Rajiv Shah commited on
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updated notebook

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  1. Prep_FINBert.ipynb +167 -0
Prep_FINBert.ipynb ADDED
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+ {
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 5,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "import os\n",
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+ "import gradio as gr\n",
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+ "from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 10,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "tokenizer = AutoTokenizer.from_pretrained(\"yiyanghkust/finbert-fls\")\n",
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+ "\n",
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+ "finbert = AutoModelForSequenceClassification.from_pretrained(\"yiyanghkust/finbert-fls\")"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 11,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "nlp = pipeline(\"text-classification\", model=finbert, tokenizer=tokenizer)\n",
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+ "results = nlp(['we expect the age of our fleet to enhance availability and reliability due to reduced downtime for repairs.',\n",
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+ " 'on an equivalent unit of production basis, general and administrative expenses declined 24 percent from 1994 to $.67 per boe.',\n",
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+ " 'we will continue to assess the need for a valuation allowance against deferred tax assets considering all available evidence obtained in future reporting periods.'])"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 15,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "<transformers.pipelines.text_classification.TextClassificationPipeline at 0x144572f40>"
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+ ]
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+ },
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+ "execution_count": 15,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": []
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 12,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "[{'label': 'Specific FLS', 'score': 0.77278733253479},\n",
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+ " {'label': 'Not FLS', 'score': 0.9905241131782532},\n",
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+ " {'label': 'Non-specific FLS', 'score': 0.975904107093811}]"
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+ ]
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+ },
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+ "execution_count": 12,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "results"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "['we expect the age of our fleet to enhance availability and reliability due to reduced downtime for repairs.',\n",
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+ " 'on an equivalent unit of production basis, general and administrative expenses declined 24 percent from 1994 to $.67 per boe.',\n",
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+ " 'we will continue to assess the need for a valuation allowance against deferred tax assets considering all available evidence obtained in future reporting periods.']]"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 19,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Running on local URL: http://127.0.0.1:7860/\n",
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+ "\n",
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+ "To create a public link, set `share=True` in `launch()`.\n"
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+ ]
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+ },
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+ {
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+ "data": {
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+ "text/html": [
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+ "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"900\" height=\"500\" allow=\"autoplay; camera; microphone;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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+ ],
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+ "text/plain": [
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+ "<IPython.core.display.HTML object>"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ }
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+ ],
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+ "source": [
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+ "title = \"Forward Looking Statement Classification with FinBERT\"\n",
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+ "description = \"This model classifies a sentence into one of the three categories: Specific FLS, Non- Specific FLS, and Not-FLS. We label a sentence as Specific FLS if it is about the future of the company, as Non-Specific FLS if it is future-oriented but could be said of any company (e.g., cautionary language or risk disclosure), and as Not-FLS if it is not about the future.\"\n",
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+ "examples =[['we expect the age of our fleet to enhance availability and reliability due to reduced downtime for repairs.'],\n",
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+ " ['on an equivalent unit of production basis, general and administrative expenses declined 24 percent from 1994 to $.67 per boe.'],\n",
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+ " ['we will continue to assess the need for a valuation allowance against deferred tax assets considering all available evidence obtained in future reporting periods.']]\n",
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+ "\n",
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+ "def get_sentiment(input_text):\n",
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+ " return nlp(input_text)\n",
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+ "\n",
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+ "iface = gr.Interface(fn=get_sentiment, \n",
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+ " inputs=\"text\", \n",
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+ " outputs=[\"text\"],\n",
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+ " title=title,\n",
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+ " description=description,\n",
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+ " examples=examples)\n",
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+ "iface.launch(debug=True)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": []
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+ }
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+ ],
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+ "metadata": {
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+ "interpreter": {
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+ "hash": "325bbc5f2b77b6a5675ad3f6ec2d9cde3e7a8993fd48d3c331b30741632a2dac"
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+ },
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+ "kernelspec": {
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+ "display_name": "Python 3.8.13 ('hf_public')",
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+ "language": "python",
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+ "name": "python3"
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+ },
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+ "language_info": {
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+ "codemirror_mode": {
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+ "name": "ipython",
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+ "version": 3
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+ },
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+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
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+ "name": "python",
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+ "nbconvert_exporter": "python",
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+ "pygments_lexer": "ipython3",
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+ "version": "3.8.13"
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+ },
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+ "orig_nbformat": 4
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+ },
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+ "nbformat": 4,
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+ "nbformat_minor": 2
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+ }