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---
license: mit
tags:
- generated_from_trainer
datasets:
- enoriega/keyword_pubmed
metrics:
- accuracy
model-index:
- name: kw_pubmed_vanilla_sentence_10000_0.0003_2
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: enoriega/keyword_pubmed sentence
type: enoriega/keyword_pubmed
args: sentence
metrics:
- name: Accuracy
type: accuracy
value: 0.6767448105720579
---
<!-- 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_vanilla_sentence_10000_0.0003_2
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 enoriega/keyword_pubmed sentence dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5883
- Accuracy: 0.6767
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 500
- total_train_batch_size: 8000
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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