metadata
license: apache-2.0
This is the finetuned model presented in MIReAD: a simple method for learning high-quality representations from scientific documents (ACL 2023).
We trained MIReAD on >500,000 PubMed and arXiv abstracts across over 2,000 journal classes. MIReAD was initialized with SciBERT weights and finetuned to predict journal class based on the abstract and title of the paper. MIReAD uses SciBERT's tokenizer.
Overall, with MIReAD you can:
- extract semantically meaningful representation using paper's abstact
- predict journal class based on paper's abstract
To load the MIReAD model:
from transformers import BertForSequenceClassification, AutoTokenizer
mpath = 'arazd/miread'
model_hub = BertForSequenceClassification.from_pretrained(mpath)
tokenizer = AutoTokenizer.from_pretrained(mpath)
To use MIReAD for feature extraction and classification:
# sample abstract text
abstr = 'Learning semantically meaningful representations from scientific documents can ...'
source_len = 512
inputs = tokenizer(abstr,
max_length = source_len,
pad_to_max_length=True,
truncation=True,
return_tensors="pt")
# classification (getting logits over 2,734 journal classes)
out = model(**inputs)
logits = out.logits
# feature extraction (getting 768-dimensional feature profiles)
out = model.bert(**inputs)
# IMPORTANT: use [CLS] token representation as document-level representation (hence, 0th idx)
feature = out.last_hidden_state[:, 0, :]