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| 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( | |
| "models/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() |