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SaylorTwift HF Staff commited on
Commit
0762d18
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1 Parent(s): 33277f2

Add 'banking_77' config data files

Browse files
.gitattributes CHANGED
@@ -17,3 +17,4 @@
17
  data/ filter=lfs diff=lfs merge=lfs -text
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  tai_safety_research/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text
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  ade_corpus_v2/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text
 
 
17
  data/ filter=lfs diff=lfs merge=lfs -text
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  tai_safety_research/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text
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  ade_corpus_v2/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text
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+ banking_77/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -46,6 +46,103 @@ dataset_info:
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  num_examples: 5000
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  download_size: 445823
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  dataset_size: 716689
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - config_name: tai_safety_research
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  features:
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  - name: Title
@@ -87,6 +184,12 @@ configs:
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  path: ade_corpus_v2/train-*
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  - split: test
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  path: ade_corpus_v2/test-*
 
 
 
 
 
 
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  - config_name: tai_safety_research
91
  data_files:
92
  - split: train
 
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  num_examples: 5000
47
  download_size: 445823
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  dataset_size: 716689
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+ - config_name: banking_77
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+ features:
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+ - name: Query
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+ dtype: string
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+ - name: ID
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+ dtype: int32
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+ - name: Label
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+ dtype:
57
+ class_label:
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+ names:
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+ '0': Unlabeled
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+ '1': Refund_not_showing_up
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+ '2': activate_my_card
62
+ '3': age_limit
63
+ '4': apple_pay_or_google_pay
64
+ '5': atm_support
65
+ '6': automatic_top_up
66
+ '7': balance_not_updated_after_bank_transfer
67
+ '8': balance_not_updated_after_cheque_or_cash_deposit
68
+ '9': beneficiary_not_allowed
69
+ '10': cancel_transfer
70
+ '11': card_about_to_expire
71
+ '12': card_acceptance
72
+ '13': card_arrival
73
+ '14': card_delivery_estimate
74
+ '15': card_linking
75
+ '16': card_not_working
76
+ '17': card_payment_fee_charged
77
+ '18': card_payment_not_recognised
78
+ '19': card_payment_wrong_exchange_rate
79
+ '20': card_swallowed
80
+ '21': cash_withdrawal_charge
81
+ '22': cash_withdrawal_not_recognised
82
+ '23': change_pin
83
+ '24': compromised_card
84
+ '25': contactless_not_working
85
+ '26': country_support
86
+ '27': declined_card_payment
87
+ '28': declined_cash_withdrawal
88
+ '29': declined_transfer
89
+ '30': direct_debit_payment_not_recognised
90
+ '31': disposable_card_limits
91
+ '32': edit_personal_details
92
+ '33': exchange_charge
93
+ '34': exchange_rate
94
+ '35': exchange_via_app
95
+ '36': extra_charge_on_statement
96
+ '37': failed_transfer
97
+ '38': fiat_currency_support
98
+ '39': get_disposable_virtual_card
99
+ '40': get_physical_card
100
+ '41': getting_spare_card
101
+ '42': getting_virtual_card
102
+ '43': lost_or_stolen_card
103
+ '44': lost_or_stolen_phone
104
+ '45': order_physical_card
105
+ '46': passcode_forgotten
106
+ '47': pending_card_payment
107
+ '48': pending_cash_withdrawal
108
+ '49': pending_top_up
109
+ '50': pending_transfer
110
+ '51': pin_blocked
111
+ '52': receiving_money
112
+ '53': request_refund
113
+ '54': reverted_card_payment?
114
+ '55': supported_cards_and_currencies
115
+ '56': terminate_account
116
+ '57': top_up_by_bank_transfer_charge
117
+ '58': top_up_by_card_charge
118
+ '59': top_up_by_cash_or_cheque
119
+ '60': top_up_failed
120
+ '61': top_up_limits
121
+ '62': top_up_reverted
122
+ '63': topping_up_by_card
123
+ '64': transaction_charged_twice
124
+ '65': transfer_fee_charged
125
+ '66': transfer_into_account
126
+ '67': transfer_not_received_by_recipient
127
+ '68': transfer_timing
128
+ '69': unable_to_verify_identity
129
+ '70': verify_my_identity
130
+ '71': verify_source_of_funds
131
+ '72': verify_top_up
132
+ '73': virtual_card_not_working
133
+ '74': visa_or_mastercard
134
+ '75': why_verify_identity
135
+ '76': wrong_amount_of_cash_received
136
+ '77': wrong_exchange_rate_for_cash_withdrawal
137
+ splits:
138
+ - name: train
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+ num_bytes: 3373
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+ num_examples: 50
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+ - name: test
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+ num_bytes: 376765
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+ num_examples: 5000
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+ download_size: 214821
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+ dataset_size: 380138
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  - config_name: tai_safety_research
147
  features:
148
  - name: Title
 
184
  path: ade_corpus_v2/train-*
185
  - split: test
186
  path: ade_corpus_v2/test-*
187
+ - config_name: banking_77
188
+ data_files:
189
+ - split: train
190
+ path: banking_77/train-*
191
+ - split: test
192
+ path: banking_77/test-*
193
  - config_name: tai_safety_research
194
  data_files:
195
  - split: train
banking_77/test-00000-of-00001.parquet ADDED
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+ oid sha256:6ac7f980fffc7b90cc931e937be2f8033f812ca3683f7c26d0fcd1abca63267c
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+ size 206428
banking_77/train-00000-of-00001.parquet ADDED
Binary file (8.39 kB). View file
 
dataset_infos.json CHANGED
@@ -57,16 +57,13 @@
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  "features": {
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  "Query": {
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  "_type": "Value"
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  },
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  "ID": {
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  "Label": {
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- "num_classes": 78,
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  "names": [
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  "Unlabeled",
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  "Refund_not_showing_up",
@@ -147,19 +144,14 @@
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  "wrong_amount_of_cash_received",
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  "wrong_exchange_rate_for_cash_withdrawal"
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  ],
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- "builder_name": "raft",
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  "config_name": "banking_77",
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  "version_str": "1.1.0",
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  "description": "Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? \n\n[RAFT](https://raft.elicit.org) is a few-shot classification benchmark that tests language models:\n\n- across multiple domains (lit review, tweets, customer interaction, etc.)\n- on economically valuable classification tasks (someone inherently cares about the task)\n- in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set)\n",
 
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  "features": {
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  "Label": {
 
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  "Unlabeled",
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  "Refund_not_showing_up",
 
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  "wrong_amount_of_cash_received",
145
  "wrong_exchange_rate_for_cash_withdrawal"
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  ],
 
 
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  "_type": "ClassLabel"
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  }
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  },
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+ "builder_name": "parquet",
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+ "dataset_name": "raft",
 
 
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  "config_name": "banking_77",
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  }
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  },
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  },
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  "terms_of_service": {
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  "description": "Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? \n\n[RAFT](https://raft.elicit.org) is a few-shot classification benchmark that tests language models:\n\n- across multiple domains (lit review, tweets, customer interaction, etc.)\n- on economically valuable classification tasks (someone inherently cares about the task)\n- in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set)\n",