---
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:
name: Text Classification
type: text-classification
dataset:
name: marker-associations-snp-binary-base
type: marker-associations-snp-binary-base
metrics:
- name: Precision
type: precision
value: 0.9384057971014492
- name: Recall
type: recall
value: 0.9055944055944056
- name: F1
type: f1
value: 0.9217081850533808
- name: Accuracy
type: accuracy
value: 0.9107505070993914
---
<!-- 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. -->
# 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