---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-v3-base-financial-inc-dec-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-financial-inc-dec-ner

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0416
- Precision: 0.9632
- Recall: 0.9704
- F1: 0.9668
- Accuracy: 0.9933

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 92   | 0.1193          | 0.625     | 0.7407 | 0.6780 | 0.9588   |
| No log        | 2.0   | 184  | 0.0522          | 0.8643    | 0.8963 | 0.88   | 0.9798   |
| No log        | 3.0   | 276  | 0.0554          | 0.8897    | 0.8963 | 0.8930 | 0.9835   |
| No log        | 4.0   | 368  | 0.0362          | 0.9416    | 0.9556 | 0.9485 | 0.9910   |
| No log        | 5.0   | 460  | 0.0315          | 0.9286    | 0.9630 | 0.9455 | 0.9918   |
| 0.1731        | 6.0   | 552  | 0.0416          | 0.9632    | 0.9704 | 0.9668 | 0.9933   |
| 0.1731        | 7.0   | 644  | 0.0496          | 0.9420    | 0.9630 | 0.9524 | 0.9910   |
| 0.1731        | 8.0   | 736  | 0.0527          | 0.9420    | 0.9630 | 0.9524 | 0.9910   |
| 0.1731        | 9.0   | 828  | 0.0604          | 0.9348    | 0.9556 | 0.9451 | 0.9895   |
| 0.1731        | 10.0  | 920  | 0.0564          | 0.9420    | 0.9630 | 0.9524 | 0.9910   |
| 0.0028        | 11.0  | 1012 | 0.0571          | 0.9493    | 0.9704 | 0.9597 | 0.9918   |
| 0.0028        | 12.0  | 1104 | 0.0570          | 0.9493    | 0.9704 | 0.9597 | 0.9918   |
| 0.0028        | 13.0  | 1196 | 0.0559          | 0.9493    | 0.9704 | 0.9597 | 0.9918   |
| 0.0028        | 14.0  | 1288 | 0.0574          | 0.9493    | 0.9704 | 0.9597 | 0.9918   |
| 0.0028        | 15.0  | 1380 | 0.0576          | 0.9493    | 0.9704 | 0.9597 | 0.9918   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1