README.md

---
license: mit
datasets:
- akaruineko/offensively-neutral
language:
- en
base_model:
- distilbert/distilbert-base-uncased
pipeline_tag: text-classification
library_name: transformers
tags:
- moderation
- filtering
- offensive
- clean
model-index:
  - name: "akaruineko/ftan-2.0"
    results:
      - task:
          type: "text-classification"
          id: "overall"
        dataset:
          type: "akaruineko/ftanch"
          name: "FTANch"
          split: "test"
        metrics:
          - type: "acc"
            value: 0.8144
            name: "Accuracy"
          - type: "p"
            value: 0.7637
            name: "Precision"
          - type: "r"
            value: 0.9890
            name: "Recall"
          - type: "f1"
            value: 0.8619
            name: "F1-Score"

      - task:
          type: "text-classification"
          id: "test"
        dataset:
          type: "akaruineko/ftanch"
          name: "FTANch"
          split: "test"
        metrics:
          - type: "acc"
            value: 0.7752
            name: "Accuracy"
          - type: "p"
            value: 0.6938
            name: "Precision"
          - type: "r"
            value: 0.9852
            name: "Recall"
          - type: "f1"
            value: 0.8142
            name: "F1-Score"

      - task:
          type: "text-classification"
          id: "test_obfuscated"
        dataset:
          type: "akaruineko/ftanch"
          name: "FTANch"
          split: "test"
        metrics:
          - type: "acc"
            value: 0.8979
            name: "Accuracy"
          - type: "p"
            value: 0.8866
            name: "Precision"
          - type: "r"
            value: 0.9942
            name: "Recall"
          - type: "f1"
            value: 0.9373
            name: "F1-Score"

      - task:
          type: "text-classification"
          id: "plain"
        dataset:
          type: "akaruineko/ftanch"
          name: "FTANch"
          split: "test"
        metrics:
          - type: "acc"
            value: 0.6670
            name: "Accuracy"
          - type: "p"
            value: 0.4398
            name: "Precision"
          - type: "r"
            value: 0.9622
            name: "Recall"
          - type: "f1"
            value: 0.6037
            name: "F1-Score"

      - task:
          type: "text-classification"
          id: "mutated"
        dataset:
          type: "akaruineko/ftanch"
          name: "FTANch"
          split: "test"
        metrics:
          - type: "acc"
            value: 0.8979
            name: "Accuracy"
          - type: "p"
            value: 0.8866
            name: "Precision"
          - type: "r"
            value: 0.9942
            name: "Recall"
          - type: "f1"
            value: 0.9373
            name: "F1-Score"
---

# ftan-2.0

**ftan-2.0** is a fine-tuned [DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased) sequence classification model for detecting offensive text.

The model predicts one of two labels:

* `clean` — non-offensive text
* `offensive` — offensive text

## Training

ftan-2.0 is the continuation of the [`akaruineko/bad-good-classifier-ru_en`](https://huggingface.co/akaruineko/bad-good-classifier-ru_en) project.

The new version was trained on the [`akaruineko/offensively-neutral`](https://huggingface.co/datasets/akaruineko/offensively-neutral) dataset, containing approximately **1.3 million text samples**.

Training used a larger dataset than the previous model and included evaluation across multiple epochs to select the best-performing checkpoint.

The best checkpoint was selected based on evaluation performance rather than simply using the final training checkpoint.

## Usage

```python
from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="akaruineko/ftan-2.0"
)

result = classifier("you are stupid")
print(result)
```

Example:

```text
[{'label': 'offensive', 'score': 0.965}]
```

## Intended Use

ftan-2.0 can be used for:

* content moderation
* filtering offensive messages
* dataset preprocessing
* text classification experiments
* moderation pipelines
* research and experimentation with text classifiers

## Limitations

This model should **not** be treated as a perfect moderation system.

Offensiveness can depend heavily on context, intent, quotation, sarcasm, reclaimed language, and the surrounding conversation. The model may therefore produce incorrect predictions for ambiguous or context-dependent text.

For example, a sentence discussing an offensive word may still receive a non-trivial offensive score even when the sentence itself is not an insult.

The model also operates on individual text inputs and does not have access to conversation history unless it is explicitly provided as input.

## Example Predictions

Some example inference results:

```text
"b****" # censored
→ offensive (0.988)

"you are stupid"
→ offensive (0.965)

"the word \"stupid\" is offensive"
→ offensive (0.695)

"beach"
→ clean (0.922)
```

These examples are illustrative and should not be interpreted as a formal benchmark.

## License

See the repository/model files for the applicable license.

## Author

Created by **akaruineko**.

This model is the **2.0** continuation of the `bad-good-classifier-ru_en` project.