Instructions to use KingTechnician/bert-base-uncased_LOGIC_Native with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KingTechnician/bert-base-uncased_LOGIC_Native with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KingTechnician/bert-base-uncased_LOGIC_Native")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KingTechnician/bert-base-uncased_LOGIC_Native") model = AutoModelForSequenceClassification.from_pretrained("KingTechnician/bert-base-uncased_LOGIC_Native") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +4 -4
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README.md
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-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.
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- Accuracy: 0.61
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- Macro Precision: 0.5758
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- Macro F1: 0.5770
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| 1.7294 | 7.0 | 812 | 1.4863 | 0.6133 | 0.5784 | 0.5823 |
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| 1.7294 | 8.0 | 928 | 1.5494 | 0.63 | 0.5924 | 0.5903 |
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| 0.3290 | 9.0 | 1044 | 1.6099 | 0.61 | 0.5733 | 0.5776 |
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### Framework versions
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.7437
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- Accuracy: 0.61
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- Macro Precision: 0.5758
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- Macro F1: 0.5770
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| 1.7294 | 7.0 | 812 | 1.4863 | 0.6133 | 0.5784 | 0.5823 |
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| 1.7294 | 8.0 | 928 | 1.5494 | 0.63 | 0.5924 | 0.5903 |
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| 0.3290 | 9.0 | 1044 | 1.6099 | 0.61 | 0.5733 | 0.5776 |
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| 0.3290 | 10.0 | 1160 | 1.6845 | 0.6167 | 0.5809 | 0.5817 |
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| 0.3290 | 11.0 | 1276 | 1.7364 | 0.6233 | 0.5811 | 0.5848 |
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| 0.3290 | 12.0 | 1392 | 1.7437 | 0.61 | 0.5758 | 0.5770 |
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### Framework versions
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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