SetFit with BAAI/bge-small-en-v1.5
This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-small-en-v1.5 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
Model Sources
Model Labels
Label |
Examples |
0 |
- '_\ni.\nSe\nNew\n~~\ned\nTy\nSw\nNe\nNw\ned\n2:\n\n \n\x0c'
- 'ne.\n\n \n \n\n \n \n\n \n\nbBo fy20 5 ‘ )\n- wi Pas BOOKING STATION\nstat” SURAT GEIS TRA: BSPORT HOT. LTE, DIMPLE COURT, 2ND FLOOR,\n H.O.: “VIRAJ IMPEX HOUSE”, 47, D' M= -toRoaD, + AT_OW. ER’S RISK oer\n' , a” MUMBAI - 400 009 Tel. : 4076 7676 sianan Gece i al CARGO iS INSUR BY CUSTOMER — PH, : 033-30821697, 22\n{ 1. Consignor’s Name & Address As. ExOme peas Br. Code\ndT ncuer\n
|
1 |
- "Posatis ils. H\n\n \n\niS\nvs\na (uf\n\noe\n\n \n\n-\n\n \n\nSarichor Pls: q\n\nPea :\n\nITEM /\n\n1. Description/ Received Reject Delivered Retur\n\n \n \n\nSPARE TX. Phat\n\n(MARKETED BY MESAPD\n\nPact eta\n\n \n\nMATERIAL RECEIPT REPORT\n\n \n\n \n \n \n\n \n\nCUM nea\n\n00 LeTlooo 0.000\n\nPAS\n\n \n \n\nELT\n\nJUPLICATE FOR TRANSPORTE?-\nOGPY (EMGISE INVOICE) RECEIVED\n\nMite ariant Eee\n\nPRAM MUIMAFE RCL RE\n\n \n\n \n\nFrys\n\n \n\not\n\nSuds oT\n\n \n \n\npeas\n\nee ase\n\n. Tax Gelig\n\nGrand Tooke\n\ni\n\nRM\n\nRate/Unit\n\nMRR SUBMITTED\nwv\n\nITH PARTY'S INVIGCE\n\nEET RY MO SSO OT Soe ELS\n\nLS.\n\n \n\n \n\n \n\nWee\n\n7; Ae 18\n\nTrcic\n\ni\nSu\n\n~s\n\n“en\n\nnny\n\x0c"
- "«= ITER /\ncit BDescription/ Received\n\nms\n\n \n \n\n \n\nIces\n\ne to\n\ntea tae\n\nhoimeryh bea\n\nPorccheninernyh Qerkees\n\nRican dec\n\nrarer:\n\nPAD RP eAR eR\n\nMeare\n\n \n\nMATERIAL RECEIPT\n\n \n\nREPORT\n\n \n\nwe ie 7\nhe\n\nSeba.\nbh ETS\n\n \n\nReject Delivered Retur\n\nTESLA y’\n\n \n\n \n\n \n\nLF PIE\n\nTAIT a\n\nSUPLICATE FOR TRANSPORTER\nOGPY (EXGISE INVOICE) RECEIVED\n\noy\n\nf\n\n“soarewe Pk Beak\nree\n\nRAF
|
Evaluation
Metrics
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
model = SetFitModel.from_pretrained("Gopal2002/Material_Receipt_Report_ZEON")
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Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
1 |
182.1336 |
1108 |
Label |
Training Sample Count |
0 |
202 |
1 |
45 |
Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
Epoch |
Step |
Training Loss |
Validation Loss |
0.0007 |
1 |
0.2952 |
- |
0.0371 |
50 |
0.2253 |
- |
0.0742 |
100 |
0.1234 |
- |
0.1114 |
150 |
0.0115 |
- |
0.1485 |
200 |
0.0036 |
- |
0.1856 |
250 |
0.0024 |
- |
0.2227 |
300 |
0.0015 |
- |
0.2598 |
350 |
0.0011 |
- |
0.2970 |
400 |
0.0009 |
- |
0.3341 |
450 |
0.0007 |
- |
0.3712 |
500 |
0.0011 |
- |
0.4083 |
550 |
0.0008 |
- |
0.4454 |
600 |
0.0008 |
- |
0.4826 |
650 |
0.0007 |
- |
0.5197 |
700 |
0.0005 |
- |
0.5568 |
750 |
0.0006 |
- |
0.5939 |
800 |
0.0005 |
- |
0.6310 |
850 |
0.0005 |
- |
0.6682 |
900 |
0.0004 |
- |
0.7053 |
950 |
0.0003 |
- |
0.7424 |
1000 |
0.0004 |
- |
0.7795 |
1050 |
0.0005 |
- |
0.8166 |
1100 |
0.0004 |
- |
0.8537 |
1150 |
0.0004 |
- |
0.8909 |
1200 |
0.0005 |
- |
0.9280 |
1250 |
0.0004 |
- |
0.9651 |
1300 |
0.0003 |
- |
1.0022 |
1350 |
0.0003 |
- |
1.0393 |
1400 |
0.0003 |
- |
1.0765 |
1450 |
0.0004 |
- |
1.1136 |
1500 |
0.0003 |
- |
1.1507 |
1550 |
0.0004 |
- |
1.1878 |
1600 |
0.0004 |
- |
1.2249 |
1650 |
0.0004 |
- |
1.2621 |
1700 |
0.0003 |
- |
1.2992 |
1750 |
0.0003 |
- |
1.3363 |
1800 |
0.0003 |
- |
1.3734 |
1850 |
0.0003 |
- |
1.4105 |
1900 |
0.0003 |
- |
1.4477 |
1950 |
0.0002 |
- |
1.4848 |
2000 |
0.0003 |
- |
1.5219 |
2050 |
0.0003 |
- |
1.5590 |
2100 |
0.0003 |
- |
1.5961 |
2150 |
0.0002 |
- |
1.6333 |
2200 |
0.0003 |
- |
1.6704 |
2250 |
0.0004 |
- |
1.7075 |
2300 |
0.0004 |
- |
1.7446 |
2350 |
0.0003 |
- |
1.7817 |
2400 |
0.0002 |
- |
1.8189 |
2450 |
0.0002 |
- |
1.8560 |
2500 |
0.0003 |
- |
1.8931 |
2550 |
0.0002 |
- |
1.9302 |
2600 |
0.0003 |
- |
1.9673 |
2650 |
0.0003 |
- |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.0
Citation
BibTeX
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}