---
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_en44
  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_en44

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: 0.1840
- Precision: 0.6942
- Recall: 0.7143
- F1: 0.7041
- Accuracy: 0.9764

## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.145         | 1.2755  | 500   | 0.1299          | 0.4548    | 0.5259 | 0.4878 | 0.9591   |
| 0.0673        | 2.5510  | 1000  | 0.0983          | 0.6367    | 0.6222 | 0.6293 | 0.9703   |
| 0.0396        | 3.8265  | 1500  | 0.1065          | 0.6064    | 0.6874 | 0.6443 | 0.9712   |
| 0.024         | 5.1020  | 2000  | 0.1177          | 0.6607    | 0.6874 | 0.6738 | 0.9738   |
| 0.0156        | 6.3776  | 2500  | 0.1214          | 0.6664    | 0.7277 | 0.6957 | 0.9750   |
| 0.0114        | 7.6531  | 3000  | 0.1301          | 0.6836    | 0.7112 | 0.6971 | 0.9752   |
| 0.0082        | 8.9286  | 3500  | 0.1263          | 0.6790    | 0.7205 | 0.6991 | 0.9758   |
| 0.0058        | 10.2041 | 4000  | 0.1426          | 0.6698    | 0.7267 | 0.6971 | 0.9751   |
| 0.0043        | 11.4796 | 4500  | 0.1452          | 0.6903    | 0.7246 | 0.7071 | 0.9762   |
| 0.0037        | 12.7551 | 5000  | 0.1531          | 0.6667    | 0.7246 | 0.6944 | 0.9757   |
| 0.0028        | 14.0306 | 5500  | 0.1634          | 0.6902    | 0.7195 | 0.7045 | 0.9764   |
| 0.0024        | 15.3061 | 6000  | 0.1628          | 0.7026    | 0.7091 | 0.7058 | 0.9763   |
| 0.002         | 16.5816 | 6500  | 0.1709          | 0.6788    | 0.7133 | 0.6956 | 0.9758   |
| 0.0017        | 17.8571 | 7000  | 0.1760          | 0.7018    | 0.7039 | 0.7028 | 0.9760   |
| 0.0015        | 19.1327 | 7500  | 0.1727          | 0.7049    | 0.7122 | 0.7085 | 0.9769   |
| 0.0012        | 20.4082 | 8000  | 0.1641          | 0.7058    | 0.7153 | 0.7105 | 0.9771   |
| 0.001         | 21.6837 | 8500  | 0.1760          | 0.7172    | 0.7008 | 0.7089 | 0.9771   |
| 0.001         | 22.9592 | 9000  | 0.1777          | 0.7049    | 0.7195 | 0.7121 | 0.9762   |
| 0.0008        | 24.2347 | 9500  | 0.1801          | 0.7131    | 0.7257 | 0.7193 | 0.9771   |
| 0.0007        | 25.5102 | 10000 | 0.1831          | 0.7049    | 0.7122 | 0.7085 | 0.9767   |
| 0.0004        | 26.7857 | 10500 | 0.1846          | 0.6960    | 0.7133 | 0.7045 | 0.9762   |
| 0.0005        | 28.0612 | 11000 | 0.1829          | 0.6995    | 0.7133 | 0.7063 | 0.9765   |
| 0.0004        | 29.3367 | 11500 | 0.1840          | 0.6942    | 0.7143 | 0.7041 | 0.9764   |


### Framework versions

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