README.md

---
library_name: transformers
tags: [text-classification, hate-speech]
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This model is fine-tuned on top of distill bert base uncased for hatespeech. Its purpose is to predict whether a given text contains hate speech or not.
Class Label are 1 for hatspeech and 0 for not.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
**Important info** This model works with binary classification and doesn't consider multilabel clssification. It detects it's either a hatespeech or not.
- **Developed by:** Ayush Dhoundiyal
- **Language(s) (NLP):** English
- **Finetuned from model:** Bert Base Uncased

### Model Sources [optional]

<!-- Provide the basic links for the model. -->
- **Paper:** https://github.com/ayushdh96/Natural-Language-Processing/blob/main/Ayush_Dhoundiyal_Project_Report.pdf
[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. -->
Pre-processing invloved basic steps like lemmtizing, stemming of words. Removing stop words and lowercasing the text to be classified. It's requested to perform these steps for good results.
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
The model provides the accuracy of 0.96, precision of 0.97. recall of 0.97 and f1 score of 0.97.