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--- |
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license: apache-2.0 |
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base_model: allenai/longformer-base-4096 |
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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: longformer_result_detection |
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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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# longformer_result_detection |
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This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1366 |
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- Precision: 0.6230 |
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- Recall: 0.6881 |
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- F1: 0.6539 |
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- Accuracy: 0.9738 |
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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: 2e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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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 | 410 | 0.0770 | 0.5744 | 0.6056 | 0.5896 | 0.9720 | |
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| 0.1137 | 2.0 | 820 | 0.0612 | 0.5608 | 0.6398 | 0.5977 | 0.9723 | |
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| 0.056 | 3.0 | 1230 | 0.0671 | 0.6374 | 0.6861 | 0.6609 | 0.9753 | |
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| 0.0347 | 4.0 | 1640 | 0.0854 | 0.6026 | 0.7505 | 0.6685 | 0.9754 | |
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| 0.0221 | 5.0 | 2050 | 0.1062 | 0.6138 | 0.6781 | 0.6444 | 0.9732 | |
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| 0.0221 | 6.0 | 2460 | 0.1116 | 0.6181 | 0.7002 | 0.6566 | 0.9750 | |
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| 0.0119 | 7.0 | 2870 | 0.1143 | 0.6405 | 0.7565 | 0.6937 | 0.9768 | |
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| 0.007 | 8.0 | 3280 | 0.1303 | 0.6712 | 0.6901 | 0.6806 | 0.9747 | |
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| 0.0058 | 9.0 | 3690 | 0.1347 | 0.6189 | 0.6861 | 0.6508 | 0.9741 | |
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| 0.0036 | 10.0 | 4100 | 0.1366 | 0.6230 | 0.6881 | 0.6539 | 0.9738 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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