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# marker-associations-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-binary-base dataset.
It achieves the following results on the evaluation set:

### Gene Results
- Precision = 0.808
- Recall = 0.940
- F1 = 0.869
- Accuracy = 0.862
- AUC = 0.944

### Chemical Results
- Precision = 0.774
- Recall = 1.0
- F1 = 0.873
- Accuracy = 0.926
- AUC = 0.964

## 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   | 88   | 0.3266          | 0.8191    | 0.8462 | 0.8324 | 0.8670   | 0.9313 |
| No log        | 2.0   | 176  | 0.3335          | 0.7870    | 0.9341 | 0.8543 | 0.8755   | 0.9465 |
| No log        | 3.0   | 264  | 0.4243          | 0.7982    | 0.9560 | 0.87   | 0.8884   | 0.9516 |
| No log        | 4.0   | 352  | 0.5388          | 0.825     | 0.7253 | 0.7719 | 0.8326   | 0.9384 |
| No log        | 5.0   | 440  | 0.7101          | 0.8537    | 0.7692 | 0.8092 | 0.8584   | 0.9416 |
| 0.1824        | 6.0   | 528  | 0.6175          | 0.8242    | 0.8242 | 0.8242 | 0.8627   | 0.9478 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["marker-associations-binary-base"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "marker-associations-binary-base", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "marker-associations-binary-base", "type": "marker-associations-binary-base"}, "metrics": [{"type": "precision", "value": 0.7981651376146789, "name": "Precision"}, {"type": "recall", "value": 0.9560439560439561, "name": "Recall"}, {"type": "f1", "value": 0.87, "name": "F1"}, {"type": "accuracy", "value": 0.8884120171673819, "name": "Accuracy"}]}]}]}
transformers pytorch bert text-classification generated_from_trainer dataset:marker-associations-binary-base license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-marker-associations-binary-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

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


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


### Gene Results


* Precision = 0.808
* Recall = 0.940
* F1 = 0.869
* Accuracy = 0.862
* AUC = 0.944


### Chemical Results


* Precision = 0.774
* Recall = 1.0
* F1 = 0.873
* Accuracy = 0.926
* AUC = 0.964


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



### Gene Results


* Precision = 0.808
* Recall = 0.940
* F1 = 0.869
* Accuracy = 0.862
* AUC = 0.944 ### Chemical Results


* Precision = 0.774
* Recall = 1.0
* F1 = 0.873
* Accuracy = 0.926
* AUC = 0.964


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
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-marker-associations-binary-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Gene Results


* Precision = 0.808
* Recall = 0.940
* F1 = 0.869
* Accuracy = 0.862
* AUC = 0.944 ### Chemical Results


* Precision = 0.774
* Recall = 1.0
* F1 = 0.873
* Accuracy = 0.926
* AUC = 0.964


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
passage: TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-marker-associations-binary-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Gene Results


* Precision = 0.808
* Recall = 0.940
* F1 = 0.869
* Accuracy = 0.862
* AUC = 0.944### Chemical Results


* Precision = 0.774
* Recall = 1.0
* F1 = 0.873
* Accuracy = 0.926
* AUC = 0.964


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