
<!-- 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-dapt-scientific-papers-pubmed-finetuned-DAGPap22

This model is a fine-tuned version of [domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed](https://huggingface.co/domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
- F1: 1.0

## 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: 6e-06
- train_batch_size: 8
- 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: 50
- num_epochs: 12
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2165        | 1.0   | 669  | 0.0218          | 0.9963   | 0.9973 |
| 0.0717        | 2.0   | 1338 | 0.0213          | 0.9964   | 0.9974 |
| 0.03          | 3.0   | 2007 | 0.0121          | 0.9983   | 0.9988 |
| 0.0165        | 4.0   | 2676 | 0.0147          | 0.9976   | 0.9982 |
| 0.0072        | 5.0   | 3345 | 0.0000          | 1.0      | 1.0    |
| 0.0055        | 6.0   | 4014 | 0.0000          | 1.0      | 1.0    |
| 0.0077        | 7.0   | 4683 | 0.0000          | 1.0      | 1.0    |
| 0.0           | 8.0   | 5352 | 0.0000          | 1.0      | 1.0    |
| 0.0           | 9.0   | 6021 | 0.0000          | 1.0      | 1.0    |
| 0.0           | 10.0  | 6690 | 0.0000          | 1.0      | 1.0    |
| 0.0           | 11.0  | 7359 | 0.0000          | 1.0      | 1.0    |
| 0.0           | 12.0  | 8028 | 0.0000          | 1.0      | 1.0    |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1

{"license": "mit", "tags": ["text-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "deberta-v3-large-dapt-scientific-papers-pubmed-finetuned-DAGPap22", "results": []}]}
transformers pytorch tensorboard deberta-v2 text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

deberta-v3-large-dapt-scientific-papers-pubmed-finetuned-DAGPap22
=================================================================


This model is a fine-tuned version of domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0000
* Accuracy: 1.0
* F1: 1.0


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: 6e-06
* train\_batch\_size: 8
* 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: 50
* num\_epochs: 12
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-06
* train\_batch\_size: 8
* 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: 50
* num\_epochs: 12
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-06
* train\_batch\_size: 8
* 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: 50
* num\_epochs: 12
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-06
* train\_batch\_size: 8
* 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: 50
* num\_epochs: 12
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1