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---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: copilot_relex_v1_with_context
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. -->
# copilot_relex_v1_with_context
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0299
- Accuracy: 0.0075
- F1: 0.0127
- Precision: 0.0064
- Recall: 0.8358
- Learning Rate: 0.0
## 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: 2e-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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Rate |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| No log | 1.0 | 26 | 0.5156 | 0.0531 | 0.0154 | 0.0078 | 0.9701 | 0.0000 |
| No log | 2.0 | 52 | 0.3270 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 3.0 | 78 | 0.1951 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 4.0 | 104 | 0.1153 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 5.0 | 130 | 0.0759 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 6.0 | 156 | 0.0584 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 7.0 | 182 | 0.0503 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 8.0 | 208 | 0.0462 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 9.0 | 234 | 0.0440 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 10.0 | 260 | 0.0427 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 11.0 | 286 | 0.0419 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 12.0 | 312 | 0.0413 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 13.0 | 338 | 0.0410 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 14.0 | 364 | 0.0407 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 15.0 | 390 | 0.0405 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 16.0 | 416 | 0.0403 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 17.0 | 442 | 0.0402 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 18.0 | 468 | 0.0400 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 19.0 | 494 | 0.0399 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 20.0 | 520 | 0.0397 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 21.0 | 546 | 0.0388 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 22.0 | 572 | 0.0388 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 23.0 | 598 | 0.0387 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 24.0 | 624 | 0.0375 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 25.0 | 650 | 0.0376 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 26.0 | 676 | 0.0369 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 27.0 | 702 | 0.0367 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 28.0 | 728 | 0.0373 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 29.0 | 754 | 0.0362 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 30.0 | 780 | 0.0361 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 31.0 | 806 | 0.0358 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 32.0 | 832 | 0.0355 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 33.0 | 858 | 0.0329 | 0.0073 | 0.0145 | 0.0073 | 0.9552 | 0.0000 |
| 0.1144 | 34.0 | 884 | 0.0327 | 0.0078 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 35.0 | 910 | 0.0328 | 0.0074 | 0.0147 | 0.0074 | 0.9701 | 0.0000 |
| 0.1144 | 36.0 | 936 | 0.0324 | 0.0075 | 0.0147 | 0.0074 | 0.9701 | 0.0000 |
| 0.1144 | 37.0 | 962 | 0.0316 | 0.0075 | 0.0147 | 0.0074 | 0.9701 | 0.0000 |
| 0.1144 | 38.0 | 988 | 0.0326 | 0.0075 | 0.0145 | 0.0073 | 0.9552 | 0.0000 |
| 0.029 | 39.0 | 1014 | 0.0312 | 0.0074 | 0.0145 | 0.0073 | 0.9552 | 0.0000 |
| 0.029 | 40.0 | 1040 | 0.0313 | 0.0072 | 0.0141 | 0.0071 | 0.9254 | 0.0000 |
| 0.029 | 41.0 | 1066 | 0.0320 | 0.0073 | 0.0143 | 0.0072 | 0.9403 | 0.0000 |
| 0.029 | 42.0 | 1092 | 0.0316 | 0.0074 | 0.0145 | 0.0073 | 0.9552 | 0.0000 |
| 0.029 | 43.0 | 1118 | 0.0310 | 0.0072 | 0.0136 | 0.0069 | 0.8955 | 0.0000 |
| 0.029 | 44.0 | 1144 | 0.0311 | 0.0072 | 0.0141 | 0.0071 | 0.9254 | 0.0000 |
| 0.029 | 45.0 | 1170 | 0.0310 | 0.0072 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.029 | 46.0 | 1196 | 0.0312 | 0.0071 | 0.0134 | 0.0067 | 0.8806 | 0.0000 |
| 0.029 | 47.0 | 1222 | 0.0308 | 0.0071 | 0.0134 | 0.0067 | 0.8806 | 0.0000 |
| 0.029 | 48.0 | 1248 | 0.0312 | 0.0072 | 0.0136 | 0.0069 | 0.8955 | 0.0000 |
| 0.029 | 49.0 | 1274 | 0.0309 | 0.0073 | 0.0136 | 0.0069 | 0.8955 | 0.0000 |
| 0.029 | 50.0 | 1300 | 0.0307 | 0.0070 | 0.0129 | 0.0065 | 0.8507 | 1e-05 |
| 0.029 | 51.0 | 1326 | 0.0303 | 0.0071 | 0.0134 | 0.0067 | 0.8806 | 0.0000 |
| 0.029 | 52.0 | 1352 | 0.0307 | 0.0073 | 0.0134 | 0.0067 | 0.8806 | 0.0000 |
| 0.029 | 53.0 | 1378 | 0.0309 | 0.0073 | 0.0134 | 0.0067 | 0.8806 | 0.0000 |
| 0.029 | 54.0 | 1404 | 0.0312 | 0.0072 | 0.0136 | 0.0069 | 0.8955 | 0.0000 |
| 0.029 | 55.0 | 1430 | 0.0303 | 0.0073 | 0.0136 | 0.0069 | 0.8955 | 9e-06 |
| 0.029 | 56.0 | 1456 | 0.0300 | 0.0071 | 0.0132 | 0.0066 | 0.8657 | 0.0000 |
| 0.029 | 57.0 | 1482 | 0.0301 | 0.0069 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.0205 | 58.0 | 1508 | 0.0302 | 0.0072 | 0.0132 | 0.0066 | 0.8657 | 0.0000 |
| 0.0205 | 59.0 | 1534 | 0.0303 | 0.0071 | 0.0129 | 0.0065 | 0.8507 | 0.0000 |
| 0.0205 | 60.0 | 1560 | 0.0308 | 0.0073 | 0.0132 | 0.0066 | 0.8657 | 0.0000 |
| 0.0205 | 61.0 | 1586 | 0.0309 | 0.0074 | 0.0136 | 0.0069 | 0.8955 | 0.0000 |
| 0.0205 | 62.0 | 1612 | 0.0306 | 0.0078 | 0.0130 | 0.0065 | 0.8507 | 0.0000 |
| 0.0205 | 63.0 | 1638 | 0.0308 | 0.0077 | 0.0130 | 0.0065 | 0.8507 | 0.0000 |
| 0.0205 | 64.0 | 1664 | 0.0303 | 0.0071 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.0205 | 65.0 | 1690 | 0.0312 | 0.0077 | 0.0132 | 0.0066 | 0.8657 | 7e-06 |
| 0.0205 | 66.0 | 1716 | 0.0304 | 0.0073 | 0.0132 | 0.0066 | 0.8657 | 0.0000 |
| 0.0205 | 67.0 | 1742 | 0.0305 | 0.0073 | 0.0132 | 0.0066 | 0.8657 | 0.0000 |
| 0.0205 | 68.0 | 1768 | 0.0304 | 0.0074 | 0.0132 | 0.0066 | 0.8657 | 0.0000 |
| 0.0205 | 69.0 | 1794 | 0.0306 | 0.0072 | 0.0129 | 0.0065 | 0.8507 | 0.0000 |
| 0.0205 | 70.0 | 1820 | 0.0314 | 0.0080 | 0.0134 | 0.0068 | 0.8806 | 6e-06 |
| 0.0205 | 71.0 | 1846 | 0.0314 | 0.0075 | 0.0132 | 0.0066 | 0.8657 | 0.0000 |
| 0.0205 | 72.0 | 1872 | 0.0307 | 0.0075 | 0.0132 | 0.0066 | 0.8657 | 0.0000 |
| 0.0205 | 73.0 | 1898 | 0.0300 | 0.0075 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.0205 | 74.0 | 1924 | 0.0301 | 0.0072 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.0205 | 75.0 | 1950 | 0.0297 | 0.0075 | 0.0132 | 0.0066 | 0.8657 | 5e-06 |
| 0.0205 | 76.0 | 1976 | 0.0306 | 0.0075 | 0.0130 | 0.0065 | 0.8507 | 0.0000 |
| 0.016 | 77.0 | 2002 | 0.0299 | 0.0073 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 78.0 | 2028 | 0.0301 | 0.0074 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 79.0 | 2054 | 0.0301 | 0.0078 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.016 | 80.0 | 2080 | 0.0306 | 0.0078 | 0.0130 | 0.0065 | 0.8507 | 0.0000 |
| 0.016 | 81.0 | 2106 | 0.0302 | 0.0073 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 82.0 | 2132 | 0.0305 | 0.0073 | 0.0129 | 0.0065 | 0.8507 | 0.0000 |
| 0.016 | 83.0 | 2158 | 0.0303 | 0.0073 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.016 | 84.0 | 2184 | 0.0302 | 0.0072 | 0.0129 | 0.0065 | 0.8507 | 0.0000 |
| 0.016 | 85.0 | 2210 | 0.0302 | 0.0072 | 0.0127 | 0.0064 | 0.8358 | 3e-06 |
| 0.016 | 86.0 | 2236 | 0.0299 | 0.0072 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 87.0 | 2262 | 0.0296 | 0.0069 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 88.0 | 2288 | 0.0299 | 0.0073 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.016 | 89.0 | 2314 | 0.0297 | 0.0072 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 90.0 | 2340 | 0.0296 | 0.0073 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 91.0 | 2366 | 0.0299 | 0.0071 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 92.0 | 2392 | 0.0293 | 0.0071 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 93.0 | 2418 | 0.0301 | 0.0073 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.016 | 94.0 | 2444 | 0.0294 | 0.0071 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 95.0 | 2470 | 0.0296 | 0.0072 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 96.0 | 2496 | 0.0298 | 0.0074 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.0136 | 97.0 | 2522 | 0.0299 | 0.0073 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.0136 | 98.0 | 2548 | 0.0298 | 0.0074 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.0136 | 99.0 | 2574 | 0.0299 | 0.0075 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.0136 | 100.0 | 2600 | 0.0299 | 0.0075 | 0.0127 | 0.0064 | 0.8358 | 0.0 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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