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--- |
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base_model: SpanBERT/spanbert-large-cased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: aspect_complaint_spanbert |
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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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# aspect_complaint_spanbert |
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This model is a fine-tuned version of [SpanBERT/spanbert-large-cased](https://huggingface.co/SpanBERT/spanbert-large-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2032 |
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- F1: 0.8368 |
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- Roc Auc: 0.8935 |
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- Accuracy: 0.5292 |
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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: 48 |
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- eval_batch_size: 48 |
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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 | F1 | Roc Auc | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
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| No log | 1.0 | 97 | 0.2810 | 0.6988 | 0.7718 | 0.0 | |
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| No log | 2.0 | 194 | 0.2209 | 0.7688 | 0.8260 | 0.2577 | |
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| No log | 3.0 | 291 | 0.2017 | 0.7958 | 0.8477 | 0.3565 | |
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| No log | 4.0 | 388 | 0.1882 | 0.8120 | 0.8678 | 0.4184 | |
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| No log | 5.0 | 485 | 0.1773 | 0.8272 | 0.8693 | 0.4416 | |
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| 0.2417 | 6.0 | 582 | 0.1732 | 0.8262 | 0.8792 | 0.4811 | |
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| 0.2417 | 7.0 | 679 | 0.1719 | 0.8331 | 0.8795 | 0.4759 | |
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| 0.2417 | 8.0 | 776 | 0.1722 | 0.8341 | 0.8814 | 0.5017 | |
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| 0.2417 | 9.0 | 873 | 0.1797 | 0.8347 | 0.8818 | 0.4923 | |
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| 0.2417 | 10.0 | 970 | 0.1856 | 0.8328 | 0.8859 | 0.5112 | |
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| 0.1287 | 11.0 | 1067 | 0.1866 | 0.8343 | 0.8868 | 0.5275 | |
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| 0.1287 | 12.0 | 1164 | 0.1868 | 0.8378 | 0.8898 | 0.5258 | |
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| 0.1287 | 13.0 | 1261 | 0.1923 | 0.8322 | 0.8864 | 0.5120 | |
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| 0.1287 | 14.0 | 1358 | 0.1916 | 0.8400 | 0.8929 | 0.5387 | |
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| 0.1287 | 15.0 | 1455 | 0.1975 | 0.8368 | 0.8919 | 0.5421 | |
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| 0.0781 | 16.0 | 1552 | 0.1974 | 0.8403 | 0.8951 | 0.5369 | |
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| 0.0781 | 17.0 | 1649 | 0.2022 | 0.8329 | 0.8911 | 0.5241 | |
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| 0.0781 | 18.0 | 1746 | 0.2012 | 0.8360 | 0.8921 | 0.5284 | |
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| 0.0781 | 19.0 | 1843 | 0.2027 | 0.8367 | 0.8936 | 0.5335 | |
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| 0.0781 | 20.0 | 1940 | 0.2032 | 0.8368 | 0.8935 | 0.5292 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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