Model: deepachalapathi/parasci3_1
Author: deepachalapathi
Pipeline: text-classification
Library: setfit
Tags: setfit, pytorch, bert, sentence-transformers, text-classification, generated_from_setfit_trainer, arxiv:2209.11055, base_model:sentence-transformers/paraphrase-TinyBERT-L6-v2, base_model:finetune:sentence-transformers/paraphrase-TinyBERT-L6-v2, region:us
Base models (finetune): id: sentence-transformers/paraphrase-TinyBERT-L6-v2
Card metadata: library_name: setfit
tags: setfit, sentence-transformers, text-classification, generated_from_setfit_trainer
metrics: accuracy
widget: part-of-speech ( pos ) tagging is a fundamental language analysis task---part-of-speech ( pos ) tagging is a fundamental nlp task , used by a wide variety of applications, the two baseline methods were implemented using scikit-learn in python---the models were implemented using scikit-learn module, semantic parsing is the task of converting a sentence into a representation of its meaning , usually in a logical form grounded in the symbols of some fixed ontology or relational database ( cite-p-21-3-3 , cite-p-21-3-4 , cite-p-21-1-11 )---for this language model , we built a trigram language model with kneser-ney smoothing using srilm from the same automatically segmented corpus, the results show that our model can clearly outperform the baselines in terms of three evaluation metrics---for the extractive or abstractive summaries , we use rouge scores , a metric used to evaluate automatic summarization performance , to measure the pairwise agreement of summaries from different annotators, language models were built with srilm , modified kneser-ney smoothing , default pruning , and order 5---the language model used was a 5-gram with modified kneserney smoothing , built with srilm toolkit
pipeline_tag: text-classification
inference: yes
base_model: sentence-transformers/paraphrase-TinyBERT-L6-v2
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Created: 2023-12-28 08:23:08