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import re |
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from nltk.stem import PorterStemmer, WordNetLemmatizer |
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def expand_list_of_lists(list_of_lists): |
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""" |
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Expands a list of lists of strings to a list of strings. |
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Args: |
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list_of_lists: A list of lists of strings. |
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Returns: |
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A list of strings. |
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""" |
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expanded_list = [] |
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for inner_list in list_of_lists: |
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for string in inner_list: |
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expanded_list.append(string) |
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return expanded_list |
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def keywords_no_companies(texts): |
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company_list = [ |
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"apple", |
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"amd", |
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"amazon", |
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"cisco", |
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"google", |
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"microsoft", |
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"nvidia", |
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"asml", |
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"intel", |
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"micron", |
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"aapl", |
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"csco", |
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"msft", |
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"asml", |
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"nvda", |
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"googl", |
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"mu", |
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"intc", |
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"amzn", |
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"amd", |
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] |
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texts = [text.split(" ") for text in texts] |
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texts = expand_list_of_lists(texts) |
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lower_texts = [text.lower() for text in texts] |
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keywords = [text for text in lower_texts if text not in company_list] |
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return keywords |
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def all_keywords_combs(texts): |
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texts = [text.split(" ") for text in texts] |
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texts = expand_list_of_lists(texts) |
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lower_texts = [text.lower() for text in texts] |
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stemmer = PorterStemmer() |
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stem_texts = [stemmer.stem(text) for text in texts] |
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lemmatizer = WordNetLemmatizer() |
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lemm_texts = [lemmatizer.lemmatize(text) for text in texts] |
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texts.extend(lower_texts) |
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texts.extend(stem_texts) |
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texts.extend(lemm_texts) |
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return texts |
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def extract_keywords(query_text, model): |
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prompt = "###Instruction: Identify the key entities that accurately describe the context.\n\nInput:{query_text}\n\n###Response:" |
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response = model.predict(prompt) |
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keywords = response.split(", ") |
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keywords = keywords_no_companies(keywords) |
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return keywords |
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def generate_alpaca_ner_prompt(query): |
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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. |
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### Instruction: |
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- The output should be in the form "Company - Value, Quarter - Value, Year - Value". |
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- The output should be in the form "Company - None, Quarter - None, Year - None", if no entities are found. |
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- Only use entities that exist in the final sentence. |
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- If Company cannot be found in the sentence, return "none" for that entity. |
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- If Quarter cannot be found in the sentence, return "none" for that entity. |
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- If Year cannot be found in the sentence, return "none" for that entity. |
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- If there is ambiguity finding the entity, return "none" for that entity. |
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### Input: |
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What was discussed regarding Services revenue performance in Apple's Q3 2020 earnings call? |
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Company - Apple, Quarter - Q3, Year - 2020 |
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How has the growth in Q1 been for the consumer market as seen by AMD? |
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Company - AMD, Quarter - Q1, Year - none |
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What was the long term view on GOOGL's cloud business growth as discussed in their earnings call? |
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Company - Google, Quarter - none, Year - none |
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What is Nvidia's outlook in the data center business in Q3 2020? |
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Company - Nvidia, Quarter - Q3, Year - 2020 |
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What are the expansion plans of Amazon in the Asia Pacific region as discussed in their earnings call? |
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Company - Amazon, Quarter - none, Year - none |
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What did the Analysts ask about CSCO's cybersecurity business in the earnings call in 2016? |
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Company - Cisco, Quarter - none, Year - 2016 |
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{query} |
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### Response:""" |
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return prompt |
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def format_entities_flan_alpaca(values): |
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""" |
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Extracts the text for each entity from the output generated by the |
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Flan-Alpaca model. |
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""" |
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try: |
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company_string, quarter_string, year_string = values.split(", ") |
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except: |
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company = None |
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quarter = None |
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year = None |
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try: |
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company = company_string.split(" - ")[1].lower() |
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company = None if company.lower() == "none" else company |
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except: |
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company = None |
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try: |
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quarter = quarter_string.split(" - ")[1] |
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quarter = None if quarter.lower() == "none" else quarter |
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except: |
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quarter = None |
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try: |
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year = year_string.split(" - ")[1] |
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year = None if year.lower() == "none" else year |
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except: |
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year = None |
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print((company, quarter, year)) |
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return company, quarter, year |
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def extract_quarter_year(string): |
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year_match = re.search(r"\d{4}", string) |
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if year_match: |
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year = year_match.group() |
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else: |
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year = None |
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quarter_match = re.search(r"Q\d", string) |
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if quarter_match: |
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quarter = "Q" + quarter_match.group()[1] |
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else: |
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quarter = None |
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return quarter, year |
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def extract_ticker_spacy(query, model): |
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doc = model(query) |
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entities = {ent.label_: ent.text for ent in doc.ents} |
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print(entities.keys()) |
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if "ORG" in entities.keys(): |
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company = entities["ORG"].lower() |
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else: |
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company = None |
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return company |
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def clean_entities(company, quarter, year): |
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company_ticker_map = { |
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"apple": "AAPL", |
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"amd": "AMD", |
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"amazon": "AMZN", |
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"cisco": "CSCO", |
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"google": "GOOGL", |
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"microsoft": "MSFT", |
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"nvidia": "NVDA", |
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"asml": "ASML", |
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"intel": "INTC", |
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"micron": "MU", |
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} |
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ticker_choice = [ |
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"AAPL", |
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"CSCO", |
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"MSFT", |
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"ASML", |
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"NVDA", |
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"GOOGL", |
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"MU", |
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"INTC", |
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"AMZN", |
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"AMD", |
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] |
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year_choice = ["2020", "2019", "2018", "2017", "2016", "All"] |
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quarter_choice = ["Q1", "Q2", "Q3", "Q4", "All"] |
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if company is not None: |
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if company in company_ticker_map.keys(): |
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ticker = company_ticker_map[company] |
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ticker_index = ticker_choice.index(ticker) |
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else: |
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ticker_index = 0 |
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else: |
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ticker_index = 0 |
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if quarter is not None: |
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if quarter in quarter_choice: |
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quarter_index = quarter_choice.index(quarter) |
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else: |
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quarter_index = len(quarter_choice) - 1 |
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else: |
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quarter_index = len(quarter_choice) - 1 |
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if year is not None: |
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if year in year_choice: |
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year_index = year_choice.index(year) |
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else: |
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year_index = len(year_choice) - 1 |
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else: |
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year_index = len(year_choice) - 1 |
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return ticker_index, quarter_index, year_index |
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