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--- |
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library_name: transformers |
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language: |
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- en |
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- hi |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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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. |
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- **Developed by:** Aakash Kumar, Debajyoti Mazumder, Jasabanta Patro |
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- **Model type:** Text Classification |
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- **Language(s) :** Hindi-English code-mixed |
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- **Parent Model:** See the [BERT multilingual base model (cased)](https://huggingface.co/google-bert/bert-base-multilingual-cased) for more information about the model. |
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## How to Get Started with the Model |
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**Details of usage** |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("debajyotimaz/codemix_hate") |
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model = AutoModelForSequenceClassification.from_pretrained("debajyotimaz/codemix_hate") |
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inputs = tokenizer("Mai tumse hate karta hun", return_tensors="pt") |
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prediction= model(input_ids=inputs['input_ids'],attention_mask=inputs['attention_mask']) |
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print(prediction.logits) |
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``` |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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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. |
