bio-lm

Model description

This model is a RoBERTa base pre-trained model that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the BioLang dataset.

Intended uses & limitations

How to use

The intended use of this model is to be fine-tuned for downstream tasks, token classification in particular.

To have a quick check of the model as-is in a fill-mask task:

from transformers import pipeline, RobertaTokenizerFast
tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512)
text = "Let us try this model to see if it <mask>."
fill_mask = pipeline(
    "fill-mask",
    model='EMBO/bio-lm',
    tokenizer=tokenizer
)
fill_mask(text)

Limitations and bias

This model should be fine-tuned on a specifi task like token classification. The model must be used with the roberta-base tokenizer.

Training data

The model was trained with a masked language modeling taskon the BioLang dataset wich includes 12Mio examples from abstracts and figure legends extracted from papers published in life sciences.

Training procedure

The training was run on a NVIDIA DGX Station with 4XTesla V100 GPUs.

Training code is available at https://github.com/source-data/soda-roberta

Command: python -m lm.train /data/json/oapmc_abstracts_figs/ MLM
Tokenizer vocab size: 50265
Training data: EMBO/biolang MLM
Training with: 12005390 examples
Evaluating on: 36713 examples
Epochs: 3.0
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
tensorboard run: lm-MLM-2021-01-27T15-17-43.113766

End of training:

trainset: 'loss': 0.8653350830078125
validation set: 'eval_loss': 0.8192330598831177, 'eval_recall': 0.8154601116513597

Eval results

Eval on test set:

recall: 0.814471959728645

EMBO
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bio-lm