---
language:
- en
license: mit
tags:
- generated_from_trainer
- deberta-v3
datasets:
- glue
metrics:
- accuracy
model-index:
- name: ds_results
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE MNLI
      type: glue
      args: mnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.874593165174939
---

<!-- 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) fine-tuned on MNLI

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4985
- Accuracy: 0.8746

## 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: 3e-05
- train_batch_size: 16
- 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: 1000
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7773        | 0.04  | 1000  | 0.5241          | 0.7984   |
| 0.546         | 0.08  | 2000  | 0.4629          | 0.8194   |
| 0.5032        | 0.12  | 3000  | 0.4704          | 0.8274   |
| 0.4711        | 0.16  | 4000  | 0.4383          | 0.8355   |
| 0.473         | 0.2   | 5000  | 0.4652          | 0.8305   |
| 0.4619        | 0.24  | 6000  | 0.4234          | 0.8386   |
| 0.4542        | 0.29  | 7000  | 0.4825          | 0.8349   |
| 0.4468        | 0.33  | 8000  | 0.3985          | 0.8513   |
| 0.4288        | 0.37  | 9000  | 0.4084          | 0.8493   |
| 0.4354        | 0.41  | 10000 | 0.3850          | 0.8533   |
| 0.423         | 0.45  | 11000 | 0.3855          | 0.8509   |
| 0.4167        | 0.49  | 12000 | 0.4122          | 0.8513   |
| 0.4129        | 0.53  | 13000 | 0.4009          | 0.8550   |
| 0.4135        | 0.57  | 14000 | 0.4136          | 0.8544   |
| 0.4074        | 0.61  | 15000 | 0.3869          | 0.8595   |
| 0.415         | 0.65  | 16000 | 0.3911          | 0.8517   |
| 0.4095        | 0.69  | 17000 | 0.3880          | 0.8593   |
| 0.4001        | 0.73  | 18000 | 0.3907          | 0.8587   |
| 0.4069        | 0.77  | 19000 | 0.3686          | 0.8630   |
| 0.3927        | 0.81  | 20000 | 0.4008          | 0.8593   |
| 0.3958        | 0.86  | 21000 | 0.3716          | 0.8639   |
| 0.4016        | 0.9   | 22000 | 0.3594          | 0.8679   |
| 0.3945        | 0.94  | 23000 | 0.3595          | 0.8679   |
| 0.3932        | 0.98  | 24000 | 0.3577          | 0.8645   |
| 0.345         | 1.02  | 25000 | 0.4080          | 0.8699   |
| 0.2885        | 1.06  | 26000 | 0.3919          | 0.8674   |
| 0.2858        | 1.1   | 27000 | 0.4346          | 0.8651   |
| 0.2872        | 1.14  | 28000 | 0.4105          | 0.8674   |
| 0.3002        | 1.18  | 29000 | 0.4133          | 0.8708   |
| 0.2954        | 1.22  | 30000 | 0.4062          | 0.8667   |
| 0.2912        | 1.26  | 31000 | 0.3972          | 0.8708   |
| 0.2958        | 1.3   | 32000 | 0.3713          | 0.8732   |
| 0.293         | 1.34  | 33000 | 0.3717          | 0.8715   |
| 0.3001        | 1.39  | 34000 | 0.3826          | 0.8716   |
| 0.2864        | 1.43  | 35000 | 0.4155          | 0.8694   |
| 0.2827        | 1.47  | 36000 | 0.4224          | 0.8666   |
| 0.2836        | 1.51  | 37000 | 0.3832          | 0.8744   |
| 0.2844        | 1.55  | 38000 | 0.4179          | 0.8699   |
| 0.2866        | 1.59  | 39000 | 0.3969          | 0.8681   |
| 0.2883        | 1.63  | 40000 | 0.4000          | 0.8683   |
| 0.2832        | 1.67  | 41000 | 0.3853          | 0.8688   |
| 0.2876        | 1.71  | 42000 | 0.3924          | 0.8677   |
| 0.2855        | 1.75  | 43000 | 0.4177          | 0.8719   |
| 0.2845        | 1.79  | 44000 | 0.3877          | 0.8724   |
| 0.2882        | 1.83  | 45000 | 0.3961          | 0.8713   |
| 0.2773        | 1.87  | 46000 | 0.3791          | 0.8740   |
| 0.2767        | 1.91  | 47000 | 0.3877          | 0.8779   |
| 0.2772        | 1.96  | 48000 | 0.4022          | 0.8690   |
| 0.2816        | 2.0   | 49000 | 0.3837          | 0.8732   |
| 0.2068        | 2.04  | 50000 | 0.4644          | 0.8720   |
| 0.1914        | 2.08  | 51000 | 0.4919          | 0.8744   |
| 0.2           | 2.12  | 52000 | 0.4870          | 0.8702   |
| 0.1904        | 2.16  | 53000 | 0.5038          | 0.8737   |
| 0.1915        | 2.2   | 54000 | 0.5232          | 0.8711   |
| 0.1956        | 2.24  | 55000 | 0.5192          | 0.8747   |
| 0.1911        | 2.28  | 56000 | 0.5215          | 0.8761   |
| 0.2053        | 2.32  | 57000 | 0.4604          | 0.8738   |
| 0.2008        | 2.36  | 58000 | 0.5162          | 0.8715   |
| 0.1971        | 2.4   | 59000 | 0.4886          | 0.8754   |
| 0.192         | 2.44  | 60000 | 0.4921          | 0.8725   |
| 0.1937        | 2.49  | 61000 | 0.4917          | 0.8763   |
| 0.1931        | 2.53  | 62000 | 0.4789          | 0.8778   |
| 0.1964        | 2.57  | 63000 | 0.4997          | 0.8721   |
| 0.2008        | 2.61  | 64000 | 0.4748          | 0.8756   |
| 0.1962        | 2.65  | 65000 | 0.4840          | 0.8764   |
| 0.2029        | 2.69  | 66000 | 0.4889          | 0.8767   |
| 0.1927        | 2.73  | 67000 | 0.4820          | 0.8758   |
| 0.1926        | 2.77  | 68000 | 0.4857          | 0.8762   |
| 0.1919        | 2.81  | 69000 | 0.4836          | 0.8749   |
| 0.1911        | 2.85  | 70000 | 0.4859          | 0.8742   |
| 0.1897        | 2.89  | 71000 | 0.4853          | 0.8766   |
| 0.186         | 2.93  | 72000 | 0.4946          | 0.8768   |
| 0.2011        | 2.97  | 73000 | 0.4851          | 0.8767   |


### Framework versions

- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3