Push model using huggingface_hub.
Browse files- README.md +92 -89
- config_setfit.json +2 -2
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README.md
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metrics:
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- accuracy
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pipeline_tag: text-classification
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inference: true
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base_model: BAAI/bge-small-en-v1.5
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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### Model Labels
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## Evaluation
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("Gopal2002/setfit_zeon")
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# Run inference
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preds = model("<s_cord-v2><s_menu><s_nm>
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```
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<!--
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### Training Set Metrics
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| Training set | Min | Median | Max |
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|:-------------|:----|:---------|:----|
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| Word count | 5 |
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| Label | Training Sample Count |
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|:------|:----------------------|
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### Training Hyperparameters
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:------:|:----:|:-------------:|:---------------:|
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### Framework Versions
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- Python: 3.10.12
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- SetFit: 1.0.
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- Sentence Transformers: 2.2.2
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- Transformers: 4.35.2
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- PyTorch: 2.1.0+cu121
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metrics:
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- accuracy
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widget:
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-*OQU<sep/><s_nm> PYCHE DESIGNCE PURCHASE ORDER</s_nm><sep/><s_nm> WHOCO SUSHINGGA
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CHOCO SUSHINGGA CHOCO SUSHINGGA SUSHINGGA SUSHINGGA SUSHINGGA SUSHINGGA SUSHINGGA
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SUSHINGGA SUSHINGGA SUSHINGGA SUSHINGGA SUSHINGGA SUSHINGGA SUSHINGGA SUSHINGGA
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SUSHINGGA SUSHINGGA SUSHINGGA SUSHINGGA SUSHINGGA SUSHINGGA SUSHINGGA SUSHINGGA
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SUSHINGGA SUSHINGGA SUSHINGGA SUSHINGGA SUSHINGGA SUSHINGGA SUSHINGGA SUSHINGGA
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POWER</s_nm><s_price> SUSHINGGANGHONGHONGHONG POWER</s_nm><s_price> SUSHINGGANGHONGHONG
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POWER</s_nm><s_price> SUSHINGGANGGANGGANGGANGGANGGANGGANGGANGGA SUSHINGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGA
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150,000</s_unitprice><s_cnt> 2</s_cnt><s_discountprice> 1,040<sep/><s_nm> A.C.B.C.B.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C.C
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JER</s_nm><s_num> JER</s_nm><s_num> JER</s_total>'
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pipeline_tag: text-classification
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inference: true
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base_model: BAAI/bge-small-en-v1.5
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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### Model Labels
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| Label | Examples |
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| 2 | <ul><li>'<s_cord-v2><s_menu><s_nm> M/S. JOSEPH SUNA</s_nm><s_num> DATABI<sep/><s_nm> Tankipaa.MIRAKU SAMBALPUR.768Q16</s_nm><s_num> DMB Nb N.9861345883<sep/><s_nm> Deals Which :Altypes of ChickenGNALOR REGUMING)</s_nm><s_num> DISALI<sep/><s_nm> WINNALIZED</s_nm><s_num> CHOCO SUSPECIALIZE</s_nm><s_num> TWICENCHE<sep/><s_nm> SHRANGKANG POWER</s_nm><s_num> LATHOCO TWICENKO:</s_nm><s_num> JERYUNG CHOCO TWICENKO:</s_nm><s_num> JERYUNG HZYGANGKAN<sep/><s_nm> DIFF-SAWALAPUKU SAMBALPUR.76801GHOLIZEG DATE</s_nm><s_num> DATE</s_nm><s_num> DATE:</s_nm><s_num> 01/01/01/01/01/01/01/01/01/01/01/01/01/01/01/01/01<sep/><s_nm> PAN No.:</s_nm><s_num> PPODATE</s_nm><s_num> 01/01/01/01/01/01/01/01/01/01/01<sep/><s_nm> DATE OPSE<sep/><s_nm> HANDUPPOWER</s_nm><s_num> 30.12221</s_num><s_price> 1,945.00</s_price><sep/><s_nm> SUSPENGGANGURG.GUSTAGUR GUSTAGANGKANGURGUSTAGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTGUTG'</li><li>'<s_cord-v2><s_menu><s_nm> GST INVOLICE</s_nm><s_price> ORIGINAL FOR KEGINGLI</s_nm><s_price> WOUCE BREGRAMING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPINGLIPPING SUSHIPPING SUSHIPPINGLIPPING SUSHIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPINGLIPPING SUSHIPPING SUSHIPPINGLIPPING SUSHIPPING SUSHIPPINGLIPPING SUSHIPPING SUSHIPPINGLIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHIPPING SUSHI'</li><li>'<s_cord-v2><s_menu><s_nm> TAX INVOICE</s_nm><s_price> ORIGINAL FOR AQUALIZE</s_nm><s_price> SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO SUSHIKO '</li></ul> |
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| 1 | <ul><li>'<s_cord-v2><s_menu><s_nm> UNDALCO INDUSTRIES LTB.</s_nm><s_unitprice> HIRAKUD POWER</s_nm><sep/><s_nm> ASH WPTCH BRIOGE</s_nm><s_unitprice> TIMOL CATE BRIOUS DATE</s_nm><s_unitprice> SUSCEE</s_nm><s_unitprice> SUSCE</s_unitprice><s_cnt> 1</s_cnt><s_price> SUSCE</s_price><sep/><s_nm> MSCED</s_nm><s_unitprice> SUSCEE</s_nm><s_unitprice> SUSCE</s_unitprice><s_cnt> 1</s_cnt><s_price> SUSCE</s_price><sep/><s_nm> MICHI CHOCO KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KESE KE'</li><li>'<s_cord-v2><s_menu><s_nm> UNDALCO INDUSTRIES LTB.</s_nm><s_unitprice> HIR A KD POWER</s_nm></s_sub><sep/><s_nm> ASH WEICH BRIOGE</s_nm><s_unitprice> 16.36.36m2</s_unitprice><s_cnt> AGE IMPL CAST SUSIC :RING LETS SUSIC SUSIC SUSIC SUSIC SUSIC SUSIC SUSCCE</s_nm></s_sub><sep/><s_nm> MSCHO</s_nm><s_unitprice> 13.45</s_unitprice><s_cnt> 1.36.36</s_cnt><s_price> 6.36</s_price><sep/><s_nm> SUSPICY TEMPLE</s_nm><s_unitprice> 14.50.13.502</s_unitprice><s_cnt> 1.00</s_cnt><s_price> 0.00</s_price><sep/><s_nm> BREAT TRIPSE TO WBLE</s_nm><s_unitprice> 13.35.5cs</s_unitprice><s_cnt> 1.00.00</s_cnt><s_price> 0.00</s_price><sep/><s_nm> BREATTY TRIPSE</s_nm><s_unitprice> 13.50</s_unitprice><s_cnt> 1.00.00.00.00</s_cnt><s_price> 0.00</s_price><sep/><s_nm> BREATTY TRIPSE</s_nm><s_unitprice> 13.50.50</s_unitprice><s_cnt> 1.00.00.00</s_cnt><s_price> 0.00</s_price><sep/><s_nm> BREATTY 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| 0 | <ul><li>'<s_cord-v2><s_menu><s_nm> HANDALCO 이미지ES LIMITED</s_nm><s_price> SUNDAYGHOCO SUSHIZEH CINCEHANGKAGHOCO SUSHIZEHANGKAGHOUSHIZEHANGKAGHOUSHIZEHANGKAGHOUSHIZEHANGKAGHOUSHIZEHANGKAGHOUSHIZEHANGKANG PURCHASE ORDER</s_nm><sep/><s_nm> WANTE CHOCO CAKE CONSULATANCE PYI LOTHO NUMPIC UPICK CHOCO CHOCO CHOCOCO SUSHIZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHZEHER</s_nm><s_discountprice>Nt.Minitie HGHOCEHINE</s_nm><s_discountprice>N.Minitie HGUMAGHO</s_nm><s_discountprice>N</s_nm><s_discountprice>N.Minitie HUMAGHO</s_nm><s_discountprice>N</s_nm><s_discountprice>N</s_discountprice><s_price> 436.0</s_price><sep/><s_nm> OxMini WHEN HUMAGHUNG</s_nm><s_discountprice> SUSHIZEHITEGHOUSHILIZEHENCE COTTING 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SUSHIBILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILI'</li><li>'<s_cord-v2><s_menu><s_nm> HINDALCO INDUSTRIES LIMITED</s_nm><s_price> GSTING&NAACHI201</s_price><sep/><s_nm> WBABUPOWER HEROGUSTAMPURGANGKANCE CHOCOLOGALINGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGANGGA'</li></ul> |
|
| 126 |
|
| 127 |
## Evaluation
|
| 128 |
|
|
|
|
| 149 |
# Download from the 🤗 Hub
|
| 150 |
model = SetFitModel.from_pretrained("Gopal2002/setfit_zeon")
|
| 151 |
# Run inference
|
| 152 |
+
preds = model("<s_cord-v2><s_menu><s_nm> HINALCO INDUSTRIES LTB. HIRAKUR</s_nm><s_unitprice> 1344</s_unitprice><s_cnt> 1</s_cnt><s_price> 4,436</s_price><sep/><s_nm> ASTRICA BRIOC</s_nm><s_unitprice> 12.082</s_unitprice><s_cnt> 1</s_cnt><s_discountprice> 12.027</s_discountprice><s_price> SUSPICY TEMPURA HIRAKUR</s_nm><s_unitprice> 12.027.00.0020</s_discountprice><s_price> PAK SUSHI HIRAKURURUR</s_nm><s_unitprice> 12.027.00.0020</s_unitprice><s_cnt> 1</s_cnt><s_discountprice> 12.027</s_discountprice><s_price> 4,436</s_price><sep/><s_nm> SUSHI SALT CALLOCALI</s_nm><s_unitprice> 12.027.0020</s_unitprice><s_cnt> 1</s_cnt><s_discountprice> 1,003</s_discountprice><s_price> 1,00</s_price></s_menu><s_sub_total><s_subtotal_price> 3,003</s_subtotal_price><s_discount_price> 3,003<sep/> 0.00</s_discount_price></s_sub_total><s_total><s_total_price> 3,00</s_total_price><s_cashprice> 3,00</s_cashprice><s_changeprice> 1,00</s_changeprice></s_total>")
|
| 153 |
```
|
| 154 |
|
| 155 |
<!--
|
|
|
|
| 181 |
### Training Set Metrics
|
| 182 |
| Training set | Min | Median | Max |
|
| 183 |
|:-------------|:----|:---------|:----|
|
| 184 |
+
| Word count | 5 | 107.8041 | 763 |
|
| 185 |
|
| 186 |
| Label | Training Sample Count |
|
| 187 |
|:------|:----------------------|
|
| 188 |
| 0 | 47 |
|
| 189 |
+
| 1 | 51 |
|
| 190 |
+
| 2 | 50 |
|
| 191 |
|
| 192 |
### Training Hyperparameters
|
| 193 |
- batch_size: (32, 32)
|
|
|
|
| 209 |
### Training Results
|
| 210 |
| Epoch | Step | Training Loss | Validation Loss |
|
| 211 |
|:------:|:----:|:-------------:|:---------------:|
|
| 212 |
+
| 0.0022 | 1 | 0.3004 | - |
|
| 213 |
+
| 0.1094 | 50 | 0.2457 | - |
|
| 214 |
+
| 0.2188 | 100 | 0.1464 | - |
|
| 215 |
+
| 0.3282 | 150 | 0.0079 | - |
|
| 216 |
+
| 0.4376 | 200 | 0.0028 | - |
|
| 217 |
+
| 0.5470 | 250 | 0.0027 | - |
|
| 218 |
+
| 0.6565 | 300 | 0.0017 | - |
|
| 219 |
+
| 0.7659 | 350 | 0.0014 | - |
|
| 220 |
+
| 0.8753 | 400 | 0.0015 | - |
|
| 221 |
+
| 0.9847 | 450 | 0.0011 | - |
|
| 222 |
+
| 1.0941 | 500 | 0.001 | - |
|
| 223 |
+
| 1.2035 | 550 | 0.0011 | - |
|
| 224 |
+
| 1.3129 | 600 | 0.001 | - |
|
| 225 |
+
| 1.4223 | 650 | 0.0011 | - |
|
| 226 |
+
| 1.5317 | 700 | 0.0011 | - |
|
| 227 |
+
| 1.6411 | 750 | 0.0009 | - |
|
| 228 |
+
| 1.7505 | 800 | 0.0008 | - |
|
| 229 |
+
| 1.8600 | 850 | 0.001 | - |
|
| 230 |
+
| 1.9694 | 900 | 0.0009 | - |
|
| 231 |
|
| 232 |
### Framework Versions
|
| 233 |
- Python: 3.10.12
|
| 234 |
+
- SetFit: 1.0.2
|
| 235 |
- Sentence Transformers: 2.2.2
|
| 236 |
- Transformers: 4.35.2
|
| 237 |
- PyTorch: 2.1.0+cu121
|
config_setfit.json
CHANGED
|
@@ -1,4 +1,4 @@
|
|
| 1 |
{
|
| 2 |
-
"
|
| 3 |
-
"
|
| 4 |
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"normalize_embeddings": false,
|
| 3 |
+
"labels": null
|
| 4 |
}
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 133462128
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:cc092c4d7ef841c1bc22a06add3b39eed7f5649f222be6e11fe7725f25999e59
|
| 3 |
size 133462128
|
model_head.pkl
CHANGED
|
@@ -1,3 +1,3 @@
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|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 10095
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:27a8885cb035357a870dacc1ff076f53eb2fc5901a25206555c68649bae6ae13
|
| 3 |
size 10095
|