Polygl0t
/

hindi-roberta-toxicity-classifier

@@ -1,199 +1,93 @@
 ---
 library_name: transformers
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 ---
 library_name: transformers
+tags:
+- toxicity
+- hindi
+license: apache-2.0
+datasets:
+- Polygl0t/hindi-toxicity-qwen-annotations
+language:
+- hi
+metrics:
+- precision
+- recall
+- accuracy
+pipeline_tag: text-classification
 ---
+# hindi-roberta Toxicity Classifier
+hindi-roberta Toxicity Classifier is an [HindRoBERTa](https://huggingface.co/l3cube-pune/hindi-roberta) based model that can be used for judging the toxicity level of a given Hindi text string.
+## Details
+For training,  we added a classification head with a single regression output to [l3cube-pune/hindi-roberta](https://huggingface.co/l3cube-pune/hindi-roberta). Only the classification head was trained, i.e., the rest of the model was frozen.
+- **Dataset:** [hindi-toxicity-qwen-annotations](https://huggingface.co/datasets/Polygl0t/hindi-toxicity-qwen-annotations)
+- **Language:** Hindi
+- **Number of Training Epochs:** 20
+- **Batch size:** 256
+- **Optimizer:** `torch.optim.AdamW`
+- **Learning Rate:** 3e-4
+- **Eval Metric:** `f1-score`
+### Confusion Matrix
+|       | **1** | **2** | **3** | **4** | **5** |
+|-------|-------|-------|-------|-------|-------|
+| **1** | 11526 | 2601  | 134   | 7     | 0     |
+| **2** | 722   | 1713  | 281   | 10    | 0     |
+| **3** | 240   | 1092  | 590   | 7     | 2     |
+| **4** | 21    | 242   | 308   | 104   | 13    |
+| **5** | 5     | 46    | 78    | 68    | 123   |
+## Usage
+Here's an example of how to use the Toxicity Classifier:
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+tokenizer = AutoTokenizer.from_pretrained("Polygl0t/hindi-roberta-toxicity-classifier")
+model = AutoModelForSequenceClassification.from_pretrained("Polygl0t/hindi-roberta-toxicity-classifier")
+model.to(device)
+text = "यह एक उदाहरण है।"
+encoded_input  =  tokenizer(text, return_tensors="pt", padding="longest", truncation=True).to(device)
+with  torch.no_grad():
+	model_output  =  model(**encoded_input)
+	logits  =  model_output.logits.squeeze(-1).float().cpu().numpy()
+# scores are produced in the range [0, 4]. To convert to the range [1, 5], we can simply add 1 to the score.
+score = logits.item() + 1
+result = {
+ "text": text,
+ "score": score,
+ "toxic_score": int(round(max(0, min(score, 4)))) + 1,
+}
+print(result)
+```
+## Cite as 🤗
+```latex
+```
+## Aknowlegments
+We gratefully acknowledge the granted access to the [Marvin cluster](https://www.hpc.uni-bonn.de/en/systems/marvin) hosted by [University of Bonn](https://www.uni-bonn.de/en) along with the support provided by its High Performance Computing & Analytics Lab.
+## License
+hindi-roberta-toxicity-classifier is licensed under the Apache License, Version 2.0. For more details, see the [LICENSE](LICENSE) file.