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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
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
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: 0.0134
- Accuracy: 0.0038
- F1: 0.0062
- Precision: 0.0031
- Recall: 0.625
- 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 | 20 | 0.5469 | 0.0994 | 0.0093 | 0.0047 | 0.8438 | 0.0000 |
| No log | 2.0 | 40 | 0.3859 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 3.0 | 60 | 0.2662 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 4.0 | 80 | 0.1781 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 5.0 | 100 | 0.1183 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 6.0 | 120 | 0.0823 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 7.0 | 140 | 0.0614 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 8.0 | 160 | 0.0494 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 9.0 | 180 | 0.0423 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 10.0 | 200 | 0.0379 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 11.0 | 220 | 0.0350 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 12.0 | 240 | 0.0331 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 13.0 | 260 | 0.0318 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 14.0 | 280 | 0.0307 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 15.0 | 300 | 0.0300 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 16.0 | 320 | 0.0294 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 17.0 | 340 | 0.0290 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 18.0 | 360 | 0.0286 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 19.0 | 380 | 0.0283 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 20.0 | 400 | 0.0300 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 21.0 | 420 | 0.0290 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 22.0 | 440 | 0.0252 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 23.0 | 460 | 0.0246 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| No log | 24.0 | 480 | 0.0242 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| 0.1127 | 25.0 | 500 | 0.0239 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| 0.1127 | 26.0 | 520 | 0.0233 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| 0.1127 | 27.0 | 540 | 0.0226 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| 0.1127 | 28.0 | 560 | 0.0224 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| 0.1127 | 29.0 | 580 | 0.0217 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
| 0.1127 | 30.0 | 600 | 0.0211 | 0.0047 | 0.0093 | 0.0047 | 0.9375 | 0.0000 |
| 0.1127 | 31.0 | 620 | 0.0206 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
| 0.1127 | 32.0 | 640 | 0.0207 | 0.0047 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
| 0.1127 | 33.0 | 660 | 0.0198 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
| 0.1127 | 34.0 | 680 | 0.0205 | 0.0047 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
| 0.1127 | 35.0 | 700 | 0.0193 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
| 0.1127 | 36.0 | 720 | 0.0198 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
| 0.1127 | 37.0 | 740 | 0.0190 | 0.0047 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
| 0.1127 | 38.0 | 760 | 0.0197 | 0.0049 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
| 0.1127 | 39.0 | 780 | 0.0185 | 0.0047 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
| 0.1127 | 40.0 | 800 | 0.0184 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
| 0.1127 | 41.0 | 820 | 0.0188 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
| 0.1127 | 42.0 | 840 | 0.0179 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
| 0.1127 | 43.0 | 860 | 0.0178 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
| 0.1127 | 44.0 | 880 | 0.0174 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
| 0.1127 | 45.0 | 900 | 0.0182 | 0.0041 | 0.0081 | 0.0041 | 0.8125 | 0.0000 |
| 0.1127 | 46.0 | 920 | 0.0171 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
| 0.1127 | 47.0 | 940 | 0.0168 | 0.0044 | 0.0087 | 0.0044 | 0.875 | 0.0000 |
| 0.1127 | 48.0 | 960 | 0.0167 | 0.0041 | 0.0081 | 0.0041 | 0.8125 | 0.0000 |
| 0.1127 | 49.0 | 980 | 0.0165 | 0.0039 | 0.0078 | 0.0039 | 0.7812 | 0.0000 |
| 0.0253 | 50.0 | 1000 | 0.0162 | 0.0039 | 0.0078 | 0.0039 | 0.7812 | 1e-05 |
| 0.0253 | 51.0 | 1020 | 0.0160 | 0.0041 | 0.0081 | 0.0041 | 0.8125 | 0.0000 |
| 0.0253 | 52.0 | 1040 | 0.0159 | 0.0038 | 0.0075 | 0.0038 | 0.75 | 0.0000 |
| 0.0253 | 53.0 | 1060 | 0.0158 | 0.0038 | 0.0075 | 0.0038 | 0.75 | 0.0000 |
| 0.0253 | 54.0 | 1080 | 0.0163 | 0.0041 | 0.0075 | 0.0038 | 0.75 | 0.0000 |
| 0.0253 | 55.0 | 1100 | 0.0160 | 0.0039 | 0.0072 | 0.0036 | 0.7188 | 9e-06 |
| 0.0253 | 56.0 | 1120 | 0.0161 | 0.0034 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
| 0.0253 | 57.0 | 1140 | 0.0156 | 0.0036 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
| 0.0253 | 58.0 | 1160 | 0.0154 | 0.0041 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
| 0.0253 | 59.0 | 1180 | 0.0155 | 0.0039 | 0.0072 | 0.0036 | 0.7188 | 0.0000 |
| 0.0253 | 60.0 | 1200 | 0.0155 | 0.0036 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
| 0.0253 | 61.0 | 1220 | 0.0154 | 0.0038 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
| 0.0253 | 62.0 | 1240 | 0.0156 | 0.0041 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
| 0.0253 | 63.0 | 1260 | 0.0152 | 0.0038 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
| 0.0253 | 64.0 | 1280 | 0.0146 | 0.0036 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
| 0.0253 | 65.0 | 1300 | 0.0147 | 0.0041 | 0.0069 | 0.0034 | 0.6875 | 7e-06 |
| 0.0253 | 66.0 | 1320 | 0.0149 | 0.0039 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
| 0.0253 | 67.0 | 1340 | 0.0148 | 0.0038 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
| 0.0253 | 68.0 | 1360 | 0.0148 | 0.0039 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
| 0.0253 | 69.0 | 1380 | 0.0143 | 0.0041 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
| 0.0253 | 70.0 | 1400 | 0.0144 | 0.0039 | 0.0062 | 0.0031 | 0.625 | 6e-06 |
| 0.0253 | 71.0 | 1420 | 0.0145 | 0.0039 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
| 0.0253 | 72.0 | 1440 | 0.0141 | 0.0038 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
| 0.0253 | 73.0 | 1460 | 0.0144 | 0.0039 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
| 0.0253 | 74.0 | 1480 | 0.0144 | 0.0039 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
| 0.019 | 75.0 | 1500 | 0.0142 | 0.0036 | 0.0062 | 0.0031 | 0.625 | 5e-06 |
| 0.019 | 76.0 | 1520 | 0.0140 | 0.0041 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
| 0.019 | 77.0 | 1540 | 0.0139 | 0.0039 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
| 0.019 | 78.0 | 1560 | 0.0140 | 0.0039 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
| 0.019 | 79.0 | 1580 | 0.0139 | 0.0038 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
| 0.019 | 80.0 | 1600 | 0.0139 | 0.0039 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
| 0.019 | 81.0 | 1620 | 0.0139 | 0.0042 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
| 0.019 | 82.0 | 1640 | 0.0136 | 0.0036 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
| 0.019 | 83.0 | 1660 | 0.0138 | 0.0041 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
| 0.019 | 84.0 | 1680 | 0.0136 | 0.0039 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
| 0.019 | 85.0 | 1700 | 0.0136 | 0.0038 | 0.0059 | 0.0030 | 0.5938 | 3e-06 |
| 0.019 | 86.0 | 1720 | 0.0136 | 0.0038 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
| 0.019 | 87.0 | 1740 | 0.0133 | 0.0038 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
| 0.019 | 88.0 | 1760 | 0.0137 | 0.0039 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
| 0.019 | 89.0 | 1780 | 0.0134 | 0.0036 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
| 0.019 | 90.0 | 1800 | 0.0133 | 0.0038 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
| 0.019 | 91.0 | 1820 | 0.0137 | 0.0041 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
| 0.019 | 92.0 | 1840 | 0.0134 | 0.0038 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
| 0.019 | 93.0 | 1860 | 0.0135 | 0.0038 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
| 0.019 | 94.0 | 1880 | 0.0134 | 0.0038 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
| 0.019 | 95.0 | 1900 | 0.0136 | 0.0039 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
| 0.019 | 96.0 | 1920 | 0.0135 | 0.0039 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
| 0.019 | 97.0 | 1940 | 0.0134 | 0.0038 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
| 0.019 | 98.0 | 1960 | 0.0134 | 0.0038 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
| 0.019 | 99.0 | 1980 | 0.0134 | 0.0038 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
| 0.0156 | 100.0 | 2000 | 0.0134 | 0.0038 | 0.0062 | 0.0031 | 0.625 | 0.0 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|