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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: aces-roberta-base-13 |
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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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# aces-roberta-base-13 |
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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: 0.5171 |
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- Precision: 0.8348 |
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- Recall: 0.8531 |
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- F1: 0.8399 |
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- Accuracy: 0.8531 |
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- F1 Who: 0.9134 |
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- F1 What: 0.8505 |
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- F1 Where: 0.8444 |
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- F1 How: 0.9391 |
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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: 1e-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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | F1 Who | F1 What | F1 Where | F1 How | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:|:-------:|:--------:|:------:| |
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| 1.2251 | 1.0 | 50 | 1.1108 | 0.6219 | 0.6941 | 0.6168 | 0.6941 | 0.0625 | 0.6856 | 0.5926 | 0.8138 | |
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| 0.6932 | 2.0 | 100 | 0.7015 | 0.7448 | 0.8031 | 0.7639 | 0.8031 | 0.8730 | 0.7932 | 0.8054 | 0.9293 | |
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| 0.5636 | 3.0 | 150 | 0.6059 | 0.8028 | 0.8289 | 0.8032 | 0.8289 | 0.8819 | 0.8095 | 0.8186 | 0.9346 | |
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| 0.4894 | 4.0 | 200 | 0.5492 | 0.8251 | 0.8499 | 0.8314 | 0.8499 | 0.9077 | 0.8402 | 0.8340 | 0.9393 | |
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| 0.4381 | 5.0 | 250 | 0.5289 | 0.8237 | 0.8523 | 0.8353 | 0.8523 | 0.9219 | 0.8497 | 0.8559 | 0.9438 | |
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| 0.4611 | 6.0 | 300 | 0.5233 | 0.8217 | 0.8507 | 0.8345 | 0.8507 | 0.9219 | 0.8346 | 0.8267 | 0.9436 | |
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| 0.3671 | 7.0 | 350 | 0.5268 | 0.8383 | 0.8507 | 0.8360 | 0.8507 | 0.9206 | 0.8485 | 0.8393 | 0.9395 | |
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| 0.3278 | 8.0 | 400 | 0.5278 | 0.8370 | 0.8507 | 0.8369 | 0.8507 | 0.9147 | 0.8448 | 0.8444 | 0.9348 | |
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| 0.3727 | 9.0 | 450 | 0.5170 | 0.8339 | 0.8547 | 0.8405 | 0.8547 | 0.9134 | 0.8549 | 0.8407 | 0.9423 | |
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| 0.372 | 10.0 | 500 | 0.5171 | 0.8348 | 0.8531 | 0.8399 | 0.8531 | 0.9134 | 0.8505 | 0.8444 | 0.9391 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.15.0 |
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- Tokenizers 0.13.3 |
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