---
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