Instructions to use Mukesh97/severity_model_checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mukesh97/severity_model_checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mukesh97/severity_model_checkpoints")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mukesh97/severity_model_checkpoints") model = AutoModelForSequenceClassification.from_pretrained("Mukesh97/severity_model_checkpoints") - Notebooks
- Google Colab
- Kaggle
Mukesh97/severity-classification
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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 the None dataset.
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It achieves the following results on the evaluation set:
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- Accuracy: 0.
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- F1 Macro: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
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### Framework versions
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2429
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- Accuracy: 0.9151
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- F1 Macro: 0.9154
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
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| 1.0321 | 1.0 | 95 | 0.6955 | 0.8117 | 0.8053 |
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| 0.2911 | 2.0 | 190 | 0.2814 | 0.9072 | 0.907 |
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| 0.1628 | 3.0 | 285 | 0.2736 | 0.8939 | 0.8928 |
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| 0.0522 | 4.0 | 380 | 0.2305 | 0.9151 | 0.916 |
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| 0.0623 | 5.0 | 475 | 0.2429 | 0.9151 | 0.9154 |
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### Framework versions
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model.safetensors
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