mann2107/BCMPIIRABSetFit
015
1---2library_name: setfit3tags:4- setfit5- sentence-transformers6- text-classification7- generated_from_setfit_trainer8base_model: sentence-transformers/paraphrase-mpnet-base-v29metrics:10- accuracy11widget:12- text: Thank you for your email. Please go ahead and issue. Please invoice in KES13- text: Hi, We are missing some invoices, can you please provide it. 02 - 12 - 202014 AGENT FEE 8900784339018 $21.00 02 - 19 - 2020 AGENT FEE 0017417554160 $22.00 0215 - 19 - 2020 AGENT FEE 0017417554143 $22.00 02 - 19 - 2020 AGENT FEE 890078338342016 $21.0017- text: I have reported this in November and not only was the trip supposed to be18 cancelled and credited I was double billed and the billing has not been corrected.19 The total credit should be $667.20. Please confirm this will be done.20- text: As promised, kindly send the ticket. Dr Ntlatlapa wants to plan for a meeting21 while in Durban.22- text: Amy Pengidore had planned to travel from Washington, DC to Chicago, IL next23 week and due to the coronavirus concerns we are looking to re-schedule her trip24 for a future date. She had airfare, car rental, and hotel scheduled and was to25 leave this Sunday, March 15th. Can you please direct us on what needs to be done26 to make changes?27pipeline_tag: text-classification28inference: true29---30 31# SetFit with sentence-transformers/paraphrase-mpnet-base-v232 33This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification.34 35The model has been trained using an efficient few-shot learning technique that involves:36 371. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.382. Training a classification head with features from the fine-tuned Sentence Transformer.39 40## Model Details41 42### Model Description43- **Model Type:** SetFit44- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)45- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance46- **Maximum Sequence Length:** 512 tokens47- **Number of Classes:** 9 classes48<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->49<!-- - **Language:** Unknown -->50<!-- - **License:** Unknown -->51 52### Model Sources53 54- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)55- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)56- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)57 58### Model Labels59| Label | Examples |60|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|61| 0 | <ul><li>'Please send me quotation for a flight for Lindelani Mkhize - East London/ Durban 31 August @ 12:00'</li><li>'I need to go to Fort Smith AR via XNA for PD days. I d like to take AA 4064 at 10:00 am arriving 11:58 am on Monday, May 11 returning on AA 4064 at 12:26 pm arriving 2:16 pm on Saturday May 16. I will need a Hertz rental. I d like to stay at the Courtyard Marriott in Fort Smith on Monday through Thursday nights checking out on Friday morning. Then I d like to stay at the Hilton Garden Inn in Bentonville AR on Walton Road Friday night checking out Saturday morning.'</li><li>'I am planning to attend a Training in to be held between Nov 22-24 2023 at Avon, France (Specific address is Corning, 7 Bis Av. de Valvins, 77210 Avon, France) I have to arrive in France on the 21st of Nov and leave on the 25th of Nov. May you please help me with the travel itinerary and accommodation quotation (within walking distance preferably), transport in France to the hotel from the airport and back. I would like to put in an overseas travel request.'</li></ul> |62| 1 | <ul><li>"Hello, Can someone help to cancel my trip in Concur? I'm unable to do it in the system. Trip from San Francisco to Minneapolis/St Paul (MDFNTI)<https://www.concursolutions.com/travelportal/triplibrary.asp>"</li><li>'Please cancel my flight for late March to Chicago and DC. Meetings have been cancelled. I am not available by phone.'</li><li>'I need to cancel the below trip due to illness in family. Could you please assist with this?'</li></ul> |63| 2 | <ul><li>'I have a travel booking question. I booked a flight for myself & a coworker, however, it was requested that we leave a couple days earlier than planned. How can I revise/move our first flight up without cancelling the whole trip? The flights home will remain the same.'</li><li>'I just received my KTN today and added it to my profile. However, I have flights in Nov booked with United and Delta. Any way to add the KTN to those reservations so the tickets come through with Pre-Check?'</li><li>"Lerato I checked Selbourne B/B, its not a nice place. Your colleague Stella booked Lindelani Mkhize in Hempston it's a beautiful place next to Garden Court, please change the accommodation from Selbourne to Hempston. This Selbourne is on the outskirt and my colleagues are not familiar with East London"</li></ul> |64| 3 | <ul><li>'Please add the below employee to our Concur system. In addition, make sure the Ghost Card is added into their profile. Lindsay Griffin lgriffin@arlingtonroe.com'</li><li>"Good afternoon - CAEP has 4 new staff members that we'd like to set - up new user profiles for. Please see the below information and let me know should anything additional be required. Last First Middle Travel Class Email Gender DOB Graham Rose - Helen Xiuqing Staff rose - helen.graham@caepnet.org Female 6/14/1995 Gumbs Mary - Frances Akua Staff mary.gumbs@caepnet.org Female 10/18/1995 Lee Elizabeth Andie Staff liz.lee@caepnet.org Female 4/23/1991 Gilchrist Gabriel Jake Staff gabriel.gilchrist@caepnet.org Male"</li><li>'Good Morning, Please create a profile for Amelia West: Name: Amelia Jean - Danielle West DOB: 05/21/1987 PH: 202 - 997 - 6592 Email: asuermann@facs.org'</li></ul> |65| 4 | <ul><li>'Invoices October 2019 Hi, My name is Lucia De Las Heras property accountant at Trion Properties. I am missing a few receipts to allocate the following charges. Would you please be able to provide a detailed invoice? 10/10/2019 FROSCH/GANT TRAVEL MBLOOMINGTON IN - 21'</li><li>'I would like to request an invoice/s for the above-mentioned employee who stayed at your establishment. Thank you for the other invoice August 2023 & the confirmation for the new reservation 01st - 04th October 2023, Thanking you in Advance!'</li><li>"Hello, Looking for an invoice for the below charge to Ryan Schulke's card - could you please assist? Vendor: United Airlines Transaction Date: 02/04/2020 Amount: $2,132.07 Ticket Number: 0167515692834"</li></ul> |66| 5 | <ul><li>'This is the second email with this trip, but I still need an itinerary for trip scheduled for January 27. Derek'</li><li>'Please send us all the flights used by G4S Kenya in the year 2022. Sorry for the short notice but we need the information by 12:00 noon today.'</li><li>'Jen Holt Can you please send me the itinerary for Jen Holt for this trip this week to Jackson Mississippi?'</li></ul> |67| 6 | <ul><li>"I've had to call off my vacation. What are my options for getting refunded?"</li><li>"Looks like I won't be traveling due to some health issues. Is getting a refund for my booking possible?"</li><li>"I've fallen ill and can't travel as planned. Can you process a refund for me?"</li></ul> |68| 7 | <ul><li>'The arrangements as stated are acceptable. Please go ahead and confirm all bookings accordingly.'</li><li>"I've reviewed the details and everything seems in order. Please proceed with the booking."</li><li>'This travel plan is satisfactory. Please secure the necessary reservations.'</li></ul> |69| 8 | <ul><li>'I need some clarification on charges for a rebooked flight. It seems higher than anticipated. Who can provide more details?'</li><li>'Wishing you and your family a very Merry Christmas and a Happy and Healthy New Year. I have one unidentified item this month, hope you can help, and as always thanks in advance. Very limited information on this. 11/21/2019 #N/A #N/A #N/A 142.45 Rail Europe North Amer'</li><li>"We've identified a mismatch between our booking records and credit card statement. Who can assist with this issue?"</li></ul> |70 71## Uses72 73### Direct Use for Inference74 75First install the SetFit library:76 77```bash78pip install setfit79```80 81Then you can load this model and run inference.82 83```python84from setfit import SetFitModel85 86# Download from the 🤗 Hub87model = SetFitModel.from_pretrained("mann2107/BCMPIIRABSetFit")88# Run inference89preds = model("Thank you for your email. Please go ahead and issue. Please invoice in KES")90```91 92<!--93### Downstream Use94 95*List how someone could finetune this model on their own dataset.*96-->97 98<!--99### Out-of-Scope Use100 101*List how the model may foreseeably be misused and address what users ought not to do with the model.*102-->103 104<!--105## Bias, Risks and Limitations106 107*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*108-->109 110<!--111### Recommendations112 113*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*114-->115 116## Training Details117 118### Training Set Metrics119| Training set | Min | Median | Max |120|:-------------|:----|:--------|:----|121| Word count | 1 | 30.4097 | 124 |122 123| Label | Training Sample Count |124|:------|:----------------------|125| 0 | 16 |126| 1 | 16 |127| 2 | 16 |128| 3 | 16 |129| 4 | 16 |130| 5 | 16 |131| 6 | 16 |132| 7 | 16 |133| 8 | 16 |134 135### Training Hyperparameters136- batch_size: (16, 2)137- num_epochs: (1, 1)138- max_steps: -1139- sampling_strategy: oversampling140- body_learning_rate: (2e-05, 1e-05)141- head_learning_rate: 0.01142- loss: CosineSimilarityLoss143- distance_metric: cosine_distance144- margin: 0.25145- end_to_end: True146- use_amp: False147- warmup_proportion: 0.1148- max_length: 512149- seed: 42150- eval_max_steps: -1151- load_best_model_at_end: True152 153### Training Results154| Epoch | Step | Training Loss | Validation Loss |155|:-------:|:--------:|:-------------:|:---------------:|156| 0.0009 | 1 | 0.2058 | - |157| 0.0434 | 50 | 0.1316 | - |158| 0.0868 | 100 | 0.0328 | - |159| 0.1302 | 150 | 0.0038 | - |160| 0.1736 | 200 | 0.0018 | - |161| 0.2170 | 250 | 0.0009 | - |162| 0.2604 | 300 | 0.002 | - |163| 0.3038 | 350 | 0.0008 | - |164| 0.3472 | 400 | 0.0006 | - |165| 0.3906 | 450 | 0.001 | - |166| 0.4340 | 500 | 0.0011 | - |167| 0.4774 | 550 | 0.0005 | - |168| 0.5208 | 600 | 0.0009 | - |169| 0.5642 | 650 | 0.0003 | - |170| 0.6076 | 700 | 0.0002 | - |171| 0.6510 | 750 | 0.0003 | - |172| 0.6944 | 800 | 0.0009 | - |173| 0.7378 | 850 | 0.0002 | - |174| 0.7812 | 900 | 0.0002 | - |175| 0.8247 | 950 | 0.0002 | - |176| 0.8681 | 1000 | 0.0004 | - |177| 0.9115 | 1050 | 0.0002 | - |178| 0.9549 | 1100 | 0.0003 | - |179| 0.9983 | 1150 | 0.0003 | - |180| **1.0** | **1152** | **-** | **0.0699** |181 182* The bold row denotes the saved checkpoint.183### Framework Versions184- Python: 3.9.16185- SetFit: 1.1.0.dev0186- Sentence Transformers: 2.2.2187- Transformers: 4.21.3188- PyTorch: 1.12.1+cu116189- Datasets: 2.4.0190- Tokenizers: 0.12.1191 192## Citation193 194### BibTeX195```bibtex196@article{https://doi.org/10.48550/arxiv.2209.11055,197 doi = {10.48550/ARXIV.2209.11055},198 url = {https://arxiv.org/abs/2209.11055},199 author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},200 keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},201 title = {Efficient Few-Shot Learning Without Prompts},202 publisher = {arXiv},203 year = {2022},204 copyright = {Creative Commons Attribution 4.0 International}205}206```207 208<!--209## Glossary210 211*Clearly define terms in order to be accessible across audiences.*212-->213 214<!--215## Model Card Authors216 217*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*218-->219 220<!--221## Model Card Contact222 223*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*224-->