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# marker-associations-snp-binary-base

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 marker-associations-snp-binary-base dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4027
- Precision: 0.9384
- Recall: 0.9056
- F1: 0.9217
- Accuracy: 0.9108
- Auc: 0.9578

## 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: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy | Auc    |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:|
| No log        | 1.0   | 153  | 0.2776          | 0.9       | 0.9441 | 0.9215 | 0.9067   | 0.9613 |
| No log        | 2.0   | 306  | 0.4380          | 0.9126    | 0.9126 | 0.9126 | 0.8986   | 0.9510 |
| No log        | 3.0   | 459  | 0.4027          | 0.9384    | 0.9056 | 0.9217 | 0.9108   | 0.9578 |
| 0.2215        | 4.0   | 612  | 0.3547          | 0.9449    | 0.8986 | 0.9211 | 0.9108   | 0.9642 |
| 0.2215        | 5.0   | 765  | 0.4465          | 0.9107    | 0.9266 | 0.9185 | 0.9047   | 0.9636 |
| 0.2215        | 6.0   | 918  | 0.5770          | 0.8970    | 0.9441 | 0.9199 | 0.9047   | 0.9666 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["marker-associations-snp-binary-base"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "marker-associations-snp-binary-base", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "marker-associations-snp-binary-base", "type": "marker-associations-snp-binary-base"}, "metrics": [{"type": "precision", "value": 0.9384057971014492, "name": "Precision"}, {"type": "recall", "value": 0.9055944055944056, "name": "Recall"}, {"type": "f1", "value": 0.9217081850533808, "name": "F1"}, {"type": "accuracy", "value": 0.9107505070993914, "name": "Accuracy"}]}]}]}
transformers pytorch bert text-classification generated_from_trainer dataset:marker-associations-snp-binary-base license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-marker-associations-snp-binary-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

marker-associations-snp-binary-base
===================================


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


* Loss: 0.4027
* Precision: 0.9384
* Recall: 0.9056
* F1: 0.9217
* Accuracy: 0.9108
* Auc: 0.9578


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: 1
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15


### Training results



### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 1
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-marker-associations-snp-binary-base #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: 1
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-marker-associations-snp-binary-base #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: 1
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15### Training results### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Tokenizers 0.10.3