fc63
/

turkish-toxic-language-detection

@@ -1,199 +1,135 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+language: tr
+tags:
+  - toxicity
+  - text-classification
+  - turkish
+  - transformers
+  - bert
+license: mit
+datasets:
+  - Overfit-GM/turkish-toxic-language
+metrics:
+  - accuracy
+  - f1
+  - precision
+  - recall
+model-index:
+  - name: Turkish Toxic Language Detection Model
+    results:
+      - task:
+          type: text-classification
+          name: Text Classification
+        dataset:
+          name: Turkish Toxic Language Dataset
+          type: Overfit-GM/turkish-toxic-language
+        metrics:
+          - name: Accuracy
+            type: accuracy
+            value: 0.96
+          - name: F1
+            type: f1
+            value: 0.96
+          - name: Precision
+            type: precision
+            value: 0.96
+          - name: Recall
+            type: recall
+            value: 0.96
 ---
+# 🇹🇷 Turkish Toxic Language Detection Model 🧠🔥
+This model is a fine-tuned version of [`dbmdz/bert-base-turkish-cased`](https://huggingface.co/dbmdz/bert-base-turkish-cased) for binary toxicity classification in **Turkish** text. It was trained using a cleaned and preprocessed version of the [`Overfit-GM/turkish-toxic-language`](https://huggingface.co/datasets/Overfit-GM/turkish-toxic-language) dataset.
+## 📊 Performance
+| Metric       | Non-Toxic | Toxic | Macro Avg |
+|--------------|-----------|-------|-----------|
+| Precision    | 0.96      | 0.95  | 0.96      |
+| Recall       | 0.95      | 0.96  | 0.96      |
+| F1-score     | 0.96      | 0.96  | 0.96      |
+| Accuracy     |           |       | **0.96**  |
+| Test Samples | 5400      | 5414  | 10814     |
+### Confusion Matrix
+|               | Pred: Non-Toxic | Pred: Toxic |
+|---------------|-----------------|-------------|
+| **True: Non-Toxic** | 5154            | 246         |
+| **True: Toxic**     | 200             | 5214        |
+## 🧪 Preprocessing Details (cleaned_corrected_text)
+The model is trained on the `cleaned_corrected_text` column, which is derived from `corrected_text` using basic regex-based cleaning steps and manual slang filtering. Here's how:
+### 🔧 Cleaning Function
+```python
+def clean_corrected_text(text):
+    text = text.lower()
+    text = re.sub(r"http\S+|www\S+|https\S+", '', text, flags=re.MULTILINE)  # URL removal
+    text = re.sub(r"@\w+", '', text)  # remove @mentions
+    text = re.sub(r"[^\w\s.,!?-]", '', text)  # remove special characters (e.g., emojis)
+    text = re.sub(r"\s+", ' ', text).strip()  # normalize whitespaces
+    return text
+```
+### 🧹 Manual Slang Filtering
+```python
+slang_words = ["kanka", "lan", "knk", "bro", "la", "birader", "kanki"]
+def remove_slang(text):
+    for word in slang_words:
+        text = text.replace(word, "")
+    return text.strip()
+```
+### ✅ Applied Steps Summary
+| Step                  | Description |
+|------------------------|-------------|
+| Lowercasing            | All text is converted to lowercase |
+| URL removal            | Removes links containing http, www, https |
+| Mention removal        | Removes @username style mentions |
+| Special character removal | Removes emojis and symbols (😊, *, %, $, ^, etc.) |
+| Whitespace normalization | Collapses multiple spaces into one |
+| Slang word removal     | Removes common informal words like "kanka", "lan", etc. |
+📌 **Conclusion**: `cleaned_corrected_text` is a lightly cleaned, non-linguistically processed text column. The model is trained directly on this.
+## 💡 Example Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+tokenizer = AutoTokenizer.from_pretrained("your-username/turkish_toxic_language_detection_model")
+model = AutoModelForSequenceClassification.from_pretrained("your-username/turkish_toxic_language_detection_model")
+def predict_toxicity(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=128)
+    outputs = model(**inputs)
+    predicted = torch.argmax(outputs.logits, dim=1).item()
+    return "Toxic" if predicted == 1 else "Non-Toxic"
+```
+## 🛠 Training Details
+- **Trainer**: Hugging Face `Trainer` API
+- **Epochs**: 3
+- **Batch size**: 16
+- **Learning Rate**: 2e-5
+- **Eval Strategy**: Epoch-based
+- **Undersampling**: Applied to balance class distribution
+## 📁 Dataset
+Dataset used: [`Overfit-GM/turkish-toxic-language`](https://huggingface.co/datasets/Overfit-GM/turkish-toxic-language)
+Final dataset size after preprocessing and balancing: 54068 samples
+---
+> "This model was developed solely for detecting harmful content. It does not reflect any personal opinions."