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---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-large-finetuned-ours-DS
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-finetuned-ours-DS
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3369
- Accuracy: 0.75
- Precision: 0.7054
- Recall: 0.6949
- F1: 0.6974
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0561 | 0.99 | 99 | 0.8773 | 0.615 | 0.4054 | 0.5584 | 0.4591 |
| 0.762 | 1.98 | 198 | 0.6514 | 0.715 | 0.6735 | 0.6672 | 0.6588 |
| 0.5661 | 2.97 | 297 | 0.6806 | 0.71 | 0.6764 | 0.6608 | 0.6435 |
| 0.3699 | 3.96 | 396 | 0.8358 | 0.71 | 0.6611 | 0.6691 | 0.6570 |
| 0.2184 | 4.95 | 495 | 1.1627 | 0.7 | 0.6597 | 0.6337 | 0.6414 |
| 0.1743 | 5.94 | 594 | 1.0544 | 0.725 | 0.6831 | 0.6949 | 0.6831 |
| 0.098 | 6.93 | 693 | 1.4757 | 0.73 | 0.6885 | 0.6902 | 0.6892 |
| 0.0813 | 7.92 | 792 | 1.8146 | 0.73 | 0.6840 | 0.6772 | 0.6800 |
| 0.0435 | 8.91 | 891 | 1.6697 | 0.755 | 0.7141 | 0.7127 | 0.7132 |
| 0.0209 | 9.9 | 990 | 1.8931 | 0.755 | 0.7102 | 0.7070 | 0.7082 |
| 0.0201 | 10.89 | 1089 | 2.1934 | 0.74 | 0.6971 | 0.6866 | 0.6907 |
| 0.0095 | 11.88 | 1188 | 2.1389 | 0.75 | 0.7014 | 0.6915 | 0.6932 |
| 0.0141 | 12.87 | 1287 | 2.1902 | 0.74 | 0.6942 | 0.6943 | 0.6936 |
| 0.0112 | 13.86 | 1386 | 2.5021 | 0.73 | 0.6889 | 0.6669 | 0.6741 |
| 0.0054 | 14.85 | 1485 | 2.3840 | 0.73 | 0.6819 | 0.6715 | 0.6746 |
| 0.0088 | 15.84 | 1584 | 2.3224 | 0.74 | 0.6909 | 0.6825 | 0.6787 |
| 0.003 | 16.83 | 1683 | 2.2641 | 0.75 | 0.7054 | 0.6949 | 0.6974 |
| 0.0017 | 17.82 | 1782 | 2.3361 | 0.75 | 0.7077 | 0.6968 | 0.7012 |
| 0.0014 | 18.81 | 1881 | 2.3041 | 0.755 | 0.7131 | 0.7009 | 0.7051 |
| 0.0083 | 19.8 | 1980 | 2.3369 | 0.75 | 0.7054 | 0.6949 | 0.6974 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
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