---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
datasets:
- nbroad/company_names
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-v3-small-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: 0.7687575810084907
    - name: Recall
      type: recall
      value: 0.7920906980896268
    - name: F1
      type: f1
      value: 0.780249736194161
    - name: Accuracy
      type: accuracy
      value: 0.9766189637193916
---

<!-- 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-company-names

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the nbroad/company_names dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0707
- Precision: 0.7688
- Recall: 0.7921
- F1: 0.7802
- Accuracy: 0.9766

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0746        | 1.0   | 2126 | 0.0657          | 0.7415    | 0.7868 | 0.7635 | 0.9753   |
| 0.0485        | 2.0   | 4252 | 0.0651          | 0.7631    | 0.7904 | 0.7765 | 0.9764   |
| 0.044         | 3.0   | 6378 | 0.0707          | 0.7688    | 0.7921 | 0.7802 | 0.9766   |


### Framework versions

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