import gradio as gr import pickle import pandas as pd data = pickle.load(open("OOV_Train_2.pkl", "rb")) data = pd.DataFrame( data, columns=["Input_Seq", "Label", "Adj_Class", "Adj", "Nn", "Hypr", "Adj_NN"] ) adjs = set(data["Adj"]) Nns = set(list(data["Nn"]) + list(data["Hypr"])) all_set = set(list(adjs) + list(Nns)) def test_input(words): word_dict = "" for w in words.split(","): if w in all_set: word_dict += "{} : in-distribution\n".format(w) else: word_dict += "{} : out-of-distribution\n".format(w) return word_dict title = "Phrase-Entailment Detection with BERT" description = """ Did you know that logically speaking **A small cat is not a small animal**, and that **A fake smile is not a smile**? Learn more by testing our BERT model tuned to perform phrase-level adjective-noun entailment. The proposed model was tuned with a section of the PLANE (**P**hrase-**L**evel **A**djective-**N**oun **E**ntailment) dataset, introduced in COLING 2022 [Bertolini et al.,](https://aclanthology.org/2022.coling-1.359/). Please note that the scope of the model is not to run lexical-entailment or hypernym detection (e.g., *"A dog is an animal*"), but to perform a very specific subset of phrase-level compositional entailment over adjective-noun phrases. The type of question you can ask the model are limited, and should have one of three forms: - An *Adjective-Noun* is a *Noun* (e.g. A red car is a car) - An *Adjective-Noun* is a *Hypernym(Noun)* (e.g. A red car is a vehicle) - An *Adjective-Noun* is a *Adjective-Hypernym(Noun)* (e.g. A red car is a red vehicle) As in the examples above, the **adjective should be the same for both phrases**, and the **Hypernym(Noun) should be a true hypernym of the selected noun**. The current model achieves an accuracy of 90% on out-of-distribution evaluation. Use the next page to check if your test-items (i.e. adjective, noun and hypernyms) were part of the training data!""" examples = [["A red car is a vehicle"], ["A fake smile is a smile"], ["A small cat is a small animal"]] interface_model = gr.Interface.load( "huggingface/lorenzoscottb/bert-base-cased-PLANE-ood-2", description=description, examples=examples, title=title, ) description_w = """ You can use this page to test if a set of words was included in the training data used to tune the model. As in the samples below, use as input a series of words separated solely by a comma (e.g. *red,car,vehicle*). """ examples_w = [["red,car,vehicle"], ["fake,smile"], ["small,cat,animal"]] interface_words = gr.Interface( fn=test_input, inputs=gr.Textbox(label="Input:word_1,word2,...,word_n"), outputs=gr.Textbox(label="In training-distribution?"), description=description_w, examples=examples_w, ) gr.TabbedInterface( [interface_model, interface_words], ["Test Model", "Check if words in/out-distribution"] ).launch()