BiodivBERT

BiodivBERT

Model description

• BiodivBERT is a domain-specific BERT based cased model for the biodiversity literature.
• It uses the tokenizer from BERTT base cased model.
• BiodivBERT is pre-trained on abstracts and full text from biodiversity literature.
• BiodivBERT is fine-tuned on two down stream tasks for Named Entity Recognition and Relation Extraction in the biodiversity domain.
• Please visit our GitHub Repo for more details.

How to use

• You can use BiodivBERT via huggingface library as follows:

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")

Training data

• BiodivBERT is pre-trained on abstracts and full text from biodiversity domain-related publications.
• We used both Elsevier and Springer APIs to crawl such data.
• We covered publications over the duration of 1990-2020.

Evaluation results

BiodivBERT overperformed both BERT_base_cased, biobert_v1.1, and BiLSTM as a baseline approach on the down stream tasks.