Sheng Lei
commited on
Commit
•
513d0e0
1
Parent(s):
2cee732
Refactor structure
Browse files- trained_model/config.json → config.json +0 -0
- csv/block_items.csv +0 -34
- genai_SDK/Seq2Seq.py +0 -250
- genai_SDK/Utilities.py +0 -563
- genai_SDK/__init__.py +0 -2
- genai_SDK/__pycache__/Seq2Seq.cpython-312.pyc +0 -0
- genai_SDK/__pycache__/Utilities.cpython-312.pyc +0 -0
- genai_SDK/__pycache__/__init__.cpython-312.pyc +0 -0
- main.py +0 -24
- trained_model/model.safetensors → model.safetensors +0 -0
- pyproject.toml +0 -23
- restrictedItems/parse.py +0 -11
- restrictedItems/predict.py +0 -40
- restrictedItems/train.py +0 -132
- trained_model/special_tokens_map.json → special_tokens_map.json +0 -0
- trained_model/tokenizer_config.json → tokenizer_config.json +0 -0
- trained_model/vocab.txt → vocab.txt +0 -0
trained_model/config.json → config.json
RENAMED
File without changes
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csv/block_items.csv
DELETED
@@ -1,34 +0,0 @@
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Date,Host,Service,Blocked Cart Items,Message
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"2024-06-25T21:51:35.523Z","""i-0ec7dc300989b0f58""","""cash-commerce-browser""","""[{\""name\"":\""Promotional Email GiftCard $10\"",\""image_url\"":\""https://target.scene7.com/is/image/Target//GUEST_337c880d-e2c0-4794-9784-962c63cf0646?qlt=80&fmt=pjpeg\""}, {\""name\"":\""Promotional Email GiftCard $10\"",\""image_url\"":\""https://target.scene7.com/is/image/Target//GUEST_337c880d-e2c0-4794-9784-962c63cf0646?qlt=80&fmt=pjpeg\""}]""","Found restricted items: [ShoppingCartProduct{name=Promotional Email GiftCard $10, image_url=https://target.scene7.com/is/image/Target//GUEST_337c880d-e2c0-4794-9784-962c63cf0646?qlt=80&fmt=pjpeg}, ShoppingCartProduct{name=Promotional Email GiftCard $10, image_url=https://target.scene7.com/is/image/Target//GUEST_337c880d-e2c0-4794-9784-962c63cf0646?qlt=80&fmt=pjpeg}]; {customer_token=C_gqed91mw3, flow_token=4s6ZZrxNlMdUWqbN59nXHURXi, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_3mfvyukp, sup_token=BRAND_57fqf0l740hpnbxev71c6d56e}"
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"2024-06-25T21:43:14.348Z","""i-0ec7dc300989b0f58""","""cash-commerce-browser""","""[{\""name\"":\""$100 Vanilla® Visa® Gift Box Gift Card (plus $5.44 Purchase Fee)\"",\""url\"":\""https://www.walmart.com/ip/seort/55844027\""}]""","Found restricted items: [ShoppingCartProduct{name=$100 Vanilla® Visa® Gift Box Gift Card (plus $5.44 Purchase Fee), url=https://www.walmart.com/ip/seort/55844027}]; {customer_token=C_86dpxgy6b, flow_token=4c546519-bc7c-4ae7-bbf0-5daf158351b1, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_fopbedhc, sup_token=null}"
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"2024-06-25T21:43:05.816Z","""i-044ba56b80a95063d""","""cash-commerce-browser""","""[{\""name\"":\""Promotional Email GiftCard $10\"",\""image_url\"":\""https://target.scene7.com/is/image/Target//GUEST_337c880d-e2c0-4794-9784-962c63cf0646?qlt=80&fmt=pjpeg\""}]""","Found restricted items: [ShoppingCartProduct{name=Promotional Email GiftCard $10, image_url=https://target.scene7.com/is/image/Target//GUEST_337c880d-e2c0-4794-9784-962c63cf0646?qlt=80&fmt=pjpeg}]; {customer_token=C_fsexdpmbh, flow_token=fRqPMaEwTcPsgLuLQ8mCvf9df, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_3mfvyukp, sup_token=BRAND_57fqf0l740hpnbxev71c6d56e}"
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"2024-06-25T21:35:36.381Z","""i-0ec7dc300989b0f58""","""cash-commerce-browser""","""[{\""name\"":\""GPS7000 GPS Tracker for Vehicles - Hidden Tracking Device for Any Vehicle - Easy Installation on Car's Battery- 10 Days of Service - Subscription Required - Low Cost Subscription Plan Options\"",\""url\"":\""https://www.amazon.com/gp/aw/d/B0BYK99LZC/ref=ox_sc_act_title_delete_4?smid=AWU3BDL6BD5T5&psc=1\"",\""image_url\"":\""https://m.media-amazon.com/images/I/61E6Cex5dUL._AC_AA135_.jpg\""}]""","Found restricted items: [ShoppingCartProduct{name=GPS7000 GPS Tracker for Vehicles - Hidden Tracking Device for Any Vehicle - Easy Installation on Car's Battery- 10 Days of Service - Subscription Required - Low Cost Subscription Plan Options, url=https://www.amazon.com/gp/aw/d/B0BYK99LZC/ref=ox_sc_act_title_delete_4?smid=AWU3BDL6BD5T5&psc=1, image_url=https://m.media-amazon.com/images/I/61E6Cex5dUL._AC_AA135_.jpg}]; {customer_token=C_5cpf4hygz, flow_token=73658682-d99e-4742-b6b4-8ebb7f1543ad, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_ih4h4tzb, sup_token=BRAND_djwngg0n5zluxwp131plevfvw}"
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"2024-06-25T21:35:24.733Z","""i-044ba56b80a95063d""","""cash-commerce-browser""","""[{\""name\"":\""GPS7000 GPS Tracker for Vehicles - Hidden Tracking Device for Any Vehicle - Easy Installation on Car's Battery- 10 Days of Service - Subscription Required - Low Cost Subscription Plan Options\"",\""url\"":\""https://www.amazon.com/gp/aw/d/B0BYK99LZC/ref=ox_sc_act_title_delete_4?smid=AWU3BDL6BD5T5&psc=1\"",\""image_url\"":\""https://m.media-amazon.com/images/I/61E6Cex5dUL._AC_AA135_.jpg\""}]""","Found restricted items: [ShoppingCartProduct{name=GPS7000 GPS Tracker for Vehicles - Hidden Tracking Device for Any Vehicle - Easy Installation on Car's Battery- 10 Days of Service - Subscription Required - Low Cost Subscription Plan Options, url=https://www.amazon.com/gp/aw/d/B0BYK99LZC/ref=ox_sc_act_title_delete_4?smid=AWU3BDL6BD5T5&psc=1, image_url=https://m.media-amazon.com/images/I/61E6Cex5dUL._AC_AA135_.jpg}]; {customer_token=C_5cpf4hygz, flow_token=73658682-d99e-4742-b6b4-8ebb7f1543ad, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_ih4h4tzb, sup_token=BRAND_djwngg0n5zluxwp131plevfvw}"
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"2024-06-25T21:35:20.107Z","""i-003fbf26706b7af3e""","""cash-commerce-browser""","""[{\""name\"":\""GPS7000 GPS Tracker for Vehicles - Hidden Tracking Device for Any Vehicle - Easy Installation on Car's Battery- 10 Days of Service - Subscription Required - Low Cost Subscription Plan Options\"",\""url\"":\""https://www.amazon.com/gp/aw/d/B0BYK99LZC/ref=ox_sc_act_title_delete_4?smid=AWU3BDL6BD5T5&psc=1\"",\""image_url\"":\""https://m.media-amazon.com/images/I/61E6Cex5dUL._AC_AA135_.jpg\""}]""","Found restricted items: [ShoppingCartProduct{name=GPS7000 GPS Tracker for Vehicles - Hidden Tracking Device for Any Vehicle - Easy Installation on Car's Battery- 10 Days of Service - Subscription Required - Low Cost Subscription Plan Options, url=https://www.amazon.com/gp/aw/d/B0BYK99LZC/ref=ox_sc_act_title_delete_4?smid=AWU3BDL6BD5T5&psc=1, image_url=https://m.media-amazon.com/images/I/61E6Cex5dUL._AC_AA135_.jpg}]; {customer_token=C_5cpf4hygz, flow_token=73658682-d99e-4742-b6b4-8ebb7f1543ad, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_ih4h4tzb, sup_token=BRAND_djwngg0n5zluxwp131plevfvw}"
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"2024-06-25T21:35:01.987Z","""i-003fbf26706b7af3e""","""cash-commerce-browser""","""[{\""name\"":\""GPS7000 GPS Tracker for Vehicles - Hidden Tracking Device for Any Vehicle - Easy Installation on Car's Battery- 10 Days of Service - Subscription Required - Low Cost Subscription Plan Options\"",\""url\"":\""https://www.amazon.com/gp/aw/d/B0BYK99LZC/ref=ox_sc_act_title_delete_4?smid=AWU3BDL6BD5T5&psc=1\"",\""image_url\"":\""https://m.media-amazon.com/images/I/61E6Cex5dUL._AC_AA135_.jpg\""}]""","Found restricted items: [ShoppingCartProduct{name=GPS7000 GPS Tracker for Vehicles - Hidden Tracking Device for Any Vehicle - Easy Installation on Car's Battery- 10 Days of Service - Subscription Required - Low Cost Subscription Plan Options, url=https://www.amazon.com/gp/aw/d/B0BYK99LZC/ref=ox_sc_act_title_delete_4?smid=AWU3BDL6BD5T5&psc=1, image_url=https://m.media-amazon.com/images/I/61E6Cex5dUL._AC_AA135_.jpg}]; {customer_token=C_5cpf4hygz, flow_token=73658682-d99e-4742-b6b4-8ebb7f1543ad, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_ih4h4tzb, sup_token=BRAND_djwngg0n5zluxwp131plevfvw}"
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"2024-06-25T21:34:07.013Z","""i-044ba56b80a95063d""","""cash-commerce-browser""","""[{\""name\"":\""$50 Vanilla Visa eGift Card\"",\""url\"":\""https://www.samsclub.com/p/vanilla-e-gift-visa-various-amount/prod25810992\"",\""image_url\"":\""https://scene7.samsclub.com/is/image/samsclub/0079936655659_A?wid=200&hei=200\""}]""","Found restricted items: [ShoppingCartProduct{name=$50 Vanilla Visa eGift Card, url=https://www.samsclub.com/p/vanilla-e-gift-visa-various-amount/prod25810992, image_url=https://scene7.samsclub.com/is/image/samsclub/0079936655659_A?wid=200&hei=200}]; {customer_token=C_haed91mp3, flow_token=Q81SuACF2yWsd6Fn6PRWDsBK4, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_dsjbdob6, sup_token=null}"
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"2024-06-25T21:33:34.552Z","""i-0ec7dc300989b0f58""","""cash-commerce-browser""","""[{\""name\"":\""GPS7000 GPS Tracker for Vehicles - Hidden Tracking Device for Any Vehicle - Easy Installation on Car's Battery- 10 Days of Service - Subscription Required - Low Cost Subscription Plan Options\"",\""url\"":\""https://www.amazon.com/gp/aw/d/B0BYK99LZC/ref=ox_sc_act_title_delete_4?smid=AWU3BDL6BD5T5&psc=1\"",\""image_url\"":\""https://m.media-amazon.com/images/I/61E6Cex5dUL._AC_AA135_.jpg\""}]""","Found restricted items: [ShoppingCartProduct{name=GPS7000 GPS Tracker for Vehicles - Hidden Tracking Device for Any Vehicle - Easy Installation on Car's Battery- 10 Days of Service - Subscription Required - Low Cost Subscription Plan Options, url=https://www.amazon.com/gp/aw/d/B0BYK99LZC/ref=ox_sc_act_title_delete_4?smid=AWU3BDL6BD5T5&psc=1, image_url=https://m.media-amazon.com/images/I/61E6Cex5dUL._AC_AA135_.jpg}]; {customer_token=C_5cpf4hygz, flow_token=73658682-d99e-4742-b6b4-8ebb7f1543ad, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_ih4h4tzb, sup_token=BRAND_djwngg0n5zluxwp131plevfvw}"
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"2024-06-25T21:31:29.788Z","""i-0c92416a905681db6""","""cash-commerce-browser""","""[{\""name\"":\""$200 Vanilla Visa Shiny Bow Gift Card (plus $6.88 Purchase Fee)\"",\""url\"":\""https://www.walmart.com/ip/seort/1042177970\""}]""","Found restricted items: [ShoppingCartProduct{name=$200 Vanilla Visa Shiny Bow Gift Card (plus $6.88 Purchase Fee), url=https://www.walmart.com/ip/seort/1042177970}]; {customer_token=C_smecd0mhm, flow_token=wnHQN78zduV3T55TsN8s0CKHA, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_fopbedhc, sup_token=BRAND_4sxo1et5hf8sug8lh500413rj}"
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"2024-06-25T21:30:38.963Z","""i-044ba56b80a95063d""","""cash-commerce-browser""","""[{\""name\"":\""$200 Vanilla Visa Shiny Bow Gift Card (plus $6.88 Purchase Fee)\"",\""image_url\"":\""https://i5.walmartimages.com/seo/200-Vanilla-Visa-Shiny-Bow-Gift-Card-plus-6-88-Purchase-Fee_1cc86583-2dcb-4677-bca4-16f685df6170.d43209d754cbb7ab3c09354d6cf906a7.jpeg?odnHeight=48&odnWidth=48&odnBg=FFFFFF\""}]""","Found restricted items: [ShoppingCartProduct{name=$200 Vanilla Visa Shiny Bow Gift Card (plus $6.88 Purchase Fee), image_url=https://i5.walmartimages.com/seo/200-Vanilla-Visa-Shiny-Bow-Gift-Card-plus-6-88-Purchase-Fee_1cc86583-2dcb-4677-bca4-16f685df6170.d43209d754cbb7ab3c09354d6cf906a7.jpeg?odnHeight=48&odnWidth=48&odnBg=FFFFFF}]; {customer_token=C_smecd0mhm, flow_token=CCvc7KfXlUWnhdqtcxORrG4Sp, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_fopbedhc, sup_token=BRAND_4sxo1et5hf8sug8lh500413rj}"
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"2024-06-25T21:29:54.170Z","""i-0ec7dc300989b0f58""","""cash-commerce-browser""","""[{\""name\"":\""$200 Vanilla Visa Shiny Bow Gift Card (plus $6.88 Purchase Fee)\"",\""url\"":\""https://www.walmart.com/ip/seort/1042177970\""}]""","Found restricted items: [ShoppingCartProduct{name=$200 Vanilla Visa Shiny Bow Gift Card (plus $6.88 Purchase Fee), url=https://www.walmart.com/ip/seort/1042177970}]; {customer_token=C_smecd0mhm, flow_token=oucI3kCcOLy76Hiv8Tb2vxgfa, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_fopbedhc, sup_token=BRAND_4sxo1et5hf8sug8lh500413rj}"
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"2024-06-25T21:26:19.296Z","""i-0c92416a905681db6""","""cash-commerce-browser""","""[{\""name\"":\""$200 Vanilla® Visa® Gift Box Gift Card (plus $6.88 Purchase Fee)\"",\""url\"":\""https://www.walmart.com/ip/seort/20625678\""}]""","Found restricted items: [ShoppingCartProduct{name=$200 Vanilla® Visa® Gift Box Gift Card (plus $6.88 Purchase Fee), url=https://www.walmart.com/ip/seort/20625678}]; {customer_token=C_002v20mt1, flow_token=iNJAgMr3PvHc07lbZAkdrnEIn, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_fopbedhc, sup_token=BRAND_4sxo1et5hf8sug8lh500413rj}"
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"2024-06-25T21:20:06.661Z","""i-003fbf26706b7af3e""","""cash-commerce-browser""","""[{\""name\"":\""Promotional Email GiftCard $20\"",\""image_url\"":\""https://target.scene7.com/is/image/Target//GUEST_337c880d-e2c0-4794-9784-962c63cf0646?qlt=80&fmt=pjpeg\""}, {\""name\"":\""Promotional Email GiftCard $10\"",\""image_url\"":\""https://target.scene7.com/is/image/Target//GUEST_337c880d-e2c0-4794-9784-962c63cf0646?qlt=80&fmt=pjpeg\""}]""","Found restricted items: [ShoppingCartProduct{name=Promotional Email GiftCard $20, image_url=https://target.scene7.com/is/image/Target//GUEST_337c880d-e2c0-4794-9784-962c63cf0646?qlt=80&fmt=pjpeg}, ShoppingCartProduct{name=Promotional Email GiftCard $10, image_url=https://target.scene7.com/is/image/Target//GUEST_337c880d-e2c0-4794-9784-962c63cf0646?qlt=80&fmt=pjpeg}]; {customer_token=C_ssdwxqyqz, flow_token=roXsSw6mIgIqe5XjF8bO8tH2o, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_3mfvyukp, sup_token=BRAND_57fqf0l740hpnbxev71c6d56e}"
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"2024-06-25T21:18:25.022Z","""i-044ba56b80a95063d""","""cash-commerce-browser""","""[{\""name\"":\""Xbox $100 Gift Card - [Digital]\"",\""url\"":\""https://www.walmart.com/ip/seort/48695417\""}]""","Found restricted items: [ShoppingCartProduct{name=Xbox $100 Gift Card - \[Digital\], url=https://www.walmart.com/ip/seort/48695417}]; {customer_token=C_h9ccehmmq, flow_token=ZichXe9bjvCk2NYseQpasHyxW, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_fopbedhc, sup_token=BRAND_4sxo1et5hf8sug8lh500413rj}"
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"2024-06-25T21:15:07.437Z","""i-0ec7dc300989b0f58""","""cash-commerce-browser""","""[{\""name\"":\""$25 Vanilla Visa Shiny Bow Gift Card (plus $3.44 Purchase Fee), quantity 2\"",\""image_url\"":\""https://i5.walmartimages.com/seo/25-Vanilla-Visa-Shiny-Bow-Gift-Card-plus-3-44-Purchase-Fee_852b53b6-7090-407d-a47b-06618b04d244.3c1e4f0aae77e8bf05fdd0660a354377.jpeg?odnHeight=48&odnWidth=48&odnBg=FFFFFF\""}, {\""name\"":\""$50 Vanilla Visa Shiny Bow Gift Card (plus $3.94 Purchase Fee), quantity 2\"",\""image_url\"":\""https://i5.walmartimages.com/seo/50-Vanilla-Visa-Shiny-Bow-Gift-Card-plus-3-94-Purchase-Fee_97c67191-c411-43a8-8174-5bed3ccfa2ed.bfc91450d0b09abcfbab003e6edf9fcf.jpeg?odnHeight=48&odnWidth=48&odnBg=FFFFFF\""}]""","Found restricted items: [ShoppingCartProduct{name=$25 Vanilla Visa Shiny Bow Gift Card (plus $3.44 Purchase Fee)\, quantity 2, image_url=https://i5.walmartimages.com/seo/25-Vanilla-Visa-Shiny-Bow-Gift-Card-plus-3-44-Purchase-Fee_852b53b6-7090-407d-a47b-06618b04d244.3c1e4f0aae77e8bf05fdd0660a354377.jpeg?odnHeight=48&odnWidth=48&odnBg=FFFFFF}, ShoppingCartProduct{name=$50 Vanilla Visa Shiny Bow Gift Card (plus $3.94 Purchase Fee)\, quantity 2, image_url=https://i5.walmartimages.com/seo/50-Vanilla-Visa-Shiny-Bow-Gift-Card-plus-3-94-Purchase-Fee_97c67191-c411-43a8-8174-5bed3ccfa2ed.bfc91450d0b09abcfbab003e6edf9fcf.jpeg?odnHeight=48&odnWidth=48&odnBg=FFFFFF}]; {customer_token=C_gawfj8yrr, flow_token=08e30143-ac4d-428a-963a-f9bbd10cfdb5, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_fopbedhc, sup_token=BRAND_4sxo1et5hf8sug8lh500413rj}"
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"2024-06-25T21:15:01.385Z","""i-003fbf26706b7af3e""","""cash-commerce-browser""","""[{\""name\"":\""$25 Vanilla Visa Shiny Bow Gift Card (plus $3.44 Purchase Fee)\"",\""image_url\"":\""https://i5.walmartimages.com/seo/25-Vanilla-Visa-Shiny-Bow-Gift-Card-plus-3-44-Purchase-Fee_852b53b6-7090-407d-a47b-06618b04d244.3c1e4f0aae77e8bf05fdd0660a354377.jpeg?odnHeight=48&odnWidth=48&odnBg=FFFFFF\""}, {\""name\"":\""$50 Vanilla Visa Shiny Bow Gift Card (plus $3.94 Purchase Fee), quantity 2\"",\""image_url\"":\""https://i5.walmartimages.com/seo/50-Vanilla-Visa-Shiny-Bow-Gift-Card-plus-3-94-Purchase-Fee_97c67191-c411-43a8-8174-5bed3ccfa2ed.bfc91450d0b09abcfbab003e6edf9fcf.jpeg?odnHeight=48&odnWidth=48&odnBg=FFFFFF\""}]""","Found restricted items: [ShoppingCartProduct{name=$25 Vanilla Visa Shiny Bow Gift Card (plus $3.44 Purchase Fee), image_url=https://i5.walmartimages.com/seo/25-Vanilla-Visa-Shiny-Bow-Gift-Card-plus-3-44-Purchase-Fee_852b53b6-7090-407d-a47b-06618b04d244.3c1e4f0aae77e8bf05fdd0660a354377.jpeg?odnHeight=48&odnWidth=48&odnBg=FFFFFF}, ShoppingCartProduct{name=$50 Vanilla Visa Shiny Bow Gift Card (plus $3.94 Purchase Fee)\, quantity 2, image_url=https://i5.walmartimages.com/seo/50-Vanilla-Visa-Shiny-Bow-Gift-Card-plus-3-94-Purchase-Fee_97c67191-c411-43a8-8174-5bed3ccfa2ed.bfc91450d0b09abcfbab003e6edf9fcf.jpeg?odnHeight=48&odnWidth=48&odnBg=FFFFFF}]; {customer_token=C_gawfj8yrr, flow_token=08e30143-ac4d-428a-963a-f9bbd10cfdb5, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_fopbedhc, sup_token=BRAND_4sxo1et5hf8sug8lh500413rj}"
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19 |
-
"2024-06-25T21:14:58.239Z","""i-003fbf26706b7af3e""","""cash-commerce-browser""","""[{\""name\"":\""$25 Vanilla Visa Shiny Bow Gift Card (plus $3.44 Purchase Fee)\"",\""image_url\"":\""https://i5.walmartimages.com/seo/25-Vanilla-Visa-Shiny-Bow-Gift-Card-plus-3-44-Purchase-Fee_852b53b6-7090-407d-a47b-06618b04d244.3c1e4f0aae77e8bf05fdd0660a354377.jpeg?odnHeight=48&odnWidth=48&odnBg=FFFFFF\""}, {\""name\"":\""$50 Vanilla Visa Shiny Bow Gift Card (plus $3.94 Purchase Fee)\"",\""image_url\"":\""https://i5.walmartimages.com/seo/50-Vanilla-Visa-Shiny-Bow-Gift-Card-plus-3-94-Purchase-Fee_97c67191-c411-43a8-8174-5bed3ccfa2ed.bfc91450d0b09abcfbab003e6edf9fcf.jpeg?odnHeight=48&odnWidth=48&odnBg=FFFFFF\""}]""","Found restricted items: [ShoppingCartProduct{name=$25 Vanilla Visa Shiny Bow Gift Card (plus $3.44 Purchase Fee), image_url=https://i5.walmartimages.com/seo/25-Vanilla-Visa-Shiny-Bow-Gift-Card-plus-3-44-Purchase-Fee_852b53b6-7090-407d-a47b-06618b04d244.3c1e4f0aae77e8bf05fdd0660a354377.jpeg?odnHeight=48&odnWidth=48&odnBg=FFFFFF}, ShoppingCartProduct{name=$50 Vanilla Visa Shiny Bow Gift Card (plus $3.94 Purchase Fee), image_url=https://i5.walmartimages.com/seo/50-Vanilla-Visa-Shiny-Bow-Gift-Card-plus-3-94-Purchase-Fee_97c67191-c411-43a8-8174-5bed3ccfa2ed.bfc91450d0b09abcfbab003e6edf9fcf.jpeg?odnHeight=48&odnWidth=48&odnBg=FFFFFF}]; {customer_token=C_gawfj8yrr, flow_token=08e30143-ac4d-428a-963a-f9bbd10cfdb5, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_fopbedhc, sup_token=BRAND_4sxo1et5hf8sug8lh500413rj}"
|
20 |
-
"2024-06-25T21:13:44.815Z","""i-0c92416a905681db6""","""cash-commerce-browser""","""[{\""name\"":\""Xbox $100 Gift Card - [Digital]\"",\""url\"":\""https://www.walmart.com/ip/seort/48695417\""}]""","Found restricted items: [ShoppingCartProduct{name=Xbox $100 Gift Card - \[Digital\], url=https://www.walmart.com/ip/seort/48695417}]; {customer_token=C_h9ccehmmq, flow_token=W9fV7QLzgEeyuDVEf2101TEnm, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_fopbedhc, sup_token=BRAND_4sxo1et5hf8sug8lh500413rj}"
|
21 |
-
"2024-06-25T21:13:36.779Z","""i-0ec7dc300989b0f58""","""cash-commerce-browser""","""[{\""name\"":\""Home Depot Birthday Cupcake eGift\"",\""url\"":\""https://www.homedepot.com/gift-cards/p/Home-Depot-Birthday-Cupcake-eGift/53L1QLAXWKVF06WJ8DLHBYT6S8/5J0QS6XYWSCJ3H9HG3F6YSRL2C\"",\""image_url\"":\""https://images.thdstatic.com/giftcards/catalog/53L1QLAXWKVF06WJ8DLHBYT6S8/xxlarge/5J0QS6XYWSCJ3H9HG3F6YSRL2C_0608202315:26:21.PNG\""}]""","Found restricted items: [ShoppingCartProduct{name=Home Depot Birthday Cupcake eGift, url=https://www.homedepot.com/gift-cards/p/Home-Depot-Birthday-Cupcake-eGift/53L1QLAXWKVF06WJ8DLHBYT6S8/5J0QS6XYWSCJ3H9HG3F6YSRL2C, image_url=https://images.thdstatic.com/giftcards/catalog/53L1QLAXWKVF06WJ8DLHBYT6S8/xxlarge/5J0QS6XYWSCJ3H9HG3F6YSRL2C_0608202315:26:21.PNG}]; {customer_token=C_ssd2wpm6w, flow_token=V53QOpItzVeaNwXwPC6cewpnt, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_kiqt4xl6, sup_token=BRAND_78qp2zjkqjto3v3xxr5ibueyv}"
|
22 |
-
"2024-06-25T21:12:33.161Z","""i-003fbf26706b7af3e""","""cash-commerce-browser""","""[{\""name\"":\""$100 Vanilla® Visa® Gift Box Gift Card (plus $5.44 Purchase Fee)\"",\""url\"":\""https://www.walmart.com/ip/seort/55844027\"",\""image_url\"":\""https://i5.walmartimages.com/seo/100-Vanilla-Visa-Gift-Box-Gift-Card-plus-5-44-Purchase-Fee_d1b5b130-1ae0-4690-a1ee-4e06ff34661a.1e16d3785d608d00f8636a7a91f40182.jpeg?odnHeight=72&odnWidth=72&odnBg=FFFFFF\""}, {\""name\"":\""$50 Vanilla Visa Shiny Bow Gift Card (plus $3.94 Purchase Fee)\"",\""url\"":\""https://www.walmart.com/ip/seort/1529427774\"",\""image_url\"":\""https://i5.walmartimages.com/seo/50-Vanilla-Visa-Shiny-Bow-Gift-Card-plus-3-94-Purchase-Fee_97c67191-c411-43a8-8174-5bed3ccfa2ed.bfc91450d0b09abcfbab003e6edf9fcf.jpeg?odnHeight=72&odnWidth=72&odnBg=FFFFFF\""}, {\""name\"":\""$25 Vanilla Visa Shiny Bow Gift Card (plus $3.44 Purchase Fee)\"",\""url\"":\""https://www.walmart.com/ip/seort/1314241502\"",\""image_url\"":\""https://i5.walmartimages.com/seo/25-Vanilla-Visa-Shiny-Bow-Gift-Card-plus-3-44-Purchase-Fee_852b53b6-7090-407d-a47b-06618b04d244.3c1e4f0aae77e8bf05fdd0660a354377.jpeg?odnHeight=72&odnWidth=72&odnBg=FFFFFF\""}]""","Found restricted items: [ShoppingCartProduct{name=$100 Vanilla® Visa® Gift Box Gift Card (plus $5.44 Purchase Fee), url=https://www.walmart.com/ip/seort/55844027, image_url=https://i5.walmartimages.com/seo/100-Vanilla-Visa-Gift-Box-Gift-Card-plus-5-44-Purchase-Fee_d1b5b130-1ae0-4690-a1ee-4e06ff34661a.1e16d3785d608d00f8636a7a91f40182.jpeg?odnHeight=72&odnWidth=72&odnBg=FFFFFF}, ShoppingCartProduct{name=$50 Vanilla Visa Shiny Bow Gift Card (plus $3.94 Purchase Fee), url=https://www.walmart.com/ip/seort/1529427774, image_url=https://i5.walmartimages.com/seo/50-Vanilla-Visa-Shiny-Bow-Gift-Card-plus-3-94-Purchase-Fee_97c67191-c411-43a8-8174-5bed3ccfa2ed.bfc91450d0b09abcfbab003e6edf9fcf.jpeg?odnHeight=72&odnWidth=72&odnBg=FFFFFF}, ShoppingCartProduct{name=$25 Vanilla Visa Shiny Bow Gift Card (plus $3.44 Purchase Fee), url=https://www.walmart.com/ip/seort/1314241502, image_url=https://i5.walmartimages.com/seo/25-Vanilla-Visa-Shiny-Bow-Gift-Card-plus-3-44-Purchase-Fee_852b53b6-7090-407d-a47b-06618b04d244.3c1e4f0aae77e8bf05fdd0660a354377.jpeg?odnHeight=72&odnWidth=72&odnBg=FFFFFF}]; {customer_token=C_gawfj8yrr, flow_token=08e30143-ac4d-428a-963a-f9bbd10cfdb5, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_fopbedhc, sup_token=BRAND_4sxo1et5hf8sug8lh500413rj}"
|
23 |
-
"2024-06-25T21:09:57.657Z","""i-003fbf26706b7af3e""","""cash-commerce-browser""","""[{\""name\"":\""Birthday Celebration Target Giftcard\"",\""url\"":\""https://www.target.com/p/birthday-celebration-target-giftcard--no-aasa/-/A-81480862\"",\""image_url\"":\""https://target.scene7.com/is/image/Target//GUEST_c2ea8817-d6da-437c-9d85-c39b2e96953d?qlt=80&fmt=pjpeg\""}]""","Found restricted items: [ShoppingCartProduct{name=Birthday Celebration Target Giftcard, url=https://www.target.com/p/birthday-celebration-target-giftcard--no-aasa/-/A-81480862, image_url=https://target.scene7.com/is/image/Target//GUEST_c2ea8817-d6da-437c-9d85-c39b2e96953d?qlt=80&fmt=pjpeg}]; {customer_token=C_8xdwa1m6n, flow_token=9RBhrYZk8g7j2WDYyM3GbfdES, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_3mfvyukp, sup_token=BRAND_57fqf0l740hpnbxev71c6d56e}"
|
24 |
-
"2024-06-25T21:09:49.051Z","""i-003fbf26706b7af3e""","""cash-commerce-browser""","""[{\""name\"":\""Birthday Celebration Target Giftcard\"",\""url\"":\""https://www.target.com/p/birthday-celebration-target-giftcard--no-aasa/-/A-81480862\"",\""image_url\"":\""https://target.scene7.com/is/image/Target//GUEST_c2ea8817-d6da-437c-9d85-c39b2e96953d?qlt=80&fmt=pjpeg\""}]""","Found restricted items: [ShoppingCartProduct{name=Birthday Celebration Target Giftcard, url=https://www.target.com/p/birthday-celebration-target-giftcard--no-aasa/-/A-81480862, image_url=https://target.scene7.com/is/image/Target//GUEST_c2ea8817-d6da-437c-9d85-c39b2e96953d?qlt=80&fmt=pjpeg}]; {customer_token=C_8xdwa1m6n, flow_token=J7liXzVSAPSDIaK4CWVLC7bBi, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_3mfvyukp, sup_token=BRAND_57fqf0l740hpnbxev71c6d56e}"
|
25 |
-
"2024-06-25T21:09:35.160Z","""i-003fbf26706b7af3e""","""cash-commerce-browser""","""[{\""name\"":\""Promotional Email GiftCard $10\"",\""image_url\"":\""https://target.scene7.com/is/image/Target//GUEST_337c880d-e2c0-4794-9784-962c63cf0646?qlt=80&fmt=pjpeg\""}]""","Found restricted items: [ShoppingCartProduct{name=Promotional Email GiftCard $10, image_url=https://target.scene7.com/is/image/Target//GUEST_337c880d-e2c0-4794-9784-962c63cf0646?qlt=80&fmt=pjpeg}]; {customer_token=C_fsexdpmbh, flow_token=jVDz28RrrN9tAkim71XsZGZMD, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_3mfvyukp, sup_token=BRAND_57fqf0l740hpnbxev71c6d56e}"
|
26 |
-
"2024-06-25T21:09:35.022Z","""i-0ec7dc300989b0f58""","""cash-commerce-browser""","""[{\""name\"":\""Birthday Celebration Target Giftcard\"",\""url\"":\""https://www.target.com/p/birthday-celebration-target-giftcard--no-aasa/-/A-81480862\"",\""image_url\"":\""\""}]""","Found restricted items: [ShoppingCartProduct{name=Birthday Celebration Target Giftcard, url=https://www.target.com/p/birthday-celebration-target-giftcard--no-aasa/-/A-81480862, image_url=}]; {customer_token=C_8xdwa1m6n, flow_token=9pZoMnaqRmVXuWQ9P9pSLefbO, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_3mfvyukp, sup_token=BRAND_57fqf0l740hpnbxev71c6d56e}"
|
27 |
-
"2024-06-25T21:09:07.723Z","""i-044ba56b80a95063d""","""cash-commerce-browser""","""[{\""name\"":\""Nintendo Switch Family Online 12 Month Gift Card [Digital]\"",\""url\"":\""https://www.walmart.com/ip/seort/875981752\""}]""","Found restricted items: [ShoppingCartProduct{name=Nintendo Switch Family Online 12 Month Gift Card \[Digital\], url=https://www.walmart.com/ip/seort/875981752}]; {customer_token=C_gq2ekammv, flow_token=03c0b437-4fc5-4967-9692-3efddd21a0d6, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_fopbedhc, sup_token=BRAND_4sxo1et5hf8sug8lh500413rj}"
|
28 |
-
"2024-06-25T21:08:46.049Z","""i-044ba56b80a95063d""","""cash-commerce-browser""","""[{\""name\"":\""Promotional Email GiftCard $10\"",\""image_url\"":\""https://target.scene7.com/is/image/Target//GUEST_337c880d-e2c0-4794-9784-962c63cf0646?qlt=80&fmt=pjpeg\""}]""","Found restricted items: [ShoppingCartProduct{name=Promotional Email GiftCard $10, image_url=https://target.scene7.com/is/image/Target//GUEST_337c880d-e2c0-4794-9784-962c63cf0646?qlt=80&fmt=pjpeg}]; {customer_token=C_fsexdpmbh, flow_token=e5xlrZ3F45YirfY5AHNFlAvtV, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_3mfvyukp, sup_token=BRAND_57fqf0l740hpnbxev71c6d56e}"
|
29 |
-
"2024-06-25T20:55:28.994Z","""i-044ba56b80a95063d""","""cash-commerce-browser""","""[{\""name\"":\""$50 Vanilla® Mastercard® Celebration Dots Gift Card (plus $3.94 Purchase Fee)\"",\""image_url\"":\""https://i5.walmartimages.com/seo/50-Vanilla-Mastercard-Celebration-Dots-Gift-Card-plus-3-94-Purchase-Fee_04e596b9-6d0a-41c9-96ed-7bdf11468cd2.d9453dcf7909a341bf31ee4dce04c833.jpeg?odnHeight=48&odnWidth=48&odnBg=FFFFFF\""}]""","Found restricted items: [ShoppingCartProduct{name=$50 Vanilla® Mastercard® Celebration Dots Gift Card (plus $3.94 Purchase Fee), image_url=https://i5.walmartimages.com/seo/50-Vanilla-Mastercard-Celebration-Dots-Gift-Card-plus-3-94-Purchase-Fee_04e596b9-6d0a-41c9-96ed-7bdf11468cd2.d9453dcf7909a341bf31ee4dce04c833.jpeg?odnHeight=48&odnWidth=48&odnBg=FFFFFF}]; {customer_token=C_nywqkaygt, flow_token=IOAzdzUmK2FLIN1KiCt8xQZQd, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_fopbedhc, sup_token=BRAND_4sxo1et5hf8sug8lh500413rj}"
|
30 |
-
"2024-06-25T20:55:26.016Z","""i-0c92416a905681db6""","""cash-commerce-browser""","""[{\""name\"":\""$50 Vanilla® Mastercard® Celebration Dots Gift Card (plus $3.94 Purchase Fee)\"",\""url\"":\""https://www.walmart.com/ip/seort/46518449\""}]""","Found restricted items: [ShoppingCartProduct{name=$50 Vanilla® Mastercard® Celebration Dots Gift Card (plus $3.94 Purchase Fee), url=https://www.walmart.com/ip/seort/46518449}]; {customer_token=C_nywqkaygt, flow_token=dEIHJrVnTIH6xsmTYspEfBcle, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_fopbedhc, sup_token=BRAND_4sxo1et5hf8sug8lh500413rj}"
|
31 |
-
"2024-06-25T20:55:13.996Z","""i-0c92416a905681db6""","""cash-commerce-browser""","""[{\""name\"":\""$50 Vanilla® Mastercard® Celebration Dots Gift Card (plus $3.94 Purchase Fee)\"",\""url\"":\""https://www.walmart.com/ip/seort/46518449\""}, {\""name\"":\""$50 Vanilla® Visa® eGift Card (plus $3.94 Purchase Fee)\"",\""url\"":\""https://www.walmart.com/ip/seort/761861368\""}]""","Found restricted items: [ShoppingCartProduct{name=$50 Vanilla® Mastercard® Celebration Dots Gift Card (plus $3.94 Purchase Fee), url=https://www.walmart.com/ip/seort/46518449}, ShoppingCartProduct{name=$50 Vanilla® Visa® eGift Card (plus $3.94 Purchase Fee), url=https://www.walmart.com/ip/seort/761861368}]; {customer_token=C_nywqkaygt, flow_token=MJgyeF7Eew00jGOPMLCG2rHyD, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_fopbedhc, sup_token=BRAND_4sxo1et5hf8sug8lh500413rj}"
|
32 |
-
"2024-06-25T20:52:39.628Z","""i-0c92416a905681db6""","""cash-commerce-browser""","""[{\""name\"":\""$50 Vanilla® Visa® eGift Card (plus $3.94 Purchase Fee)\"",\""url\"":\""https://www.walmart.com/ip/seort/761861368\""}]""","Found restricted items: [ShoppingCartProduct{name=$50 Vanilla® Visa® eGift Card (plus $3.94 Purchase Fee), url=https://www.walmart.com/ip/seort/761861368}]; {customer_token=C_nywqkaygt, flow_token=WJm0vCQG5TcMjVjTvpMqrfOGs, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_fopbedhc, sup_token=BRAND_4sxo1et5hf8sug8lh500413rj}"
|
33 |
-
"2024-06-25T20:52:36.059Z","""i-0ec7dc300989b0f58""","""cash-commerce-browser""","""[{\""name\"":\""$50 Vanilla Visa Shiny Bow Gift Card (plus $3.94 Purchase Fee)\"",\""url\"":\""https://www.walmart.com/ip/seort/1529427774\""}, {\""name\"":\""$50 Vanilla® Visa® eGift Card (plus $3.94 Purchase Fee)\"",\""url\"":\""https://www.walmart.com/ip/seort/761861368\""}]""","Found restricted items: [ShoppingCartProduct{name=$50 Vanilla Visa Shiny Bow Gift Card (plus $3.94 Purchase Fee), url=https://www.walmart.com/ip/seort/1529427774}, ShoppingCartProduct{name=$50 Vanilla® Visa® eGift Card (plus $3.94 Purchase Fee), url=https://www.walmart.com/ip/seort/761861368}]; {customer_token=C_nywqkaygt, flow_token=a5YVHg3kkZn8fb3e3TT1b9GlZ, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_fopbedhc, sup_token=BRAND_4sxo1et5hf8sug8lh500413rj}"
|
34 |
-
"2024-06-25T20:52:02.686Z","""i-0ec7dc300989b0f58""","""cash-commerce-browser""","""[{\""name\"":\""Delta Airlines Wedding $250 Gift Card (Email Delivery)\"",\""url\"":\""https://www.target.com/p/delta-airlines-wedding--250-gift-card--email-delivery---no-aasa/-/A-89537719\"",\""image_url\"":\""https://target.scene7.com/is/image/Target//GUEST_0de9a4fb-7f0e-4d85-806f-e841d7664a59?qlt=80&fmt=pjpeg\""}]""","Found restricted items: [ShoppingCartProduct{name=Delta Airlines Wedding $250 Gift Card (Email Delivery), url=https://www.target.com/p/delta-airlines-wedding--250-gift-card--email-delivery---no-aasa/-/A-89537719, image_url=https://target.scene7.com/is/image/Target//GUEST_0de9a4fb-7f0e-4d85-806f-e841d7664a59?qlt=80&fmt=pjpeg}]; {customer_token=C_80exppmwa, flow_token=pnOyvjetUVkuZfhIOQwOQCINH, payment_method=PAYMENT_METHOD_SINGLE_USE_PAYMENT, merchant_token=M_3mfvyukp, sup_token=BRAND_57fqf0l740hpnbxev71c6d56e}"
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genai_SDK/Seq2Seq.py
DELETED
@@ -1,250 +0,0 @@
|
|
1 |
-
#!/usr/bin/env python
|
2 |
-
# coding: utf-8
|
3 |
-
import torch
|
4 |
-
import torch.nn as nn
|
5 |
-
import torch.optim as optim
|
6 |
-
from torch.utils.data import DataLoader, random_split
|
7 |
-
import pandas as pd
|
8 |
-
from tqdm import tqdm
|
9 |
-
import time
|
10 |
-
from .Utilities import LanguageDataset
|
11 |
-
|
12 |
-
class Seq2Seq():
|
13 |
-
"""
|
14 |
-
Base class for Seq2Seq (text-generation models). This class will be inherited by wrappers of transformers like GPT2
|
15 |
-
and T5.
|
16 |
-
|
17 |
-
Attributes:
|
18 |
-
|
19 |
-
Methods:
|
20 |
-
|
21 |
-
"""
|
22 |
-
|
23 |
-
def __init__(self, gpu=0, max_length=0, model_path=None):
|
24 |
-
|
25 |
-
# Load Seq2Seq to device based on available hardware
|
26 |
-
if torch.cuda.is_available():
|
27 |
-
self.device = torch.device('cuda')
|
28 |
-
else:
|
29 |
-
try:
|
30 |
-
self.device = torch.device('mps') # Apple Silicon
|
31 |
-
except Exception:
|
32 |
-
self.device = torch.device('cpu')
|
33 |
-
|
34 |
-
# GPU that model will run on
|
35 |
-
self.gpu = gpu
|
36 |
-
|
37 |
-
# Model specs
|
38 |
-
if model_path: self.model = torch.load(model_path).to(self.device)
|
39 |
-
else: self.model = None
|
40 |
-
self.model_name = ""
|
41 |
-
self.tokenizer = None
|
42 |
-
self.max_length = max_length
|
43 |
-
|
44 |
-
# Training specs
|
45 |
-
self.train_loader = None
|
46 |
-
self.valid_loader = None
|
47 |
-
self.results = pd.DataFrame(columns=['epoch', 'model_arch', 'batch_size', 'gpu', 'training_loss', 'validation_loss', 'epoch_duration_sec'])
|
48 |
-
|
49 |
-
def load_data(self, df, batch_size, train_ratio=0.8):
|
50 |
-
self.batch_size = batch_size
|
51 |
-
dataset = LanguageDataset(df, self.tokenizer)
|
52 |
-
train_size = int(0.8*len(dataset))
|
53 |
-
valid_size = len(dataset) - train_size
|
54 |
-
train_data, valid_data = random_split(dataset, [train_size, valid_size])
|
55 |
-
self.max_length = dataset.max_length
|
56 |
-
self.train_loader = DataLoader(train_data, batch_size=self.batch_size, shuffle=True)
|
57 |
-
self.valid_loader = DataLoader(valid_data, batch_size=self.batch_size)
|
58 |
-
|
59 |
-
""" Return training results """
|
60 |
-
def summary(self):
|
61 |
-
return self.results
|
62 |
-
|
63 |
-
""" Save model to path """
|
64 |
-
def to_pt(self, path):
|
65 |
-
torch.save(self.model, path)
|
66 |
-
|
67 |
-
|
68 |
-
class GPT2(Seq2Seq):
|
69 |
-
"""
|
70 |
-
This is the GPT2 implementation of Seq2Seq.
|
71 |
-
"""
|
72 |
-
|
73 |
-
def __init__(self, gpu, model_name, batch_size=16):
|
74 |
-
super().__init__(gpu, max_length=0)
|
75 |
-
from transformers import GPT2Tokenizer, GPT2LMHeadModel
|
76 |
-
self.model_name = model_name
|
77 |
-
self.model = GPT2LMHeadModel.from_pretrained(self.model_name).to(self.device)
|
78 |
-
self.tokenizer = GPT2Tokenizer.from_pretrained(self.model_name)
|
79 |
-
self.tokenizer.pad_token = self.tokenizer.eos_token
|
80 |
-
|
81 |
-
def train(self, num_epochs=3, train_ratio=0.8):
|
82 |
-
criterion = nn.CrossEntropyLoss(ignore_index=self.tokenizer.pad_token_id)
|
83 |
-
optimizer = optim.Adam(self.model.parameters(), lr=5e-4)
|
84 |
-
|
85 |
-
# Init a results dataframe
|
86 |
-
|
87 |
-
results = pd.DataFrame(columns=['epoch', 'transformer', 'batch_size', 'gpu',
|
88 |
-
'training_loss', 'validation_loss', 'epoch_duration_sec'])
|
89 |
-
# The training loop
|
90 |
-
for epoch in range(num_epochs):
|
91 |
-
start_time = time.time() # Start the timer for the epoch
|
92 |
-
|
93 |
-
# Training
|
94 |
-
## This line tells the self.model we're in 'learning mode'
|
95 |
-
self.model.train()
|
96 |
-
epoch_training_loss = 0
|
97 |
-
train_iterator = tqdm(self.train_loader,
|
98 |
-
desc=f"Training Epoch {epoch + 1}/{num_epochs} Batch Size: {self.batch_size}, Transformer: {self.model_name}")
|
99 |
-
for batch in train_iterator:
|
100 |
-
optimizer.zero_grad()
|
101 |
-
inputs = batch['input_ids'].squeeze(1).to(self.device)
|
102 |
-
targets = inputs.clone()
|
103 |
-
outputs = self.model(input_ids=inputs, labels=targets)
|
104 |
-
loss = outputs.loss
|
105 |
-
loss.backward()
|
106 |
-
optimizer.step()
|
107 |
-
train_iterator.set_postfix({'Training Loss': loss.item()})
|
108 |
-
epoch_training_loss += loss.item()
|
109 |
-
avg_epoch_training_loss = epoch_training_loss / len(train_iterator)
|
110 |
-
|
111 |
-
# Validation
|
112 |
-
## This line below tells the self.model to 'stop learning'
|
113 |
-
self.model.eval()
|
114 |
-
epoch_validation_loss = 0
|
115 |
-
total_loss = 0
|
116 |
-
valid_iterator = tqdm(self.valid_loader, desc=f"Validation Epoch {epoch + 1}/{num_epochs}")
|
117 |
-
with torch.no_grad():
|
118 |
-
for batch in valid_iterator:
|
119 |
-
inputs = batch['input_ids'].squeeze(1).to(self.device)
|
120 |
-
targets = inputs.clone()
|
121 |
-
outputs = self.model(input_ids=inputs, labels=targets)
|
122 |
-
loss = outputs.loss
|
123 |
-
total_loss += loss
|
124 |
-
valid_iterator.set_postfix({'Validation Loss': loss.item()})
|
125 |
-
epoch_validation_loss += loss.item()
|
126 |
-
|
127 |
-
avg_epoch_validation_loss = epoch_validation_loss / len(self.valid_loader)
|
128 |
-
|
129 |
-
end_time = time.time() # End the timer for the epoch
|
130 |
-
epoch_duration_sec = end_time - start_time # Calculate the duration in seconds
|
131 |
-
|
132 |
-
new_row = {'transformer': self.model_name,
|
133 |
-
'batch_size': self.batch_size,
|
134 |
-
'gpu': self.gpu,
|
135 |
-
'epoch': epoch + 1,
|
136 |
-
'training_loss': avg_epoch_training_loss,
|
137 |
-
'validation_loss': avg_epoch_validation_loss,
|
138 |
-
'epoch_duration_sec': epoch_duration_sec} # Add epoch_duration to the dataframe
|
139 |
-
|
140 |
-
self.results.loc[len(self.results)] = new_row
|
141 |
-
print(f"Epoch: {epoch + 1}, Validation Loss: {total_loss / len(self.valid_loader)}")
|
142 |
-
|
143 |
-
def generate_text(self, input_str, top_k=16, top_p=0.95, temperature=1.0, repetition_penalty=1.2):
|
144 |
-
# Encode string to tokens
|
145 |
-
input_ids= self.tokenizer.encode(input_str, return_tensors='pt').to(self.device)
|
146 |
-
|
147 |
-
# Feed tokens to model and get outcome tokens
|
148 |
-
output = self.model.generate(
|
149 |
-
input_ids,
|
150 |
-
max_length=self.max_length,
|
151 |
-
num_return_sequences=1,
|
152 |
-
do_sample=True,
|
153 |
-
top_k=top_k,
|
154 |
-
top_p=top_p,
|
155 |
-
temperature=temperature,
|
156 |
-
repetition_penalty=repetition_penalty
|
157 |
-
)
|
158 |
-
|
159 |
-
# Decode tokens to string
|
160 |
-
decoded_output = self.tokenizer.decode(output[0], skip_special_tokens=True)
|
161 |
-
return decoded_output
|
162 |
-
|
163 |
-
class FlanT5(Seq2Seq):
|
164 |
-
"""
|
165 |
-
This is the T5 implementation of Seq2Seq - it is designed to support T5 models of various sizes.
|
166 |
-
"""
|
167 |
-
def __init__(self, gpu, model_name, batch_size=16):
|
168 |
-
super().__init__(gpu, max_length=0)
|
169 |
-
from transformers import T5ForConditionalGeneration, T5Tokenizer
|
170 |
-
self.model_name = model_name
|
171 |
-
self.model = T5ForConditionalGeneration.from_pretrained(self.model_name).to(self.device)
|
172 |
-
self.tokenizer = T5Tokenizer.from_pretrained(self.model_name)
|
173 |
-
self.tokenizer.pad_token = self.tokenizer.eos_token
|
174 |
-
|
175 |
-
def train(self, num_epochs=3, train_ratio=0.8):
|
176 |
-
criterion = nn.CrossEntropyLoss(ignore_index=self.tokenizer.pad_token_id)
|
177 |
-
optimizer = optim.Adam(self.model.parameters(), lr=5e-4)
|
178 |
-
|
179 |
-
# Init a results dataframe
|
180 |
-
|
181 |
-
self.results = pd.DataFrame(columns=['epoch', 'transformer', 'batch_size', 'gpu',
|
182 |
-
'training_loss', 'validation_loss', 'epoch_duration_sec'])
|
183 |
-
# The training loop
|
184 |
-
for epoch in range(num_epochs):
|
185 |
-
start_time = time.time() # Start the timer for the epoch
|
186 |
-
|
187 |
-
# Training
|
188 |
-
## This line tells the model we're in 'learning mode'
|
189 |
-
self.model.train()
|
190 |
-
epoch_training_loss = 0
|
191 |
-
train_iterator = tqdm(self.train_loader,
|
192 |
-
desc=f"Training Epoch {epoch + 1}/{num_epochs} Batch Size: {self.batch_size}, Transformer: {self.model_name}")
|
193 |
-
for batch in train_iterator:
|
194 |
-
optimizer.zero_grad()
|
195 |
-
inputs = batch['input_ids'].squeeze(1).to(self.device)
|
196 |
-
targets = batch['labels'].squeeze(1).to(self.device)
|
197 |
-
outputs = self.model(input_ids=inputs, labels=targets)
|
198 |
-
loss = outputs.loss
|
199 |
-
loss.backward()
|
200 |
-
optimizer.step()
|
201 |
-
train_iterator.set_postfix({'Training Loss': loss.item()})
|
202 |
-
epoch_training_loss += loss.item()
|
203 |
-
avg_epoch_training_loss = epoch_training_loss / len(train_iterator)
|
204 |
-
|
205 |
-
# Validation
|
206 |
-
## This line below tells the model to 'stop learning'
|
207 |
-
self.model.eval()
|
208 |
-
epoch_validation_loss = 0
|
209 |
-
total_loss = 0
|
210 |
-
valid_iterator = tqdm(self.valid_loader, desc=f"Validation Epoch {epoch + 1}/{num_epochs}")
|
211 |
-
with torch.no_grad():
|
212 |
-
for batch in valid_iterator:
|
213 |
-
inputs = batch['input_ids'].squeeze(1).to(self.device)
|
214 |
-
targets = batch['labels'].squeeze(1).to(self.device)
|
215 |
-
outputs = self.model(input_ids=inputs, labels=targets)
|
216 |
-
loss = outputs.loss
|
217 |
-
total_loss += loss
|
218 |
-
valid_iterator.set_postfix({'Validation Loss': loss.item()})
|
219 |
-
epoch_validation_loss += loss.item()
|
220 |
-
|
221 |
-
avg_epoch_validation_loss = epoch_validation_loss / len(self.valid_loader)
|
222 |
-
|
223 |
-
end_time = time.time() # End the timer for the epoch
|
224 |
-
epoch_duration_sec = end_time - start_time # Calculate the duration in seconds
|
225 |
-
|
226 |
-
new_row = {'transformer': self.model_name,
|
227 |
-
'batch_size': self.batch_size,
|
228 |
-
'gpu': self.gpu,
|
229 |
-
'epoch': epoch + 1,
|
230 |
-
'training_loss': avg_epoch_training_loss,
|
231 |
-
'validation_loss': avg_epoch_validation_loss,
|
232 |
-
'epoch_duration_sec': epoch_duration_sec} # Add epoch_duration to the dataframe
|
233 |
-
|
234 |
-
self.results.loc[len(self.results)] = new_row
|
235 |
-
print(f"Epoch: {epoch + 1}, Validation Loss: {total_loss / len(self.valid_loader)}")
|
236 |
-
|
237 |
-
def generate_text(self, input_str, top_k=16, top_p=0.95, temperature=1.0, repetition_penalty=1.2):
|
238 |
-
# Encode input string into tensors via the FlanT5 tokenizer
|
239 |
-
input_ids = self.tokenizer.encode(input_str, return_tensors='pt', max_length=self.max_length, truncation=True).to(self.device)
|
240 |
-
# Run tensors through model to get output tensor values
|
241 |
-
output_ids = self.model.generate(input_ids,
|
242 |
-
max_length=self.max_length,
|
243 |
-
do_sample=True,
|
244 |
-
top_k=top_k,
|
245 |
-
top_p=top_p,
|
246 |
-
temperature=temperature,
|
247 |
-
repetition_penalty=repetition_penalty)
|
248 |
-
# Decode output tensors to text vi
|
249 |
-
output_str = self.tokenizer.decode(output_ids[0], skip_special_tokens=True)
|
250 |
-
return output_str
|
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|
genai_SDK/Utilities.py
DELETED
@@ -1,563 +0,0 @@
|
|
1 |
-
#!/usr/bin/env python
|
2 |
-
# coding: utf-8
|
3 |
-
import pandas as pd
|
4 |
-
from torch.utils.data import Dataset
|
5 |
-
|
6 |
-
class LanguageDataset(Dataset):
|
7 |
-
def __init__(self, df, tokenizer):
|
8 |
-
# Make sure data is compatible
|
9 |
-
if len(df.columns) !=2:
|
10 |
-
raise Exception("Dataset can only have two columns!")
|
11 |
-
|
12 |
-
self.data = df.to_dict(orient='records')
|
13 |
-
self.tokenizer = tokenizer
|
14 |
-
|
15 |
-
# set the length of smallest square needed
|
16 |
-
self.max_length = smallest_square_length(df)
|
17 |
-
self.labels = df.columns
|
18 |
-
|
19 |
-
def __len__(self):
|
20 |
-
return len(self.data)
|
21 |
-
def __getitem__(self, i):
|
22 |
-
X = self.data[i][self.labels[0]]
|
23 |
-
Y = self.data[i][self.labels[1]]
|
24 |
-
if str(type(self.tokenizer)) == "<class 'transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer'>":
|
25 |
-
return self.tokenizer.encode_plus(X + ' | ' + Y,
|
26 |
-
return_tensors='pt',
|
27 |
-
max_length = self.max_length,
|
28 |
-
padding='max_length',
|
29 |
-
truncation=True)
|
30 |
-
elif str(type(self.tokenizer)) == "<class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>":
|
31 |
-
input_tokens = self.tokenizer.encode_plus(
|
32 |
-
X,
|
33 |
-
max_length=self.max_length,
|
34 |
-
padding='max_length',
|
35 |
-
truncation=True,
|
36 |
-
return_tensors='pt'
|
37 |
-
)
|
38 |
-
target_tokens = self.tokenizer.encode_plus(
|
39 |
-
Y,
|
40 |
-
max_length=self.max_length,
|
41 |
-
padding='max_length',
|
42 |
-
truncation=True,
|
43 |
-
return_tensors='pt'
|
44 |
-
)
|
45 |
-
return {
|
46 |
-
'input_ids': input_tokens['input_ids'].squeeze(),
|
47 |
-
# 'attention_mask': input_tokens['attention_mask'].squeeze(),
|
48 |
-
'labels': target_tokens['input_ids'].squeeze()
|
49 |
-
}
|
50 |
-
|
51 |
-
|
52 |
-
|
53 |
-
def smallest_square_length(df):
|
54 |
-
col1 = df[df.columns[0]].astype(str).apply(lambda x: len(x)).max()
|
55 |
-
col2 = df[df.columns[1]].astype(str).apply(lambda x: len(x)).max()
|
56 |
-
|
57 |
-
max_length = max(col1, col2)
|
58 |
-
|
59 |
-
x = 2
|
60 |
-
while x < max_length:
|
61 |
-
x = x * 2
|
62 |
-
|
63 |
-
return x
|
64 |
-
|
65 |
-
def levenshtein_distance(str1, str2):
|
66 |
-
"""
|
67 |
-
Computes the Levenshtein distance between two strings.
|
68 |
-
Parameters:
|
69 |
-
str1 (str): The first string.
|
70 |
-
str2 (str): The second string.
|
71 |
-
Returns:
|
72 |
-
int: The Levenshtein distance between the two strings.
|
73 |
-
"""
|
74 |
-
m, n = len(str1), len(str2)
|
75 |
-
dp = [[0] * (n + 1) for _ in range(m + 1)]
|
76 |
-
|
77 |
-
for i in range(m + 1):
|
78 |
-
dp[i][0] = i
|
79 |
-
|
80 |
-
for j in range(n + 1):
|
81 |
-
dp[0][j] = j
|
82 |
-
|
83 |
-
for i in range(1, m + 1):
|
84 |
-
for j in range(1, n + 1):
|
85 |
-
if str1[i - 1] == str2[j - 1]:
|
86 |
-
dp[i][j] = dp[i - 1][j - 1]
|
87 |
-
else:
|
88 |
-
dp[i][j] = 1 + min(dp[i - 1][j], dp[i][j - 1], dp[i - 1][j - 1])
|
89 |
-
|
90 |
-
return dp[m][n]
|
91 |
-
|
92 |
-
def grid_search(model, tokenizer, input_str, topK_values, topP_values, temperature_values, repetition_penalty_values, expected_output):
|
93 |
-
"""
|
94 |
-
Conducts a grid search over specified hyperparameters to find the best text generation settings (GPT series).
|
95 |
-
Parameters:
|
96 |
-
- model: The pre-trained model used for text generation.
|
97 |
-
- tokenizer: The tokenizer associated with the model.
|
98 |
-
- input_str: The input string to the model for text generation.
|
99 |
-
- topK_values: A list of integer values for the topK sampling hyperparameter.
|
100 |
-
- topP_values: A list of float values for the topP (nucleus) sampling hyperparameter.
|
101 |
-
- temperature_values: A list of float values for the temperature setting of the model.
|
102 |
-
- repetition_penalty_values: A list of float values for penalizing repetitions in the generated text.
|
103 |
-
- expected_output: The expected output string against which generated texts are evaluated using the Levenshtein distance.
|
104 |
-
Returns:
|
105 |
-
- results: A pandas DataFrame containing the combination of hyperparameters, the generated output for each combination, and its Levenshtein distance from the expected output.
|
106 |
-
Notes:
|
107 |
-
- The function prints out the best hyperparameters found during the search, based on the smallest Levenshtein distance.
|
108 |
-
- Levenshtein distance measures the number of edits required to transform one string into another.
|
109 |
-
"""
|
110 |
-
results = pd.DataFrame(columns=['topK', 'topP', 'temperature', 'repetition_penalty', 'generated_output', 'levenshtein_distance'])
|
111 |
-
min_distance = 9999999
|
112 |
-
for topK in topK_values:
|
113 |
-
for topP in topP_values:
|
114 |
-
for temperature in temperature_values:
|
115 |
-
for repetition_penalty in repetition_penalty_values:
|
116 |
-
# try:
|
117 |
-
generated_output = model.generate_text(input_str, topK, topP, temperature, repetition_penalty)
|
118 |
-
# print(generated_output)
|
119 |
-
distance = levenshtein_distance(generated_output, expected_output)
|
120 |
-
if distance < min_distance:
|
121 |
-
print(f'topK={topK}, topP={topP}, temperature={temperature}, repetition_penalty={repetition_penalty}, levenshtein_distance={distance}')
|
122 |
-
min_distance = distance
|
123 |
-
|
124 |
-
new_row = {'topK': topK,
|
125 |
-
'topP': topP,
|
126 |
-
'temperature': temperature,
|
127 |
-
'repetition_penalty': repetition_penalty,
|
128 |
-
'generated_output': generated_output,
|
129 |
-
'levenshtein_distance': distance
|
130 |
-
}
|
131 |
-
results.loc[len(results)] = new_row
|
132 |
-
|
133 |
-
return results.sort_values(by='levenshtein_distance', ascending=True)
|
134 |
-
|
135 |
-
|
136 |
-
def to_coreml(gpt_model, path=''):
|
137 |
-
import torch
|
138 |
-
|
139 |
-
device = torch.device('mps')
|
140 |
-
|
141 |
-
if torch.cuda.is_available():
|
142 |
-
device = torch.device('cuda')
|
143 |
-
else:
|
144 |
-
try:
|
145 |
-
device = torch.device('mps') # Apple Silicon
|
146 |
-
except Exception:
|
147 |
-
device = torch.device('cpu')
|
148 |
-
if path != '': lm_head_model = torch.load(path, map_location=device)
|
149 |
-
else: lm_head_model = gpt_model.model
|
150 |
-
|
151 |
-
"""
|
152 |
-
Recreate the Core ML model from scratch using
|
153 |
-
coremltools' neural_network.NeuralNetworkBuilder
|
154 |
-
"""
|
155 |
-
import coremltools
|
156 |
-
import coremltools.models.datatypes as datatypes
|
157 |
-
from coremltools.models import neural_network as neural_network
|
158 |
-
from coremltools.models.utils import save_spec
|
159 |
-
import numpy as np
|
160 |
-
import torch
|
161 |
-
model_name = 'model'
|
162 |
-
|
163 |
-
model = lm_head_model.transformer
|
164 |
-
|
165 |
-
wte = model.wte.weight.data.cpu().numpy().transpose() # shape (768, 50257) /!\ i hate this
|
166 |
-
wpe = model.wpe.weight.data.cpu().numpy().transpose() # shape (768, 1024)
|
167 |
-
|
168 |
-
sequence_length = 128
|
169 |
-
steps = model.config.n_layer
|
170 |
-
|
171 |
-
# build model
|
172 |
-
input_features = [
|
173 |
-
('input_ids', datatypes.Array(sequence_length)),
|
174 |
-
('position_ids', datatypes.Array(sequence_length)),
|
175 |
-
]
|
176 |
-
output_features = [('output_logits', None)]
|
177 |
-
|
178 |
-
builder = neural_network.NeuralNetworkBuilder(
|
179 |
-
input_features,
|
180 |
-
output_features,
|
181 |
-
mode=None,
|
182 |
-
disable_rank5_shape_mapping=True,
|
183 |
-
)
|
184 |
-
builder.add_expand_dims(
|
185 |
-
name='input_ids_expanded_to_rank5',
|
186 |
-
input_name='input_ids',
|
187 |
-
output_name='input_ids_expanded_to_rank5',
|
188 |
-
axes=(1, 2, 3, 4)
|
189 |
-
)
|
190 |
-
builder.add_expand_dims(
|
191 |
-
name='position_ids_expanded_to_rank5',
|
192 |
-
input_name='position_ids',
|
193 |
-
output_name='position_ids_expanded_to_rank5',
|
194 |
-
axes=(1, 2, 3, 4)
|
195 |
-
)
|
196 |
-
builder.add_embedding(
|
197 |
-
name='token_embeddings',
|
198 |
-
input_name='input_ids_expanded_to_rank5',
|
199 |
-
output_name='token_embeddings',
|
200 |
-
W=wte,
|
201 |
-
b=None,
|
202 |
-
input_dim=50257,
|
203 |
-
output_channels=768,
|
204 |
-
has_bias=False,
|
205 |
-
)
|
206 |
-
builder.add_embedding(
|
207 |
-
name='positional_embeddings',
|
208 |
-
input_name='position_ids_expanded_to_rank5',
|
209 |
-
output_name='positional_embeddings',
|
210 |
-
W=wpe,
|
211 |
-
b=None,
|
212 |
-
input_dim=1024,
|
213 |
-
output_channels=768,
|
214 |
-
has_bias=False,
|
215 |
-
)
|
216 |
-
|
217 |
-
# Input:, Output: (seq, 1, 768, 1, 1)
|
218 |
-
builder.add_add_broadcastable(
|
219 |
-
name='embeddings_addition',
|
220 |
-
input_names=['token_embeddings', 'positional_embeddings'],
|
221 |
-
output_name=f'{0}_previous_block'
|
222 |
-
)
|
223 |
-
|
224 |
-
for i in range(steps):
|
225 |
-
print(i)
|
226 |
-
ln_weight = model.h[i].ln_1.weight.data.cpu().numpy().reshape((1, 1, 768, 1, 1))
|
227 |
-
ln_bias = model.h[i].ln_1.bias.data.cpu().numpy().reshape((1, 1, 768, 1, 1))
|
228 |
-
ln_epsilon = model.h[i].ln_1.eps
|
229 |
-
|
230 |
-
builder.add_mvn(
|
231 |
-
name=f"{i}_block_ln_1",
|
232 |
-
input_name=f"{i}_previous_block",
|
233 |
-
# output_name=f"{i}_block_ln_1_output",
|
234 |
-
output_name=f"{i}_block_ln_1",
|
235 |
-
across_channels=True,
|
236 |
-
normalize_variance=True,
|
237 |
-
epsilon=ln_epsilon
|
238 |
-
)
|
239 |
-
|
240 |
-
builder.add_scale(
|
241 |
-
name=f"{i}_block_ln_1_scaled",
|
242 |
-
input_name=f"{i}_block_ln_1",
|
243 |
-
output_name=f"{i}_block_ln_1_scaled",
|
244 |
-
W=ln_weight,
|
245 |
-
b=ln_bias,
|
246 |
-
has_bias=True,
|
247 |
-
shape_scale=[768],
|
248 |
-
shape_bias=[768]
|
249 |
-
)
|
250 |
-
|
251 |
-
builder.add_transpose(
|
252 |
-
name=f"{i}_block_ln_1_reshape",
|
253 |
-
input_name=f"{i}_block_ln_1_scaled",
|
254 |
-
output_name=f"{i}_block_ln_1_scaled_transposed",
|
255 |
-
axes=(1, 0, 2, 3, 4)
|
256 |
-
)
|
257 |
-
|
258 |
-
|
259 |
-
conv_1D_bias = model.h[i].attn.c_attn.bias.data.cpu().numpy().reshape((1, 1, 2304, 1, 1))
|
260 |
-
conv_1D_weights = model.h[i].attn.c_attn.weight.cpu().data.numpy().transpose().reshape((1, 768, 2304, 1, 1))
|
261 |
-
|
262 |
-
builder.add_inner_product(
|
263 |
-
name=f"{i}_block_attn_conv",
|
264 |
-
input_name=f"{i}_block_ln_1_scaled_transposed",
|
265 |
-
output_name=f"{i}_block_attn_conv",
|
266 |
-
input_channels=768,
|
267 |
-
output_channels=2304,
|
268 |
-
W=conv_1D_weights,
|
269 |
-
b=conv_1D_bias,
|
270 |
-
has_bias=True
|
271 |
-
)
|
272 |
-
|
273 |
-
builder.add_split(
|
274 |
-
name=f"{i}_block_attn_qkv_split",
|
275 |
-
input_name=f"{i}_block_attn_conv",
|
276 |
-
output_names=[f"{i}_block_attn_q", f"{i}_block_attn_k", f"{i}_block_attn_v"]
|
277 |
-
)
|
278 |
-
|
279 |
-
builder.add_rank_preserving_reshape(
|
280 |
-
name=f"{i}_block_attn_q_reshape",
|
281 |
-
input_name=f"{i}_block_attn_q",
|
282 |
-
output_name=f"{i}_block_attn_q_reshape",
|
283 |
-
output_shape=(1, 1, sequence_length, 12, 64)
|
284 |
-
)
|
285 |
-
|
286 |
-
builder.add_transpose(
|
287 |
-
name=f"{i}_block_attn_q_reshape_permuted",
|
288 |
-
input_name=f"{i}_block_attn_q_reshape",
|
289 |
-
output_name=f"{i}_block_attn_q_reshape_permuted",
|
290 |
-
axes=(0, 1, 3, 2, 4)
|
291 |
-
)
|
292 |
-
|
293 |
-
builder.add_rank_preserving_reshape(
|
294 |
-
name=f"{i}_block_attn_k_reshape",
|
295 |
-
input_name=f"{i}_block_attn_k",
|
296 |
-
output_name=f"{i}_block_attn_k_reshape",
|
297 |
-
output_shape=(1, 1, sequence_length, 12, 64)
|
298 |
-
)
|
299 |
-
|
300 |
-
builder.add_transpose(
|
301 |
-
name=f"{i}_block_attn_k_reshape_permuted",
|
302 |
-
input_name=f"{i}_block_attn_k_reshape",
|
303 |
-
output_name=f"{i}_block_attn_k_reshape_permuted",
|
304 |
-
axes=(0, 1, 3, 4, 2)
|
305 |
-
)
|
306 |
-
|
307 |
-
builder.add_rank_preserving_reshape(
|
308 |
-
name=f"{i}_block_attn_v_reshape",
|
309 |
-
input_name=f"{i}_block_attn_v",
|
310 |
-
output_name=f"{i}_block_attn_v_reshape",
|
311 |
-
output_shape=(1, 1, sequence_length, 12, 64)
|
312 |
-
)
|
313 |
-
|
314 |
-
builder.add_transpose(
|
315 |
-
name=f"{i}_block_attn_v_reshape_permuted",
|
316 |
-
input_name=f"{i}_block_attn_v_reshape",
|
317 |
-
output_name=f"{i}_block_attn_v_reshape_permuted",
|
318 |
-
axes=(0, 1, 3, 2, 4)
|
319 |
-
)
|
320 |
-
|
321 |
-
builder.add_batched_mat_mul(
|
322 |
-
name=f"{i}_block_attn_qv_matmul",
|
323 |
-
input_names=[f"{i}_block_attn_q_reshape_permuted", f"{i}_block_attn_k_reshape_permuted"],
|
324 |
-
output_name=f"{i}_block_attn_qv_matmul"
|
325 |
-
)
|
326 |
-
|
327 |
-
builder.add_scale(
|
328 |
-
name=f"{i}_block_attn_qv_matmul_scaled",
|
329 |
-
input_name=f"{i}_block_attn_qv_matmul",
|
330 |
-
output_name=f"{i}_block_attn_qv_matmul_scaled",
|
331 |
-
W=np.array(1/8),
|
332 |
-
b=0,
|
333 |
-
has_bias=False
|
334 |
-
)
|
335 |
-
|
336 |
-
bias_0 = model.h[i].attn.bias
|
337 |
-
nd = ns = sequence_length
|
338 |
-
b = (model.h[i].attn.bias[:, :, ns-nd:ns, :ns]).unsqueeze(0)
|
339 |
-
|
340 |
-
builder.add_scale(
|
341 |
-
name=f"{i}_block_attn_bias",
|
342 |
-
input_name=f"{i}_block_attn_qv_matmul_scaled",
|
343 |
-
output_name=f"{i}_block_attn_bias",
|
344 |
-
W=b,
|
345 |
-
b=None,
|
346 |
-
has_bias=False,
|
347 |
-
shape_scale=[1, sequence_length, sequence_length]
|
348 |
-
)
|
349 |
-
|
350 |
-
bias_constant_0 = -1e4 * torch.logical_not(b)
|
351 |
-
|
352 |
-
builder.add_bias(
|
353 |
-
name=f"{i}_block_attn_afterbias",
|
354 |
-
input_name=f"{i}_block_attn_bias",
|
355 |
-
output_name=f"{i}_block_attn_afterbias",
|
356 |
-
# output_name=f"output_logits",
|
357 |
-
b=bias_constant_0,
|
358 |
-
shape_bias=[1, sequence_length, sequence_length],
|
359 |
-
)
|
360 |
-
|
361 |
-
builder.add_squeeze(
|
362 |
-
name=f"{i}_squeezit",
|
363 |
-
input_name=f"{i}_block_attn_afterbias",
|
364 |
-
output_name=f"{i}_squeezit",
|
365 |
-
axes=[0, 1]
|
366 |
-
)
|
367 |
-
|
368 |
-
builder.add_softmax(
|
369 |
-
name=f"{i}_block_attn_softmax",
|
370 |
-
input_name=f"{i}_squeezit",
|
371 |
-
output_name=f"{i}_block_attn_softmax",
|
372 |
-
)
|
373 |
-
|
374 |
-
builder.add_expand_dims(
|
375 |
-
name=f"{i}_expandit",
|
376 |
-
input_name=f"{i}_block_attn_softmax",
|
377 |
-
output_name=f"{i}_expandit",
|
378 |
-
axes=[0, 1]
|
379 |
-
)
|
380 |
-
|
381 |
-
builder.add_batched_mat_mul(
|
382 |
-
name=f"{i}_block_full_attention",
|
383 |
-
input_names=[f"{i}_expandit", f"{i}_block_attn_v_reshape_permuted"],
|
384 |
-
output_name=f"{i}_block_full_attention"
|
385 |
-
)
|
386 |
-
|
387 |
-
builder.add_transpose(
|
388 |
-
name=f"{i}_block_full_attention_merged_t",
|
389 |
-
input_name=f"{i}_block_full_attention",
|
390 |
-
output_name=f"{i}_block_full_attention_merged_t",
|
391 |
-
axes=[0, 1, 3, 2, 4]
|
392 |
-
)
|
393 |
-
|
394 |
-
builder.add_rank_preserving_reshape(
|
395 |
-
name=f"{i}_block_full_attention_merged",
|
396 |
-
input_name=f"{i}_block_full_attention_merged_t",
|
397 |
-
output_name=f"{i}_block_full_attention_merged",
|
398 |
-
output_shape=[1, 1, 1, sequence_length, 768]
|
399 |
-
)
|
400 |
-
|
401 |
-
builder.add_transpose(
|
402 |
-
name=f"{i}_block_attn_conv_proj_t",
|
403 |
-
input_name=f"{i}_block_full_attention_merged",
|
404 |
-
output_name=f"{i}_block_attn_conv_proj_t",
|
405 |
-
axes=[0, 3, 4, 1, 2]
|
406 |
-
)
|
407 |
-
|
408 |
-
conv_1D_proj_bias = model.h[i].attn.c_proj.bias.data.cpu().numpy().reshape((1, 1, 768, 1, 1))
|
409 |
-
conv_1D_proj_weights = model.h[i].attn.c_proj.weight.data.cpu().numpy().transpose().reshape((1, 768, 768, 1, 1))
|
410 |
-
|
411 |
-
# Input:, Output: (1, 3, 768, 1, 1)
|
412 |
-
builder.add_inner_product(
|
413 |
-
name=f"{i}_block_attn_conv_proj",
|
414 |
-
input_name=f"{i}_block_attn_conv_proj_t",
|
415 |
-
output_name=f"{i}_block_attn_conv_proj",
|
416 |
-
input_channels=768,
|
417 |
-
output_channels=768,
|
418 |
-
W=conv_1D_proj_weights,
|
419 |
-
b=conv_1D_proj_bias,
|
420 |
-
has_bias=True
|
421 |
-
)
|
422 |
-
|
423 |
-
# Input: (seq, 1, 768, 1, 1), Output: (1, seq, 768, 1, 1)
|
424 |
-
builder.add_transpose(
|
425 |
-
name=f"{i}_previous_block_t",
|
426 |
-
input_name=f'{i}_previous_block',
|
427 |
-
output_name=f"{i}_previous_block_t",
|
428 |
-
axes=[1, 0, 2, 3, 4]
|
429 |
-
)
|
430 |
-
|
431 |
-
# Input: [(1, seq, 768, 1, 1), (1, seq, 768, 1, 1)], Output: (1, seq, 768, 1, 1)
|
432 |
-
builder.add_add_broadcastable(
|
433 |
-
name=f"{i}_block_xa_sum",
|
434 |
-
input_names=[f"{i}_previous_block_t", f"{i}_block_attn_conv_proj"],
|
435 |
-
output_name=f"{i}_block_xa_sum",
|
436 |
-
# output_name=f"output_logits"
|
437 |
-
)
|
438 |
-
|
439 |
-
ln_2_weight = model.h[i].ln_2.weight.data.cpu().numpy().reshape((1, 1, 768, 1, 1))
|
440 |
-
ln_2_bias = model.h[i].ln_2.bias.data.cpu().numpy().reshape((1, 1, 768, 1, 1))
|
441 |
-
ln_2_epsilon = model.h[i].ln_2.eps
|
442 |
-
|
443 |
-
# Input: (1, seq, 768, 1, 1), Output:
|
444 |
-
builder.add_mvn(
|
445 |
-
name=f"{i}_block_ln_2",
|
446 |
-
input_name=f"{i}_block_xa_sum",
|
447 |
-
output_name=f"{i}_block_ln_2",
|
448 |
-
across_channels=True,
|
449 |
-
normalize_variance=True,
|
450 |
-
epsilon=ln_2_epsilon
|
451 |
-
)
|
452 |
-
|
453 |
-
builder.add_scale(
|
454 |
-
name=f"{i}_block_ln_2_scaled",
|
455 |
-
input_name=f"{i}_block_ln_2",
|
456 |
-
# output_name=f"output_logits",
|
457 |
-
output_name=f"{i}_block_ln_2_scaled",
|
458 |
-
W=ln_2_weight,
|
459 |
-
b=ln_2_bias,
|
460 |
-
has_bias=True,
|
461 |
-
shape_scale=[768],
|
462 |
-
shape_bias=[768]
|
463 |
-
)
|
464 |
-
|
465 |
-
mlp_conv_1D_fc_bias = model.h[i].mlp.c_fc.bias.data.cpu().numpy().reshape((1, 1, 3072, 1, 1))
|
466 |
-
mlp_conv_1D_fc_weights = model.h[i].mlp.c_fc.weight.data.cpu().numpy().transpose().reshape((1, 768, 3072, 1, 1))
|
467 |
-
|
468 |
-
# Input:, Output: (1, 3, 3072, 1, 1)
|
469 |
-
builder.add_inner_product(
|
470 |
-
name=f"{i}_block_mlp_conv_fc",
|
471 |
-
input_name=f"{i}_block_ln_2_scaled",
|
472 |
-
output_name=f"{i}_block_mlp_conv_fc",
|
473 |
-
# output_name=f"output_logits",
|
474 |
-
input_channels=768,
|
475 |
-
output_channels=3072,
|
476 |
-
W=mlp_conv_1D_fc_weights,
|
477 |
-
b=mlp_conv_1D_fc_bias,
|
478 |
-
has_bias=True
|
479 |
-
)
|
480 |
-
|
481 |
-
builder.add_gelu(
|
482 |
-
name=f"{i}_block_mlp_gelu",
|
483 |
-
input_name=f"{i}_block_mlp_conv_fc",
|
484 |
-
output_name=f"{i}_block_mlp_gelu",
|
485 |
-
# output_name=f"output_logits",
|
486 |
-
mode='TANH_APPROXIMATION'
|
487 |
-
)
|
488 |
-
|
489 |
-
mlp_conv_1D_proj_bias = model.h[i].mlp.c_proj.bias.data.cpu().numpy().reshape((1, 1, 768, 1, 1))
|
490 |
-
mlp_conv_1D_proj_weights = model.h[i].mlp.c_proj.weight.data.cpu().numpy().transpose().reshape((1, 3072, 768, 1, 1))
|
491 |
-
|
492 |
-
# Input:, Output: (1, 3, 3072, 1, 1)
|
493 |
-
builder.add_inner_product(
|
494 |
-
name=f"{i}_block_mlp_conv_proj",
|
495 |
-
input_name=f"{i}_block_mlp_gelu",
|
496 |
-
output_name=f"{i}_block_mlp_conv_proj",
|
497 |
-
# output_name=f"output_logits",
|
498 |
-
input_channels=3072,
|
499 |
-
output_channels=768,
|
500 |
-
W=mlp_conv_1D_proj_weights,
|
501 |
-
b=mlp_conv_1D_proj_bias,
|
502 |
-
has_bias=True
|
503 |
-
)
|
504 |
-
|
505 |
-
builder.add_add_broadcastable(
|
506 |
-
name=f"{i}_block_xm_sum",
|
507 |
-
input_names=[f"{i}_block_xa_sum", f"{i}_block_mlp_conv_proj"],
|
508 |
-
# output_name=f"output_logits"
|
509 |
-
output_name=f"{i + 1}_previous_block_final"
|
510 |
-
)
|
511 |
-
|
512 |
-
builder.add_transpose(
|
513 |
-
name=f"{i}_block_xm_sum_t",
|
514 |
-
input_name=f"{i + 1}_previous_block_final",
|
515 |
-
output_name=f"{i + 1}_previous_block",
|
516 |
-
axes=[1, 0, 2, 3, 4]
|
517 |
-
)
|
518 |
-
|
519 |
-
|
520 |
-
ln_f_weight = model.ln_f.weight.data.cpu().numpy().reshape((1, 1, 768, 1, 1))
|
521 |
-
ln_f_bias = model.ln_f.bias.data.cpu().numpy().reshape((1, 1, 768, 1, 1))
|
522 |
-
ln_f_epsilon = model.ln_f.eps
|
523 |
-
|
524 |
-
# Input: (1, seq, 768, 1, 1), Output:
|
525 |
-
builder.add_mvn(
|
526 |
-
name=f"ln_f",
|
527 |
-
input_name=f"{steps}_previous_block_final",
|
528 |
-
output_name=f"ln_f",
|
529 |
-
# output_name=f"output_logits",
|
530 |
-
across_channels=True,
|
531 |
-
normalize_variance=True,
|
532 |
-
epsilon=ln_f_epsilon
|
533 |
-
)
|
534 |
-
|
535 |
-
builder.add_scale(
|
536 |
-
name=f"ln_f_scaled",
|
537 |
-
input_name=f"ln_f",
|
538 |
-
output_name=f"ln_f_scaled",
|
539 |
-
# output_name=f"output_logits",
|
540 |
-
W=ln_f_weight,
|
541 |
-
b=ln_f_bias,
|
542 |
-
has_bias=True,
|
543 |
-
shape_scale=[768],
|
544 |
-
shape_bias=[768]
|
545 |
-
)
|
546 |
-
|
547 |
-
lm_head_weights = lm_head_model.lm_head.weight.data.cpu().numpy().reshape((1, 50257, 768, 1, 1))
|
548 |
-
|
549 |
-
builder.add_inner_product(
|
550 |
-
name="lm_head",
|
551 |
-
input_name="ln_f_scaled",
|
552 |
-
output_name="output_logits",
|
553 |
-
input_channels=768,
|
554 |
-
output_channels=50257,
|
555 |
-
W=lm_head_weights,
|
556 |
-
b=None,
|
557 |
-
has_bias=False
|
558 |
-
)
|
559 |
-
|
560 |
-
# compile spec to model
|
561 |
-
mlmodel = coremltools.models.MLModel(builder.spec)
|
562 |
-
|
563 |
-
save_spec(builder.spec, f'{model_name}-{sequence_length}-{steps}.mlmodel')
|
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|
genai_SDK/__init__.py
DELETED
@@ -1,2 +0,0 @@
|
|
1 |
-
from .Utilities import *
|
2 |
-
from .Seq2Seq import *
|
|
|
|
|
|
genai_SDK/__pycache__/Seq2Seq.cpython-312.pyc
DELETED
Binary file (13.4 kB)
|
|
genai_SDK/__pycache__/Utilities.cpython-312.pyc
DELETED
Binary file (21.4 kB)
|
|
genai_SDK/__pycache__/__init__.cpython-312.pyc
DELETED
Binary file (241 Bytes)
|
|
main.py
DELETED
@@ -1,24 +0,0 @@
|
|
1 |
-
from datasets import load_dataset, DatasetDict, Dataset
|
2 |
-
import pandas as pd
|
3 |
-
import ast
|
4 |
-
|
5 |
-
import genai_SDK
|
6 |
-
from genai_SDK.Seq2Seq import GPT2
|
7 |
-
|
8 |
-
# Load dataset from huggingface
|
9 |
-
dataset = load_dataset("erwanlc/cocktails_recipe_no_brand")
|
10 |
-
|
11 |
-
# Convert to a pandas dataframe
|
12 |
-
data = [{'title': item['title'], 'raw_ingredients': item['raw_ingredients']} for item in dataset['train']]
|
13 |
-
df = pd.DataFrame(data)
|
14 |
-
|
15 |
-
# Just extract the ingredient names, nothing else
|
16 |
-
df.raw_ingredients = df.raw_ingredients.apply(lambda x: ', '.join([y[1] for y in ast.literal_eval(x)]))
|
17 |
-
#display(df.head())
|
18 |
-
|
19 |
-
model = GPT2(gpu=0, model_name="distilgpt2")
|
20 |
-
model.load_data(df=df, batch_size=8)
|
21 |
-
|
22 |
-
model.train(num_epochs=2)
|
23 |
-
|
24 |
-
print(model.generate_text("Annual Planning"))
|
|
|
|
|
|
|
|
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|
|
|
trained_model/model.safetensors → model.safetensors
RENAMED
File without changes
|
pyproject.toml
DELETED
@@ -1,23 +0,0 @@
|
|
1 |
-
[tool.poetry]
|
2 |
-
name = "hackweek2024-sup-genai-tools"
|
3 |
-
version = "0.1.0"
|
4 |
-
description = ""
|
5 |
-
authors = ["Your Name <you@example.com>"]
|
6 |
-
readme = "README.md"
|
7 |
-
|
8 |
-
[tool.poetry.dependencies]
|
9 |
-
python = "^3.12"
|
10 |
-
torch = "^2.3.1"
|
11 |
-
torchtext = "^0.18.0"
|
12 |
-
transformers = "^4.41.2"
|
13 |
-
sentencepiece = "^0.2.0"
|
14 |
-
pandas = "^2.2.2"
|
15 |
-
tqdm = "^4.66.4"
|
16 |
-
datasets = "^2.20.0"
|
17 |
-
scikit-learn = "^1.5.0"
|
18 |
-
accelerate = "^0.31.0"
|
19 |
-
|
20 |
-
|
21 |
-
[build-system]
|
22 |
-
requires = ["poetry-core"]
|
23 |
-
build-backend = "poetry.core.masonry.api"
|
|
|
|
|
|
|
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|
|
restrictedItems/parse.py
DELETED
@@ -1,11 +0,0 @@
|
|
1 |
-
import csv, json
|
2 |
-
|
3 |
-
with open('csv/block_items.csv', newline='') as csvfile:
|
4 |
-
reader = csv.reader(csvfile, delimiter=',', quotechar='"')
|
5 |
-
for i, row in enumerate(reader):
|
6 |
-
if i == 0:
|
7 |
-
continue
|
8 |
-
items = json.loads(json.loads(row[3]))
|
9 |
-
|
10 |
-
for item in items:
|
11 |
-
print(item['name'])
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
|
restrictedItems/predict.py
DELETED
@@ -1,40 +0,0 @@
|
|
1 |
-
from transformers import BertTokenizer, BertForSequenceClassification
|
2 |
-
import torch
|
3 |
-
|
4 |
-
# Load the trained model and tokenizer
|
5 |
-
model = BertForSequenceClassification.from_pretrained("/Users/slei/hackweek2024-sup-genai-tools/hackweek2024-sup-genai-tools/trained_model")
|
6 |
-
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
7 |
-
|
8 |
-
|
9 |
-
# Function to predict the class of a single input text
|
10 |
-
def predict(text):
|
11 |
-
# Preprocess the input text
|
12 |
-
inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True)
|
13 |
-
|
14 |
-
# Make predictions
|
15 |
-
with torch.no_grad():
|
16 |
-
outputs = model(**inputs)
|
17 |
-
|
18 |
-
# Get the predicted class
|
19 |
-
logits = outputs.logits
|
20 |
-
predicted_class = torch.argmax(logits, dim=1).item()
|
21 |
-
|
22 |
-
return predicted_class
|
23 |
-
|
24 |
-
label_map = {0: 'Allowed Item', 1: 'Restricted Item'}
|
25 |
-
|
26 |
-
def main():
|
27 |
-
while True:
|
28 |
-
# Prompting the user for input
|
29 |
-
user_input = input("Enter something: ")
|
30 |
-
|
31 |
-
predicted_class = predict(user_input)
|
32 |
-
|
33 |
-
# Map the predicted class to a human-readable label
|
34 |
-
predicted_label = label_map[predicted_class]
|
35 |
-
|
36 |
-
# Displaying the user input
|
37 |
-
print(f'The item "{user_input}" is classified as: "{predicted_label}"')
|
38 |
-
|
39 |
-
if __name__ == "__main__":
|
40 |
-
main()
|
|
|
|
|
|
|
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restrictedItems/train.py
DELETED
@@ -1,132 +0,0 @@
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from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments
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from sklearn.model_selection import train_test_split
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import torch
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# Load dataset
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# Assume dataset is a list of tuples (text, label)
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# dataset = [("item description 1", 0), ("item description 2", 1), ...]
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restricted_dataset = [
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("Promotional Email GiftCard $10", 1),
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("$100 Vanilla® Visa", 1),
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("Promotional Email GiftCard $10", 1),
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("GPS7000 GPS Tracker for", 1),
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("$50 Vanilla Visa eGift", 1),
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("GPS7000 GPS Tracker for", 1),
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("$200 Vanilla Visa Shiny", 1),
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("$200 Vanilla Visa Shiny", 1),
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("$200 Vanilla Visa Shiny", 1),
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("$200 Vanilla® Visa", 1),
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("Promotional Email GiftCard $20", 1),
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("Xbox $100 Gift Card", 1),
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("$25 Vanilla Visa Shiny", 1),
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("$25 Vanilla Visa Shiny", 1),
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("$25 Vanilla Visa Shiny", 1),
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("Xbox $100 Gift Card", 1),
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("Home Depot Birthday Cupcake", 1),
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("$100 Vanilla® Visa", 1),
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("Birthday Celebration Target Giftcard", 1),
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("Birthday Celebration Target Giftcard", 1),
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("Promotional Email GiftCard $10", 1),
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("Birthday Celebration Target Giftcard", 1),
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("Nintendo Switch Family Online", 1),
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("Promotional Email GiftCard $10", 1),
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("$50 Vanilla® Mastercard", 1),
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("$50 Vanilla® Mastercard", 1),
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("$50 Vanilla® Mastercard", 1),
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("$50 Vanilla® Visa", 1),
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("$50 Vanilla Visa Shiny", 1),
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("Delta Airlines Wedding $250", 1)
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]
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normal_dataset =[
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("Kerrygold Grass-Fed Pure Irish Garlic & Herb Butter Stick, 3", 0),
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("bettergoods Garlic, Parmesan, & Basil Butter, 3 oz", 0),
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("Birds Eye Savory Herb Riced Cauliflower, 10 oz (Frozen)", 0),
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("Great Value Root Blend, Beets, Carrots, Parsnips and Sweet Potatoes", 0),
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("Fresh Blueberries, 18 oz Container", 0),
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("Mixpresso 3 Piece Black Canisters Sets For The Kitchen, Kitchen Jars With", 0),
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("Freshness Guaranteed Chicken Breast Tenderloins, 2.25 - 3.2", 0),
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("Kiolbassa Smoked Meats Beef Hickory Smoked Sausage, 4 links - 13oz", 0),
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("Hot Pockets Frozen Snacks, Pepperoni Pizza Buttery Crust, 5 Sandwiches", 0),
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("Kool Aid Jammers Tropical Punch Kids Drink 0% Juice Box Pouches, 10", 0),
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("Frito-Lay Flavor Mix Variety Pack Snack Chips, 1oz Bags, 18 Count", 0),
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("State Fair Classic Corn Dogs, 42.7 oz, 16 Count", 0),
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("ASURION 2 Year Sporting Goods Protection Plan ($175 - $199.99)", 0),
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("6% Incline Walking Pad Treadmill 320+ lb Capacity, Under The Desk", 0),
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("Renpure Biotin & Collagen Thickening Conditioner for All Hair Types, 32 fl", 0),
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("Renpure Biotin & Collagen Thickening Hair Shampoo for All Hair Types, 32", 0),
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("eos Shea Better Body Lotion for Dry Skin, Vanilla Cashmere, 16 fl", 0),
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("Degree Ultra Clear Long Lasting Men's Antiperspirant Deodorant Dry Spray,", 0),
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("Tide PODS Liquid Laundry Detergent, Original Scent, HE Compatible, 42 Count", 0),
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("DEER PARK Brand 100% Natural Spring Water, 16.9-ounce", 0),
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("Great Value Milk Whole Vitamin D Gallon Plastic Jug", 0),
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("Jumbo Russet Potatoes Whole Fresh, 8 lb Bag", 0),
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("Great Value Butter Pecan Flavored Ice Cream, 16 fl oz", 0),
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("Beef Lean Stew Meat, 1.0 - 1.5 lb Tray", 0),
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("Great Value Spaghetti 16oz", 0),
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("Great Value Flavored with Meat Pasta Sauce, 24 oz", 0),
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("Kentucky Kernel Original Seasoned Flour, Coating Mix for Frying, Value Size", 0)
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]
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dataset = restricted_dataset + normal_dataset
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# Split dataset
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train_texts, val_texts, train_labels, val_labels = train_test_split([item[0] for item in dataset], [item[1] for item in dataset], test_size=0.2)
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import pdb; pdb.set_trace()
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# Load pre-trained BERT tokenizer
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tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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# Tokenize data
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train_encodings = tokenizer(train_texts, truncation=True, padding=True)
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val_encodings = tokenizer(val_texts, truncation=True, padding=True)
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# Convert to torch Dataset
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class ShoppingCartDataset(torch.utils.data.Dataset):
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def __init__(self, encodings, labels):
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self.encodings = encodings
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self.labels = labels
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def __getitem__(self, idx):
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item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
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item['labels'] = torch.tensor(self.labels[idx])
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return item
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def __len__(self):
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return len(self.labels)
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train_dataset = ShoppingCartDataset(train_encodings, train_labels)
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val_dataset = ShoppingCartDataset(val_encodings, val_labels)
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# Load pre-trained BERT model
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model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
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# Training arguments
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training_args = TrainingArguments(
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output_dir='../results',
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num_train_epochs=3,
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per_device_train_batch_size=16,
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per_device_eval_batch_size=16,
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warmup_steps=500,
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weight_decay=0.01,
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logging_dir='./logs',
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logging_steps=10,
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)
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# Trainer
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=train_dataset,
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eval_dataset=val_dataset,
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)
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# Train model
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trainer.train()
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# Evaluate model
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trainer.evaluate()
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model.save_pretrained('trained_model')
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trained_model/special_tokens_map.json → special_tokens_map.json
RENAMED
File without changes
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trained_model/tokenizer_config.json → tokenizer_config.json
RENAMED
File without changes
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trained_model/vocab.txt → vocab.txt
RENAMED
File without changes
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