Add SetFit model
Browse files- 1_Pooling/config.json +10 -0
- README.md +316 -0
- config.json +26 -0
- config_sentence_transformers.json +9 -0
- config_setfit.json +4 -0
- model.safetensors +3 -0
- model_head.pkl +3 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +64 -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": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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README.md
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---
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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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- accuracy
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widget:
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- text: Tiong Bahru Plaza, DDC-L2-5, AHU-L2-03 trip alarm
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- text: 'Tiong Bahru Plaza, DDC L4-1, PAU-L4-03 supply air temperature (Units: °C).2'
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- text: Tiong Bahru Plaza, DDC-L20, AHU 20-1 VSD CONTROL
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- text: 'Tiong Bahru Plaza, VAV 19-7, Discharge Air Flow (Units: m3/h)'
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- text: Tiong Bahru Plaza, DDC-L2-5, PAU-L2-02 VSD control
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pipeline_tag: text-classification
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inference: true
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base_model: sentence-transformers/paraphrase-MiniLM-L3-v2
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model-index:
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- name: SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2
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results:
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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: 0.9863861386138614
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name: Accuracy
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---
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# SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2
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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 [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2) 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:** [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2)
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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:** 128 tokens
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- **Number of Classes:** 44 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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| 28 | <ul><li>'Tiong Bahru Plaza, UC800_102002_Chiller_2, Chilled Water Flow'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Chiller 1 CHW Flowrate'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CHW Flowrate'</li></ul> |
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| 33 | <ul><li>'Tiong Bahru Plaza, UC800_102005, Cond Water Flow'</li><li>'Tiong Bahru Plaza, UC800_102005, Condenser Water Flow'</li><li>'Tiong Bahru Plaza, UC800_102004, Condenser Water Flow'</li></ul> |
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| 2 | <ul><li>'Tiong Bahru Plaza, DDC-L6, AHU 4-1 FAD Feedback'</li><li>'Tiong Bahru Plaza, DDC-L2-2, AHU-L2-05 FAD feedback'</li><li>'Tiong Bahru Plaza, DDC L14-1, AHU 13-1 FAD Feedback'</li></ul> |
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| 42 | <ul><li>'Tiong Bahru Plaza, UC800_102004, Cond Leaving Water Temp (Units: °C)'</li><li>'Tiong Bahru Plaza, UC800_102004, Cond Leaving Water Temp (Units: °C)'</li><li>'Tiong Bahru Plaza, UC800_102005, Cond Entering Water Temp (Units: °C)'</li></ul> |
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| 31 | <ul><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CWR Temperature'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Head CWR Temp'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CWR Temp'</li></ul> |
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| 9 | <ul><li>'Tiong Bahru Plaza, DDC L4-1, PAU-L4-02 supply air temperature (Units: °C).2'</li><li>'Tiong Bahru Plaza, DDC-L1-4, PAU-L1-05 supply air temperature (Units: °C)'</li><li>'Tiong Bahru Plaza, DDC-L1-4, PAU-L1-03 supply air temperature (Units: °C)'</li></ul> |
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| 1 | <ul><li>'Tiong Bahru Plaza, DDC L4-1, PAU-L4-02 static pressure (Units: Pa)'</li><li>'Tiong Bahru Plaza, DDC-L2-5, PAU-L2-02 static pressure (Units: Pa)'</li><li>'Tiong Bahru Plaza, DDC-L1-4, PAU-L1-05 Static pressure (Units: Pa)'</li></ul> |
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| 25 | <ul><li>'Tiong Bahru Plaza, DDC-L1-3, AHU-L2-02 VSD control'</li><li>'Tiong Bahru Plaza, DDC-L2-5, PAU-L2-04 VSD control'</li><li>'Tiong Bahru Plaza, DDC-L3-2, AHU-6-2A VSD control'</li></ul> |
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| 12 | <ul><li>'Tiong Bahru Plaza, DDC-9-1, AHU 8-1 On/Off Status'</li><li>'Tiong Bahru Plaza, DDC-9-1, AHU 7-1 On/Off Status'</li><li>'Tiong Bahru Plaza, DDC-L1-5, PAU-L1-06 on/off status'</li></ul> |
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| 8 | <ul><li>'Tiong Bahru Plaza, DDC-L1-3, AHU-L1-02 returm air temperature (Units: °C)'</li><li>'Tiong Bahru Plaza, DDC-L1-1, AHU-L1-01 returm air temperature (Units: °C)'</li><li>'Tiong Bahru Plaza, DDC L2-4, AHU L2-04 returm air temperature (Units: °C)'</li></ul> |
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| 24 | <ul><li>'Tiong Bahru Plaza, DDC-L20, PAHU TR-1 TEMPERATURE (Units: °C)'</li></ul> |
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| 13 | <ul><li>'Tiong Bahru Plaza, DDC-L17, AHU 17-1 Smoke Alarm'</li><li>'Tiong Bahru Plaza, DDC-9-1, AHU 9-1 Smoke Alarm'</li><li>'Tiong Bahru Plaza, DDC-B1-6, AHU-B1-1 Smoke Alarm'</li></ul> |
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| 30 | <ul><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Head CWS Temp'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CWS Temperature'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CWS Temp'</li></ul> |
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| 5 | <ul><li>'Tiong Bahru Plaza, DDC-L20, AHU 19-1 OCT (Units: °C)'</li><li>'Tiong Bahru Plaza, DDC-L17, AHU 16-1 OCT (Units: °C)'</li><li>'Tiong Bahru Plaza, DDC L14-1, AHU 13-1 OCT (Units: °C)'</li></ul> |
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| 43 | <ul><li>'Tiong Bahru Plaza, VAV 19-11, Air Valve Position (Units: %)'</li><li>'Tiong Bahru Plaza, VAV 19-13, Air Valve Position (Units: %)'</li><li>'Tiong Bahru Plaza, VAV 19-16, Air Valve Position (Units: %)'</li></ul> |
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| 14 | <ul><li>'Tiong Bahru Plaza, DDC-L1-5, AHU-L3-04A trip alarm'</li><li>'Tiong Bahru Plaza, DDC-9-1, AHU 9-1 Trip Alarm'</li><li>'Tiong Bahru Plaza, DDC-L17, AHU 16-1 Trip Alarm'</li></ul> |
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| 36 | <ul><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Chiller 3 CHWR Temperature'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CWR Temperature'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Chiller 3 CWR Temperature'</li></ul> |
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| 34 | <ul><li>'Tiong Bahru Plaza, UC800_3, Operating Mode'</li><li>'Tiong Bahru Plaza, UC800_101001_Chiller_1, Operating Mode'</li><li>'CT 3-1 Switch Mode'</li></ul> |
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| 18 | <ul><li>'Tiong Bahru Plaza, DDC-L2-5, PAU-L2-04 switch mode'</li><li>'Tiong Bahru Plaza, DDC-L3-3, AHU-L3-1 switch mode'</li><li>'Tiong Bahru Plaza, DDC L2-4, AHU L2-04 switch mode'</li></ul> |
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| 6 | <ul><li>'Tiong Bahru Plaza, DDC-L17, AHU 16-1 Valve Control (Units: %)'</li><li>'Tiong Bahru Plaza, DDC-L20, AHU 20-1 VALVE CONTROL (Units: %)'</li><li>'Tiong Bahru Plaza, DDC L12, AHU 10-1 VALVE CONTROL (Units: %)'</li></ul> |
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| 17 | <ul><li>'Tiong Bahru Plaza, VAV 19-12, Discharge Air Temperature (Units: °C)'</li><li>'Tiong Bahru Plaza, VAV 19-15, Discharge Air Temperature (Units: °C)'</li><li>'Tiong Bahru Plaza, VAV 19-7, Discharge Air Temperature (Units: °C)'</li></ul> |
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| 0 | <ul><li>'Tiong Bahru Plaza, DDC L14-1, AHU 13-1 CO2 Reading (Units: ppm).1'</li><li>'Tiong Bahru Plaza, DDC-L1-5, AHU-L3-04A CO2 sensor reading (Units: ppm)'</li><li>'Tiong Bahru Plaza, DDC-L20, Co2 Level 18'</li></ul> |
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| 29 | <ul><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CHWR Temperature'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CHWR Temperature'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CHWR Temp'</li></ul> |
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| 15 | <ul><li>'Tiong Bahru Plaza, VAV 19-6, Space Temperature (Units: °C).1'</li><li>'Tiong Bahru Plaza, VAV-19-3, Space Temperature (Units: °C)'</li><li>'Tiong Bahru Plaza, VAV 19-17A, Space Temperature (Units: °C)'</li></ul> |
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| 11 | <ul><li>'DDC-CH-3:CH 3 Start/Stop Control'</li><li>'Tiong Bahru Plaza, DDC-L1-4, PAU-L1-05 Start/Stop Control'</li><li>'Tiong Bahru Plaza, DDC L14-1, AHU12-1 Start/Stop'</li></ul> |
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| 32 | <ul><li>'Tiong Bahru Plaza, UC800_101001_Chiller_1, Chilled Water Flow'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Chiller 4 CHW Flowrate'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Chiller 3 CHW Flowrate'</li></ul> |
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| 27 | <ul><li>'Tiong Bahru Plaza, LSB-U4-1, Active Power kW'</li><li>'Tiong Bahru Plaza, MB-1-S2, Active Power kW'</li><li>'Tiong Bahru Plaza, MB-1-S3, Active Power kW'</li></ul> |
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| 41 | <ul><li>' Chiller SC:Header Differential Pressure'</li></ul> |
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| 40 | <ul><li>'Wet Bulb Temperature'</li></ul> |
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| 3 | <ul><li>'Tiong Bahru Plaza, DDC-L6, AHU 4-1 FAD Control'</li><li>'Tiong Bahru Plaza, DDC-L20, L20 Fresh air damper control (Units: %)'</li><li>'Tiong Bahru Plaza, DDC-L2-2, AHU-L2-05 FAD control (Units: %)'</li></ul> |
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| 22 | <ul><li>'Tiong Bahru Plaza, VAV 19-2, Active Setpoint (Units: °C)'</li><li>'Tiong Bahru Plaza, VAV 18-17, Active Setpoint (Units: °C)'</li><li>'Tiong Bahru Plaza, VAV 19-16, Active Setpoint (Units: °C)'</li></ul> |
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| 21 | <ul><li>'Tiong Bahru Plaza, VAV 19-19, Air Flow Setpoint Active (Units: m3/h)'</li><li>'Tiong Bahru Plaza, VAV 19-22, Air Flow Setpoint Active (Units: m3/h)'</li><li>'Tiong Bahru Plaza, VAV 19-9, Air Flow Setpoint Active (Units: m3/h)'</li></ul> |
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| 26 | <ul><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CHWS Temperature'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CHWS Temperature'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CHWS Temp'</li></ul> |
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| 23 | <ul><li>'Tiong Bahru Plaza, DDC B1-3, Pau-B1-02-DP Sensor'</li></ul> |
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| 16 | <ul><li>'Tiong Bahru Plaza, VAV 19-9, Discharge Air Flow (Units: m3/h)'</li><li>'Tiong Bahru Plaza, VAV 19-18, Discharge Air Flow (Units: m3/h)'</li><li>'Tiong Bahru Plaza, VAV 19-15, Discharge Air Flow (Units: m3/h)'</li></ul> |
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| 39 | <ul><li>'Tiong Bahru Plaza, UC800_3, Evaporator Refrigerant Pressure - Circuit 1 (Units: Pa)'</li><li>'Tiong Bahru Plaza, UC800_102002_Chiller_2, Evaporator Refrigerant Pressure - Circuit 1 (Units: Pa)'</li><li>'Tiong Bahru Plaza, UC800_101001_Chiller_1, Evaporator Refrigerant Pressure - Circuit 1 (Units: Pa)'</li></ul> |
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| 7 | <ul><li>'Tiong Bahru Plaza, DDC L4-1, PAU-L4-02 modulating valve feedback (Units: %).3'</li><li>'Tiong Bahru Plaza, DDC-L2-2, AHU-L2-05 modulating valve feedback'</li><li>'Tiong Bahru Plaza, DDC B1-4, PAU-B1-1 modulating valve feedback'</li></ul> |
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| 37 | <ul><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Chiller 4 CWS Temperature'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CWS Temperature'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Chiller 3 CWS Temperature'</li></ul> |
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| 20 | <ul><li>'Tiong Bahru Plaza, DDC_L4-3, Outdoor temperature (Units: °C)'</li><li>'Tiong Bahru Plaza, DDC_L4-3, Outdoor temperature (Units: °C).2'</li><li>'Tiong Bahru Plaza, DDC_L4-3, Outdoor temperature (Units: °C).1'</li></ul> |
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| 35 | <ul><li>'Tiong Bahru Plaza, UC800_102002_Chiller_2, Condenser Saturated Refrigerant Temperature Circuit 1 (Units: °C)'</li><li>'Tiong Bahru Plaza, UC800_3, Condenser Saturated Refrigerant Temperature Circuit 1 (Units: °C)'</li><li>'Tiong Bahru Plaza, UC800_102002_Chiller_2, Evaporator Saturated Refrigerant Temperature - Circuit 1 (Units: °C)'</li></ul> |
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| 4 | <ul><li>'Tiong Bahru Plaza, DDC-9-1, AHU 9-1 Flow'</li><li>'Tiong Bahru Plaza, DDC-L6, AHU 5-3 Flow'</li><li>'Tiong Bahru Plaza, DDC-L6, AHU 4-1 Flow'</li></ul> |
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106 |
+
| 19 | <ul><li>'Tiong Bahru Plaza, DDC_L4-3, Outdoor humidity (Units: %).2'</li><li>'Tiong Bahru Plaza, DDC_L4-3, Outdoor humidity (Units: %)'</li><li>'Tiong Bahru Plaza, DDC_L4-3, Outdoor humidity (Units: %).1'</li></ul> |
|
107 |
+
| 38 | <ul><li>'Tiong Bahru Plaza, UC800_3, Entering Condenser Water (Units: °C)'</li><li>'Tiong Bahru Plaza, UC800_102002_Chiller_2, Entering Condenser Water (Units: °C)'</li><li>'Tiong Bahru Plaza, UC800_101001_Chiller_1, Entering Condenser Water (Units: °C)'</li></ul> |
|
108 |
+
| 10 | <ul><li>'Tiong Bahru Plaza, DDC-L1-3, AHU-L2-02 VSD feedback'</li><li>'DDC-CH-3:CWP 3 VSD Feedback'</li><li>'Tiong Bahru Plaza, DDC-L2-8, PAU-L2-03 VSD feedback'</li></ul> |
|
109 |
+
|
110 |
+
## Evaluation
|
111 |
+
|
112 |
+
### Metrics
|
113 |
+
| Label | Accuracy |
|
114 |
+
|:--------|:---------|
|
115 |
+
| **all** | 0.9864 |
|
116 |
+
|
117 |
+
## Uses
|
118 |
+
|
119 |
+
### Direct Use for Inference
|
120 |
+
|
121 |
+
First install the SetFit library:
|
122 |
+
|
123 |
+
```bash
|
124 |
+
pip install setfit
|
125 |
+
```
|
126 |
+
|
127 |
+
Then you can load this model and run inference.
|
128 |
+
|
129 |
+
```python
|
130 |
+
from setfit import SetFitModel
|
131 |
+
|
132 |
+
# Download from the 🤗 Hub
|
133 |
+
model = SetFitModel.from_pretrained("Varun1010/all-MiniLM-L6-v2-polaris-more")
|
134 |
+
# Run inference
|
135 |
+
preds = model("Tiong Bahru Plaza, DDC-L20, AHU 20-1 VSD CONTROL")
|
136 |
+
```
|
137 |
+
|
138 |
+
<!--
|
139 |
+
### Downstream Use
|
140 |
+
|
141 |
+
*List how someone could finetune this model on their own dataset.*
|
142 |
+
-->
|
143 |
+
|
144 |
+
<!--
|
145 |
+
### Out-of-Scope Use
|
146 |
+
|
147 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
148 |
+
-->
|
149 |
+
|
150 |
+
<!--
|
151 |
+
## Bias, Risks and Limitations
|
152 |
+
|
153 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
154 |
+
-->
|
155 |
+
|
156 |
+
<!--
|
157 |
+
### Recommendations
|
158 |
+
|
159 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
160 |
+
-->
|
161 |
+
|
162 |
+
## Training Details
|
163 |
+
|
164 |
+
### Training Set Metrics
|
165 |
+
| Training set | Min | Median | Max |
|
166 |
+
|:-------------|:----|:-------|:----|
|
167 |
+
| Word count | 3 | 8.7134 | 13 |
|
168 |
+
|
169 |
+
| Label | Training Sample Count |
|
170 |
+
|:------|:----------------------|
|
171 |
+
| 0 | 4 |
|
172 |
+
| 1 | 4 |
|
173 |
+
| 2 | 4 |
|
174 |
+
| 3 | 4 |
|
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+
| 4 | 4 |
|
176 |
+
| 5 | 4 |
|
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+
| 6 | 4 |
|
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+
| 7 | 4 |
|
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+
| 8 | 4 |
|
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+
| 9 | 4 |
|
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+
| 10 | 4 |
|
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+
| 11 | 4 |
|
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+
| 12 | 4 |
|
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+
| 13 | 4 |
|
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+
| 14 | 4 |
|
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+
| 15 | 4 |
|
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+
| 16 | 4 |
|
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+
| 17 | 4 |
|
189 |
+
| 18 | 4 |
|
190 |
+
| 19 | 3 |
|
191 |
+
| 20 | 3 |
|
192 |
+
| 21 | 4 |
|
193 |
+
| 22 | 4 |
|
194 |
+
| 23 | 1 |
|
195 |
+
| 24 | 1 |
|
196 |
+
| 25 | 4 |
|
197 |
+
| 26 | 4 |
|
198 |
+
| 27 | 4 |
|
199 |
+
| 28 | 4 |
|
200 |
+
| 29 | 4 |
|
201 |
+
| 30 | 3 |
|
202 |
+
| 31 | 3 |
|
203 |
+
| 32 | 4 |
|
204 |
+
| 33 | 4 |
|
205 |
+
| 34 | 4 |
|
206 |
+
| 35 | 4 |
|
207 |
+
| 36 | 4 |
|
208 |
+
| 37 | 4 |
|
209 |
+
| 38 | 3 |
|
210 |
+
| 39 | 3 |
|
211 |
+
| 40 | 1 |
|
212 |
+
| 41 | 1 |
|
213 |
+
| 42 | 3 |
|
214 |
+
| 43 | 4 |
|
215 |
+
|
216 |
+
### Training Hyperparameters
|
217 |
+
- batch_size: (64, 64)
|
218 |
+
- num_epochs: (5, 5)
|
219 |
+
- max_steps: -1
|
220 |
+
- sampling_strategy: oversampling
|
221 |
+
- body_learning_rate: (2e-05, 1e-05)
|
222 |
+
- head_learning_rate: 0.01
|
223 |
+
- loss: CosineSimilarityLoss
|
224 |
+
- distance_metric: cosine_distance
|
225 |
+
- margin: 0.25
|
226 |
+
- end_to_end: False
|
227 |
+
- use_amp: False
|
228 |
+
- warmup_proportion: 0.1
|
229 |
+
- seed: 42
|
230 |
+
- eval_max_steps: -1
|
231 |
+
- load_best_model_at_end: False
|
232 |
+
|
233 |
+
### Training Results
|
234 |
+
| Epoch | Step | Training Loss | Validation Loss |
|
235 |
+
|:------:|:----:|:-------------:|:---------------:|
|
236 |
+
| 0.0027 | 1 | 0.1538 | - |
|
237 |
+
| 0.1330 | 50 | 0.0495 | - |
|
238 |
+
| 0.2660 | 100 | 0.0655 | - |
|
239 |
+
| 0.3989 | 150 | 0.0336 | - |
|
240 |
+
| 0.5319 | 200 | 0.0282 | - |
|
241 |
+
| 0.6649 | 250 | 0.0207 | - |
|
242 |
+
| 0.7979 | 300 | 0.0184 | - |
|
243 |
+
| 0.9309 | 350 | 0.0163 | - |
|
244 |
+
| 1.0638 | 400 | 0.0088 | - |
|
245 |
+
| 1.1968 | 450 | 0.0307 | - |
|
246 |
+
| 1.3298 | 500 | 0.0153 | - |
|
247 |
+
| 1.4628 | 550 | 0.0079 | - |
|
248 |
+
| 1.5957 | 600 | 0.02 | - |
|
249 |
+
| 1.7287 | 650 | 0.0165 | - |
|
250 |
+
| 1.8617 | 700 | 0.0087 | - |
|
251 |
+
| 1.9947 | 750 | 0.0236 | - |
|
252 |
+
| 2.1277 | 800 | 0.0108 | - |
|
253 |
+
| 2.2606 | 850 | 0.0071 | - |
|
254 |
+
| 2.3936 | 900 | 0.0137 | - |
|
255 |
+
| 2.5266 | 950 | 0.0104 | - |
|
256 |
+
| 2.6596 | 1000 | 0.0054 | - |
|
257 |
+
| 2.7926 | 1050 | 0.0058 | - |
|
258 |
+
| 2.9255 | 1100 | 0.0052 | - |
|
259 |
+
| 3.0585 | 1150 | 0.0053 | - |
|
260 |
+
| 3.1915 | 1200 | 0.004 | - |
|
261 |
+
| 3.3245 | 1250 | 0.0047 | - |
|
262 |
+
| 3.4574 | 1300 | 0.0176 | - |
|
263 |
+
| 3.5904 | 1350 | 0.0046 | - |
|
264 |
+
| 3.7234 | 1400 | 0.0139 | - |
|
265 |
+
| 3.8564 | 1450 | 0.0043 | - |
|
266 |
+
| 3.9894 | 1500 | 0.0042 | - |
|
267 |
+
| 4.1223 | 1550 | 0.0112 | - |
|
268 |
+
| 4.2553 | 1600 | 0.0091 | - |
|
269 |
+
| 4.3883 | 1650 | 0.0045 | - |
|
270 |
+
| 4.5213 | 1700 | 0.009 | - |
|
271 |
+
| 4.6543 | 1750 | 0.0097 | - |
|
272 |
+
| 4.7872 | 1800 | 0.0049 | - |
|
273 |
+
| 4.9202 | 1850 | 0.0036 | - |
|
274 |
+
|
275 |
+
### Framework Versions
|
276 |
+
- Python: 3.10.12
|
277 |
+
- SetFit: 1.0.3
|
278 |
+
- Sentence Transformers: 2.6.1
|
279 |
+
- Transformers: 4.38.2
|
280 |
+
- PyTorch: 2.2.1+cu121
|
281 |
+
- Datasets: 2.18.0
|
282 |
+
- Tokenizers: 0.15.2
|
283 |
+
|
284 |
+
## Citation
|
285 |
+
|
286 |
+
### BibTeX
|
287 |
+
```bibtex
|
288 |
+
@article{https://doi.org/10.48550/arxiv.2209.11055,
|
289 |
+
doi = {10.48550/ARXIV.2209.11055},
|
290 |
+
url = {https://arxiv.org/abs/2209.11055},
|
291 |
+
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
|
292 |
+
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
|
293 |
+
title = {Efficient Few-Shot Learning Without Prompts},
|
294 |
+
publisher = {arXiv},
|
295 |
+
year = {2022},
|
296 |
+
copyright = {Creative Commons Attribution 4.0 International}
|
297 |
+
}
|
298 |
+
```
|
299 |
+
|
300 |
+
<!--
|
301 |
+
## Glossary
|
302 |
+
|
303 |
+
*Clearly define terms in order to be accessible across audiences.*
|
304 |
+
-->
|
305 |
+
|
306 |
+
<!--
|
307 |
+
## Model Card Authors
|
308 |
+
|
309 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
310 |
+
-->
|
311 |
+
|
312 |
+
<!--
|
313 |
+
## Model Card Contact
|
314 |
+
|
315 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
316 |
+
-->
|
config.json
ADDED
@@ -0,0 +1,26 @@
|
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|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "sentence-transformers/paraphrase-MiniLM-L3-v2",
|
3 |
+
"architectures": [
|
4 |
+
"BertModel"
|
5 |
+
],
|
6 |
+
"attention_probs_dropout_prob": 0.1,
|
7 |
+
"classifier_dropout": null,
|
8 |
+
"gradient_checkpointing": false,
|
9 |
+
"hidden_act": "gelu",
|
10 |
+
"hidden_dropout_prob": 0.1,
|
11 |
+
"hidden_size": 384,
|
12 |
+
"initializer_range": 0.02,
|
13 |
+
"intermediate_size": 1536,
|
14 |
+
"layer_norm_eps": 1e-12,
|
15 |
+
"max_position_embeddings": 512,
|
16 |
+
"model_type": "bert",
|
17 |
+
"num_attention_heads": 12,
|
18 |
+
"num_hidden_layers": 3,
|
19 |
+
"pad_token_id": 0,
|
20 |
+
"position_embedding_type": "absolute",
|
21 |
+
"torch_dtype": "float32",
|
22 |
+
"transformers_version": "4.38.2",
|
23 |
+
"type_vocab_size": 2,
|
24 |
+
"use_cache": true,
|
25 |
+
"vocab_size": 30522
|
26 |
+
}
|
config_sentence_transformers.json
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
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|
1 |
+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "2.0.0",
|
4 |
+
"transformers": "4.7.0",
|
5 |
+
"pytorch": "1.9.0+cu102"
|
6 |
+
},
|
7 |
+
"prompts": {},
|
8 |
+
"default_prompt_name": null
|
9 |
+
}
|
config_setfit.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
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 sha256:6a95a2f416768e0050891704a228b9ec4ba90839c91ba3b058f13918f07aa0c1
|
3 |
+
size 69565312
|
model_head.pkl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:a5679d900f1fa8fa87531403ef44f6c3ef24e65ad6596d627c4fd98cc109e142
|
3 |
+
size 136711
|
modules.json
ADDED
@@ -0,0 +1,14 @@
|
|
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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 |
+
]
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 128,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
special_tokens_map.json
ADDED
@@ -0,0 +1,37 @@
|
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|
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|
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|
3 |
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|
4 |
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|
5 |
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|
6 |
+
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|
7 |
+
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|
8 |
+
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|
9 |
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|
10 |
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|
11 |
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|
12 |
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"normalized": false,
|
13 |
+
"rstrip": false,
|
14 |
+
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|
15 |
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|
16 |
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"pad_token": {
|
17 |
+
"content": "[PAD]",
|
18 |
+
"lstrip": false,
|
19 |
+
"normalized": false,
|
20 |
+
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|
21 |
+
"single_word": false
|
22 |
+
},
|
23 |
+
"sep_token": {
|
24 |
+
"content": "[SEP]",
|
25 |
+
"lstrip": false,
|
26 |
+
"normalized": false,
|
27 |
+
"rstrip": false,
|
28 |
+
"single_word": false
|
29 |
+
},
|
30 |
+
"unk_token": {
|
31 |
+
"content": "[UNK]",
|
32 |
+
"lstrip": false,
|
33 |
+
"normalized": false,
|
34 |
+
"rstrip": false,
|
35 |
+
"single_word": false
|
36 |
+
}
|
37 |
+
}
|
tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,64 @@
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|
1 |
+
{
|
2 |
+
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|
3 |
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|
4 |
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|
5 |
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|
6 |
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|
7 |
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|
8 |
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|
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|
10 |
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|
11 |
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|
12 |
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|
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|
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|
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|
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|
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|
18 |
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|
19 |
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|
20 |
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|
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|
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|
23 |
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|
24 |
+
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|
25 |
+
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|
26 |
+
},
|
27 |
+
"102": {
|
28 |
+
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|
29 |
+
"lstrip": false,
|
30 |
+
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|
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 |
+
"max_length": 128,
|
50 |
+
"model_max_length": 512,
|
51 |
+
"never_split": null,
|
52 |
+
"pad_to_multiple_of": null,
|
53 |
+
"pad_token": "[PAD]",
|
54 |
+
"pad_token_type_id": 0,
|
55 |
+
"padding_side": "right",
|
56 |
+
"sep_token": "[SEP]",
|
57 |
+
"stride": 0,
|
58 |
+
"strip_accents": null,
|
59 |
+
"tokenize_chinese_chars": true,
|
60 |
+
"tokenizer_class": "BertTokenizer",
|
61 |
+
"truncation_side": "right",
|
62 |
+
"truncation_strategy": "longest_first",
|
63 |
+
"unk_token": "[UNK]"
|
64 |
+
}
|
vocab.txt
ADDED
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|