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# mdeberta-v3-base-finetuned-recores

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6094
- Accuracy: 0.2011

## 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 3000
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.6112        | 1.0   | 1047  | 1.6094          | 0.1901   |
| 1.608         | 2.0   | 2094  | 1.6094          | 0.1873   |
| 1.6127        | 3.0   | 3141  | 1.6095          | 0.1983   |
| 1.6125        | 4.0   | 4188  | 1.6094          | 0.2424   |
| 1.6118        | 5.0   | 5235  | 1.6094          | 0.1956   |
| 1.6181        | 6.0   | 6282  | 1.6094          | 0.2094   |
| 1.6229        | 7.0   | 7329  | 1.6095          | 0.1680   |
| 1.6125        | 8.0   | 8376  | 1.6094          | 0.1736   |
| 1.6134        | 9.0   | 9423  | 1.6094          | 0.2066   |
| 1.6174        | 10.0  | 10470 | 1.6093          | 0.2204   |
| 1.6161        | 11.0  | 11517 | 1.6096          | 0.2121   |
| 1.6198        | 12.0  | 12564 | 1.6094          | 0.2039   |
| 1.6182        | 13.0  | 13611 | 1.6094          | 0.2287   |
| 1.6208        | 14.0  | 14658 | 1.6094          | 0.2287   |
| 1.6436        | 15.0  | 15705 | 1.6092          | 0.2287   |
| 1.6209        | 16.0  | 16752 | 1.6094          | 0.2094   |
| 1.6097        | 17.0  | 17799 | 1.6094          | 0.2094   |
| 1.6115        | 18.0  | 18846 | 1.6094          | 0.2149   |
| 1.6249        | 19.0  | 19893 | 1.6094          | 0.1956   |
| 1.6201        | 20.0  | 20940 | 1.6094          | 0.1763   |
| 1.6217        | 21.0  | 21987 | 1.6094          | 0.1956   |
| 1.6193        | 22.0  | 23034 | 1.6094          | 0.1846   |
| 1.6171        | 23.0  | 24081 | 1.6095          | 0.1983   |
| 1.6123        | 24.0  | 25128 | 1.6095          | 0.1846   |
| 1.6164        | 25.0  | 26175 | 1.6094          | 0.2011   |


### Framework versions

- Transformers 4.19.0
- Pytorch 1.10.1+cu102
- Datasets 2.2.1
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "mdeberta-v3-base-finetuned-recores", "results": []}]}
transformers pytorch tensorboard deberta-v2 multiple-choice generated_from_trainer license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #deberta-v2 #multiple-choice #generated_from_trainer #license-mit #endpoints_compatible #region-us 

mdeberta-v3-base-finetuned-recores
==================================


This model is a fine-tuned version of microsoft/mdeberta-v3-base on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.6094
* Accuracy: 0.2011


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: 2e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 3000
* num\_epochs: 25


### Training results



### Framework versions


* Transformers 4.19.0
* Pytorch 1.10.1+cu102
* Datasets 2.2.1
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 3000
* num\_epochs: 25 ### Training results ### Framework versions


* Transformers 4.19.0
* Pytorch 1.10.1+cu102
* Datasets 2.2.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #deberta-v2 #multiple-choice #generated_from_trainer #license-mit #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 3000
* num\_epochs: 25 ### Training results ### Framework versions


* Transformers 4.19.0
* Pytorch 1.10.1+cu102
* Datasets 2.2.1
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #deberta-v2 #multiple-choice #generated_from_trainer #license-mit #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 3000
* num\_epochs: 25### Training results### Framework versions


* Transformers 4.19.0
* Pytorch 1.10.1+cu102
* Datasets 2.2.1
* Tokenizers 0.12.1