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--- |
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license: mit |
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base_model: SCUT-DLVCLab/lilt-roberta-en-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: lilt-roBERTa-en-base-sroie |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/miss1dua/huggingface/runs/r5thm7ml) |
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# lilt-roBERTa-en-base-sroie |
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This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0348 |
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- Address: {'precision': 0.92, 'recall': 0.9279538904899135, 'f1': 0.9239598278335724, 'number': 347} |
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- Company: {'precision': 0.9405099150141643, 'recall': 0.9567723342939481, 'f1': 0.9485714285714285, 'number': 347} |
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- Date: {'precision': 0.9827586206896551, 'recall': 0.9855907780979827, 'f1': 0.9841726618705036, 'number': 347} |
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- Total: {'precision': 0.9131652661064426, 'recall': 0.9394812680115274, 'f1': 0.9261363636363636, 'number': 347} |
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- Overall Precision: 0.9389 |
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- Overall Recall: 0.9524 |
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- Overall F1: 0.9456 |
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- Overall Accuracy: 0.9954 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Address | Company | Date | Total | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.0536 | 6.3291 | 500 | 0.0261 | {'precision': 0.9067796610169492, 'recall': 0.9250720461095101, 'f1': 0.9158345221112697, 'number': 347} | {'precision': 0.9273743016759777, 'recall': 0.9567723342939481, 'f1': 0.9418439716312057, 'number': 347} | {'precision': 0.9828080229226361, 'recall': 0.9884726224783862, 'f1': 0.985632183908046, 'number': 347} | {'precision': 0.883008356545961, 'recall': 0.9135446685878963, 'f1': 0.8980169971671388, 'number': 347} | 0.9246 | 0.9460 | 0.9352 | 0.9949 | |
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| 0.0058 | 12.6582 | 1000 | 0.0326 | {'precision': 0.9176136363636364, 'recall': 0.930835734870317, 'f1': 0.9241773962804005, 'number': 347} | {'precision': 0.9323943661971831, 'recall': 0.9538904899135446, 'f1': 0.9430199430199431, 'number': 347} | {'precision': 0.9827586206896551, 'recall': 0.9855907780979827, 'f1': 0.9841726618705036, 'number': 347} | {'precision': 0.8910081743869209, 'recall': 0.9423631123919308, 'f1': 0.9159663865546217, 'number': 347} | 0.9304 | 0.9532 | 0.9416 | 0.9950 | |
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| 0.0019 | 18.9873 | 1500 | 0.0348 | {'precision': 0.92, 'recall': 0.9279538904899135, 'f1': 0.9239598278335724, 'number': 347} | {'precision': 0.9405099150141643, 'recall': 0.9567723342939481, 'f1': 0.9485714285714285, 'number': 347} | {'precision': 0.9827586206896551, 'recall': 0.9855907780979827, 'f1': 0.9841726618705036, 'number': 347} | {'precision': 0.9131652661064426, 'recall': 0.9394812680115274, 'f1': 0.9261363636363636, 'number': 347} | 0.9389 | 0.9524 | 0.9456 | 0.9954 | |
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### Framework versions |
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- Transformers 4.42.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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