Spaces:
Build error
Build error
File size: 6,903 Bytes
c5e4524 36ce206 a7fc504 36ce206 a7fc504 1227998 a7fc504 2493f1d a7fc504 1227998 a7fc504 c5e4524 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 |
import re
from nltk.stem import PorterStemmer, WordNetLemmatizer
# Keyword Extraction
def expand_list_of_lists(list_of_lists):
"""
Expands a list of lists of strings to a list of strings.
Args:
list_of_lists: A list of lists of strings.
Returns:
A list of strings.
"""
expanded_list = []
for inner_list in list_of_lists:
for string in inner_list:
expanded_list.append(string)
return expanded_list
def keywords_no_companies(texts):
# Company list (to remove companies from extracted entities)
company_list = [
"apple",
"amd",
"amazon",
"cisco",
"google",
"microsoft",
"nvidia",
"asml",
"intel",
"micron",
"aapl",
"csco",
"msft",
"asml",
"nvda",
"googl",
"mu",
"intc",
"amzn",
"amd",
]
texts = [text.split(" ") for text in texts]
texts = expand_list_of_lists(texts)
# Convert all strings to lowercase.
lower_texts = [text.lower() for text in texts]
keywords = [text for text in lower_texts if text not in company_list]
return keywords
def all_keywords_combs(texts):
texts = [text.split(" ") for text in texts]
texts = expand_list_of_lists(texts)
# Convert all strings to lowercase.
lower_texts = [text.lower() for text in texts]
# Stem the words in each string.
stemmer = PorterStemmer()
stem_texts = [stemmer.stem(text) for text in texts]
# Lemmatize the words in each string.
lemmatizer = WordNetLemmatizer()
lemm_texts = [lemmatizer.lemmatize(text) for text in texts]
texts.extend(lower_texts)
texts.extend(stem_texts)
texts.extend(lemm_texts)
return texts
def extract_keywords(query_text, model):
prompt = "###Instruction: Identify the key entities that accurately describe the context.\n\nInput:{query_text}\n\n###Response:"
#prompt = f"###Instruction:Extract the important keywords which describe the context accurately.\n\nInput:{query_text}\n\n###Response:"
response = model.predict(prompt)
keywords = response.split(", ")
keywords = keywords_no_companies(keywords)
return keywords
# Entity Extraction
def generate_alpaca_ner_prompt(query):
prompt = f"""Below is an instruction that describes a task, paired with an input that provides further context. Use the following guidelines to extract the entities representing the Company, Quarter, and Year in the sentence.
### Instruction:
- The output should be in the form "Company - Value, Quarter - Value, Year - Value".
- The output should be in the form "Company - None, Quarter - None, Year - None", if no entities are found.
- Only use entities that exist in the final sentence.
- If Company cannot be found in the sentence, return "none" for that entity.
- If Quarter cannot be found in the sentence, return "none" for that entity.
- If Year cannot be found in the sentence, return "none" for that entity.
- If there is ambiguity finding the entity, return "none" for that entity.
### Input:
What was discussed regarding Services revenue performance in Apple's Q3 2020 earnings call?
Company - Apple, Quarter - Q3, Year - 2020
How has the growth in Q1 been for the consumer market as seen by AMD?
Company - AMD, Quarter - Q1, Year - none
What was the long term view on GOOGL's cloud business growth as discussed in their earnings call?
Company - Google, Quarter - none, Year - none
What is Nvidia's outlook in the data center business in Q3 2020?
Company - Nvidia, Quarter - Q3, Year - 2020
What are the expansion plans of Amazon in the Asia Pacific region as discussed in their earnings call?
Company - Amazon, Quarter - none, Year - none
What did the Analysts ask about CSCO's cybersecurity business in the earnings call in 2016?
Company - Cisco, Quarter - none, Year - 2016
{query}
### Response:"""
return prompt
def format_entities_flan_alpaca(values):
"""
Extracts the text for each entity from the output generated by the
Flan-Alpaca model.
"""
try:
company_string, quarter_string, year_string = values.split(", ")
except:
company = None
quarter = None
year = None
try:
company = company_string.split(" - ")[1].lower()
company = None if company.lower() == "none" else company
except:
company = None
try:
quarter = quarter_string.split(" - ")[1]
quarter = None if quarter.lower() == "none" else quarter
except:
quarter = None
try:
year = year_string.split(" - ")[1]
year = None if year.lower() == "none" else year
except:
year = None
print((company, quarter, year))
return company, quarter, year
def extract_quarter_year(string):
# Extract year from string
year_match = re.search(r"\d{4}", string)
if year_match:
year = year_match.group()
else:
year = None
# Extract quarter from string
quarter_match = re.search(r"Q\d", string)
if quarter_match:
quarter = "Q" + quarter_match.group()[1]
else:
quarter = None
return quarter, year
def extract_ticker_spacy(query, model):
doc = model(query)
entities = {ent.label_: ent.text for ent in doc.ents}
print(entities.keys())
if "ORG" in entities.keys():
company = entities["ORG"].lower()
else:
company = None
return company
def clean_entities(company, quarter, year):
company_ticker_map = {
"apple": "AAPL",
"amd": "AMD",
"amazon": "AMZN",
"cisco": "CSCO",
"google": "GOOGL",
"microsoft": "MSFT",
"nvidia": "NVDA",
"asml": "ASML",
"intel": "INTC",
"micron": "MU",
}
ticker_choice = [
"AAPL",
"CSCO",
"MSFT",
"ASML",
"NVDA",
"GOOGL",
"MU",
"INTC",
"AMZN",
"AMD",
]
year_choice = ["2020", "2019", "2018", "2017", "2016", "All"]
quarter_choice = ["Q1", "Q2", "Q3", "Q4", "All"]
if company is not None:
if company in company_ticker_map.keys():
ticker = company_ticker_map[company]
ticker_index = ticker_choice.index(ticker)
else:
ticker_index = 0
else:
ticker_index = 0
if quarter is not None:
if quarter in quarter_choice:
quarter_index = quarter_choice.index(quarter)
else:
quarter_index = len(quarter_choice) - 1
else:
quarter_index = len(quarter_choice) - 1
if year is not None:
if year in year_choice:
year_index = year_choice.index(year)
else:
year_index = len(year_choice) - 1
else:
year_index = len(year_choice) - 1
return ticker_index, quarter_index, year_index
|