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| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| from datetime import datetime | |
| import os | |
| import re | |
| from huggingface_hub import hf_hub_url, cached_download | |
| from gensim.models.fasttext import load_facebook_model | |
| ACCESS_KEY = os.environ.get('ACCESS_KEY') | |
| # Setup model | |
| url = hf_hub_url(repo_id="simonschoe/call2vec", filename="model.bin") | |
| cached_download(url) | |
| model = load_facebook_model(cached_download(url)) | |
| def semantic_search(_input, n): | |
| """ Perform semantic search """ | |
| _input = re.split('[,;\n]', _input) | |
| _input = [s.strip().lower().replace(' ', '_') for s in _input if s] | |
| if _input[0] != ACCESS_KEY: | |
| with open('log.txt', 'a') as f: | |
| f.write(str(datetime.now()) + '+++' + '___'.join(_input) + '\n') | |
| if len(_input) > 1: | |
| avg_input = np.stack([model.wv[w] for w in _input], axis=0).mean(axis=0) | |
| nearest_neighbours = model.wv.most_similar(positive=avg_input, topn=n) | |
| frequencies = [model.wv.get_vecattr(nn[0], 'count') for nn in nearest_neighbours] | |
| else: | |
| nearest_neighbours = model.wv.most_similar(positive=_input[0], topn=n) | |
| frequencies = [model.wv.get_vecattr(nn[0], 'count') for nn in nearest_neighbours] | |
| if _input[0] == ACCESS_KEY: | |
| with open('log.txt', 'r') as f: | |
| prompts = f.readlines() | |
| prompts = [p.strip().split('+++') for p in prompts] | |
| result = pd.DataFrame(prompts, | |
| columns=['Time', 'Prompt']) | |
| else: | |
| result = pd.DataFrame([(a[0],a[1],b) for a,b in zip(nearest_neighbours, frequencies)], | |
| columns=['Token', 'Cosine Similarity', 'Corpus Frequency']) | |
| result.to_csv('result.csv') | |
| return result, 'result.csv', '\n'.join(_input) | |
| app = gr.Blocks() | |
| with app: | |
| gr.Markdown("# Call2Vec") | |
| gr.Markdown("## Semantic Search in Quarterly Earnings Conference Calls") | |
| with gr.Row(): | |
| 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. | |
| """ | |
| ) | |
| with gr.Column(): | |
| text_in = gr.Textbox(lines=1, placeholder="Insert text", label="Search Query") | |
| with gr.Row(): | |
| n = gr.Slider(value=50, minimum=5, maximum=250, step=5, label="Number of Neighbours") | |
| compute_bt = gr.Button("Start\nSearch") | |
| df_out = gr.Dataframe(interactive=False) | |
| f_out = gr.File(interactive=False, label="Download") | |
| gr.Examples( | |
| examples = [["transformation", 3], ["climate_change", 3], ["risk, political_risk, uncertainty", 5]], | |
| inputs = [text_in, n], | |
| outputs = [df_out, f_out, text_in], | |
| fn = semantic_search, | |
| cache_examples=True | |
| ) | |
| gr.Markdown( | |
| """ | |
| <div style='text-align: center;'>Call2Vec by X and Y</center></div> | |
| <p class="aligncenter"><img 'id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=simonschoe.call2vec&left_color=green&right_color=blue" /></p> | |
| """ | |
| ) | |
| compute_bt.click(semantic_search, inputs=[text_in, n], outputs=[df_out, f_out, text_in]) | |
| app.launch() |