---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
datasets:
- nbroad/company_names
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-v3-base-company-names
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: nbroad/company_names
      type: nbroad/company_names
    metrics:
    - name: Precision
      type: precision
      value: 0.7739696312364425
    - name: Recall
      type: recall
      value: 0.7962863774326013
    - name: F1
      type: f1
      value: 0.7849694196330357
    - name: Accuracy
      type: accuracy
      value: 0.9769126125154315
---

<!-- 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-company-names

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the nbroad/company_names dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0693
- Precision: 0.7740
- Recall: 0.7963
- F1: 0.7850
- Accuracy: 0.9769

## 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: 8e-05
- train_batch_size: 48
- 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_ratio: 0.1
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0752        | 1.0   | 2126 | 0.0664          | 0.7416    | 0.7979 | 0.7687 | 0.9757   |
| 0.0484        | 2.0   | 4252 | 0.0652          | 0.7725    | 0.7903 | 0.7813 | 0.9768   |
| 0.0415        | 3.0   | 6378 | 0.0693          | 0.7740    | 0.7963 | 0.7850 | 0.9769   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.14.1