Annotators},
+      author={Daniil Moskovskiy and Nikita Sushko and Sergey Pletenev and Elena Tutubalina and Alexander Panchenko},
+      year={2025},
+      eprint={2502.06394},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.06394},
+}
+```
 ## Model Card Authors [optional]
+[Daniil Moskovskiy](https://huggingface.co/etomoscow)
 ## Model Card Contact
+For any questions, please contact: [Daniil Moskovskiy]([email protected])