Instructions to use Shruthikaa/muril_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shruthikaa/muril_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Shruthikaa/muril_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Shruthikaa/muril_classification") model = AutoModelForSequenceClassification.from_pretrained("Shruthikaa/muril_classification") - Notebooks
- Google Colab
- Kaggle
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# muril_classification
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This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muril-base-cased) on
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It achieves the following results on the evaluation set:
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- Loss: 0.4072
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- Accuracy: 0.843
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# muril_classification
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This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muril-base-cased) on Hate and Offensive Comments dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4072
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- Accuracy: 0.843
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