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an easy-to-use interface to fine-tuned BERT models for computing semantic similarity in clinical and web text. that's it.

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I load the model and try to predict the similarity between sentence A and sentence B. when I change the order of these sentences (i change the place of sentence...

Hi, I attend the 2019 n2c2 task3, and I found your model is useful om my data, and I'm keeping on study of task 3, so can you give me...

I used your model to predict similarity on variety of data-sets, and I was able to use it, but In places I needed only the word embedding, could you help...

I used this model on quora qa data set (http://qim.fs.quoracdn.net/quora_duplicate_questions.tsv). Performance of the model is below: -----------------|Model_output - 0 | |Model_output - 1 is_duplicate -0 | 218,328 | 36,696 is_duplicate...

Hi, can you share all the parameters of the clinical finetuned model? thank you !

`from semantic_text_similarity.models import WebBertSimilarity from semantic_text_similarity.models import ClinicalBertSimilarity web_model = WebBertSimilarity(device='cpu', batch_size=10) #defaults to GPU prediction clinical_model = ClinicalBertSimilarity(device='cuda', batch_size=10) #defaults to GPU prediction web_model.predict([("She won an olympic gold medal","The...