Create README.md

README.md

@@ -0,0 +1,80 @@
+---
+license: apache-2.0
+pipeline_tag: text-classification
+tags:
+- not-for-all-audiences
+---
+```markdown
+---
+license: apache-2.0
+pipeline_tag: text-classification
+---
+# Model Card: Fine-Tuned DistilBERT for Offensive/Hate Speech Detection
+
+## Model Description
+
+The **Fine-Tuned DistilBERT** is a variant of the BERT transformer model,
+distilled for efficient performance while maintaining high accuracy.
+It has been adapted and fine-tuned for the specific task of offensive/hate speech detection in text data.
+
+The model, named "distilbert-base-uncased," is pre-trained on a substantial amount of text data,
+which allows it to capture semantic nuances and contextual information present in natural language text.
+It has been fine-tuned with meticulous attention to hyperparameter settings, including batch size and learning rate, to ensure optimal model performance for the offensive/hate speech detection task.
+
+During the fine-tuning process, a batch size suitable for efficient computation and learning was chosen.
+Additionally, a learning rate was selected to strike a balance between rapid convergence and steady optimization,
+ ensuring the model not only learns quickly but also steadily refines its capabilities throughout training.
+
+This model has been trained on a proprietary dataset specifically designed for offensive/hate speech detection.
+ The dataset consists of text samples, each labeled as "non-offensive" or "offensive."
+The diversity within the dataset allowed the model to learn to identify offensive content accurately.
+
+The goal of this meticulous training process is to equip the model with the ability to detect offensive and hate speech in text data effectively. The result is a model ready to contribute significantly to content moderation and safety, while maintaining high standards of accuracy and reliability.
+
+## Intended Uses & Limitations
+
+### Intended Uses
+- **Offensive/Hate Speech Detection**: The primary intended use of this model is to detect offensive or hate speech in text data. It is well-suited for filtering and identifying inappropriate content in various applications.
+
+### How to Use
+To use this model for offensive/hate speech detection, you can follow these steps:
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="Falconsai/offensive_speech_detection")
+text = "Your text to classify here."
+result = classifier(text)
+
+```
+
+### Limitations
+- **Specialized Task Fine-Tuning**: While the model is adept at offensive/hate speech detection, its performance may vary when applied to other natural language processing tasks.
+- Users interested in employing this model for different tasks should explore fine-tuned versions available in the model hub for optimal results.
+
+## Training Data
+
+The model's training data includes a proprietary dataset designed for offensive/hate speech detection. This dataset comprises a diverse collection of text samples, categorized into "non-offensive" and "offensive" classes. The training process aimed to equip the model with the ability to distinguish between offensive and non-offensive content effectively.
+
+### Training Stats
+- Evaluation Loss: *Insert Evaluation Loss*
+- Evaluation Accuracy: *Insert Evaluation Accuracy*
+- Evaluation Runtime: *Insert Evaluation Runtime*
+- Evaluation Samples per Second: *Insert Evaluation Samples per Second*
+- Evaluation Steps per Second: *Insert Evaluation Steps per Second*
+
+**Note:** Specific evaluation statistics should be provided based on the model's performance.
+
+## Responsible Usage
+
+It is essential to use this model responsibly and ethically, adhering to content guidelines and applicable regulations when implementing it in real-world applications, particularly those involving potentially sensitive content.
+
+## References
+
+- [Hugging Face Model Hub](https://huggingface.co/models)
+- [DistilBERT Paper](https://arxiv.org/abs/1910.01108)
+
+**Disclaimer:** The model's performance may be influenced by the quality and representativeness of the data it was fine-tuned on. Users are encouraged to assess the model's suitability for their specific applications and datasets.
+```
+
+This refactored model card provides information about a Fine-Tuned DistilBERT model for offensive/hate speech detection, including its intended uses, limitations, training data, responsible usage guidelines, and references. Please replace the placeholders such as "Insert Evaluation Loss" with specific evaluation statistics as needed.