---
library_name: transformers
language:
- en
- hi
---

# Model Card for Model ID

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



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model is for Hindi-English code-mixed hate detection.

- **Developed by:** Aakash Kumar, Debajyoti Mazumder, Jasabanta Patro
- **Model type:** Text Classification
- **Language(s) :** Hindi-English code-mixed
- **Parent Model:** See the [BERT multilingual base model (cased)](https://huggingface.co/google-bert/bert-base-multilingual-cased) for more information about the model.

## How to Get Started with the Model

**Details of usage**


```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("debajyotimaz/codemix_hate")
model = AutoModelForSequenceClassification.from_pretrained("debajyotimaz/codemix_hate")
inputs = tokenizer("Mai tumse hate karta hun", return_tensors="pt")
prediction= model(input_ids=inputs['input_ids'],attention_mask=inputs['attention_mask'])
print(prediction.logits)
```

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->
We use the F1 score of positive class as the evaluation metric for training of our model because it takes into account the Acc, Pre and Rec values.