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| import gradio as gr | |
| from transformers import pipeline | |
| from transformers_interpret import SequenceClassificationExplainer | |
| from bs4 import BeautifulSoup | |
| # Setup model | |
| classifier = pipeline("text-classification", model="simonschoe/TransformationTransformer") | |
| explainer = SequenceClassificationExplainer(classifier.model, classifier.tokenizer) | |
| legend = """ | |
| <div style="text-align: center; display: block; margin-left: auto; margin-right: auto; border-top: 1px solid; margin-top: 5px; padding-top: 5px;"><b>Legend: </b><span style="display: inline-block; width: 10px; height: 10px; border: 1px solid; background-color: hsl(0, 75%, 60%)"></span> Generic <span style="display: inline-block; width: 10px; height: 10px; border: 1px solid; background-color: hsl(120, 75%, 50%)"></span> Transformation </div> | |
| """ | |
| def classify(_input): | |
| """ | |
| wrapper method to compute label 1 probability and explanation for given input | |
| """ | |
| result = classifier(_input)[0] | |
| score = result['score'] | |
| if result['label'] == 'LABEL_0': | |
| score = 1-score | |
| # getting visualization | |
| attributions = explainer(_input) | |
| html = explainer.visualize().__html__() | |
| soup = BeautifulSoup(html, 'html.parser') | |
| explanation = soup.find_all('td')[-1].__str__().replace('td', 'div') | |
| # adding legend to word importance explanation | |
| result_html = explanation + legend | |
| return score, result_html | |
| app = gr.Blocks() | |
| with app: | |
| gr.Markdown("# Call2Vec") | |
| gr.Markdown("## Semantic Search in Quarterly Earnings Conference Calls") | |
| with gr.Row(): | |
| with gr.Column(): | |
| text_in = gr.Textbox(lines=1, placeholder="Insert text", label="Search Query") | |
| with gr.Row(): | |
| compute_bt = gr.Button("Calculate") | |
| score_out = gr.Number(label="Label 1 probability", interactive=False) | |
| html_out = gr.HTML(label="Explanation") | |
| with gr.Column(): | |
| gr.Markdown( | |
| """ | |
| #### Project Description | |
| Call2Vec is a [fastText](https://fasttext.cc/) word embedding model trained via [Gensim](https://radimrehurek.com/gensim/). It maps each token in the vocabulary into a dense, 300-dimensional vector space, designed for performing semantic search. | |
| The model is trained on a large sample of quarterly earnings conference calls, held by U.S. firms during the 2006-2022 period. In particular, the training data is restriced to the (rather sponentous) executives' remarks of the Q&A section of the call. The data has been preprocessed prior to model training via stop word removal, lemmatization, named entity masking, and coocurrence modeling. | |
| """ | |
| ) | |
| gr.Markdown( | |
| """ | |
| #### App usage | |
| The model is intented to be used for **semantic search**: It encodes the search query (entered in the textbox on the right) in a dense vector space and finds semantic neighbours, i.e., token which frequently occur within similar contexts in the underlying training data. | |
| The model allows for two use cases: | |
| 1. *Single Search:* The input query consists of a single word. When provided a bi-, tri-, or even fourgram, the quality of the model output depends on the presence of the query token in the model's vocabulary. N-grams should be concated by an underscore (e.g., "machine_learning" or "artifical_intelligence"). | |
| 2. *Multi Search:* The input query may consist of several words or n-grams, seperated by comma, semi-colon or newline. It then computes the average vector over all inputs and performs semantic search based on the average input token. | |
| """ | |
| ) | |
| gr.Examples( | |
| examples=[["Now Accord networks is a company in video, and he led the sales team, and the marketing group at Accord, and he took it from start up, sound familiar, it's from start up to $60 million company in two years."], ["Another test sentence"], ["Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam"]], | |
| inputs=[text_in], | |
| outputs=[score_out, html_out], | |
| fn=classify, | |
| cache_examples=True | |
| ) | |
| gr.Markdown( | |
| """ | |
| <p style="text-align: center;"> | |
| Call2Vec by X and Y | |
| <br> | |
| <img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=simonschoe.call2vec&left_color=green&right_color=blue" style="display: block; margin-left: auto; margin-right: auto;"/> | |
| </p> | |
| """ | |
| ) | |
| compute_bt.click(classify, inputs=[text_in], outputs=[score_out, html_out]) | |
| app.launch() | |