scibert-scivocab-uncased_pub_section
- original model file name: textclassifer_scibert_scivocab_uncased_pubmed_full
- This is a fine-tuned checkpoint of
allenai/scibert_scivocab_uncased
for document section text classification - possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RESULTS,
usage in python
install transformers as needed: pip install -U transformers
run the following, changing the example text to your use case:
from transformers import pipeline
model_tag = "ml4pubmed/scibert-scivocab-uncased_pub_section"
classifier = pipeline(
'text-classification',
model=model_tag,
)
prompt = """
Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.
"""
classifier(
prompt,
) # classify the sentence
metadata
training_metrics
date_run: Apr-25-2022_t-03
huggingface_tag: allenai/scibert_scivocab_uncased
training_parameters
date_run: Apr-25-2022_t-03
huggingface_tag: allenai/scibert_scivocab_uncased
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