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---
base_model: microsoft/mdeberta-v3-base
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: scenario-non-kd-scr-ner-half-mdeberta_data-univner_full44
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-non-kd-scr-ner-half-mdeberta_data-univner_full44
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3028
- Precision: 0.6277
- Recall: 0.5869
- F1: 0.6066
- Accuracy: 0.9615
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 44
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.36 | 0.2910 | 500 | 0.2872 | 0.2875 | 0.1173 | 0.1666 | 0.9284 |
| 0.2389 | 0.5821 | 1000 | 0.2086 | 0.3476 | 0.2561 | 0.2949 | 0.9388 |
| 0.1727 | 0.8731 | 1500 | 0.1810 | 0.4363 | 0.3748 | 0.4033 | 0.9465 |
| 0.1338 | 1.1641 | 2000 | 0.1644 | 0.4626 | 0.4675 | 0.4650 | 0.9514 |
| 0.1122 | 1.4552 | 2500 | 0.1560 | 0.4983 | 0.5063 | 0.5023 | 0.9538 |
| 0.1033 | 1.7462 | 3000 | 0.1504 | 0.5354 | 0.5128 | 0.5238 | 0.9564 |
| 0.0919 | 2.0373 | 3500 | 0.1475 | 0.5073 | 0.5452 | 0.5256 | 0.9558 |
| 0.0665 | 2.3283 | 4000 | 0.1547 | 0.5536 | 0.5578 | 0.5557 | 0.9583 |
| 0.066 | 2.6193 | 4500 | 0.1513 | 0.5345 | 0.5760 | 0.5545 | 0.9581 |
| 0.0642 | 2.9104 | 5000 | 0.1489 | 0.5750 | 0.5683 | 0.5716 | 0.9605 |
| 0.0495 | 3.2014 | 5500 | 0.1597 | 0.5769 | 0.5711 | 0.5740 | 0.9600 |
| 0.0437 | 3.4924 | 6000 | 0.1643 | 0.5848 | 0.5680 | 0.5763 | 0.9603 |
| 0.0444 | 3.7835 | 6500 | 0.1615 | 0.5884 | 0.5898 | 0.5891 | 0.9607 |
| 0.0409 | 4.0745 | 7000 | 0.1723 | 0.5869 | 0.5761 | 0.5815 | 0.9606 |
| 0.0313 | 4.3655 | 7500 | 0.1740 | 0.5871 | 0.5930 | 0.5900 | 0.9606 |
| 0.032 | 4.6566 | 8000 | 0.1682 | 0.5911 | 0.6031 | 0.5971 | 0.9611 |
| 0.0304 | 4.9476 | 8500 | 0.1771 | 0.6070 | 0.5783 | 0.5923 | 0.9613 |
| 0.0228 | 5.2386 | 9000 | 0.1843 | 0.5817 | 0.6045 | 0.5929 | 0.9608 |
| 0.0216 | 5.5297 | 9500 | 0.1841 | 0.5938 | 0.6142 | 0.6038 | 0.9609 |
| 0.0232 | 5.8207 | 10000 | 0.1957 | 0.5816 | 0.5998 | 0.5906 | 0.9600 |
| 0.0201 | 6.1118 | 10500 | 0.1982 | 0.6049 | 0.5963 | 0.6006 | 0.9611 |
| 0.0153 | 6.4028 | 11000 | 0.2040 | 0.5919 | 0.6057 | 0.5987 | 0.9602 |
| 0.0165 | 6.6938 | 11500 | 0.2039 | 0.6000 | 0.5988 | 0.5994 | 0.9609 |
| 0.0165 | 6.9849 | 12000 | 0.2076 | 0.5963 | 0.5913 | 0.5938 | 0.9606 |
| 0.0121 | 7.2759 | 12500 | 0.2178 | 0.6015 | 0.5833 | 0.5923 | 0.9604 |
| 0.012 | 7.5669 | 13000 | 0.2186 | 0.6206 | 0.5902 | 0.6050 | 0.9613 |
| 0.0126 | 7.8580 | 13500 | 0.2218 | 0.5882 | 0.6191 | 0.6033 | 0.9600 |
| 0.0098 | 8.1490 | 14000 | 0.2296 | 0.6164 | 0.5911 | 0.6035 | 0.9617 |
| 0.0091 | 8.4400 | 14500 | 0.2332 | 0.5986 | 0.5976 | 0.5981 | 0.9607 |
| 0.0097 | 8.7311 | 15000 | 0.2322 | 0.6053 | 0.5996 | 0.6024 | 0.9613 |
| 0.0089 | 9.0221 | 15500 | 0.2355 | 0.6174 | 0.6034 | 0.6103 | 0.9612 |
| 0.0064 | 9.3132 | 16000 | 0.2440 | 0.6306 | 0.5835 | 0.6061 | 0.9614 |
| 0.0071 | 9.6042 | 16500 | 0.2451 | 0.6220 | 0.5761 | 0.5982 | 0.9609 |
| 0.0073 | 9.8952 | 17000 | 0.2461 | 0.6203 | 0.5990 | 0.6095 | 0.9616 |
| 0.0064 | 10.1863 | 17500 | 0.2506 | 0.6213 | 0.5900 | 0.6052 | 0.9615 |
| 0.005 | 10.4773 | 18000 | 0.2547 | 0.6226 | 0.5970 | 0.6096 | 0.9617 |
| 0.0058 | 10.7683 | 18500 | 0.2553 | 0.6374 | 0.5897 | 0.6126 | 0.9620 |
| 0.0054 | 11.0594 | 19000 | 0.2624 | 0.6232 | 0.5840 | 0.6030 | 0.9617 |
| 0.0044 | 11.3504 | 19500 | 0.2655 | 0.6262 | 0.5946 | 0.6100 | 0.9620 |
| 0.0048 | 11.6414 | 20000 | 0.2654 | 0.6154 | 0.5989 | 0.6070 | 0.9616 |
| 0.0042 | 11.9325 | 20500 | 0.2724 | 0.6306 | 0.5806 | 0.6046 | 0.9616 |
| 0.004 | 12.2235 | 21000 | 0.2707 | 0.6052 | 0.5920 | 0.5985 | 0.9607 |
| 0.0035 | 12.5146 | 21500 | 0.2714 | 0.5962 | 0.5986 | 0.5974 | 0.9607 |
| 0.0041 | 12.8056 | 22000 | 0.2755 | 0.6263 | 0.5858 | 0.6053 | 0.9616 |
| 0.0035 | 13.0966 | 22500 | 0.2842 | 0.6350 | 0.5814 | 0.6071 | 0.9614 |
| 0.0033 | 13.3877 | 23000 | 0.2763 | 0.6317 | 0.5868 | 0.6084 | 0.9614 |
| 0.0028 | 13.6787 | 23500 | 0.2831 | 0.6141 | 0.5976 | 0.6057 | 0.9616 |
| 0.0031 | 13.9697 | 24000 | 0.2797 | 0.6141 | 0.6064 | 0.6102 | 0.9614 |
| 0.0024 | 14.2608 | 24500 | 0.2873 | 0.5980 | 0.6038 | 0.6009 | 0.9611 |
| 0.0025 | 14.5518 | 25000 | 0.2913 | 0.6055 | 0.5980 | 0.6017 | 0.9612 |
| 0.003 | 14.8428 | 25500 | 0.2885 | 0.6208 | 0.5843 | 0.6020 | 0.9615 |
| 0.0023 | 15.1339 | 26000 | 0.2923 | 0.6255 | 0.5849 | 0.6045 | 0.9618 |
| 0.0019 | 15.4249 | 26500 | 0.2875 | 0.6221 | 0.6015 | 0.6116 | 0.9619 |
| 0.0027 | 15.7159 | 27000 | 0.2898 | 0.6241 | 0.5967 | 0.6101 | 0.9619 |
| 0.0024 | 16.0070 | 27500 | 0.2943 | 0.6146 | 0.5895 | 0.6018 | 0.9612 |
| 0.0016 | 16.2980 | 28000 | 0.2996 | 0.6199 | 0.5928 | 0.6060 | 0.9614 |
| 0.0022 | 16.5891 | 28500 | 0.3028 | 0.6277 | 0.5869 | 0.6066 | 0.9615 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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