---
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_en55
  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_en55

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: 0.3347
- Precision: 0.5325
- Recall: 0.4752
- F1: 0.5022
- Accuracy: 0.9624

## 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: 55
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1623        | 1.2755  | 500   | 0.1875          | 0.4252    | 0.3002 | 0.3519 | 0.9534   |
| 0.0693        | 2.5510  | 1000  | 0.1568          | 0.4472    | 0.4689 | 0.4578 | 0.9583   |
| 0.0358        | 3.8265  | 1500  | 0.1803          | 0.5255    | 0.4472 | 0.4832 | 0.9611   |
| 0.0189        | 5.1020  | 2000  | 0.2008          | 0.5077    | 0.4793 | 0.4931 | 0.9608   |
| 0.0098        | 6.3776  | 2500  | 0.2203          | 0.4759    | 0.5207 | 0.4973 | 0.9599   |
| 0.0072        | 7.6531  | 3000  | 0.2339          | 0.5105    | 0.4783 | 0.4939 | 0.9613   |
| 0.0046        | 8.9286  | 3500  | 0.2526          | 0.5327    | 0.4638 | 0.4958 | 0.9612   |
| 0.003         | 10.2041 | 4000  | 0.2658          | 0.5150    | 0.4793 | 0.4965 | 0.9604   |
| 0.0021        | 11.4796 | 4500  | 0.2805          | 0.5541    | 0.4669 | 0.5067 | 0.9616   |
| 0.0015        | 12.7551 | 5000  | 0.2782          | 0.5156    | 0.4959 | 0.5055 | 0.9621   |
| 0.0018        | 14.0306 | 5500  | 0.3001          | 0.5448    | 0.4720 | 0.5058 | 0.9614   |
| 0.0014        | 15.3061 | 6000  | 0.2938          | 0.5354    | 0.5083 | 0.5215 | 0.9629   |
| 0.0012        | 16.5816 | 6500  | 0.3020          | 0.5554    | 0.5135 | 0.5336 | 0.9633   |
| 0.0008        | 17.8571 | 7000  | 0.3080          | 0.5345    | 0.4886 | 0.5105 | 0.9627   |
| 0.0007        | 19.1327 | 7500  | 0.3184          | 0.5346    | 0.4638 | 0.4967 | 0.9626   |
| 0.0004        | 20.4082 | 8000  | 0.3198          | 0.5233    | 0.4762 | 0.4986 | 0.9612   |
| 0.0005        | 21.6837 | 8500  | 0.3303          | 0.5583    | 0.4658 | 0.5079 | 0.9617   |
| 0.0004        | 22.9592 | 9000  | 0.3305          | 0.5575    | 0.4969 | 0.5255 | 0.9629   |
| 0.0003        | 24.2347 | 9500  | 0.3357          | 0.5440    | 0.4669 | 0.5025 | 0.9617   |
| 0.0002        | 25.5102 | 10000 | 0.3258          | 0.5404    | 0.5124 | 0.5260 | 0.9624   |
| 0.0002        | 26.7857 | 10500 | 0.3360          | 0.5587    | 0.4928 | 0.5237 | 0.9628   |
| 0.0001        | 28.0612 | 11000 | 0.3318          | 0.5208    | 0.4803 | 0.4997 | 0.9618   |
| 0.0001        | 29.3367 | 11500 | 0.3347          | 0.5325    | 0.4752 | 0.5022 | 0.9624   |


### Framework versions

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