---
base_model: microsoft/mdeberta-v3-base
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: scenario-kd-pre-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-kd-pre-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: 46.6459
- Precision: 0.8272
- Recall: 0.8335
- F1: 0.8303
- Accuracy: 0.9822

## 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: 8
- eval_batch_size: 32
- seed: 44
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 149.9231      | 0.2911 | 500   | 110.8748        | 0.5070    | 0.1208 | 0.1951 | 0.9347   |
| 101.1776      | 0.5822 | 1000  | 93.5222         | 0.7123    | 0.6586 | 0.6844 | 0.9701   |
| 89.9222       | 0.8732 | 1500  | 86.5450         | 0.7487    | 0.7276 | 0.7380 | 0.9748   |
| 83.6167       | 1.1643 | 2000  | 81.4135         | 0.7818    | 0.7501 | 0.7656 | 0.9769   |
| 78.8225       | 1.4554 | 2500  | 77.5955         | 0.7905    | 0.7547 | 0.7722 | 0.9777   |
| 75.3094       | 1.7465 | 3000  | 74.1885         | 0.7825    | 0.7798 | 0.7812 | 0.9783   |
| 71.9149       | 2.0375 | 3500  | 71.4168         | 0.7893    | 0.8020 | 0.7956 | 0.9790   |
| 68.8017       | 2.3286 | 4000  | 68.6904         | 0.8194    | 0.7778 | 0.7981 | 0.9794   |
| 66.2935       | 2.6197 | 4500  | 66.3018         | 0.7981    | 0.8070 | 0.8025 | 0.9802   |
| 64.1282       | 2.9108 | 5000  | 64.3227         | 0.7988    | 0.8130 | 0.8059 | 0.9803   |
| 61.983        | 3.2019 | 5500  | 62.6362         | 0.8141    | 0.8114 | 0.8128 | 0.9808   |
| 60.0914       | 3.4929 | 6000  | 60.8145         | 0.8106    | 0.8149 | 0.8127 | 0.9808   |
| 58.497        | 3.7840 | 6500  | 59.2819         | 0.8126    | 0.8158 | 0.8142 | 0.9812   |
| 57.0173       | 4.0751 | 7000  | 58.0187         | 0.8126    | 0.7990 | 0.8058 | 0.9804   |
| 55.5793       | 4.3662 | 7500  | 56.7794         | 0.8033    | 0.8240 | 0.8135 | 0.9808   |
| 54.4031       | 4.6573 | 8000  | 55.5089         | 0.8072    | 0.8287 | 0.8178 | 0.9812   |
| 53.2147       | 4.9483 | 8500  | 54.5450         | 0.8128    | 0.8094 | 0.8111 | 0.9810   |
| 52.0438       | 5.2394 | 9000  | 53.6043         | 0.8145    | 0.8222 | 0.8184 | 0.9814   |
| 51.102        | 5.5305 | 9500  | 52.6326         | 0.8100    | 0.8261 | 0.818  | 0.9811   |
| 50.3841       | 5.8216 | 10000 | 51.8428         | 0.8138    | 0.8300 | 0.8219 | 0.9815   |
| 49.4812       | 6.1126 | 10500 | 51.1615         | 0.8192    | 0.8296 | 0.8244 | 0.9819   |
| 48.7273       | 6.4037 | 11000 | 50.4750         | 0.8156    | 0.8201 | 0.8178 | 0.9813   |
| 48.1157       | 6.6948 | 11500 | 49.8869         | 0.8190    | 0.8259 | 0.8224 | 0.9818   |
| 47.4821       | 6.9859 | 12000 | 49.2946         | 0.8203    | 0.8279 | 0.8241 | 0.9819   |
| 46.889        | 7.2770 | 12500 | 48.8428         | 0.8178    | 0.8224 | 0.8201 | 0.9816   |
| 46.3939       | 7.5680 | 13000 | 48.3821         | 0.8264    | 0.8224 | 0.8244 | 0.9819   |
| 46.0878       | 7.8591 | 13500 | 47.9867         | 0.8210    | 0.8272 | 0.8241 | 0.9817   |
| 45.669        | 8.1502 | 14000 | 47.6715         | 0.8207    | 0.8257 | 0.8232 | 0.9818   |
| 45.3064       | 8.4413 | 14500 | 47.3744         | 0.8167    | 0.8336 | 0.8251 | 0.9818   |
| 45.0768       | 8.7324 | 15000 | 47.1812         | 0.8221    | 0.8235 | 0.8228 | 0.9821   |
| 44.8212       | 9.0234 | 15500 | 46.9769         | 0.8172    | 0.8274 | 0.8223 | 0.9816   |
| 44.6107       | 9.3145 | 16000 | 46.8141         | 0.8204    | 0.8298 | 0.8250 | 0.9819   |
| 44.4495       | 9.6056 | 16500 | 46.7872         | 0.8189    | 0.8285 | 0.8236 | 0.9819   |
| 44.51         | 9.8967 | 17000 | 46.6459         | 0.8272    | 0.8335 | 0.8303 | 0.9822   |


### Framework versions

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