
<!-- 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. -->

# kw_pubmed_1000_0.0003

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 keyword_pubmed_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7086
- Accuracy: 0.3394

## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 250
- total_train_batch_size: 8000
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.09  | 4    | 4.3723          | 0.3436   |
| 6.0386        | 0.17  | 8    | 4.2113          | 0.3442   |
| 3.7573        | 0.26  | 12   | 4.2079          | 0.3634   |
| 2.9944        | 0.35  | 16   | 4.3370          | 0.3513   |
| 2.7048        | 0.44  | 20   | 4.8594          | 0.3067   |
| 2.7048        | 0.52  | 24   | 4.4929          | 0.3383   |
| 2.9458        | 0.61  | 28   | 4.5146          | 0.3408   |
| 2.3783        | 0.7   | 32   | 4.5680          | 0.3430   |
| 2.2485        | 0.78  | 36   | 4.5095          | 0.3477   |
| 2.1701        | 0.87  | 40   | 4.4971          | 0.3449   |
| 2.1701        | 0.96  | 44   | 4.7051          | 0.3321   |
| 2.0861        | 1.07  | 48   | 4.7615          | 0.3310   |
| 2.4168        | 1.15  | 52   | 4.7086          | 0.3394   |


### Framework versions

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

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["keyword_pubmed_dataset"], "metrics": ["accuracy"], "model-index": [{"name": "kw_pubmed_1000_0.0003", "results": [{"task": {"type": "fill-mask", "name": "Masked Language Modeling"}, "dataset": {"name": "keyword_pubmed_dataset", "type": "keyword_pubmed_dataset", "args": "sentence"}, "metrics": [{"type": "accuracy", "value": 0.33938523162661094, "name": "Accuracy"}]}]}]}
transformers pytorch bert fill-mask generated_from_trainer dataset:keyword_pubmed_dataset license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #dataset-keyword_pubmed_dataset #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

kw\_pubmed\_1000\_0.0003
========================


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


* Loss: 4.7086
* Accuracy: 0.3394


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: 0.0003
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 250
* total\_train\_batch\_size: 8000
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* 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: 0.0003
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 250
* total\_train\_batch\_size: 8000
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* 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 #bert #fill-mask #generated_from_trainer #dataset-keyword_pubmed_dataset #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 250
* total\_train\_batch\_size: 8000
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* 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 #bert #fill-mask #generated_from_trainer #dataset-keyword_pubmed_dataset #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 250
* total\_train\_batch\_size: 8000
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* 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