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(): 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("Search") df_out = gr.Dataframe(interactive=False) f_out = gr.File(interactive=False, label="Download") 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 = [ ["transformation", 20], ["climate_change", 50], ["risk, political_risk, uncertainty", 250], ], inputs = [text_in, n], outputs = [df_out, f_out, text_in], fn = semantic_search, cache_examples=True ) gr.Markdown( """
Call2Vec by X and Y