mann2107/BCMPIIRAB
05
1---2library_name: setfit3tags:4- setfit5- sentence-transformers6- text-classification7- generated_from_setfit_trainer8base_model: sentence-transformers/all-MiniLM-L6-v29metrics:10- accuracy11widget:12- text: The itinerary meets our requirements, please book as proposed.13- text: 'Please may you kindly send us the invoices for our stay at the Protea hotel14 in Cape Town from 20/07/2023 - 22/07/2023. The four confirmation numbers from15 the vouchers are as follows: 74733068 74731210 74729566 74727187'16- text: Can you please tell me if this Flight ticket for Shaun Connolly was charge17 to the LBP travel card.18- text: I am very confused on the itineraries I've received for Michelle Curtin. Can19 you please send me an updated itinerary with her actual travel schedule?20- text: I got a call late Friday afternoon to move our meeting of today. The rental21 company was supposed to drop off the car for me at 13:00. Can you please call22 and find out if they can deliver it before 10 this morning. Preferably 9. Sorry23 for the inconvenience.24pipeline_tag: text-classification25inference: true26model-index:27- name: SetFit with sentence-transformers/all-MiniLM-L6-v228 results:29 - task:30 type: text-classification31 name: Text Classification32 dataset:33 name: Unknown34 type: unknown35 split: test36 metrics:37 - type: accuracy38 value: 0.87539 name: Accuracy40---41 42# SetFit with sentence-transformers/all-MiniLM-L6-v243 44This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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.45 46The model has been trained using an efficient few-shot learning technique that involves:47 481. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.492. Training a classification head with features from the fine-tuned Sentence Transformer.50 51## Model Details52 53### Model Description54- **Model Type:** SetFit55- **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)56- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance57- **Maximum Sequence Length:** 256 tokens58- **Number of Classes:** 9 classes59<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->60<!-- - **Language:** Unknown -->61<!-- - **License:** Unknown -->62 63### Model Sources64 65- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)66- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)67- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)68 69### Model Labels70| Label | Examples |71|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|72| 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> |73| 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> |74| 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> |75| 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> |76| 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> |77| 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> |78| 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> |79| 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> |80| 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> |81 82## Evaluation83 84### Metrics85| Label | Accuracy |86|:--------|:---------|87| **all** | 0.875 |88 89## Uses90 91### Direct Use for Inference92 93First install the SetFit library:94 95```bash96pip install setfit97```98 99Then you can load this model and run inference.100 101```python102from setfit import SetFitModel103 104# Download from the 🤗 Hub105model = SetFitModel.from_pretrained("mann2107/BCMPIIRAB")106# Run inference107preds = model("The itinerary meets our requirements, please book as proposed.")108```109 110<!--111### Downstream Use112 113*List how someone could finetune this model on their own dataset.*114-->115 116<!--117### Out-of-Scope Use118 119*List how the model may foreseeably be misused and address what users ought not to do with the model.*120-->121 122<!--123## Bias, Risks and Limitations124 125*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*126-->127 128<!--129### Recommendations130 131*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*132-->133 134## Training Details135 136### Training Set Metrics137| Training set | Min | Median | Max |138|:-------------|:----|:--------|:----|139| Word count | 1 | 30.4097 | 124 |140 141| Label | Training Sample Count |142|:------|:----------------------|143| 0 | 16 |144| 1 | 16 |145| 2 | 16 |146| 3 | 16 |147| 4 | 16 |148| 5 | 16 |149| 6 | 16 |150| 7 | 16 |151| 8 | 16 |152 153### Training Hyperparameters154- batch_size: (16, 16)155- num_epochs: (1, 1)156- max_steps: -1157- sampling_strategy: oversampling158- body_learning_rate: (2e-05, 1e-05)159- head_learning_rate: 0.01160- loss: CosineSimilarityLoss161- distance_metric: cosine_distance162- margin: 0.25163- end_to_end: False164- use_amp: False165- warmup_proportion: 0.1166- seed: 42167- eval_max_steps: -1168- load_best_model_at_end: True169 170### Training Results171| Epoch | Step | Training Loss | Validation Loss |172|:-------:|:--------:|:-------------:|:---------------:|173| 0.0009 | 1 | 0.1977 | - |174| 0.0434 | 50 | 0.1642 | - |175| 0.0868 | 100 | 0.1034 | - |176| 0.1302 | 150 | 0.05 | - |177| 0.1736 | 200 | 0.0177 | - |178| 0.2170 | 250 | 0.0128 | - |179| 0.2604 | 300 | 0.0148 | - |180| 0.3038 | 350 | 0.0109 | - |181| 0.3472 | 400 | 0.0059 | - |182| 0.3906 | 450 | 0.004 | - |183| 0.4340 | 500 | 0.0036 | - |184| 0.4774 | 550 | 0.0064 | - |185| 0.5208 | 600 | 0.0042 | - |186| 0.5642 | 650 | 0.002 | - |187| 0.6076 | 700 | 0.0017 | - |188| 0.6510 | 750 | 0.002 | - |189| 0.6944 | 800 | 0.0026 | - |190| 0.7378 | 850 | 0.0019 | - |191| 0.7812 | 900 | 0.0017 | - |192| 0.8247 | 950 | 0.0017 | - |193| 0.8681 | 1000 | 0.0015 | - |194| 0.9115 | 1050 | 0.0009 | - |195| 0.9549 | 1100 | 0.002 | - |196| 0.9983 | 1150 | 0.0008 | - |197| **1.0** | **1152** | **-** | **0.0732** |198 199* The bold row denotes the saved checkpoint.200### Framework Versions201- Python: 3.9.16202- SetFit: 1.1.0.dev0203- Sentence Transformers: 2.2.2204- Transformers: 4.21.3205- PyTorch: 1.12.1+cu116206- Datasets: 2.4.0207- Tokenizers: 0.12.1208 209## Citation210 211### BibTeX212```bibtex213@article{https://doi.org/10.48550/arxiv.2209.11055,214 doi = {10.48550/ARXIV.2209.11055},215 url = {https://arxiv.org/abs/2209.11055},216 author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},217 keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},218 title = {Efficient Few-Shot Learning Without Prompts},219 publisher = {arXiv},220 year = {2022},221 copyright = {Creative Commons Attribution 4.0 International}222}223```224 225<!--226## Glossary227 228*Clearly define terms in order to be accessible across audiences.*229-->230 231<!--232## Model Card Authors233 234*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*235-->236 237<!--238## Model Card Contact239 240*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*241-->