---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- recall
- precision
model-index:
- name: deberta-v3-small-finetuned-ner-2048
  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-small-finetuned-ner-2048

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0036
- Recall: 0.9920
- Precision: 0.9866
- Fbeta Score: 0.9918

## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Recall | Precision | Fbeta Score |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:---------:|:-----------:|
| 0.0085        | 1.0   | 3186  | 0.0058          | 0.9786 | 0.9693    | 0.9782      |
| 0.0032        | 2.0   | 6373  | 0.0036          | 0.9869 | 0.9764    | 0.9865      |
| 0.004         | 3.0   | 9559  | 0.0037          | 0.9791 | 0.9892    | 0.9795      |
| 0.0014        | 4.0   | 12746 | 0.0035          | 0.9908 | 0.9817    | 0.9905      |
| 0.0019        | 5.0   | 15932 | 0.0038          | 0.9903 | 0.9806    | 0.9899      |
| 0.0022        | 6.0   | 19119 | 0.0032          | 0.9929 | 0.9861    | 0.9927      |
| 0.0005        | 7.0   | 22305 | 0.0031          | 0.9906 | 0.9894    | 0.9905      |
| 0.0003        | 8.0   | 25492 | 0.0033          | 0.9915 | 0.9855    | 0.9913      |
| 0.0001        | 9.0   | 28678 | 0.0036          | 0.9920 | 0.9866    | 0.9918      |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2