Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use KingTechnician/roberta-base_LOGIC_Native with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KingTechnician/roberta-base_LOGIC_Native with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KingTechnician/roberta-base_LOGIC_Native")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KingTechnician/roberta-base_LOGIC_Native") model = AutoModelForSequenceClassification.from_pretrained("KingTechnician/roberta-base_LOGIC_Native") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +15 -15
- model.safetensors +1 -1
README.md
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.
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- Accuracy: 0.
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- Macro Precision: 0.
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- Macro F1: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro Precision | Macro F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:--------:|
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| No log | 1.0 | 116 | 2.2572 | 0.3633 | 0.3744 | 0.3280 |
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| No log | 2.0 | 232 | 1.6881 | 0.
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| No log | 3.0 | 348 | 1.
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| No log | 4.0 | 464 | 1.
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### Framework versions
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.1872
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- Accuracy: 0.6367
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- Macro Precision: 0.6085
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- Macro F1: 0.5927
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro Precision | Macro F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:--------:|
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| No log | 1.0 | 116 | 2.2572 | 0.3633 | 0.3744 | 0.3280 |
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| No log | 2.0 | 232 | 1.6881 | 0.4933 | 0.4444 | 0.4351 |
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| No log | 3.0 | 348 | 1.5136 | 0.5767 | 0.5515 | 0.5475 |
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| No log | 4.0 | 464 | 1.5064 | 0.6033 | 0.5808 | 0.5616 |
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| 1.4911 | 5.0 | 580 | 1.5690 | 0.5967 | 0.5912 | 0.5609 |
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| 1.4911 | 6.0 | 696 | 1.5927 | 0.6267 | 0.6002 | 0.5907 |
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| 1.4911 | 7.0 | 812 | 1.6903 | 0.6267 | 0.5964 | 0.5876 |
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| 1.4911 | 8.0 | 928 | 1.8527 | 0.6167 | 0.5924 | 0.5848 |
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| 0.2082 | 9.0 | 1044 | 2.0450 | 0.6267 | 0.6208 | 0.5933 |
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| 0.2082 | 10.0 | 1160 | 2.0799 | 0.63 | 0.5922 | 0.5852 |
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| 0.2082 | 11.0 | 1276 | 2.1676 | 0.6333 | 0.6069 | 0.5889 |
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| 0.2082 | 12.0 | 1392 | 2.1872 | 0.6367 | 0.6085 | 0.5927 |
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### Framework versions
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model.safetensors
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size 498646636
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