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---
license: mit
tags:
- generated_from_trainer
datasets:
- keyword_pubmed_dataset
metrics:
- accuracy
model-index:
- name: kw_pubmed_1000_0.0003
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: keyword_pubmed_dataset
type: keyword_pubmed_dataset
args: sentence
metrics:
- name: Accuracy
type: accuracy
value: 0.33938523162661094
---
<!-- 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
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