Update README.md

README.md

@@ -11,6 +11,22 @@ pipeline_tag: text-classification
 tags:
 - hate speech
 ---
+# MetaHateBERT
+
+## Model Description
+
+This is a fine-tuned BERT model specifically designed to detect hate speech in text. The model is based on the `bert-base-uncased` architecture and has been fine-tuned on a custom dataset for the task of binary text classification, where the labels are `no hate` and `hate`.
+
+## Intended Uses & Limitations
+
+### Intended Uses
+- **Hate Speech Detection**: This model is intended for detecting hate speech in social media comments, forums, and other text data sources.
+- **Content Moderation**: Can be used by platforms to automatically flag potentially harmful content.
+
+### Limitations
+- **Biases**: The model may carry biases present in the training data.
+- **False Positives/Negatives**: It's not perfect and may misclassify some instances.
+- **Domain Specificity**: Performance may vary across different domains.
 
 ## Citation
 
@@ -32,5 +48,21 @@ If you use this model, please cite the following reference:
 }
 ```
 
+## Usage
+
+### Inference
+
+To use this model, you can load it via the `transformers` library:
+
+```python
+from transformers import pipeline
+
+# Load the model
+classifier = pipeline("text-classification", model="irlab-udc/MetaHateBERT")
+
+# Test the model
+result = classifier("Your input text here")
+print(result)  # Should print the labels "no hate" or "hate"
+
 ## Acknowledgements
 The authors thank the funding from the Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101073351. The authors also thank the financial support supplied by the Consellería de Cultura, Educación, Formación Profesional e Universidades (accreditation 2019-2022 ED431G/01, ED431B 2022/33) and the European Regional Development Fund, which acknowledges the CITIC Research Center in ICT of the University of A Coruña as a Research Center of the Galician University System and the project PID2022-137061OB-C21 (Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Proyectos de Generación de Conocimiento; supported by the European Regional Development Fund). The authors also thank the funding of project PLEC2021-007662 (MCIN/AEI/10.13039/501100011033, Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Plan de Recuperación, Transformación y Resiliencia, Unión Europea-Next Generation EU).