Push model using huggingface_hub.
Browse files- 1_Pooling/config.json +7 -0
- README.md +255 -0
- config.json +31 -0
- config_sentence_transformers.json +7 -0
- config_setfit.json +4 -0
- model.safetensors +3 -0
- model_head.pkl +3 -0
- modules.json +20 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +57 -0
- vocab.txt +0 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 384,
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"pooling_mode_cls_token": true,
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"pooling_mode_mean_tokens": false,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false
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}
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README.md
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| 1 |
+
---
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| 2 |
+
library_name: setfit
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tags:
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- setfit
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- sentence-transformers
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- text-classification
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- generated_from_setfit_trainer
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metrics:
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| 9 |
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- accuracy
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| 10 |
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widget:
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| 11 |
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- text: <s_cord-v2><s_menu><s_nm> HINADALCO INDUSTRIES LIMITED</s_nm>GSTIH</s_nm><s_price>
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214,000</s_price><sep/><s_nm> WHARADOPOWER HHARADOPOWER HHARADOPOWER HEROUPPOWER
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HEROUPPOWER HEROUPPOWER HEROUPPOWER HEROUPPOWER</s_nm><sep/><s_nm> PHOCE 006.3636.FxG
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+
CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCOCO CHOCO CHOCOCO
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CHOCOCO CHOCOCO CHOCOCO CHOCOCO CHOCOCO CHOCOCO CHOCOCO CHOCOCO CHOCOCO CHOCOCO
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CHOCOCO CHOCOCO CHOCOCO CHOCOCO CHOCOCO CHOCOCO CHOCOCO CHOCOCO CHOCOCO CHOCOCO
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CHOCOCO CHOCOCO CHOCOCO CHOCOCO CHOCOCO CHOCOCO CHOCOCO CHOCOCO CHOCOCO CHOCOCO
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CHOCOCO CHOCOCO CHOCOCO CHOCOCOCO CHOCOCO CHOCOCOCO CHOCOCOCO CHOCOCOCO CHOCOCOCO
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| 19 |
+
CHOCOCOCO CHOCOCOCOCO CHOCOCOCOCO CHOCOCOCOCOCO CHOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCO
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- text: <s_cord-v2><s_menu><s_nm> HNDALCO INDUSTRIES LIWITED</s_nm><s_discountprice>
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+
-5</s_discountprice><s_price> SUSHIBILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILI
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- text: <s_cord-v2><s_menu><s_nm> DUDHALA ROAD LINES</s_nm><s_num> P.O. HILLArud.
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Dstc Sambalum</s_nm><s_num> JCHOCOLOGY</s_num><s_price> SUSHI<s_etc> 3834</s_etc></s_sub_total><s_total><s_total_price>
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19,942</s_total_price><s_total_etc> 19,94</s_total_etc><s_emoneyprice> 19,942</s_total_etc><s_emoneyprice>
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19,942</s_total_etc><s_emoneyprice> 19,942</s_total_etc><s_emoneyprice> 19,942</s_total_etc></s_total>
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| 26 |
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- text: <s_cord-v2><s_menu><s_nm> HINDALCO INDUSTRIES LIMITED</s_nm><s_price> SUSHICHEdzi
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| 27 |
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- Odiha<sep/><s_nm> HEROUP PORE CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO
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CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO
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CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO
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CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO
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CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO
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CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO
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CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO
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CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO
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CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO
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CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO
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CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO
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CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO
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CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO
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CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO
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CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO
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CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO
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CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO
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CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO
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CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO CHOCO
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CHOCO CHOCO CHOCO
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| 47 |
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- text: <s_cord-v2><s_menu><s_nm> M/S ANIL KUMAR SAHU</s_nm><s_num> ATIVO-HIRAKUD</s_nm><s_num>
|
| 48 |
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AVAILI<s_etc> MOMOBILE NO. 97735729<sep/><s_nm> DIST - SAMBALPUR 769016. ODAISA</s_nm><s_num>
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| 49 |
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DIST -</s_num><s_price> AWAILLIO =よいu=<s_sub_total><s_subtotal_price> 529.6gnall.com</s_subtotal_price><s_discount_price>
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| 50 |
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HUNDALCO KINGUSUSTRIES LITED TAK INVOLICE</s_nm><s_num> HIRAKUD POWER</s_nm><s_num>
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| 51 |
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(SSUEDIUMGR RULAG POWER</s_nm><s_num> AVAI HIRAKUD</s_nm><s_num> AVOR<s_num> AVAIJI</s_num><s_price>
|
| 52 |
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97736363636<sep/><s_nm> SAMBALPUR 798016.001KA</s_nm><s_num> DIST -</s_num><s_price>
|
| 53 |
+
447.20</s_price><sep/><s_nm> SATE COOE-</s_nm><s_num> 21</s_num><s_price> 11,030.020</s_price><sep/><s_nm>
|
| 54 |
+
GSTUN =</s_nm><s_num> 21AACH1201R122</s_nm><s_num> GST-</s_num><s_price> 11,030.020</s_price><sep/><s_nm>
|
| 55 |
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AACH1201 PO. NO. P/PO/S/S/RVR/V/192/0976</s_num><s_price> 2,8402</s_price><sep/><s_nm>
|
| 56 |
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DETAILS O COGING (HWHIPPED)</s_nm><s_num> P DATE</s_nm><s_num> PY DATE</s_nm><s_num>
|
| 57 |
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JIKAYU POWER</s_nm><s_num> JATE OF ISSUE</s_nm><s_num> DIST SAMBALPUR - 798016.00H</s_nm><s_num>
|
| 58 |
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JULY - 20116.0<sep/> JULY - SLI</s_nm><s_num> JANUARY - 2020 HW/S/S/C</s_nm><s_num>
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| 59 |
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SLINGGA CJANTITY UOM N. SPICYPICYE</s_nm><s_num> JANNABLE VALUE</s_nm><s_num>
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| 60 |
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SCI1764</s_num><s_price> (RS)</s_price><sep/><s_nm> ASH TRANSPOTATIONTO VARIOUS</s_nm><s_num>
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SPICYE</s_nm><s_num> JULYE</s_nm><s_num> JULYE</s_nm><s_num> JULY JANUARY - 2020</s_nm><s_num>
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| 62 |
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SLIY -<sep/> SUS/AC</s_nm><s_num> SLIYMPH/SCALF</s_nm><s_num> SLIY SLIY SPICYMPYMPE</s_nm><s_num>
|
| 63 |
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SLIY SPICYMPYPICYPICYE</s_nm><s_num> JANJAN TWINJANJANJAN SUSPICY SLIY SLIY SLIY
|
| 64 |
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SPICYMPPICY SPICYMPE</s_nm><s_num> SPICYPICYPICYPICYPICYPICYE</s_nm><s_num> SLIY
|
| 65 |
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SATE TAKABLE VALUE</s_nm><s_num> SLIF</s_nm><s_num> SLIF</s_nm><s_num> SLIF</s_nm><s_num>
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| 66 |
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SLIF</s_nm><s_num> SLIF</s_nm><s_num> SPICYMPE</s_nm><s_num> SLIF</s_nm><s_num>
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SPICYPICYPICYE</s_nm><s_num> SPICYPICYPICYE</s_nm><s_num> SPICYPICYE</s_nm><s_num>
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| 68 |
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SPICYPICYE</s_nm><s_num> SPICYPICYE</s_nm><s_num> SPICYE</s_nm><s_num> SPICYE</s_nm><s_num>
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SPICYE</s_nm><s_num> SPICYE</s_nm><s_num> SPICYE</s_nm><s_num> SPICYE</s_nm><s_num>
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SPICYE</s_nm><s_num> SPICYE</s_nm><s_num> SPICYE</s_nm><s_num> SPICYE</s_nm><s_num>
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| 71 |
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SPICYE</s_nm><s_num> SPICYE</s_nm><s_num> SPICYE</s_nm><s_num> SPICYE</s_nm><s_num>
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| 72 |
+
JULY JANUARY - 79016.016.00Hadviseur JULY - 2019 JULY - SLIF SLIF SLIF</s_nm><s_num>
|
| 73 |
+
SLIF SLIF/AC</s_nm><s_num> SLIF
|
| 74 |
+
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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| 77 |
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model-index:
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| 78 |
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- name: SetFit with BAAI/bge-small-en-v1.5
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results:
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| 80 |
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- task:
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type: text-classification
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name: Text Classification
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dataset:
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name: Unknown
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type: unknown
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split: test
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metrics:
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- type: accuracy
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value: 1.0
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name: Accuracy
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---
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+
|
| 93 |
+
# SetFit with BAAI/bge-small-en-v1.5
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| 94 |
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|
| 95 |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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The model has been trained using an efficient few-shot learning technique that involves:
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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2. Training a classification head with features from the fine-tuned Sentence Transformer.
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## Model Details
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### Model Description
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- **Model Type:** SetFit
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- **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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- **Maximum Sequence Length:** 512 tokens
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- **Number of Classes:** 3 classes
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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### Model Sources
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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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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|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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| 2 | <ul><li>'<s_cord-v2><s_menu><s_nm> M/S ANIL KUMAR SAHU</s_nm><s_num> ATIVO-</s_num><s_discountprice> IMALID</s_discountprice><s_price> 9777362</s_price><sep/><s_nm> DIST - SAMBALPUR 769016. ODAH</s_nm><s_num> DIST -</s_num><s_discountprice> SUSHIKA</s_nm><s_num> DETAILIC ORESUPENT (BILLED TO)</s_nm><s_num> DATAIS NOVOICE</s_nm><s_num> SUSHI HUNDALCO INJUSTRIES LITC. (SSUEUNDER RULLE 46 GST/QGST RULAG)</s_nm><s_num> AV<sep/><s_nm> AYAPO HIRAKUD POWER</s_nm><s_num> AVYE</s_nm><s_num> AVYE DIST - SAMBALPUR 798016.001A</s_nm><s_num> AVOCUE NO. 487.20</s_num><s_price> 4877.00</s_price><sep/><s_nm> SATE COD:</s_nm><s_num> 21</s_num><s_price> 11,03020</s_price><sep/><s_nm> GSTIN =</s_nm><s_num> 21AACH1201R122</s_nm><s_num> GSTINE -<sep/><s_nm> AACH1201R</s_nm><s_num> PO. NO. 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Tripathy,Maa Tarini Residency</s_nm><s_num> BUdharaja,Sambalpuri</s_nm><s_num> 303545912</s_num><s_price> 4,327</s_price><sep/><s_nm> EMAIL: pratapktripathy @reidfmail.com</s_nm><s_num> 303545912</s_num><s_price> 12.86.10</s_price></s_menu><s_sub_total><s_subtotal_price> 25,092.28</s_subtotal_price><s_discount_price> 3,314</s_subtotal_price><s_discount_price> 3,314</s_discount_price><s_tax_price> 17,28.27</s_tax_price></s_sub_total><s_total><s_total_price> 17,28.27</s_total_price><s_total_etc> 3,36314</s_total_etc><s_creditcardprice> 3,36314</s_creditcardprice></s_total>'</li><li>'<s_cord-v2><s_menu><s_nm> ACC</s_nm><s_price> TAX INVQICE</s_nm><s_price> ACC Uimited</s_nm><s_price> ---</s_price><sep/><s_nm> ACC Uimited</s_nm><s_unitprice> ---classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classically-classclassically-classically-classically-classically-classically-classically-classically-classically-classclassically-classically-classically-classically-classically-classically-classically-classically-classically-classclassically-classically-classically-classically-classclassically-classically-classically-classically-classclassically-classically-classically-classically-classclassically-classically-classclassically-classically-classclassically-classically-classclassically-classclassically-classclassically-classclassically-classclassically-classclassically-classclassically-classclassically-classclassclassically-classclassically-classclassically-classclassclassically-classclassclassically-classclassclassically-classclassclassically-classclassclassically-classclassclassclassically-classclassclassically-classclassclassclassically-classclassclassically-classclassclassclassically-classclassclassclassically-classclassclassclassically-classclassclassclassically-classclassclassclassically-classclassclassclassclassically-classclassclassclassically-classclassclassclassclassically-classclassclassclassclassically-classclassclassclassclassically-'</li></ul> |
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OUT</s_nm><s_num> 271-</s_num><s_price> 90.44</s_price><sep/><s_nm> DATE VEHICLE</s_nm><s_num> SLINO TIME</s_nm><s_num> AWT</s_nm><s_num> AVAT</s_nm><s_num> AVAT</s_nm><s_num> AVAT</s_nm><s_num> AVAT</s_nm><s_num> AVAT</s_nm><s_num> AVAT</s_nm><s_num> AVAT</s_nm><s_num> AVAT</s_nm><s_num> AVAT</s_nm><s_num> AVAT</s_nm><s_num> AVAT</s_nm><s_num> AVAT</s_nm><s_num> AVAT</s_nm><s_num> AVAT</s_nm><s_num> AVAT</s_nm><s_num> AVAT</s_nm><s_num> AVAT</s_nm><s_num> AVAT</s_nm><s_num> AVAT</s_nm><s_num> AVGARAH 15.60</s_num><s_price> 14.64</s_num><s_price> 13.95</s_num><s_price> 13.95</s_price><sep/><s_nm> SHREE SAL</s_nm><s_num> AVAI</s_nm><s_num> AVGARH 13.95</s_num><s_price> 14.64</s_num><s_price> 13.91</s_price><sep/><s_nm> SHREE SAL</s_nm><s_num> 31.08</s_num><s_price> 13.95</s_num><s_price> 31.95</s_price><sep/><s_nm> SHREE SAL</s_nm><s_num> 31.00</s_num><s_price> 13.95</s_num><s_price> 20.04</s_num><s_price> 9.28</s_num><s_price> 12.04</s_num><s_price> 20.04</s_num><s_price> 20.04</s_num><s_price> 9.28</s_num><s_price> 12.04</s_num><s_price> 20.04</s_num><s_price> 20.04</s_num><s_price> 9.28</s_num><s_price> 12.04</s_num><s_price> 20.04</s_num><s_price> 20.04</s_num><s_price> 9.28</s_num><s_price> 12.04</s_num><s_price> 12.04</s_num><s_price> 20.04</s_num><s_price> 20.04</s_num><s_price> 9.28</s_num><s_price> 12.04</s_num><s_price> 31.95</s_num><s_price> 4129</s_num><s_price> 9.28</s_num><s_price> 12.04</s_num><s_price> 12.04</s_num><s_price> 4129</s_num><s_price> 9.28</s_num><s_price> 12.95</s_price></s_menu><s_sub_total><s_subtotal_price> 57.70</s_num><s_price> 4129</s_num><s_price> 12.04</s_num><s_price> 12.04</s_num><s_price> 17.70</s_num><s_price> 4129</s_num><s_price> 12.04</s_num><s_price> 17.70</s_num><s_price> 4196</s_num><s_price> 4196</s_num><s_price> 6.57</s_num><s_price> 12.04</s_num><s_price> 17.70</s_num><s_price> 17.70</s_num><s_price> 17.00</s_num><s_price> 17.00</s_num><s_price> 17.00</s_num><s_price> 17.00</s_num><s_price> 17.00</s_num><s_price> 17.00</s_num><s_price> 17.00</s_num><s_price> 17.00</s_num><s_price> 12.04</s_num><s_price> 17.00</s_num><s_price> 17.000.00</s_price></s_menu><s_sub_total><s_subtotal_price> 90.44</s_subtotal_price><s_discount_price> 13.91</s_num><s_price> 13.91</s_subtotal_price><s_discount_price> 13.91</s_subtotal_price><s_discount_price> 13.95</s_num><s_price> 13.95</s_num><s_price> 12.00</s_num><s_price> 12.00</s_num><s_price> 12.00</s_num><s_price> 12.00</s_num><s_price> 12.00</s_num><s_price> 17.00</s_num><s_price> 12.00</s_num><s_price> 17.000.00</s_num><s_price> 12.000.00<s_nm> SHRK</s_num><s_price>'</li><li>'<s_cord-v2><s_menu><s_nm> SHRI SAL BRICKS & BLOCKS</s_nm><s_num> DATE VEHGLE適合</s_nm><s_num> SUNDA OUT</s_nm><s_num> BRICKS PLANT</s_nm><s_num> ADDRESS WT</s_nm><s_num> 0412</s_num><s_price> WH</s_nm><s_num> 041-2</s_price><sep/><s_nm> SHREE SAL BARGARH 14.53</s_price><sep/><s_nm> 20119</s_nm><s_num> 0412</s_num><s_price> 14.53</s_price><sep/><s_nm> WHRE SAL BAKGARH 1545</s_nm><s_num> 051-</s_num><s_price> BARGARH</s_nm><s_num> 2019</s_price><sep/><s_nm> WHRE SALI</s_nm><s_num> 1012</s_num><s_price> 18.09</s_price><sep/><s_nm> WHRE SALI</s_nm><s_num> 1012</s_num><s_price> BARGARH 16.05</s_price><sep/><s_nm> 1012</s_nm><s_num> 0147</s_num><s_unitprice> 12.19</s_unitprice><s_cnt> 3,8</s_cnt><s_price> 10.0</s_price><sep/><s_nm> 20112</s_cnt><s_price> 011</s_num><s_unitprice> 12.13</s_price><sep/><s_nm> WHRE SALI</s_nm><s_num> SAI</s_nm><s_num> SAI</s_nm><s_num> SAI</s_nm><s_num> SAI</s_nm><s_num> BARGARH 17.61</s_num><s_price> 13.618</s_price><sep/><s_nm> SHREE SALI</s_nm><s_num> SAI</s_nm><s_num> SAI</s_nm><s_num> SAI</s_nm><s_num> SAI</s_nm><s_num> SAI</s_nm><s_num> SAI</s_nm><s_num> 13,636<sep/><s_nm> 2011</s_num><s_price> 13.13</s_price><sep/><s_nm> WHITE</s_nm><s_num> JANJARY - NET</s_nm><s_num> DATE VEHGLEGLE NO SLNO. OUT</s_nm><s_num> DRICKS PLANT</s_nm><s_num> DORESE WT</s_nm><s_num> 0601</s_num><s_price> WT</s_nm><s_num> 014.452</s_cnt><s_price> 16.15</s_price><sep/><s_nm> SHREE SAL BANGKAM</s_nm><s_num> 0.21</s_num><s_price> 15.73</s_price><sep/><s_nm> SHREE SAL BRICK BARGAH 15.73</s_nm><s_num> 15,73</s_num><s_price> 16.97</s_price><sep/><s_nm> SHREE SAL BRICK BARGARH</s_nm><s_num> 144.85</s_num><s_price> 15.17</s_price></s_menu><s_sub_total><s_subtotal_price> 114.85</s_subtotal_price><s_discount_price> 13.13</s_subtotal_price><s_discount_price> 13.13</s_subtotal_price><s_discount_price> 13.13</s_discount_price><s_tax_price> 4,3</s_etc></s_sub_total><s_total><s_total_price> 114.85</s_total_price><s_cashprice> 0.18</s_cashprice></s_total>'</li></ul> |
|
| 126 |
+
|
| 127 |
+
## Evaluation
|
| 128 |
+
|
| 129 |
+
### Metrics
|
| 130 |
+
| Label | Accuracy |
|
| 131 |
+
|:--------|:---------|
|
| 132 |
+
| **all** | 1.0 |
|
| 133 |
+
|
| 134 |
+
## Uses
|
| 135 |
+
|
| 136 |
+
### Direct Use for Inference
|
| 137 |
+
|
| 138 |
+
First install the SetFit library:
|
| 139 |
+
|
| 140 |
+
```bash
|
| 141 |
+
pip install setfit
|
| 142 |
+
```
|
| 143 |
+
|
| 144 |
+
Then you can load this model and run inference.
|
| 145 |
+
|
| 146 |
+
```python
|
| 147 |
+
from setfit import SetFitModel
|
| 148 |
+
|
| 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> DUDHALA ROAD LINES</s_nm><s_num> P.O. HILLArud. Dstc Sambalum</s_nm><s_num> JCHOCOLOGY</s_num><s_price> SUSHI<s_etc> 3834</s_etc></s_sub_total><s_total><s_total_price> 19,942</s_total_price><s_total_etc> 19,94</s_total_etc><s_emoneyprice> 19,942</s_total_etc><s_emoneyprice> 19,942</s_total_etc><s_emoneyprice> 19,942</s_total_etc><s_emoneyprice> 19,942</s_total_etc></s_total>")
|
| 153 |
+
```
|
| 154 |
+
|
| 155 |
+
<!--
|
| 156 |
+
### Downstream Use
|
| 157 |
+
|
| 158 |
+
*List how someone could finetune this model on their own dataset.*
|
| 159 |
+
-->
|
| 160 |
+
|
| 161 |
+
<!--
|
| 162 |
+
### Out-of-Scope Use
|
| 163 |
+
|
| 164 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
| 165 |
+
-->
|
| 166 |
+
|
| 167 |
+
<!--
|
| 168 |
+
## Bias, Risks and Limitations
|
| 169 |
+
|
| 170 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
| 171 |
+
-->
|
| 172 |
+
|
| 173 |
+
<!--
|
| 174 |
+
### Recommendations
|
| 175 |
+
|
| 176 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
| 177 |
+
-->
|
| 178 |
+
|
| 179 |
+
## Training Details
|
| 180 |
+
|
| 181 |
+
### Training Set Metrics
|
| 182 |
+
| Training set | Min | Median | Max |
|
| 183 |
+
|:-------------|:----|:---------|:----|
|
| 184 |
+
| Word count | 6 | 137.5652 | 378 |
|
| 185 |
+
|
| 186 |
+
| Label | Training Sample Count |
|
| 187 |
+
|:------|:----------------------|
|
| 188 |
+
| 0 | 7 |
|
| 189 |
+
| 1 | 9 |
|
| 190 |
+
| 2 | 7 |
|
| 191 |
+
|
| 192 |
+
### Training Hyperparameters
|
| 193 |
+
- batch_size: (32, 32)
|
| 194 |
+
- num_epochs: (1, 1)
|
| 195 |
+
- max_steps: -1
|
| 196 |
+
- sampling_strategy: oversampling
|
| 197 |
+
- body_learning_rate: (2e-05, 1e-05)
|
| 198 |
+
- head_learning_rate: 0.01
|
| 199 |
+
- loss: CosineSimilarityLoss
|
| 200 |
+
- distance_metric: cosine_distance
|
| 201 |
+
- margin: 0.25
|
| 202 |
+
- end_to_end: False
|
| 203 |
+
- use_amp: False
|
| 204 |
+
- warmup_proportion: 0.1
|
| 205 |
+
- seed: 42
|
| 206 |
+
- eval_max_steps: -1
|
| 207 |
+
- load_best_model_at_end: False
|
| 208 |
+
|
| 209 |
+
### Training Results
|
| 210 |
+
| Epoch | Step | Training Loss | Validation Loss |
|
| 211 |
+
|:------:|:----:|:-------------:|:---------------:|
|
| 212 |
+
| 0.0909 | 1 | 0.2752 | - |
|
| 213 |
+
|
| 214 |
+
### Framework Versions
|
| 215 |
+
- Python: 3.10.12
|
| 216 |
+
- SetFit: 1.0.1
|
| 217 |
+
- Sentence Transformers: 2.2.2
|
| 218 |
+
- Transformers: 4.35.2
|
| 219 |
+
- PyTorch: 2.1.0+cu121
|
| 220 |
+
- Datasets: 2.16.1
|
| 221 |
+
- Tokenizers: 0.15.0
|
| 222 |
+
|
| 223 |
+
## Citation
|
| 224 |
+
|
| 225 |
+
### BibTeX
|
| 226 |
+
```bibtex
|
| 227 |
+
@article{https://doi.org/10.48550/arxiv.2209.11055,
|
| 228 |
+
doi = {10.48550/ARXIV.2209.11055},
|
| 229 |
+
url = {https://arxiv.org/abs/2209.11055},
|
| 230 |
+
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
|
| 231 |
+
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
|
| 232 |
+
title = {Efficient Few-Shot Learning Without Prompts},
|
| 233 |
+
publisher = {arXiv},
|
| 234 |
+
year = {2022},
|
| 235 |
+
copyright = {Creative Commons Attribution 4.0 International}
|
| 236 |
+
}
|
| 237 |
+
```
|
| 238 |
+
|
| 239 |
+
<!--
|
| 240 |
+
## Glossary
|
| 241 |
+
|
| 242 |
+
*Clearly define terms in order to be accessible across audiences.*
|
| 243 |
+
-->
|
| 244 |
+
|
| 245 |
+
<!--
|
| 246 |
+
## Model Card Authors
|
| 247 |
+
|
| 248 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| 249 |
+
-->
|
| 250 |
+
|
| 251 |
+
<!--
|
| 252 |
+
## Model Card Contact
|
| 253 |
+
|
| 254 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
| 255 |
+
-->
|
config.json
ADDED
|
@@ -0,0 +1,31 @@
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|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "/root/.cache/torch/sentence_transformers/BAAI_bge-small-en-v1.5/",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"BertModel"
|
| 5 |
+
],
|
| 6 |
+
"attention_probs_dropout_prob": 0.1,
|
| 7 |
+
"classifier_dropout": null,
|
| 8 |
+
"hidden_act": "gelu",
|
| 9 |
+
"hidden_dropout_prob": 0.1,
|
| 10 |
+
"hidden_size": 384,
|
| 11 |
+
"id2label": {
|
| 12 |
+
"0": "LABEL_0"
|
| 13 |
+
},
|
| 14 |
+
"initializer_range": 0.02,
|
| 15 |
+
"intermediate_size": 1536,
|
| 16 |
+
"label2id": {
|
| 17 |
+
"LABEL_0": 0
|
| 18 |
+
},
|
| 19 |
+
"layer_norm_eps": 1e-12,
|
| 20 |
+
"max_position_embeddings": 512,
|
| 21 |
+
"model_type": "bert",
|
| 22 |
+
"num_attention_heads": 12,
|
| 23 |
+
"num_hidden_layers": 12,
|
| 24 |
+
"pad_token_id": 0,
|
| 25 |
+
"position_embedding_type": "absolute",
|
| 26 |
+
"torch_dtype": "float32",
|
| 27 |
+
"transformers_version": "4.35.2",
|
| 28 |
+
"type_vocab_size": 2,
|
| 29 |
+
"use_cache": true,
|
| 30 |
+
"vocab_size": 30522
|
| 31 |
+
}
|
config_sentence_transformers.json
ADDED
|
@@ -0,0 +1,7 @@
|
|
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|
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|
| 1 |
+
{
|
| 2 |
+
"__version__": {
|
| 3 |
+
"sentence_transformers": "2.2.2",
|
| 4 |
+
"transformers": "4.28.1",
|
| 5 |
+
"pytorch": "1.13.0+cu117"
|
| 6 |
+
}
|
| 7 |
+
}
|
config_setfit.json
ADDED
|
@@ -0,0 +1,4 @@
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|
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"normalize_embeddings": false,
|
| 3 |
+
"labels": null
|
| 4 |
+
}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f720739c5cf7b45c5aee25bf329780aaccf9fb3087d6f0c1379f79d001cec6bd
|
| 3 |
+
size 133462128
|
model_head.pkl
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:31685ed0242ed9566ca18e30dbbb7dc5db3acb9d77d5ea3d6eeaa2ea24f8565a
|
| 3 |
+
size 10095
|
modules.json
ADDED
|
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
+
"idx": 0,
|
| 4 |
+
"name": "0",
|
| 5 |
+
"path": "",
|
| 6 |
+
"type": "sentence_transformers.models.Transformer"
|
| 7 |
+
},
|
| 8 |
+
{
|
| 9 |
+
"idx": 1,
|
| 10 |
+
"name": "1",
|
| 11 |
+
"path": "1_Pooling",
|
| 12 |
+
"type": "sentence_transformers.models.Pooling"
|
| 13 |
+
},
|
| 14 |
+
{
|
| 15 |
+
"idx": 2,
|
| 16 |
+
"name": "2",
|
| 17 |
+
"path": "2_Normalize",
|
| 18 |
+
"type": "sentence_transformers.models.Normalize"
|
| 19 |
+
}
|
| 20 |
+
]
|
sentence_bert_config.json
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"max_seq_length": 512,
|
| 3 |
+
"do_lower_case": true
|
| 4 |
+
}
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"cls_token": "[CLS]",
|
| 3 |
+
"mask_token": "[MASK]",
|
| 4 |
+
"pad_token": "[PAD]",
|
| 5 |
+
"sep_token": "[SEP]",
|
| 6 |
+
"unk_token": "[UNK]"
|
| 7 |
+
}
|
tokenizer.json
ADDED
|
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|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,57 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"0": {
|
| 4 |
+
"content": "[PAD]",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"100": {
|
| 12 |
+
"content": "[UNK]",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
+
"101": {
|
| 20 |
+
"content": "[CLS]",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
+
"102": {
|
| 28 |
+
"content": "[SEP]",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"103": {
|
| 36 |
+
"content": "[MASK]",
|
| 37 |
+
"lstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
}
|
| 43 |
+
},
|
| 44 |
+
"clean_up_tokenization_spaces": true,
|
| 45 |
+
"cls_token": "[CLS]",
|
| 46 |
+
"do_basic_tokenize": true,
|
| 47 |
+
"do_lower_case": true,
|
| 48 |
+
"mask_token": "[MASK]",
|
| 49 |
+
"model_max_length": 512,
|
| 50 |
+
"never_split": null,
|
| 51 |
+
"pad_token": "[PAD]",
|
| 52 |
+
"sep_token": "[SEP]",
|
| 53 |
+
"strip_accents": null,
|
| 54 |
+
"tokenize_chinese_chars": true,
|
| 55 |
+
"tokenizer_class": "BertTokenizer",
|
| 56 |
+
"unk_token": "[UNK]"
|
| 57 |
+
}
|
vocab.txt
ADDED
|
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See raw diff
|
|
|