---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: deberta-v3-base-finetuned-ner
  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. -->

# deberta-v3-base-finetuned-ner

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9895
- Overall Precision: 0.5201
- Overall Recall: 0.3319
- Overall F1: 0.4052
- Overall Accuracy: 0.9326
- Datasetname F1: 0.4952
- Hyperparametername F1: 0.48
- Hyperparametervalue F1: 0.5
- Methodname F1: 0.3933
- Metricname F1: 0.2488
- Metricvalue F1: 0.2456
- Taskname F1: 0.6393

## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Datasetname F1 | Hyperparametername F1 | Hyperparametervalue F1 | Methodname F1 | Metricname F1 | Metricvalue F1 | Taskname F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:---------------------:|:----------------------:|:-------------:|:-------------:|:--------------:|:-----------:|
| No log        | 1.0   | 141  | 1.2556          | 0.2784            | 0.1520         | 0.1967     | 0.9212           | 0.0            | 0.3478                | 0.2581                 | 0.3750        | 0.0           | 0.0            | 0.0556      |
| No log        | 2.0   | 282  | 0.8945          | 0.3020            | 0.5096         | 0.3793     | 0.9088           | 0.5            | 0.1538                | 0.2778                 | 0.3540        | 0.4566        | 0.0896         | 0.3756      |
| No log        | 3.0   | 423  | 1.0233          | 0.3702            | 0.4518         | 0.4069     | 0.9268           | 0.4211         | 0.2647                | 0.3333                 | 0.3529        | 0.4658        | 0.1613         | 0.5270      |
| 0.6352        | 4.0   | 564  | 1.1734          | 0.4316            | 0.4390         | 0.4352     | 0.9310           | 0.4854         | 0.3462                | 0.3415                 | 0.4352        | 0.4269        | 0.2295         | 0.5827      |
| 0.6352        | 5.0   | 705  | 1.3147          | 0.4840            | 0.4540         | 0.4685     | 0.9390           | 0.5143         | 0.5                   | 0.625                  | 0.5739        | 0.3495        | 0.2333         | 0.5865      |
| 0.6352        | 6.0   | 846  | 2.1441          | 0.5618            | 0.3405         | 0.4240     | 0.9373           | 0.5185         | 0.5581                | 0.6061                 | 0.4898        | 0.2365        | 0.1071         | 0.6126      |
| 0.6352        | 7.0   | 987  | 1.9895          | 0.5201            | 0.3319         | 0.4052     | 0.9326           | 0.4952         | 0.48                  | 0.5                    | 0.3933        | 0.2488        | 0.2456         | 0.6393      |


### Framework versions

- Transformers 4.23.1
- Pytorch 1.12.1+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1