---
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-full-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-full-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: 0.3003
- Precision: 0.6230
- Recall: 0.5993
- F1: 0.6109
- Accuracy: 0.9631

## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3129        | 0.2910  | 500   | 0.2430          | 0.3687    | 0.2001 | 0.2594 | 0.9351   |
| 0.201         | 0.5821  | 1000  | 0.1893          | 0.3603    | 0.3711 | 0.3656 | 0.9430   |
| 0.1493        | 0.8731  | 1500  | 0.1664          | 0.4946    | 0.4279 | 0.4588 | 0.9519   |
| 0.1081        | 1.1641  | 2000  | 0.1566          | 0.5297    | 0.5299 | 0.5298 | 0.9563   |
| 0.0881        | 1.4552  | 2500  | 0.1487          | 0.5472    | 0.5748 | 0.5607 | 0.9581   |
| 0.0825        | 1.7462  | 3000  | 0.1487          | 0.5918    | 0.5183 | 0.5526 | 0.9594   |
| 0.0721        | 2.0373  | 3500  | 0.1490          | 0.5893    | 0.5660 | 0.5774 | 0.9613   |
| 0.0454        | 2.3283  | 4000  | 0.1648          | 0.5981    | 0.5498 | 0.5730 | 0.9609   |
| 0.0457        | 2.6193  | 4500  | 0.1633          | 0.5882    | 0.5934 | 0.5908 | 0.9617   |
| 0.046         | 2.9104  | 5000  | 0.1505          | 0.6074    | 0.5959 | 0.6016 | 0.9627   |
| 0.0302        | 3.2014  | 5500  | 0.1771          | 0.6159    | 0.5788 | 0.5968 | 0.9620   |
| 0.0249        | 3.4924  | 6000  | 0.1871          | 0.6064    | 0.5751 | 0.5903 | 0.9615   |
| 0.0271        | 3.7835  | 6500  | 0.1806          | 0.6146    | 0.5882 | 0.6011 | 0.9628   |
| 0.0235        | 4.0745  | 7000  | 0.1966          | 0.6161    | 0.5804 | 0.5977 | 0.9626   |
| 0.0152        | 4.3655  | 7500  | 0.2110          | 0.6071    | 0.5887 | 0.5978 | 0.9621   |
| 0.0165        | 4.6566  | 8000  | 0.1978          | 0.6008    | 0.6174 | 0.6090 | 0.9620   |
| 0.0164        | 4.9476  | 8500  | 0.2096          | 0.6029    | 0.5750 | 0.5886 | 0.9611   |
| 0.011         | 5.2386  | 9000  | 0.2174          | 0.6055    | 0.6027 | 0.6041 | 0.9626   |
| 0.0101        | 5.5297  | 9500  | 0.2234          | 0.5919    | 0.6080 | 0.5999 | 0.9615   |
| 0.0109        | 5.8207  | 10000 | 0.2246          | 0.6148    | 0.5975 | 0.6060 | 0.9623   |
| 0.0099        | 6.1118  | 10500 | 0.2228          | 0.6115    | 0.6164 | 0.6139 | 0.9626   |
| 0.0062        | 6.4028  | 11000 | 0.2401          | 0.6099    | 0.6060 | 0.6079 | 0.9623   |
| 0.0073        | 6.6938  | 11500 | 0.2560          | 0.6161    | 0.5897 | 0.6026 | 0.9621   |
| 0.0082        | 6.9849  | 12000 | 0.2488          | 0.6008    | 0.5914 | 0.5960 | 0.9614   |
| 0.0049        | 7.2759  | 12500 | 0.2573          | 0.6155    | 0.5832 | 0.5989 | 0.9620   |
| 0.0057        | 7.5669  | 13000 | 0.2583          | 0.6320    | 0.5882 | 0.6093 | 0.9628   |
| 0.0058        | 7.8580  | 13500 | 0.2601          | 0.6040    | 0.6188 | 0.6113 | 0.9623   |
| 0.0044        | 8.1490  | 14000 | 0.2676          | 0.5962    | 0.6006 | 0.5984 | 0.9616   |
| 0.0039        | 8.4400  | 14500 | 0.2747          | 0.6194    | 0.5930 | 0.6059 | 0.9624   |
| 0.004         | 8.7311  | 15000 | 0.2796          | 0.6080    | 0.5776 | 0.5924 | 0.9614   |
| 0.0044        | 9.0221  | 15500 | 0.2836          | 0.6095    | 0.5875 | 0.5983 | 0.9623   |
| 0.0028        | 9.3132  | 16000 | 0.2907          | 0.6315    | 0.5891 | 0.6095 | 0.9631   |
| 0.003         | 9.6042  | 16500 | 0.2962          | 0.6212    | 0.5787 | 0.5992 | 0.9626   |
| 0.0038        | 9.8952  | 17000 | 0.2864          | 0.6232    | 0.5823 | 0.6021 | 0.9625   |
| 0.0029        | 10.1863 | 17500 | 0.2912          | 0.6240    | 0.5892 | 0.6061 | 0.9623   |
| 0.0023        | 10.4773 | 18000 | 0.2990          | 0.6344    | 0.5728 | 0.6020 | 0.9625   |
| 0.0028        | 10.7683 | 18500 | 0.2953          | 0.6186    | 0.5965 | 0.6073 | 0.9628   |
| 0.0021        | 11.0594 | 19000 | 0.2989          | 0.6216    | 0.5988 | 0.6100 | 0.9630   |
| 0.0017        | 11.3504 | 19500 | 0.3025          | 0.6161    | 0.6057 | 0.6108 | 0.9631   |
| 0.0023        | 11.6414 | 20000 | 0.2973          | 0.6148    | 0.6057 | 0.6102 | 0.9629   |
| 0.0021        | 11.9325 | 20500 | 0.3003          | 0.6230    | 0.5993 | 0.6109 | 0.9631   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1