Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use DL-Project/hatespeech_distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DL-Project/hatespeech_distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DL-Project/hatespeech_distilbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DL-Project/hatespeech_distilbert") model = AutoModelForSequenceClassification.from_pretrained("DL-Project/hatespeech_distilbert") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.0640
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- Accuracy: 0.7544
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- Recall: 0.7930
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9977
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- Accuracy: 0.7737
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- Recall: 0.8118
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- Precision: 0.7526
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- F1: 0.7811
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And the following results on the test set:
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- Loss: 1.0640
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- Accuracy: 0.7544
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- Recall: 0.7930
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