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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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Dataset used to train ml4pubmed/scibert-scivocab-uncased_pub_section