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--- |
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license: mit |
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base_model: roberta-large |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: roberta-large-fomc |
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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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# roberta-large-fomc |
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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: 0.7874 |
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- Accuracy: 0.6660 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:--------:| |
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| No log | 0.0083 | 1 | 1.0582 | 0.4980 | |
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| 1.0574 | 0.2149 | 26 | 1.0428 | 0.4980 | |
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| 1.0702 | 0.4215 | 51 | 1.0500 | 0.4980 | |
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| 1.1065 | 0.6281 | 76 | 1.0374 | 0.4980 | |
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| 1.0241 | 0.8347 | 101 | 1.0391 | 0.4980 | |
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| 1.0324 | 1.0 | 121 | 1.0191 | 0.4980 | |
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| 1.0324 | 1.0413 | 126 | 1.0097 | 0.4980 | |
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| 0.9751 | 1.2479 | 151 | 1.0542 | 0.4737 | |
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| 1.0134 | 1.4545 | 176 | 0.9746 | 0.5931 | |
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| 0.9276 | 1.6612 | 201 | 0.8633 | 0.5648 | |
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| 0.8469 | 1.8678 | 226 | 0.7729 | 0.6538 | |
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| 0.7992 | 2.0 | 242 | 0.7874 | 0.6660 | |
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| 0.8853 | 2.0744 | 251 | 0.8597 | 0.6680 | |
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| 0.6466 | 2.2810 | 276 | 0.7767 | 0.6498 | |
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| 0.778 | 2.4876 | 301 | 1.0588 | 0.6498 | |
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| 0.7202 | 2.6942 | 326 | 0.7493 | 0.6721 | |
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| 0.7108 | 2.9008 | 351 | 0.8892 | 0.6397 | |
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| 0.6354 | 3.0 | 363 | 0.8265 | 0.6579 | |
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| 0.7704 | 3.1074 | 376 | 0.7833 | 0.6781 | |
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| 0.6867 | 3.3140 | 401 | 0.9702 | 0.6478 | |
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| 0.6973 | 3.5207 | 426 | 1.0300 | 0.6700 | |
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| 0.6682 | 3.7273 | 451 | 0.8206 | 0.6781 | |
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| 0.6605 | 3.9339 | 476 | 0.8862 | 0.6822 | |
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| 0.8521 | 4.0 | 484 | 0.8093 | 0.6316 | |
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| 0.6442 | 4.1405 | 501 | 0.9483 | 0.6437 | |
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| 0.577 | 4.3471 | 526 | 0.8860 | 0.6883 | |
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| 0.5252 | 4.5537 | 551 | 0.8797 | 0.7045 | |
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| 0.5274 | 4.7603 | 576 | 0.7289 | 0.7024 | |
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| 0.467 | 4.9669 | 601 | 0.8224 | 0.6903 | |
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| 0.467 | 5.0 | 605 | 0.8218 | 0.6903 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 1.12.0 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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