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---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: deberta-v3-small_v1_no_entities_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. -->
# deberta-v3-small_v1_no_entities_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: 0.0315
- Accuracy: 0.0062
- F1: 0.0086
- Precision: 0.0043
- Recall: 0.9070
- 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Rate |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| No log | 1.0 | 191 | 0.0385 | 0.0047 | 0.0094 | 0.0047 | 1.0 | 0.0000 |
| No log | 2.0 | 382 | 0.0282 | 0.0047 | 0.0094 | 0.0047 | 1.0 | 0.0000 |
| 0.1139 | 3.0 | 573 | 0.0274 | 0.0047 | 0.0094 | 0.0047 | 1.0 | 0.0000 |
| 0.1139 | 4.0 | 764 | 0.0270 | 0.0047 | 0.0094 | 0.0047 | 1.0 | 0.0000 |
| 0.1139 | 5.0 | 955 | 0.0271 | 0.0047 | 0.0094 | 0.0047 | 1.0 | 0.0000 |
| 0.0317 | 6.0 | 1146 | 0.0269 | 0.0047 | 0.0094 | 0.0047 | 1.0 | 0.0000 |
| 0.0317 | 7.0 | 1337 | 0.0271 | 0.0047 | 0.0094 | 0.0047 | 1.0 | 0.0000 |
| 0.0316 | 8.0 | 1528 | 0.0264 | 0.0047 | 0.0094 | 0.0047 | 1.0 | 0.0000 |
| 0.0316 | 9.0 | 1719 | 0.0261 | 0.0047 | 0.0094 | 0.0047 | 1.0 | 0.0000 |
| 0.0316 | 10.0 | 1910 | 0.0261 | 0.0047 | 0.0094 | 0.0047 | 1.0 | 0.0000 |
| 0.0299 | 11.0 | 2101 | 0.0263 | 0.0047 | 0.0094 | 0.0047 | 1.0 | 0.0000 |
| 0.0299 | 12.0 | 2292 | 0.0262 | 0.0047 | 0.0094 | 0.0047 | 1.0 | 0.0000 |
| 0.0299 | 13.0 | 2483 | 0.0260 | 0.0047 | 0.0094 | 0.0047 | 1.0 | 0.0000 |
| 0.0294 | 14.0 | 2674 | 0.0263 | 0.0047 | 0.0094 | 0.0047 | 1.0 | 0.0000 |
| 0.0294 | 15.0 | 2865 | 0.0259 | 0.0047 | 0.0094 | 0.0047 | 1.0 | 0.0000 |
| 0.026 | 16.0 | 3056 | 0.0262 | 0.0050 | 0.0094 | 0.0047 | 1.0 | 0.0000 |
| 0.026 | 17.0 | 3247 | 0.0265 | 0.0050 | 0.0092 | 0.0046 | 0.9767 | 0.0000 |
| 0.026 | 18.0 | 3438 | 0.0270 | 0.0048 | 0.0093 | 0.0047 | 0.9884 | 0.0000 |
| 0.0224 | 19.0 | 3629 | 0.0272 | 0.0056 | 0.0090 | 0.0045 | 0.9535 | 0.0000 |
| 0.0224 | 20.0 | 3820 | 0.0271 | 0.0055 | 0.0091 | 0.0046 | 0.9651 | 0.0000 |
| 0.0197 | 21.0 | 4011 | 0.0271 | 0.0052 | 0.0090 | 0.0045 | 0.9535 | 0.0000 |
| 0.0197 | 22.0 | 4202 | 0.0270 | 0.0050 | 0.0090 | 0.0045 | 0.9535 | 0.0000 |
| 0.0197 | 23.0 | 4393 | 0.0271 | 0.0056 | 0.0090 | 0.0045 | 0.9535 | 0.0000 |
| 0.0172 | 24.0 | 4584 | 0.0275 | 0.0053 | 0.0089 | 0.0045 | 0.9419 | 0.0000 |
| 0.0172 | 25.0 | 4775 | 0.0273 | 0.0053 | 0.0089 | 0.0045 | 0.9419 | 1e-05 |
| 0.0172 | 26.0 | 4966 | 0.0282 | 0.0061 | 0.0087 | 0.0044 | 0.9186 | 0.0000 |
| 0.0152 | 27.0 | 5157 | 0.0281 | 0.0060 | 0.0088 | 0.0044 | 0.9302 | 0.0000 |
| 0.0152 | 28.0 | 5348 | 0.0281 | 0.0058 | 0.0088 | 0.0044 | 0.9302 | 0.0000 |
| 0.0138 | 29.0 | 5539 | 0.0277 | 0.0059 | 0.0088 | 0.0044 | 0.9302 | 0.0000 |
| 0.0138 | 30.0 | 5730 | 0.0292 | 0.0056 | 0.0089 | 0.0045 | 0.9419 | 0.0000 |
| 0.0138 | 31.0 | 5921 | 0.0287 | 0.0061 | 0.0088 | 0.0044 | 0.9302 | 0.0000 |
| 0.0124 | 32.0 | 6112 | 0.0289 | 0.0059 | 0.0087 | 0.0044 | 0.9186 | 0.0000 |
| 0.0124 | 33.0 | 6303 | 0.0300 | 0.0062 | 0.0086 | 0.0043 | 0.9070 | 0.0000 |
| 0.0124 | 34.0 | 6494 | 0.0293 | 0.0057 | 0.0087 | 0.0044 | 0.9186 | 0.0000 |
| 0.0113 | 35.0 | 6685 | 0.0297 | 0.0059 | 0.0089 | 0.0045 | 0.9419 | 6e-06 |
| 0.0113 | 36.0 | 6876 | 0.0293 | 0.0060 | 0.0086 | 0.0043 | 0.9070 | 0.0000 |
| 0.0106 | 37.0 | 7067 | 0.0295 | 0.0060 | 0.0085 | 0.0043 | 0.8953 | 0.0000 |
| 0.0106 | 38.0 | 7258 | 0.0301 | 0.0063 | 0.0086 | 0.0043 | 0.9070 | 0.0000 |
| 0.0106 | 39.0 | 7449 | 0.0300 | 0.0063 | 0.0085 | 0.0043 | 0.8953 | 0.0000 |
| 0.0092 | 40.0 | 7640 | 0.0297 | 0.0057 | 0.0086 | 0.0043 | 0.9070 | 0.0000 |
| 0.0092 | 41.0 | 7831 | 0.0299 | 0.0061 | 0.0086 | 0.0043 | 0.9070 | 0.0000 |
| 0.0091 | 42.0 | 8022 | 0.0298 | 0.0064 | 0.0086 | 0.0043 | 0.9070 | 0.0000 |
| 0.0091 | 43.0 | 8213 | 0.0302 | 0.0061 | 0.0087 | 0.0044 | 0.9186 | 0.0000 |
| 0.0091 | 44.0 | 8404 | 0.0307 | 0.0062 | 0.0086 | 0.0043 | 0.9070 | 0.0000 |
| 0.0082 | 45.0 | 8595 | 0.0310 | 0.0062 | 0.0086 | 0.0043 | 0.9070 | 0.0000 |
| 0.0082 | 46.0 | 8786 | 0.0308 | 0.0062 | 0.0087 | 0.0044 | 0.9186 | 0.0000 |
| 0.0082 | 47.0 | 8977 | 0.0314 | 0.0062 | 0.0087 | 0.0044 | 0.9186 | 0.0000 |
| 0.0081 | 48.0 | 9168 | 0.0314 | 0.0064 | 0.0087 | 0.0044 | 0.9186 | 0.0000 |
| 0.0081 | 49.0 | 9359 | 0.0315 | 0.0062 | 0.0086 | 0.0043 | 0.9070 | 0.0000 |
| 0.0077 | 50.0 | 9550 | 0.0315 | 0.0062 | 0.0086 | 0.0043 | 0.9070 | 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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