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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-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-13 |
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This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4600 |
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- Precision: 0.8364 |
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- Recall: 0.8452 |
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- F1: 0.8383 |
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- Accuracy: 0.8452 |
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- F1 Who: 0.9189 |
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- F1 What: 0.8621 |
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- F1 Where: 0.9231 |
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- F1 How: 0.9141 |
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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: 5 |
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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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| 1.9849 | 0.35 | 20 | 1.4123 | 0.5426 | 0.6351 | 0.5494 | 0.6351 | 0.1026 | 0.6222 | 0.3232 | 0.7857 | |
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| 1.2159 | 0.7 | 40 | 0.9450 | 0.6559 | 0.7188 | 0.6592 | 0.7188 | 0.6780 | 0.7539 | 0.7071 | 0.7882 | |
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| 0.8634 | 1.05 | 60 | 0.6885 | 0.7652 | 0.7994 | 0.7725 | 0.7994 | 0.9067 | 0.8152 | 0.8070 | 0.8940 | |
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| 0.6777 | 1.4 | 80 | 0.6144 | 0.7650 | 0.7946 | 0.7711 | 0.7946 | 0.9189 | 0.7876 | 0.8039 | 0.9085 | |
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| 0.6051 | 1.75 | 100 | 0.5485 | 0.8126 | 0.8278 | 0.8150 | 0.8278 | 0.9315 | 0.8362 | 0.8148 | 0.9241 | |
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| 0.5511 | 2.11 | 120 | 0.5264 | 0.8113 | 0.8167 | 0.8036 | 0.8167 | 0.9315 | 0.8444 | 0.8257 | 0.9199 | |
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| 0.486 | 2.46 | 140 | 0.4867 | 0.8230 | 0.8357 | 0.8248 | 0.8357 | 0.9315 | 0.8539 | 0.9091 | 0.9048 | |
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| 0.4813 | 2.81 | 160 | 0.4767 | 0.8285 | 0.8278 | 0.8213 | 0.8278 | 0.9189 | 0.8701 | 0.9076 | 0.9135 | |
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| 0.4494 | 3.16 | 180 | 0.5042 | 0.8152 | 0.8199 | 0.8126 | 0.8199 | 0.9315 | 0.8427 | 0.8333 | 0.8956 | |
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| 0.4018 | 3.51 | 200 | 0.4802 | 0.8248 | 0.8357 | 0.8249 | 0.8357 | 0.9189 | 0.8736 | 0.8780 | 0.9357 | |
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| 0.4205 | 3.86 | 220 | 0.4723 | 0.8340 | 0.8389 | 0.8346 | 0.8389 | 0.9189 | 0.8636 | 0.9138 | 0.8986 | |
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| 0.3535 | 4.21 | 240 | 0.4669 | 0.8324 | 0.8452 | 0.8364 | 0.8452 | 0.9189 | 0.8571 | 0.9138 | 0.9167 | |
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| 0.3808 | 4.56 | 260 | 0.4585 | 0.8349 | 0.8452 | 0.8383 | 0.8452 | 0.9189 | 0.8621 | 0.9231 | 0.9141 | |
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| 0.3491 | 4.91 | 280 | 0.4600 | 0.8364 | 0.8452 | 0.8383 | 0.8452 | 0.9189 | 0.8621 | 0.9231 | 0.9141 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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