Update README.md
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README.md
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example_title: "Sentiment Analysis"
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text: |-
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Input: I'm feeling quite sad and sorry for myself but ill snap out of it soon.
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Output: sadness
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Input: I can have for a treat or if i am feeling festive.
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Output:
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example_title: "ADE Corpus V2"
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text: |-
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Label the sentence based on whether it is related to an adverse drug effect (ADE). Details are described below:
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Drugs: Names of drugs and chemicals that include brand names, trivial names, abbreviations and systematic names were annotated. Mentions of drugs or chemicals should strictly be in a therapeutic context. This category does not include the names of metabolites, reaction byproducts, or hospital chemicals (e.g. surgical equipment disinfectants).
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Adverse effect: Mentions of adverse effects include signs, symptoms, diseases, disorders, acquired abnormalities, deficiencies, organ damage or death that strictly occur as a consequence of drug intake.
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Possible labels:
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1. ADE-related
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2. not ADE-related
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Sentence: A challenge with clozapine was feasible and showed no clinical symptoms of eosinophilia.
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Label: not ADE-related
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Sentence: CONCLUSIONS: These results suggest that clozapine may cause TD; however, the prevalence is low and the severity is relatively mild, with no or mild self-reported discomfort.
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Label: ADE-related
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Sentence: Best-corrected visual acuity measurements were performed at every visit.
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Label: not ADE-related
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Sentence: These cases were considered unusual in light of the short delay of their onset after initiation of immunosuppressive therapy and their fulminant course: 3 of these patients died of PCP occurring during the first month of treatment with prednisone.
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Label: ADE-related
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Sentence: The INR should be monitored more frequently when bosentan is initiated, adjusted, or discontinued in patients taking warfarin.
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Label: not ADE-related
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Sentence: NEH must be considered in lupus patients receiving cytotoxic agents to avoid inappropriate use of corticosteroids or antibiotics in this self-limited condition.
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Label:
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example_title: Banking77
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text: |-
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The following is a banking customer service query. Classify the query into one of the 77 categories available.
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Possible labels:
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1. Refund_not_showing_up
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2. activate_my_card
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3. age_limit
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4. apple_pay_or_google_pay
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5. atm_support
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6. automatic_top_up
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7. balance_not_updated_after_bank_transfer
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8. balance_not_updated_after_cheque_or_cash_deposit
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9. beneficiary_not_allowed
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10. cancel_transfer
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11. card_about_to_expire
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12. card_acceptance
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13. card_arrival
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14. card_delivery_estimate
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15. card_linking
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16. card_not_working
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17. card_payment_fee_charged
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18. card_payment_not_recognised
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19. card_payment_wrong_exchange_rate
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20. card_swallowed
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21. cash_withdrawal_charge
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22. cash_withdrawal_not_recognised
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23. change_pin
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24. compromised_card
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25. contactless_not_working
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26. country_support
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27. declined_card_payment
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28. declined_cash_withdrawal
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29. declined_transfer
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30. direct_debit_payment_not_recognised
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31. disposable_card_limits
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32. edit_personal_details
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33. exchange_charge
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34. exchange_rate
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35. exchange_via_app
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36. extra_charge_on_statement
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37. failed_transfer
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38. fiat_currency_support
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39. get_disposable_virtual_card
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40. get_physical_card
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41. getting_spare_card
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42. getting_virtual_card
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43. lost_or_stolen_card
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44. lost_or_stolen_phone
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45. order_physical_card
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46. passcode_forgotten
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47. pending_card_payment
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48. pending_cash_withdrawal
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49. pending_top_up
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50. pending_transfer
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51. pin_blocked
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52. receiving_money
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53. request_refund
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54. reverted_card_payment?
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55. supported_cards_and_currencies
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56. terminate_account
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57. top_up_by_bank_transfer_charge
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58. top_up_by_card_charge
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59. top_up_by_cash_or_cheque
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60. top_up_failed
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61. top_up_limits
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62. top_up_reverted
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63. topping_up_by_card
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64. transaction_charged_twice
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65. transfer_fee_charged
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66. transfer_into_account
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67. transfer_not_received_by_recipient
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68. transfer_timing
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69. unable_to_verify_identity
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70. verify_my_identity
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71. verify_source_of_funds
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72. verify_top_up
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73. virtual_card_not_working
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74. visa_or_mastercard
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75. why_verify_identity
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76. wrong_amount_of_cash_received
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77. wrong_exchange_rate_for_cash_withdrawal
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Query: My card payment was not successful.
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Label: declined_card_payment
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Query: Is it possible for me to change my PIN number?
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Label: change_pin
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Query: limits on top ups
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Label: top_up_limits
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Query: I live in the EU - can I get a card?
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Label: country_support
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Query: How can I tell the source for my available funds?
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Label: verify_source_of_funds
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Query: Why am I getting declines when trying to make a purchase online?
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Label:
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example_title:
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text: |-
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Possible labels:
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1. not overruling
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2. overruling
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Sentence: see mciver, 134 n.c.app. at 588, 518 s.e.2d at 526.
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Label: not overruling
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Sentence: accordingly, to the extent of any conflict nemecek v. state, 621 s.w.2d 404 (tex.cr.app. 1980) is overruled.
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Label: overruling
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Sentence: the following facts are taken from the administrative record.
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Label: not overruling
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Sentence: see scott, supra at 352; commonwealth v. ruffin, 475 mass. 1003, 1004 (2016).
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Label:
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example_title: "Tweet Eval Hate"
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text: |-
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1. hate speech
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2. not hate speech
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Tweet: #TakeAKnee trump's a chump! @user is the real deal-helping so many on his journey through greatness! Even @user can't stand #IQ45-stands to CLEAN UP while he's in prison. One hot, lucky #Immigrant babe! #OHi12 #VoteBlue @user in the #Columbus area. #BlueWave baby!
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Label: not hate speech
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Tweet: HOW REFRESHING! In South Korea, there is no such thing as 'political correctness" when it comes to dealing with Muslim refugee wannabes via @user
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Label: hate speech
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Tweet: New to Twitter-- any men on here know what the process is to get #verified?
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Label: not hate speech
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Tweet: UK Pensioner Faces 350 Lashes In Saudi Arabia why does this country exist it does nothing for migrants picks on old men no help from anyone
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Label: not hate speech
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Tweet: RT @user Her:I don't get what u want outta this relationship Him:Well, I was only looking for a bj but u kept coming back
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Label: not hate speech
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Tweet: Dont worry @user you are and will always be the most hysterical woman.
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Label:
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---
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<h1 style="font-size: 42px">GPT-JT<h1/>
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example_title: "Sentiment Analysis"
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text: |-
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The task is to label the post's emotion as sadness, joy, love, anger, fear, or surprise.
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Input: I'm feeling quite sad and sorry for myself but ill snap out of it soon.
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Output: sadness
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Input: I can have for a treat or if i am feeling festive.
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Output:
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example_title: "Country Currency"
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text: |-
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Return the currency of the given country.
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Input: Switzerland
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Output: Swiss Franc
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Input: India
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Output:
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example_title: "Tweet Eval Hate"
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text: |-
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1. hate speech
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2. not hate speech
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Tweet: HOW REFRESHING! In South Korea, there is no such thing as 'political correctness" when it comes to dealing with Muslim refugee wannabes via @user
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Label: hate speech
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Tweet: New to Twitter-- any men on here know what the process is to get #verified?
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Label: not hate speech
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Tweet: Dont worry @user you are and will always be the most hysterical woman.
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Label:
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example_title: "Entity Recognition"
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text: |-
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Extract all the names of people, places, and organizations from the following sentences.
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Sentence: Satya Nadella, the CEO of Microsoft, was visiting the Bahamas last May.
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Entities: Satya Nadella, Microsoft, Bahamas
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Sentence: Pacific Northwest cities include Seattle and Portland, which I have visited with Vikash.
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Entities:
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example_title: "Data Clearning"
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text: |-
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Format the data into a CSV file:
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Input: Jane Doe [email protected] (520) 382 2435
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Output: Jane Doe,[email protected],520-382-2435
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Input: Peter Lee (510) 333-2429 email: [email protected]
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Output:
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---
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<h1 style="font-size: 42px">GPT-JT<h1/>
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