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---
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
|