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---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: scenario-TCR-XLMR-XCOPA_data-xcopa_all
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. -->
# scenario-TCR-XLMR-XCOPA_data-xcopa_all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6932
- Accuracy: 0.4558
- F1: 0.4185
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 0.38 | 5 | 0.6932 | 0.49 | 0.4715 |
| No log | 0.77 | 10 | 0.6932 | 0.4925 | 0.4634 |
| No log | 1.15 | 15 | 0.6931 | 0.5033 | 0.4669 |
| No log | 1.54 | 20 | 0.6932 | 0.4683 | 0.4491 |
| No log | 1.92 | 25 | 0.6932 | 0.4883 | 0.4689 |
| No log | 2.31 | 30 | 0.6932 | 0.4808 | 0.4716 |
| No log | 2.69 | 35 | 0.6931 | 0.5058 | 0.4785 |
| No log | 3.08 | 40 | 0.6932 | 0.4867 | 0.4615 |
| No log | 3.46 | 45 | 0.6932 | 0.4533 | 0.4090 |
| No log | 3.85 | 50 | 0.6932 | 0.4592 | 0.4190 |
| No log | 4.23 | 55 | 0.6931 | 0.5167 | 0.4858 |
| No log | 4.62 | 60 | 0.6931 | 0.5133 | 0.4832 |
| No log | 5.0 | 65 | 0.6931 | 0.525 | 0.5128 |
| No log | 5.38 | 70 | 0.6931 | 0.5158 | 0.5072 |
| No log | 5.77 | 75 | 0.6931 | 0.5275 | 0.5158 |
| No log | 6.15 | 80 | 0.6931 | 0.5333 | 0.5302 |
| No log | 6.54 | 85 | 0.6931 | 0.5075 | 0.4883 |
| No log | 6.92 | 90 | 0.6931 | 0.5042 | 0.4893 |
| No log | 7.31 | 95 | 0.6930 | 0.5042 | 0.4979 |
| No log | 7.69 | 100 | 0.6932 | 0.4883 | 0.4623 |
| No log | 8.08 | 105 | 0.6932 | 0.4617 | 0.4159 |
| No log | 8.46 | 110 | 0.6932 | 0.4425 | 0.4085 |
| No log | 8.85 | 115 | 0.6931 | 0.4992 | 0.4705 |
| No log | 9.23 | 120 | 0.6931 | 0.4975 | 0.4668 |
| No log | 9.62 | 125 | 0.6931 | 0.4867 | 0.4510 |
| No log | 10.0 | 130 | 0.6931 | 0.4817 | 0.4456 |
| No log | 10.38 | 135 | 0.6931 | 0.4808 | 0.4442 |
| No log | 10.77 | 140 | 0.6932 | 0.49 | 0.4565 |
| No log | 11.15 | 145 | 0.6932 | 0.4908 | 0.4579 |
| No log | 11.54 | 150 | 0.6931 | 0.5067 | 0.4761 |
| No log | 11.92 | 155 | 0.6931 | 0.5083 | 0.4887 |
| No log | 12.31 | 160 | 0.6931 | 0.5217 | 0.4974 |
| No log | 12.69 | 165 | 0.6932 | 0.4967 | 0.4499 |
| No log | 13.08 | 170 | 0.6931 | 0.5158 | 0.4872 |
| No log | 13.46 | 175 | 0.6931 | 0.5225 | 0.5065 |
| No log | 13.85 | 180 | 0.6931 | 0.5475 | 0.5323 |
| No log | 14.23 | 185 | 0.6931 | 0.5383 | 0.5224 |
| No log | 14.62 | 190 | 0.6932 | 0.4925 | 0.4558 |
| No log | 15.0 | 195 | 0.6932 | 0.4783 | 0.4340 |
| No log | 15.38 | 200 | 0.6931 | 0.5267 | 0.5120 |
| No log | 15.77 | 205 | 0.6931 | 0.5158 | 0.4890 |
| No log | 16.15 | 210 | 0.6931 | 0.53 | 0.5179 |
| No log | 16.54 | 215 | 0.6932 | 0.48 | 0.4497 |
| No log | 16.92 | 220 | 0.6932 | 0.4558 | 0.4133 |
| No log | 17.31 | 225 | 0.6932 | 0.4742 | 0.4290 |
| No log | 17.69 | 230 | 0.6931 | 0.4967 | 0.4838 |
| No log | 18.08 | 235 | 0.6931 | 0.5325 | 0.5126 |
| No log | 18.46 | 240 | 0.6931 | 0.5242 | 0.5090 |
| No log | 18.85 | 245 | 0.6931 | 0.5242 | 0.5013 |
| No log | 19.23 | 250 | 0.6932 | 0.4683 | 0.4273 |
| No log | 19.62 | 255 | 0.6931 | 0.5158 | 0.4890 |
| No log | 20.0 | 260 | 0.6932 | 0.4533 | 0.4225 |
| No log | 20.38 | 265 | 0.6932 | 0.4658 | 0.4282 |
| No log | 20.77 | 270 | 0.6932 | 0.4667 | 0.4326 |
| No log | 21.15 | 275 | 0.6932 | 0.4725 | 0.4403 |
| No log | 21.54 | 280 | 0.6932 | 0.4667 | 0.4306 |
| No log | 21.92 | 285 | 0.6932 | 0.4658 | 0.4272 |
| No log | 22.31 | 290 | 0.6932 | 0.4625 | 0.4256 |
| No log | 22.69 | 295 | 0.6932 | 0.4617 | 0.4232 |
| No log | 23.08 | 300 | 0.6932 | 0.465 | 0.4247 |
| No log | 23.46 | 305 | 0.6932 | 0.455 | 0.4223 |
| No log | 23.85 | 310 | 0.6931 | 0.4558 | 0.4226 |
| No log | 24.23 | 315 | 0.6931 | 0.4875 | 0.4638 |
| No log | 24.62 | 320 | 0.6931 | 0.4642 | 0.4315 |
| No log | 25.0 | 325 | 0.6931 | 0.4508 | 0.4153 |
| No log | 25.38 | 330 | 0.6932 | 0.4558 | 0.4185 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
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