---
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-pre-ner-full-mdeberta_data-univner_en66
  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-pre-ner-full-mdeberta_data-univner_en66

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: 0.1822
- Precision: 0.7036
- Recall: 0.7226
- F1: 0.7130
- Accuracy: 0.9771

## 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: 66
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1469        | 1.2755  | 500   | 0.1352          | 0.4213    | 0.4658 | 0.4425 | 0.9588   |
| 0.066         | 2.5510  | 1000  | 0.1071          | 0.5530    | 0.6429 | 0.5945 | 0.9700   |
| 0.0349        | 3.8265  | 1500  | 0.1015          | 0.6313    | 0.7267 | 0.6756 | 0.9738   |
| 0.0222        | 5.1020  | 2000  | 0.1179          | 0.6442    | 0.7516 | 0.6937 | 0.9748   |
| 0.0137        | 6.3776  | 2500  | 0.1166          | 0.6736    | 0.7350 | 0.7030 | 0.9760   |
| 0.009         | 7.6531  | 3000  | 0.1178          | 0.6533    | 0.7236 | 0.6866 | 0.9753   |
| 0.0068        | 8.9286  | 3500  | 0.1312          | 0.6976    | 0.7091 | 0.7033 | 0.9763   |
| 0.0048        | 10.2041 | 4000  | 0.1438          | 0.6793    | 0.7215 | 0.6998 | 0.9754   |
| 0.0038        | 11.4796 | 4500  | 0.1479          | 0.6906    | 0.7371 | 0.7131 | 0.9761   |
| 0.0031        | 12.7551 | 5000  | 0.1503          | 0.6745    | 0.7422 | 0.7068 | 0.9760   |
| 0.0025        | 14.0306 | 5500  | 0.1489          | 0.6886    | 0.7143 | 0.7012 | 0.9768   |
| 0.0019        | 15.3061 | 6000  | 0.1565          | 0.6755    | 0.7350 | 0.7040 | 0.9757   |
| 0.0018        | 16.5816 | 6500  | 0.1564          | 0.7004    | 0.7453 | 0.7222 | 0.9771   |
| 0.0014        | 17.8571 | 7000  | 0.1661          | 0.7066    | 0.7381 | 0.7220 | 0.9771   |
| 0.0013        | 19.1327 | 7500  | 0.1724          | 0.6811    | 0.7164 | 0.6983 | 0.9757   |
| 0.0008        | 20.4082 | 8000  | 0.1760          | 0.6788    | 0.7329 | 0.7048 | 0.9763   |
| 0.0007        | 21.6837 | 8500  | 0.1777          | 0.6900    | 0.7350 | 0.7118 | 0.9768   |
| 0.0008        | 22.9592 | 9000  | 0.1821          | 0.6795    | 0.7464 | 0.7114 | 0.9761   |
| 0.0006        | 24.2347 | 9500  | 0.1888          | 0.6715    | 0.7660 | 0.7157 | 0.9763   |
| 0.0005        | 25.5102 | 10000 | 0.1830          | 0.6973    | 0.7298 | 0.7132 | 0.9767   |
| 0.0005        | 26.7857 | 10500 | 0.1805          | 0.7045    | 0.7257 | 0.7149 | 0.9769   |
| 0.0004        | 28.0612 | 11000 | 0.1812          | 0.6963    | 0.7215 | 0.7087 | 0.9765   |
| 0.0003        | 29.3367 | 11500 | 0.1822          | 0.7036    | 0.7226 | 0.7130 | 0.9771   |


### Framework versions

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