Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use KingTechnician/roberta-base_LOGIC_LRTC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KingTechnician/roberta-base_LOGIC_LRTC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KingTechnician/roberta-base_LOGIC_LRTC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KingTechnician/roberta-base_LOGIC_LRTC") model = AutoModelForSequenceClassification.from_pretrained("KingTechnician/roberta-base_LOGIC_LRTC") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +16 -16
- 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: 1.
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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 | 1.
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| No log | 2.0 | 232 | 1.
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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: 1.9662
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- Accuracy: 0.6933
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- Macro Precision: 0.6567
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- Macro F1: 0.6467
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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 | 1.5639 | 0.41 | 0.4728 | 0.3904 |
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| No log | 2.0 | 232 | 1.1497 | 0.5833 | 0.5578 | 0.5659 |
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| No log | 3.0 | 348 | 1.1332 | 0.63 | 0.6070 | 0.6126 |
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| No log | 4.0 | 464 | 1.2206 | 0.6767 | 0.6478 | 0.6249 |
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| 0.9898 | 5.0 | 580 | 1.3936 | 0.6267 | 0.5943 | 0.5994 |
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| 0.9898 | 6.0 | 696 | 1.5044 | 0.6633 | 0.6153 | 0.6181 |
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| 0.9898 | 7.0 | 812 | 1.7037 | 0.69 | 0.6467 | 0.6392 |
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| 0.9898 | 8.0 | 928 | 1.8787 | 0.6833 | 0.6484 | 0.6307 |
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| 0.0978 | 9.0 | 1044 | 1.9438 | 0.69 | 0.6580 | 0.6500 |
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| 0.0978 | 10.0 | 1160 | 1.9061 | 0.69 | 0.6368 | 0.6413 |
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| 0.0978 | 11.0 | 1276 | 1.9510 | 0.69 | 0.6478 | 0.6478 |
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| 0.0978 | 12.0 | 1392 | 1.9662 | 0.6933 | 0.6567 | 0.6467 |
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### Framework versions
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model.safetensors
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