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Open Source Model Profile · razent

SciFive-base-Pubmed_PMC

SciFive-base-Pubmed_PMC is a 223M-parameter T5 model record from razent. Its model card presents SciFive as a text-to-text transformer for biomedical literature.

Publisher
razent
Task
text-classification
Model type
t5
License
Unknown
Library
transformers
Publication status
Accepted · not indexed

Model overview

SciFive-base-Pubmed_PMC is published by razent as a T5-based model record. Safetensors metadata reports 222903552 parameters, and the captured configuration identifies T5ForConditionalGeneration. According to the model card, it presents the SciFive PubMed and PMC base model for biomedical literature.

Recorded capabilities

223M T5 conditional generation

Captured config identifies T5ForConditionalGeneration and Safetensors metadata reports 222903552 parameters.

Biomedical text-to-text framing

According to the model card, the record presents SciFive as a text-to-text transformer model for biomedical literature.

PubMed and PMC dataset tags

Hub tags list pubmed and pmc/open_access datasets with text2text-generation, question-answering, and text-generation task tags.

Documented Transformers usage

The model card gives AutoTokenizer and AutoModelForSeq2SeqLM loading code with a generate call using max_length 256.

Use cases in the source record

  • Biomedical text-to-text experiments over PubMed and PMC-style literature using the documented T5 setup.
  • Seq2SeqLM generation trials that follow the card's tokenizer encoding and max_length 256 generate call.

Limitations and unknowns

  • No license value was extracted from this record.
  • No evaluation results were extracted from this record.
  • No context-window value was extracted from this record.
  • Provider state is historical snapshot data, not independently refreshed current availability.
  • Paper and repository details come from the publisher model card and have not been independently verified by Ethen.

Source and provenance

Source: razent/SciFive-base-Pubmed_PMC

Captured: Unknown. Processed: 2026-09-07T19:34:56.503652+00:00.

SciFive Pubmed+PMC Base Introduction Paper: SciFive: a text-to-text transformer model for biomedical literature Authors: Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet How to use For more details, do check out our Github repo . from transformers import AutoTokenizer, AutoModelForSeq2SeqLM ​ tokenizer = AutoTokenizer.from_pretrained( "razent/SciFive-base-Pubmed_PMC" ) model = AutoModelForSeq2SeqLM.from_pretrained( "razent/SciFive-base-Pubmed_PMC" ) ​ sentence = "Identification of APC2 , a homologue of the adenomatous polyposis coli tumour suppressor ." text = sentence…

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