
# bert-paper-classifier

This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract) on the dataset from [González-Márquez et al., 2023](https://www.biorxiv.org/content/10.1101/2023.04.10.536208v1).

## Intended uses & limitations

This model is intended to predict the category given the paper title (and optionally its abstract) — for the biomedical papers. For example, it is likely to predict `virology` as a category for the paper with a title containing `COVID-19`.

So far only a subset of the PubMed dataset has been used for training. Future improvements to this model can come with using the full dataset with a combination of titles and abstracts for the fine-tuning as well as extending the training set to the preprints from bioRxiv and/or arXiv.

## Training procedure

The code for the model fine-tuning can be found [in the respective notebook](https://huggingface.co/oracat/bert-paper-classifier/blob/main/finetuning-pubmed.ipynb).

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-paper-classifier", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 


# bert-paper-classifier

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract on the dataset from González-Márquez et al., 2023.

## Intended uses & limitations

This model is intended to predict the category given the paper title (and optionally its abstract) — for the biomedical papers. For example, it is likely to predict 'virology' as a category for the paper with a title containing 'COVID-19'.

So far only a subset of the PubMed dataset has been used for training. Future improvements to this model can come with using the full dataset with a combination of titles and abstracts for the fine-tuning as well as extending the training set to the preprints from bioRxiv and/or arXiv.

## Training procedure

The code for the model fine-tuning can be found in the respective notebook.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
# bert-paper-classifier

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract on the dataset from González-Márquez et al., 2023. ## Intended uses & limitations

This model is intended to predict the category given the paper title (and optionally its abstract) — for the biomedical papers. For example, it is likely to predict 'virology' as a category for the paper with a title containing 'COVID-19'.

So far only a subset of the PubMed dataset has been used for training. Future improvements to this model can come with using the full dataset with a combination of titles and abstracts for the fine-tuning as well as extending the training set to the preprints from bioRxiv and/or arXiv. ## Training procedure

The code for the model fine-tuning can be found in the respective notebook. ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3 ### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # bert-paper-classifier

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract on the dataset from González-Márquez et al., 2023. ## Intended uses & limitations

This model is intended to predict the category given the paper title (and optionally its abstract) — for the biomedical papers. For example, it is likely to predict 'virology' as a category for the paper with a title containing 'COVID-19'.

So far only a subset of the PubMed dataset has been used for training. Future improvements to this model can come with using the full dataset with a combination of titles and abstracts for the fine-tuning as well as extending the training set to the preprints from bioRxiv and/or arXiv. ## Training procedure

The code for the model fine-tuning can be found in the respective notebook. ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3 ### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
# bert-paper-classifier

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract on the dataset from González-Márquez et al., 2023.## Intended uses & limitations

This model is intended to predict the category given the paper title (and optionally its abstract) — for the biomedical papers. For example, it is likely to predict 'virology' as a category for the paper with a title containing 'COVID-19'.

So far only a subset of the PubMed dataset has been used for training. Future improvements to this model can come with using the full dataset with a combination of titles and abstracts for the fine-tuning as well as extending the training set to the preprints from bioRxiv and/or arXiv.## Training procedure

The code for the model fine-tuning can be found in the respective notebook.### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3