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