---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: deberta-v3-base-orgs-v2
  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-orgs-v2

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9632
- F1: 0.7927
- Loss: 0.1186
- Precision: 0.8127
- Recall: 0.7735

## 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: 0.0003
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Accuracy | F1     | Validation Loss | Precision | Recall |
|:-------------:|:-----:|:----:|:--------:|:------:|:---------------:|:---------:|:------:|
| 0.0706        | 0.7   | 600  | 0.9602   | 0.7690 | 0.1138          | 0.7590    | 0.7793 |
| 0.0526        | 1.4   | 1200 | 0.9617   | 0.7870 | 0.1113          | 0.7942    | 0.7799 |
| 0.0409        | 2.11  | 1800 | 0.9627   | 0.7875 | 0.1125          | 0.7911    | 0.7839 |
| 0.0376        | 2.81  | 2400 | 0.9632   | 0.7927 | 0.1186          | 0.8127    | 0.7735 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0a0+32f93b1
- Datasets 2.15.0
- Tokenizers 0.15.0