lighteternal
/

BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-mnli

Text Classification

textual-entailment

Inference Endpoints

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lighteternal commited on Oct 26, 2021

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1 Parent(s): 40b7109

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@@ -38,7 +38,7 @@ hypothesis = 'EpCAM is downregulated in breast cancer.'
 # run through model pre-trained on MNLI
 x = tokenizer.encode(premise, hypothesis, return_tensors='pt',
                      truncation_strategy='only_first')
-logits = model(x.to(device))[0]
 probs = logits.softmax(dim=1)
 print('Probabilities for entailment, neutral, contradiction \n', np.around(probs.cpu().

 # run through model pre-trained on MNLI
 x = tokenizer.encode(premise, hypothesis, return_tensors='pt',
                      truncation_strategy='only_first')
+logits = model(x)[0]
 probs = logits.softmax(dim=1)
 print('Probabilities for entailment, neutral, contradiction \n', np.around(probs.cpu().