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---
language:
- en
license: mit
tags:
- generated_from_trainer
- deberta-v3
datasets:
- glue
metrics:
- accuracy
model-index:
- name: deberta-v3-small
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE SST2
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.9403669724770642
- task:
type: text-classification
name: Text Classification
dataset:
name: glue
type: glue
config: sst2
split: validation
metrics:
- name: Accuracy
type: accuracy
value: 0.9403669724770642
verified: true
- name: Precision
type: precision
value: 0.9375
verified: true
- name: Recall
type: recall
value: 0.9459459459459459
verified: true
- name: AUC
type: auc
value: 0.9804217184474193
verified: true
- name: F1
type: f1
value: 0.9417040358744394
verified: true
- name: loss
type: loss
value: 0.21338027715682983
verified: true
---
<!-- 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 SST2
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2134
- Accuracy: 0.9404
## 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
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.176 | 1.0 | 4210 | 0.2134 | 0.9404 |
| 0.1254 | 2.0 | 8420 | 0.2362 | 0.9415 |
| 0.0957 | 3.0 | 12630 | 0.3187 | 0.9335 |
| 0.0673 | 4.0 | 16840 | 0.3039 | 0.9266 |
| 0.0457 | 5.0 | 21050 | 0.3521 | 0.9312 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|