Instructions to use mervenoyan/PubMedBERT-QNLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mervenoyan/PubMedBERT-QNLI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mervenoyan/PubMedBERT-QNLI")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mervenoyan/PubMedBERT-QNLI") model = AutoModelForSequenceClassification.from_pretrained("mervenoyan/PubMedBERT-QNLI") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
PubMedBERT Abstract + Full Text Fine-Tuned on QNLI Task
Use case: You can use it to search through a document for a given question, to see if your question is answered in that document.
LABEL0 is "not entailment" meaning your question is not answered by the context and LABEL1 is "entailment" meaning your question is answered.
Example input: [CLS] Your question [SEP] The context to be searched in [SEP]
Link to the original model: https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
Credits to the paper:
@misc{pubmedbert, author = {Yu Gu and Robert Tinn and Hao Cheng and Michael Lucas and Naoto Usuyama and Xiaodong Liu and Tristan Naumann and Jianfeng Gao and Hoifung Poon}, title = {Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing}, year = {2020}, eprint = {arXiv:2007.15779}, }
- Downloads last month
- 22