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# PubMedBert-abstract-cord19-v2

This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the [pritamdeka/cord-19-abstract](https://huggingface.co/datasets/pritamdeka/cord-19-abstract) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2371
- Accuracy: 0.7247

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 4.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.27          | 0.53  | 5000  | 1.2425          | 0.7236   |
| 1.2634        | 1.06  | 10000 | 1.3123          | 0.7141   |
| 1.3041        | 1.59  | 15000 | 1.3583          | 0.7072   |
| 1.3829        | 2.12  | 20000 | 1.3590          | 0.7121   |
| 1.3069        | 2.65  | 25000 | 1.3506          | 0.7154   |
| 1.2921        | 3.18  | 30000 | 1.3448          | 0.7160   |
| 1.2731        | 3.7   | 35000 | 1.3375          | 0.7178   |


### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["pritamdeka/cord-19-abstract"], "metrics": ["accuracy"], "base_model": "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext", "model-index": [{"name": "pubmedbert-abstract-cord19", "results": [{"task": {"type": "fill-mask", "name": "Masked Language Modeling"}, "dataset": {"name": "pritamdeka/cord-19-abstract", "type": "pritamdeka/cord-19-abstract", "args": "fulltext"}, "metrics": [{"type": "accuracy", "value": 0.7246798699728464, "name": "Accuracy"}]}]}]}
transformers pytorch bert fill-mask generated_from_trainer dataset:pritamdeka/cord-19-abstract base_model:microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #dataset-pritamdeka/cord-19-abstract #base_model-microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

PubMedBert-abstract-cord19-v2
=============================


This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the pritamdeka/cord-19-abstract dataset.
It achieves the following results on the evaluation set:


* Loss: 1.2371
* Accuracy: 0.7247


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: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10000
* num\_epochs: 4.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10000
* num\_epochs: 4.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #dataset-pritamdeka/cord-19-abstract #base_model-microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10000
* num\_epochs: 4.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #dataset-pritamdeka/cord-19-abstract #base_model-microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10000
* num\_epochs: 4.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0