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BiodivBERT

Biodiversity domain language model. It is fine-tuned on 2 downstream tasks for Named Entity Recognition (NER) and Relation Extraction (RE) using various state-of-the-art datasets. BiodivBERT!

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Pre-training

BiodivBERT Pre-training involves data crawling and pre-traing task.

Data Crawling

  • Pre-training from scratch requires a large corpus of unlabeled text. We are interested in the biodiversity domain, thus, we crawl two well known publishers in the Life Sciences: Elsevier and Springer.
  • We provide our data_crawling script in this repo crawling!

To crawl the data:

  • Make sure to create elsevier_config.json and springer_config.json with your API key
  • Adjust the settings to the target API from the config.py, enable the correct API you want to crawl.
  • Launch main.py to will crawl either the abstracts or the full text based on the settings you select in the config.py file.
  • To ensure you will process unique DOIs and to avoid duplicate text at the end, run filter_DOI.py and specify the correct target, e.g., targets=['Springer']
    • This will create a text file with unique DOIs.
  • To crawl the full text, you should ensure that OPENACCESS=True and FULL=True in the config.py
  • The main will download the PDFs from Springer and the actual parsed text from Elsevier.
  • To obtain the full text from Springer, GROBID service must be hosted and the client is up and runing, for more information on how to setup GROBID, please visit their page and we recommend to use docker to establish the service.
    • We provide a wrapper for the client at run_grobid.py
  • A data clean step is mandatory here, to do so, please run data_clean.py it will clean directory by directory based on the configuration.
  • To create an train and evaluation sets, you can use train_test_split.py.

Pre-training Code

  • Pre-training code is published under this repo \pre-training
  • We support 2 config.py files, one for abstracts and one for full text pre-training.
    • Before you start pre-training, make sure you change the root=Your data folder
    • You can adjust the hyperparams, e.g., we select pre_device_batch_size=16 is the maximum that could fit in a single V100 GPU in our case.
  • We recommend using the datasets library by HuggingFace to support efficient data loading.

Fine-tuning

  • We have fine-tuned BiodivBERT on two down stream tasks: Named Entity Recognition & Relation Extration using the state-of-the-art datasets from biodiversity domain.

Named Entity Recognition

  • Datasets:
    • COPIOUS
    • QEMP
    • BiodivNER
    • Species-800
    • LINNAEUS
  • Code:
    • We have fine-tuned BiodivBERT for /NER on a single TPU provided by ColabPro for few hours per dataset.

Relation Extraction

  • Datasets:
    • GAD
    • EU-ADR
    • BioRelEx
    • BiodivRE
  • Same as in NER, We have fine-tuned BiodivBERT for /RE on a single TPU provided by ColabPro for few hours per dataset.

How to Use BiodivBERT

  1. Masked Language Model
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("NoYo25/BiodivBERT")

model = AutoModelForMaskedLM.from_pretrained("NoYo25/BiodivBERT")
  1. Token Classification - Named Entity Recognition
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("NoYo25/BiodivBERT")

model = AutoModelForTokenClassification.from_pretrained("NoYo25/BiodivBERT")
  1. Sequence Classification - Relation Extraction
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("NoYo25/BiodivBERT")

model = AutoModelForSequenceClassification.from_pretrained("NoYo25/BiodivBERT")

Citation