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README.md

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+---
+language: 
+- en
+- hi
+license: mit
+tags:
+- text-classification
+- hate-speech-detection
+- xlm-roberta
+- multilingual
+datasets:
+- hasoc2019
+metrics:
+- accuracy
+- f1
+pipeline_tag: text-classification
+widget:
+- text: "I love everyone in this community!"
+  example_title: "Positive Example"
+- text: "This person is terrible and should be banned"
+  example_title: "Negative Example"
+---
+
+# Hate Speech Detector (XLM-RoBERTa)
+
+Multilingual hate speech detection model fine-tuned on HASOC 2019 dataset.
+
+## Model Description
+
+This model detects hate speech in English and Hindi text using XLM-RoBERTa base as the backbone.
+
+**Languages:** English, Hindi  
+**Task:** Binary Text Classification (Hate Speech / Not Hate Speech)  
+**Base Model:** xlm-roberta-base
+
+## Intended Uses
+
+- Content moderation
+- Social media monitoring  
+- Research purposes
+
+## How to Use
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("archich/hate-speech-detector")
+model = AutoModelForSequenceClassification.from_pretrained("archich/hate-speech-detector")
+
+# Example text
+text = "Your text here"
+
+# Tokenize
+inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=256)
+
+# Predict
+with torch.no_grad():
+    outputs = model(**inputs)
+    probs = torch.softmax(outputs.logits, dim=1)
+    prediction = torch.argmax(probs, dim=1).item()
+
+labels = ["NOT_HATE_SPEECH", "HATE_SPEECH"]
+print(f"Prediction: {labels[prediction]} ({probs[0][prediction].item():.2%} confidence)")
+```
+
+## Training Data
+
+Trained on HASOC 2019 (Hate Speech and Offensive Content Identification) dataset containing:
+- Hindi posts from social media
+- English posts from social media
+
+## Label Mapping
+
+- `0`: NOT_HATE_SPEECH - Normal, non-offensive content
+- `1`: HATE_SPEECH - Hateful or offensive content (HOF)
+
+## Limitations & Ethical Considerations
+
+⚠️ **Important Notice:**
+
+- This model is intended to **assist** human moderators, not replace them
+- May contain biases from training data
+- Context and cultural nuances are important - manual review recommended
+- False positives are possible
+- Should not be the sole decision-maker for content removal
+
+## Performance
+
+Training details and metrics available in model files.
+
+## Citation
+
+If you use this model, please cite:
+
+```
+@misc{hate-speech-detector,
+  author = {archich},
+  title = {Multilingual Hate Speech Detector},
+  year = {2024},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/archich/hate-speech-detector}}
+}
+```