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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:3820 |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: samsung ms23h3125ak/ms23h3125ak |
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sentences: |
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- Canon EOS M50 + 15-45mm IS STM |
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- Bosch KIV32X23GB Integrated |
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- Indesit DIF04B1 Integrated |
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- Samsung MS23H3125AK Black |
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- Samsung RB29FWRNDBC Black |
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- Hisense RQ560N4WC1 |
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- Samsung UE32M5520 |
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- Nikon CoolPix A10 |
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- Hotpoint RPD10457JKK |
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- HP Intel Xeon X5670 2.93GHz Socket 1366 3200MHz bus Upgrade Tray |
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- Indesit DFG15B1S Silver |
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- Samsung WW10M86DQOO |
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- Bosch SMV46MX00G Integrated |
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- LG 49SK8100PLA |
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- Nikon CoolPix W300 |
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- AMD Ryzen 3 1300X 3.5GHz Box |
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- LG OLED65B8PLA |
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- Samsung Galaxy J5 SM-J530 |
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- LG 65UK6500PLA |
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- Siemens WM14T391GB |
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- Apple iPhone SE 32GB |
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- source_sentence: lg oled65c8pla |
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sentences: |
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- Beko LCSM1545W White |
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- Bosch KAN90VI20G Stainless Steel |
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- Canon PowerShot SX60 HS |
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- Hotpoint WMAQF621P |
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- Apple iPhone 7 Plus 32GB |
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- Hotpoint FFU4DK Black |
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- Fujifilm Finepix XP130 |
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- Bosch WAN24108GB |
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- LG OLED65E8PLA |
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- Intel Core i7-8700K 3.7GHz Box |
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- Fujifilm X-Pro2 |
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- LG OLED65C8PLA |
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- Samsung UE55NU8000 |
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- LG 49LK5900PLA |
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- Apple iPhone 8 64GB |
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- Samsung UE65NU7100 |
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- AEG L6FBG942R |
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- AMD Ryzen 7 1700 3GHz Box |
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- Panasonic TX-49FX750B |
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- Bosch WKD28351GB |
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- Bosch GUD15A50GB Integrated |
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- source_sentence: 15.748 cm 6.2 2960 x 1440 samoled octa core 2.3ghz quad 1.7gh |
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sentences: |
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- Apple iPhone SE 32GB |
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- Apple iPhone X 64GB |
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- LG 55SK9500PLA |
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- Sony Cyber-shot DSC-WX500 |
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- Samsung Galaxy A5 SM-A520F |
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- Apple iPhone 8 Plus 64GB |
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- Indesit IWDD7123 |
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- Bosch SMS67MW01G White |
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- Bosch KGV33XW30G White |
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- Samsung WW80K5413UW |
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- AMD Ryzen 3 1300X 3.5GHz Box |
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- Bosch WAW28750GB |
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- Samsung Galaxy S8+ 64GB |
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- Bosch KGN39VW35G White |
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- Intel Core i7-7700K 4.2GHz Box |
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- Hotpoint RZAAV22P White |
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- Samsung UE49NU8000 |
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- HP AMD Opteron 6276 2.3GHz Upgrade Tray |
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- Praktica Luxmedia Z250 |
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- Hotpoint HFC2B19SV White |
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- Hisense RB385N4EW1 White |
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- source_sentence: boxed processor amd ryzen 3 1200 4 x 3.1 ghz quad |
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sentences: |
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- Bosch KGN36HI32 Stainless Steel |
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- Bosch SMS24AW01G White |
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- Hotpoint WDAL8640P |
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- Doro 6050 |
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- Samsung QE55Q7FN |
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- AMD Ryzen 3 1200 3.1GHz Box |
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- Samsung UE55NU7500 |
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- Huawei Honor 10 128GB Dual SIM |
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- Sony Xperia L1 |
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- Hotpoint FFU4DK Black |
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- Hoover DXOC 68C3B |
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- Sony Xperia XA1 |
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- Nikon D7200 + 18-105mm VR |
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- HP Intel Xeon DP E5640 2.66GHz Socket 1366 1066MHz bus Upgrade Tray |
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- Samsung UE49NU8000 |
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- Panasonic Lumix DMC-FT30 |
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- Hotpoint FDL 9640K UK |
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- Apple iPhone 6S Plus 128GB |
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- Nikon D5600 + AF-P 18-55mm VR |
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- HP AMD Opteron 6238 2.6GHz Upgrade Tray |
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- Apple iPhone SE 32GB |
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- source_sentence: lg 49uk6300plb/49uk6300plb |
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sentences: |
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- Bosch KIR24V20GB Integrated |
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- Bosch WAWH8660GB |
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- Intel Core i5-7600K 3.80GHz Box |
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- Sony Bravia KD-65AF8 |
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- Samsung RL4362FBASL Stainless Steel |
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- Bosch SMI50C15GB Silver |
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- Apple iPhone XS Max 256GB |
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- Fujifilm X-T100 + XC 15-45/f3.5-5.6 OIS PZ |
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- Bosch KGN36VW35G White |
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- Samsung WW70K5410UW |
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- Samsung Galaxy J6 |
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- LG 49UK6300PLB |
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- Doro Secure 580 |
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- Sony Xperia XZ1 Compact |
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- Bosch SMV50C10GB Integrated |
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- Bosch KGN34VB35G Black |
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- Panasonic NN-E27JWMBPQ White |
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- Samsung WW10M86DQOA/EU |
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- LG 55SK9500PLA |
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- Samsung QE65Q8DN |
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- Canon EOS 80D |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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model-index: |
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- name: SentenceTransformer |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: Product Category Retrieval Test |
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type: Product-Category-Retrieval-Test |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.8085774058577406 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.9476987447698745 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.9644351464435147 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9769874476987448 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.8085774058577406 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.3158995815899582 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.19288702928870294 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09769874476987449 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.8085774058577406 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.9476987447698745 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.9644351464435147 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9769874476987448 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.9041917131034228 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.879607906621505 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.8805000617705705 |
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name: Cosine Map@100 |
|
--- |
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|
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# SentenceTransformer |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 512-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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|
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## Model Details |
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) --> |
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- **Maximum Sequence Length:** 384 tokens |
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- **Output Dimensionality:** 512 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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|
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### Model Sources |
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|
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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|
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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): SentenceTransformer( |
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(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) |
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) |
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``` |
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|
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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|
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("llmvetter/embedding_finetune") |
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# Run inference |
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sentences = [ |
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'lg 49uk6300plb/49uk6300plb', |
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'LG 49UK6300PLB', |
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'Samsung Galaxy J6', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 512] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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|
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<!-- |
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### Direct Usage (Transformers) |
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|
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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|
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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|
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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## Evaluation |
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|
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### Metrics |
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#### Information Retrieval |
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* Dataset: `Product-Category-Retrieval-Test` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
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|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.8086 | |
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| cosine_accuracy@3 | 0.9477 | |
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| cosine_accuracy@5 | 0.9644 | |
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| cosine_accuracy@10 | 0.977 | |
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| cosine_precision@1 | 0.8086 | |
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| cosine_precision@3 | 0.3159 | |
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| cosine_precision@5 | 0.1929 | |
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| cosine_precision@10 | 0.0977 | |
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| cosine_recall@1 | 0.8086 | |
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| cosine_recall@3 | 0.9477 | |
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| cosine_recall@5 | 0.9644 | |
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| cosine_recall@10 | 0.977 | |
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| **cosine_ndcg@10** | **0.9042** | |
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| cosine_mrr@10 | 0.8796 | |
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| cosine_map@100 | 0.8805 | |
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|
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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|
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
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## Training Details |
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|
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### Training Dataset |
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#### Unnamed Dataset |
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|
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* Size: 3,820 training samples |
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* Columns: <code>sentence_0</code>, <code>sentence_1</code>, <code>sentence_2</code>, <code>sentence_3</code>, <code>sentence_4</code>, <code>sentence_5</code>, <code>sentence_6</code>, <code>sentence_7</code>, <code>sentence_8</code>, <code>sentence_9</code>, <code>sentence_10</code>, <code>sentence_11</code>, <code>sentence_12</code>, <code>sentence_13</code>, <code>sentence_14</code>, <code>sentence_15</code>, <code>sentence_16</code>, <code>sentence_17</code>, <code>sentence_18</code>, <code>sentence_19</code>, <code>sentence_20</code>, and <code>sentence_21</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | sentence_2 | sentence_3 | sentence_4 | sentence_5 | sentence_6 | sentence_7 | sentence_8 | sentence_9 | sentence_10 | sentence_11 | sentence_12 | sentence_13 | sentence_14 | sentence_15 | sentence_16 | sentence_17 | sentence_18 | sentence_19 | sentence_20 | sentence_21 | |
|
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | |
|
| details | <ul><li>min: 4 tokens</li><li>mean: 18.41 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.94 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.11 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.15 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.89 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.89 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.98 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.07 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.04 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.84 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.82 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.81 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.05 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.92 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.18 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.07 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.93 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.02 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.04 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.02 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.95 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.86 tokens</li><li>max: 30 tokens</li></ul> | |
|
* Samples: |
|
| sentence_0 | sentence_1 | sentence_2 | sentence_3 | sentence_4 | sentence_5 | sentence_6 | sentence_7 | sentence_8 | sentence_9 | sentence_10 | sentence_11 | sentence_12 | sentence_13 | sentence_14 | sentence_15 | sentence_16 | sentence_17 | sentence_18 | sentence_19 | sentence_20 | sentence_21 | |
|
|:---------------------------------------------------------------------|:----------------------------------------|:---------------------------------------------|:-------------------------------------|:-------------------------------------|:--------------------------------------|:----------------------------------------------|:----------------------------------|:---------------------------------|:----------------------------------------------|:-----------------------------------------------------------------------------|:---------------------------------------------|:------------------------------------|:--------------------------------------------|:---------------------------------------------|:----------------------------------------|:-------------------------------------------------|:-------------------------------|:------------------------------------------|:---------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------| |
|
| <code>sony kd49xf8505bu 49 4k ultra hd tv</code> | <code>Sony Bravia KD-49XF8505</code> | <code>Intel Core i7-8700K 3.7GHz Box</code> | <code>Bosch WAN24100GB</code> | <code>AMD FX-6300 3.5GHz Box</code> | <code>Bosch WIW28500GB</code> | <code>Bosch KGN36VL35G Stainless Steel</code> | <code>Indesit XWDE751480XS</code> | <code>CAT S41 Dual SIM</code> | <code>Sony Xperia XA1 Ultra 32GB</code> | <code>Samsung Galaxy J6</code> | <code>Samsung QE55Q7FN</code> | <code>Bosch KGN39VW35G White</code> | <code>Intel Core i5 7400 3.0GHz Box</code> | <code>Neff C17UR02N0B Stainless Steel</code> | <code>Samsung RR39M7340SA Silver</code> | <code>Samsung RB41J7255SR Stainless Steel</code> | <code>Hoover DXOC 68C3B</code> | <code>Canon PowerShot SX730 HS</code> | <code>Samsung RR39M7340BC Black</code> | <code>Praktica Luxmedia WP240</code> | <code>HP Intel Xeon DP E5506 2.13GHz Socket 1366 800MHz bus Upgrade Tray</code> | |
|
| <code>doro 8040 4g sim free mobile phone black</code> | <code>Doro 8040</code> | <code>Bosch HMT75M551 Stainless Steel</code> | <code>Bosch SMI50C15GB Silver</code> | <code>Samsung WW90K5413UX</code> | <code>Panasonic Lumix DMC-TZ70</code> | <code>Sony KD-49XF7073</code> | <code>Nikon CoolPix W100</code> | <code>Samsung WD90J6A10AW</code> | <code>Bosch CFA634GS1B Stainless Steel</code> | <code>HP AMD Opteron 8425 HE 2.1GHz Socket F 4800MHz bus Upgrade Tray</code> | <code>Canon EOS 800D + 18-55mm IS STM</code> | <code>Samsung UE50NU7400</code> | <code>Apple iPhone 6S 128GB</code> | <code>Samsung RS52N3313SA/EU Graphite</code> | <code>Bosch WAW325H0GB</code> | <code>Sony Bravia KD-55AF8</code> | <code>Sony Alpha 6500</code> | <code>Doro 5030</code> | <code>LG GSL761WBXV Black</code> | <code>Bosch SMS67MW00G White</code> | <code>AEG L6FBG942R</code> | |
|
| <code>fridgemaster muz4965 undercounter freezer white a rated</code> | <code>Fridgemaster MUZ4965 White</code> | <code>Samsung UE49NU7100</code> | <code>Nikon CoolPix A10</code> | <code>Samsung UE55NU7100</code> | <code>Samsung QE55Q7FN</code> | <code>Bosch KGN49XL30G Stainless Steel</code> | <code>Samsung UE49NU7500</code> | <code>LG 55UK6300PLB</code> | <code>Hoover DXOC 68C3B</code> | <code>Panasonic Lumix DMC-FZ2000</code> | <code>Panasonic Lumix DMC-TZ80</code> | <code>Bosch WKD28541GB</code> | <code>Apple iPhone 6 32GB</code> | <code>Sony Bravia KDL-32WE613</code> | <code>Lec TF50152W White</code> | <code>Bosch KGV36VW32G White</code> | <code>Bosch WAYH8790GB</code> | <code>Samsung RS68N8240B1/EU Black</code> | <code>Sony Xperia XZ1</code> | <code>HP Intel Xeon DP E5506 2.13GHz Socket 1366 800MHz bus Upgrade Tray</code> | <code>Sharp R372WM White</code> | |
|
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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|
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `num_train_epochs`: 8 |
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- `multi_dataset_batch_sampler`: round_robin |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1 |
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- `num_train_epochs`: 8 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: linear |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.0 |
|
- `warmup_steps`: 0 |
|
- `log_level`: passive |
|
- `log_level_replica`: warning |
|
- `log_on_each_node`: True |
|
- `logging_nan_inf_filter`: True |
|
- `save_safetensors`: True |
|
- `save_on_each_node`: False |
|
- `save_only_model`: False |
|
- `restore_callback_states_from_checkpoint`: False |
|
- `no_cuda`: False |
|
- `use_cpu`: False |
|
- `use_mps_device`: False |
|
- `seed`: 42 |
|
- `data_seed`: None |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: False |
|
- `fp16`: False |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: None |
|
- `local_rank`: 0 |
|
- `ddp_backend`: None |
|
- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
|
- `debug`: [] |
|
- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
|
- `label_names`: None |
|
- `load_best_model_at_end`: False |
|
- `ignore_data_skip`: False |
|
- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
|
- `hub_private_repo`: None |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `include_for_metrics`: [] |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `use_liger_kernel`: False |
|
- `eval_use_gather_object`: False |
|
- `average_tokens_across_devices`: False |
|
- `prompts`: None |
|
- `batch_sampler`: batch_sampler |
|
- `multi_dataset_batch_sampler`: round_robin |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | Product-Category-Retrieval-Test_cosine_ndcg@10 | |
|
|:------:|:----:|:-------------:|:----------------------------------------------:| |
|
| 1.0 | 120 | - | 0.7406 | |
|
| 2.0 | 240 | - | 0.8437 | |
|
| 3.0 | 360 | - | 0.8756 | |
|
| 4.0 | 480 | - | 0.8875 | |
|
| 4.1667 | 500 | 2.5302 | - | |
|
| 5.0 | 600 | - | 0.8963 | |
|
| 6.0 | 720 | - | 0.9015 | |
|
| 7.0 | 840 | - | 0.9042 | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.11.10 |
|
- Sentence Transformers: 3.3.1 |
|
- Transformers: 4.47.1 |
|
- PyTorch: 2.5.1+cu124 |
|
- Accelerate: 1.2.1 |
|
- Datasets: 3.2.0 |
|
- Tokenizers: 0.21.0 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
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