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---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
datasets:
- boolq
metrics:
- accuracy
model-index:
- name: deberta-v3-large_boolq
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: boolq
type: boolq
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.882262996941896
---
<!-- 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-large_boolq
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co./microsoft/deberta-v3-large) on the boolq dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5796
- Accuracy: 0.8823
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.85 | 250 | 0.3265 | 0.8740 |
| 0.3786 | 1.69 | 500 | 0.3212 | 0.8844 |
| 0.3786 | 2.54 | 750 | 0.4205 | 0.8838 |
| 0.1324 | 3.39 | 1000 | 0.5393 | 0.8832 |
| 0.1324 | 4.24 | 1250 | 0.5796 | 0.8823 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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