Model: NoYo25/BiodivBERT
Author: NoYo25
Pipeline: token-classification
Library: transformers
Tags: transformers, pytorch, bert, fill-mask, bert-base-cased, biodiversity, token-classification, sequence-classification, en, license:apache-2.0, endpoints_compatible, region:us
Card metadata: language: en
thumbnail: https://www.fusion.uni-jena.de/fusionmedia/fusionpictures/fusion-service/fusion-transp.png?height=383&width=680
tags: bert-base-cased, biodiversity, token-classification, sequence-classification
license: apache-2.0
citation: Abdelmageed, N., Löffler, F., & König-Ries, B. (2023). BiodivBERT: a Pre-Trained Language Model for the Biodiversity Domain.
paper: https://ceur-ws.org/Vol-3415/paper-7.pdf
metrics: f1, precision, recall, accuracy
evaluation datasets: url: https://doi.org/10.5281/zenodo.6554208, named entity recognition: COPIOUS, QEMP, BiodivNER, LINNAEUS, Species800, relation extraction: GAD, EU-ADR, BiodivRE, BioRelEx
training_data: crawling-keywords: biodivers, genetic diversity, omic diversity, phylogenetic diversity, soil diversity, population diversity, species diversity, ecosystem diversity, functional diversity, microbial diversity, corpora: (+Abs) Springer and Elsevier abstracts in the duration of 1990-2020, (+Abs+Full) Springer and Elsevier abstracts and open access full publication text in the duration of 1990-2020
pre-training-hyperparams: MAX_LEN = 512, MLM_PROP = 0.15, num_train_epochs = 3, per_device_train_batch_size = 16, per_device_eval_batch_size = 16, gradient_accumulation_steps = 4
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Created: 2022-05-16 13:02:40