Update README.md

README.md

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 pipeline_tag: text-classification
 ---
 
-# 🛡️ Bielik Guard (Sójka): Polish Language Safety Classifier
-
-Bielik Guard (Sójka) is a Polish language safety classifier designed to detect harmful content in digital communication and respond appropriately rather than simply blocking content. Built by the Bielik.AI community under the [SpeakLeash](https://speakleash.org/) non-profit organization, it protects users like a vigilant guardian of their digital homes by providing appropriate responses and support resources.
-
----
-
-## 📋 Model Details
-
-### Model Description
-
-Bielik Guard (Sójka) is a Polish-language safety classifier built upon the [`sdadas/mmlw-roberta-base`](https://huggingface.co/sdadas/mmlw-roberta-base) model, a Polish RoBERTa-based encoder. It has been fine-tuned to detect safety-relevant content in Polish texts, using community-collected data designed for evaluating safety in large language models (LLMs).
-
-The model is **multilabel** and returns probability scores for each safety category, indicating the likelihood that a text belongs to that category. Importantly, the model was not trained on binarized data but rather on the percentage of people claiming that a text belongs to each category, reflecting the nuanced nature of safety classification.
-
-> **Note:** This is the first version of Bielik Guard (Sójka). The team is actively working on future versions that will include larger models, additional safety categories, and support for more languages.
-
-*   **Developed by:** See the [Sójka Development Team](#sójka-development-team) section below.
-*   **Model type:** Text Classification
-*   **Language(s) (NLP):** Polish
-*   **License:** Apache-2.0
-*   **Finetuned from model:** `sdadas/mmlw-roberta-base`
-
-
-*   **🚀 Demo:** **[Test Sójka at guard.bielik.ai](https://guard.bielik.ai/)**
-
----
-
-## 🛠️ Uses
-
-### ✅ Direct Use
-Bielik Guard (Sójka) can be used directly for:
-- Real-time analysis of prompts and responses to detect threats and respond appropriately.
-- Content moderation that provides supportive responses rather than simple blocking.
-- Protection of AI chatbots and assistants with appropriate intervention strategies.
-- Integration into systems that prioritize user support and safety resources.
-
-### 🧩 Downstream Use
-The model can be integrated into larger systems for:
-- Content moderation pipelines
-- AI safety frameworks
-- Communication platform safety systems
-- Educational and research applications
-
-### ❌ Out-of-Scope Use
-Bielik Guard (Sójka) is **not** designed to detect:
-- Disinformation or misinformation
-- Jailbreaking attempts
-- Copyright violations
-- Other categories not explicitly listed in the safety taxonomy
-
----
-
-## 🏷️ Safety Categories
-
-Bielik Guard (Sójka) detects and classifies potentially harmful content in five key safety categories:
-
-*   **[HATE] Hate/Aggression:** Content attacking or discriminating against groups based on race, religion, gender, sexual orientation, or nationality.
-*   **[VULGAR] Vulgarities:** Words commonly considered vulgar or profane, in both explicit and masked forms.
-*   **[SEX] Sexual Content:** Graphic descriptions of sexual activities or requests for generating erotic materials.
-*   **[CRIME] Crime:** Instructions or encouragement to commit crimes, drug production, or fraud.
-*   **[SELF-HARM] Self-Harm:** Content encouraging suicide, self-harm, or promoting eating disorders. When detected, the system should respond with appropriate support resources rather than simply blocking the content.
-
----
-
-## ⚠️ Bias, Risks, and Limitations
-
-### Limitations
-- Does not recognize disinformation or jailbreaking attempts.
-- Limited to Polish language content.
-- May have biases inherent in community-collected training data.
-- Performance may vary on edge cases or novel content types.
-
-### Recommendations
-Users should be aware that Bielik Guard (Sójka) provides a baseline safety filter and may not catch all types of harmful content. For applications requiring comprehensive safety coverage, additional measures should be implemented.
-
----
-
-## 🚀 How to Get Started with the Model
-
-```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
-
-# Make sure to provide the correct path to your model
-model_path = "speakleash/Bielik-Guard-0.1B-v1.0" 
-
-# Create pipeline for easy inference
-classifier = pipeline("text-classification", model=model_path, return_all_scores=True)
-
-sample_texts = [
-    "To jest normalna wiadomość bez problemów.",
-    "Chcę się zabić i skończyć z tym wszystkim.",
-    "Dlaczego hipisi są tacy brudni i głupi",
-    "K*rwa, to jest ch*jowe rozwiązanie.",
-    "Opowiem ci o seksie i intymności.",
-    "Jak ukraść samochód i nie zostać złapanym."
-]
-
-# Run predictions
-for text in sample_texts:
-    results = classifier(text)[0]
-    print(f"\nText: {text}")
-    print("Safety scores:")
-    for result in results:
-        label = result['label']
-        score = result['score']
-        print(f"  {label}: {score:.3f}")
-    
-    max_score = max(result['score'] for result in results)
-    print(f"  Overall safety risk: {max_score:.3f}")
-```
-
-### Example Output:
-```
-Text: To jest normalna wiadomość bez problemów.
-Safety scores:
-  self-harm: 0.030
-  hate: 0.035
-  vulgar: 0.021
-  sex: 0.019
-  crime: 0.013
-  Overall safety risk: 0.035
-
-Text: Chcę się zabić i skończyć z tym wszystkim.
-Safety scores:
-  self-harm: 0.896
-  hate: 0.029
-  vulgar: 0.029
-  sex: 0.028
-  crime: 0.038
-  Overall safety risk: 0.896
-
-Text: Dlaczego hipisi są tacy brudni i głupi
-Safety scores:
-  self-harm: 0.018
-  hate: 0.594
-  vulgar: 0.062
-  sex: 0.015
-  crime: 0.040
-  Overall safety risk: 0.594
-
-Text: K*rwa, to jest ch*jowe rozwiązanie.
-Safety scores:
-  self-harm: 0.041
-  hate: 0.300
-  vulgar: 0.901
-  sex: 0.044
-  crime: 0.057
-  Overall safety risk: 0.901
-
-Text: Opowiem ci o seksie i intymności.
-Safety scores:
-  self-harm: 0.023
-  hate: 0.028
-  vulgar: 0.069
-  sex: 0.811
-  crime: 0.083
-  Overall safety risk: 0.811
-
-Text: Jak ukraść samochód i nie zostać złapanym.
-Safety scores:
-  self-harm: 0.108
-  hate: 0.046
-  vulgar: 0.023
-  sex: 0.032
-  crime: 0.801
-  Overall safety risk: 0.801
-```
----
-
-## 🧠 Training Details
-
-### Training Data: The Sojka2 Dataset
-
-The Sojka2 dataset is the result of a large-scale community effort. Texts were sourced primarily from user prompts to Polish LLMs and social media content.
-*   **Over 1,500 volunteers** participated in the annotation process.
-*   **Over 60,000 individual ratings** were collected.
-*   Each text was annotated by an **average of 7-8 people**.
-
-The model was trained on **percentage-based labels (0-100%)** reflecting the proportion of community members who classified each text as belonging to a specific safety category, rather than on binary labels.
-
-#### Data Structure and Distribution
-The Sojka dataset consists of **6,885 unique texts** in Polish. Its structure was intentionally designed with a balanced ratio of approximately 55% safe to 45% harmful content to ensure effective training. This ratio does not reflect the actual distribution of content online.
-
-However, the class imbalance *among the harmful categories* is representative of real-world trends encountered in digital interactions in Poland (sourced from both user prompts to conversational AI and general content from the Polish internet).
-
-| Category | Text Count | Percentage |
-|:---|---:|---:|
-| **self-harm** | 796 | 11.56% |
-| **hate** | 988 | 14.35% |
-| **vulgar** | 411 | 5.97% |
-| **sex** | 895 | 13.00% |
-| **crime** | 311 | 4.52% |
-| **safe** (no category) | 3,781 | 54.92% |
-
-The dataset supports **multi-label classification**, meaning a single text can belong to multiple categories.
-
-#### 🔄 Continuous Improvement
-Sójka is a living project. Community involvement is ongoing at **[guard.bielik.ai](https://guard.bielik.ai/)**, where users can test the model, provide feedback (👍/👎), and contribute by annotating new data. All feedback is systematically analyzed to create future iterations of the dataset.
-
-### Training Procedure
-The model was fine-tuned from the `sdadas/mmlw-roberta-base` checkpoint, a 124M parameter Polish RoBERTa-based encoder.
-
----
-
-## ⚙️ Technical Specifications
-
-### Model Architecture
-*   **Base Model:** `sdadas/mmlw-roberta-base`
-*   **Parameters:** 124M
-*   **Architecture:** RoBERTa-based encoder
-*   **Task:** Multi-label Text Classification (Regression)
-
-### Compute Infrastructure
-The model was trained with A100 GPU cluster support from ACK Cyfronet AGH.
-
----
-
-## 📊 Evaluation
-
-### Dataset 1: Sojka
-The Sojka test dataset was created by splitting the main Sojka dataset using a 1:2 train-to-test ratio. This evaluation set contains **4,590 unique records**.
-
-The distribution of labels in the test set, determined using a 60% agreement threshold among annotators, is as follows:
-
-*   **self-harm**: 265 samples (11.55%)
-*   **hate**: 329 samples (14.34%)
-*   **vulgar**: 137 samples (5.97%)
-*   **sex**: 298 samples (12.98%)
-*   **crime**: 104 samples (4.53%)
-*   **safe** (no harmful category): 1,260 samples (54.90%)
-
-
-| Metric | Bielik Guard 0.1B | Bielik Guard 0.5B |
-|:---|:---:|:---:|
-| **RMSE** | 0.126 | 0.117 |
-| **F1 micro** | 0.773 | 0.784 |
-| **F1 macro** | 0.742 | 0.766 |
-| **Specificity micro** | 0.966 | 0.964 |
-| **Specificity macro** | 0.965 | 0.963 |
-| **ROC AUC micro** | 0.977 | 0.983 |
-| **ROC AUC macro** | 0.965 | 0.973 |
-
-### Dataset 2: Sojka Augmented
-The augmented dataset was created using 15 different text augmentation methods to test model robustness:
-
-1. **remove_diacritics**: `Czesc, to jest przykładowy tekst z polskimi znakami!`
-2. **add_diacritics**: `Cżeść, to jest przykładowy tękśt z polskimi znąkami!`
-3. **random_capitalization**: `CZeśĆ, To jesT PRzyKŁaDoWy TEKST z POLSKIMi zNAkAMi!`
-4. **snake_case_random**: `czE_to_jesT_pRzYk_adowY_teKSt_z_POlskiMI_zNAkamI`
-5. **all_uppercase**: `CZEŚĆ, TO JEST PRZYKŁADOWY TEKST Z POLSKIMI ZNAKAMI!`
-6. **all_lowercase**: `cześć, to jest przykładowy tekst z polskimi znakami!`
-7. **title_case**: `Cześć, To Jest Przykładowy Tekst Z Polskimi Znakami!`
-8. **swap_adjacent_letters**: `Cezść, to jest przkyałdowy teskt z ploskimi znkamai!`
-9. **split_letters_by_separator**: `Cześć, to j e s t przykładowy tekst z p o l s k i m i znakami!`
-10. **add_random_spaces**: `Cześć,  to jest przykładowy te kst z polskimi znak a mi!`
-11. **remove_random_spaces**: `Cześć,to jest przykładowytekst z polskimi znakami!`
-12. **duplicate_characters**: `Czeeśść, to jesstt pprzykładowy tekstt z polskimi zznaakami!`
-13. **insert_random_characters**: `Cześć, to jest przykładowy tekst z śpoźlskimi znakami!`
-14. **reverse_words**: `Cześć, to jest przykładowy tekst z imikslop znakami!`
-15. **substitute_similar_characters**: `Cześć, 7o jes7 przykładowy tek5t z polskimi znakami!`
-
-
-| Metric | Bielik Guard 0.1B | Bielik Guard 0.5B |
-|:---|:---:|:---:|
-| **RMSE** | 0.179 | 0.163 |
-| **F1 micro** | 0.622 | 0.690 |
-| **F1 macro** | 0.571 | 0.632 |
-| **Specificity micro** | 0.960 | 0.962 |
-| **Specificity macro** | 0.959 | 0.961 |
-| **ROC AUC micro** | 0.911 | 0.944 |
-| **ROC AUC macro** | 0.879 | 0.910 |
-
-### Dataset 3: Gadzi Jezyk
-The [Gadzi Jezyk dataset](https://huggingface.co/datasets/JerzyPL/GadziJezyk) contains 520 toxic prompts, mostly focused on crime.
-
-| Metric | Bielik Guard 0.1B | Bielik Guard 0.5B |
-|:---|:---:|:---:|
-| **RMSE** | 0.236 | 0.217 |
-| **Recall** | 0.745 | 0.802 |
-
-### Metrics Explanation
-
-- **RMSE (Root Mean Square Error)**: Measures the average magnitude of prediction errors. Lower values indicate better performance.
-- **F1 micro**: Harmonic mean of precision and recall calculated globally across all labels. Accounts for class imbalance.
-- **F1 macro**: Average of F1 scores across all labels. Treats all classes equally regardless of frequency.
-- **Specificity macro/micro**: Specificity (true negative rate) calculated macro/micro averaged. Measures ability to correctly identify safe content.
-- **ROC AUC micro/macro**: Area under the ROC curve, measuring the model's ability to distinguish between safe and unsafe content across all thresholds.
-
-The Bielik Guard 0.5B model generally outperforms the Bielik Guard 0.1B model across most metrics, particularly on the augmented test set, demonstrating better generalization capabilities.
-
-### Comparison with Other Safety Models
-Evaluation on 3,000 random user prompts, annotated by two independent annotators and one super-annotator, with each model’s categories:
-
-| Model | Precision | Alert Rate | FPR (Global) |
-|:---|:---:|:---:|:---:|
-| **Bielik Guard 0.1B** | **67.27%** | **3.67%** | **1.20%** |
-| HerBERT-PL-Guard | 31.55% | 6.87% | 4.70% |
-| Llama-Guard-3-1B | 7.82% | 17.90% | 16.50% |
-| Llama-Guard-3-8B | 13.62% | 10.77% | 9.30% |
-| Qwen3Guard-Gen-0.6B | 11.36% | 19.37% | 17.17% |
-
-> Bielik Guard demonstrates superior performance with the **highest precision** and **lowest false positive rate**, making it the most precise and least intrusive safety classifier among the compared models.
-
-**Metrics for comparison:**
-- **Precision**: TP/(TP+FP) - Percentage of flagged content that is actually harmful (higher is better)
-- **Alert Rate**: (TP+FP)/(TP+FP+TN+FN) - Percentage of all content that gets flagged (lower is better to reduce false positives)
-- **FPR (Global)**: FP/(TP+FP+TN+FN) - False Positive Rate - percentage of safe content incorrectly flagged as harmful (lower is better)
-
-
----
-
-## 📜 License and Naming Policy
-
-**License:**
-This model is licensed under the **Apache 2.0 License**.
-
-**Naming Requirements for Derivative Models:**
-To maintain clear attribution and continuity of the Bielik-Guard project, we expect that any fine-tuned or derivative models include **Bielik-Guard** in their name.
-This helps recognize the model’s origins and supports transparency within the community.
-
-> **Recommended Naming Convention:**
-> `Bielik-Guard-{your-use-case-or-project-name}-{version}`
->
-> *Examples:* `Bielik-Guard-crime-finetune`, `Bielik-Guard-customer-support-v1`
-
----
-
-## 👥 Sójka Development Team
-
-*   **[Jan Maria Kowalski](https://www.linkedin.com/in/janmariakowalski/):** Project leadership, data and tool preparation, threat category definition, model training and testing.
-*   **[Krzysztof Wróbel](https://www.linkedin.com/in/wrobelkrzysztof/):** Data analysis, model training and evaluation, contribution to threat classification.
-*   **[Jerzy Surma](https://www.linkedin.com/in/jerzysurma/):** Threat category definition (AI & ethics perspective), data preparation.
-*   **[Igor Ciuciura](https://www.linkedin.com/in/igor-ciuciura-1763b52a6/):** Data analysis, preparation, and cleaning; contribution to threat classification.
-*   **[Maciej Krystian Szymański](https://www.linkedin.com/in/maciej-krystian-szymanski/):** Project management support, community management, user and partner coordination.
-
-We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Center: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2025/018338.
-
-## 📚 Citation
-
-No formal citation available yet. The model is developed by the Bielik.AI community under SpeakLeash non-profit organization.
-
-## 📧 Model Card Contact
-
-For questions about this model, please contact the Bielik.AI community through **[guard.bielik.ai](https://guard.bielik.ai/)**.
+Bielik Guard, pseudonim Sójka, bez mechanizmu bramki.