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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: bpmn-task-extractor |
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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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# bpmn-task-extractor |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0970 |
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- Precision: 0.95 |
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- Recall: 0.95 |
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- F1: 0.9500 |
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- Accuracy: 0.9888 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 1 | 1.0813 | 0.3077 | 0.2 | 0.2424 | 0.6404 | |
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| No log | 2.0 | 2 | 0.7296 | 0.4783 | 0.55 | 0.5116 | 0.7191 | |
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| No log | 3.0 | 3 | 0.5097 | 0.6111 | 0.55 | 0.5789 | 0.8090 | |
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| No log | 4.0 | 4 | 0.3683 | 0.7059 | 0.6 | 0.6486 | 0.8652 | |
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| No log | 5.0 | 5 | 0.2926 | 0.75 | 0.6 | 0.6667 | 0.8539 | |
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| No log | 6.0 | 6 | 0.2268 | 0.7647 | 0.65 | 0.7027 | 0.8764 | |
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| No log | 7.0 | 7 | 0.1699 | 0.7778 | 0.7 | 0.7368 | 0.9101 | |
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| No log | 8.0 | 8 | 0.1273 | 0.8 | 0.8 | 0.8000 | 0.9438 | |
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| No log | 9.0 | 9 | 0.1061 | 0.95 | 0.95 | 0.9500 | 0.9888 | |
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| No log | 10.0 | 10 | 0.0970 | 0.95 | 0.95 | 0.9500 | 0.9888 | |
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### Framework versions |
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- Transformers 4.21.3 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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