---
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Thank you for your email. Please go ahead and issue. Please invoice in KES
- text: Hi, We are missing some invoices, can you please provide it. 02 - 12 - 2020
AGENT FEE 8900784339018 $21.00 02 - 19 - 2020 AGENT FEE 0017417554160 $22.00 02
- 19 - 2020 AGENT FEE 0017417554143 $22.00 02 - 19 - 2020 AGENT FEE 8900783383420
$21.00
- text: We need your assistance with the payment for the recent office supplies order.
Let us know once it's done.
- text: I have reported this in November and not only was the trip supposed to be
cancelled and credited I was double billed and the billing has not been corrected.
The total credit should be $667.20. Please confirm this will be done.
- text: The invoice for the travel arrangements needs to be settled. Kindly provide
payment confirmation.
inference: true
---
# SetFit with sentence-transformers/all-MiniLM-L6-v2
This 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.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 14 classes
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | - 'Please send me quotation for a flight for Lindelani Mkhize - East London/ Durban 31 August @ 12:00'
- "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."
- 'Can you please send me flight quotations for Mr Mthetho Sovara for travel to Bologna, Italy as per details below: 7 Oct: JHB to Bologna, Italy 14 Oct: Bologna, Italy to JHB'
|
| 1 | - 'I need to cancel my flight booking from London Heathrow to JFK, New York, scheduled for August 15th, 2024. The booking reference is XJ12345.'
- 'Please cancel my flight for late March to Chicago and DC. Meetings have been cancelled. I am not available by phone.'
- 'I need to cancel the below trip due to illness in family. Could you please assist with this?'
|
| 2 | - 'I need to change the departure time for my one-way flight from SFO to LAX on October 15th. Could you please reschedule it to a later flight around 6:00 PM on the same day?'
- 'Can you please extend my hotel reservation at the Marriott in Denver from November 19th to November 23rd, 2024? Originally, I was scheduled to check out on the 19th.'
- "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"
|
| 3 | - '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'
- "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"
- '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'
|
| 4 | - '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'
- 'I would like to request an invoice/s for the above-mentioned employee who stayed at your establishment.'
- "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"
|
| 5 | - 'This is the second email with this trip, but I still need an itinerary for trip scheduled for January 27. Derek'
- '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.'
- 'Jen Holt Can you please send me the itinerary for Jen Holt for this trip this week to Jackson Mississippi?'
|
| 6 | - "I've had to call off my vacation. What are my options for getting refunded?"
- "Looks like I won't be traveling due to some health issues. Is getting a refund for my booking possible?"
- "I've fallen ill and can't travel as planned. Can you process a refund for me?"
|
| 7 | - 'The arrangements as stated are acceptable. Please go ahead and confirm all bookings accordingly.'
- "I've reviewed the details and everything seems in order. Please proceed with the booking."
- 'This travel plan is satisfactory. Please secure the necessary reservations.'
|
| 8 | - 'I need some clarification on charges for a rebooked flight. It seems higher than anticipated. Who can provide more details?'
- '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'
- "We've identified a mismatch between our booking records and credit card statement. Who can assist with this issue?"
|
| 9 | - 'I booked a hotel in Berlin for next month, but the confirmation email I received has the wrong dates. Can you please correct this and resend the confirmation?'
- "I need to arrange a shuttle for our team from the airport to the conference venue, but I haven't received any confirmation yet. Can someone check on this for me?"
- "When trying to book a flight for our CEO, the system shows an error stating 'payment not processed.' Can you assist in resolving this issue quickly?"
|
| 10 | - 'Please assist with payment for the conference room booking at Hilton last week.'
- 'Kindly process the invoice for the catering services provided during the annual company meeting.'
- "Supplier, please find a statement with all invoices listed due for the IT maintenance services. If you've already paid, please forward proof and date of payment. Thank you for your support."
|
| 11 | - "Congratulations! You've been selected to win a brand new iPhone 14. Click here to claim your prize now!"
- 'Get rich quick! Invest in our exclusive cryptocurrency and watch your money grow 10x in just a month. Limited time offer!'
- 'Your PayPal account has been compromised. Please click here to verify your information and secure your account.'
|
| 12 | - 'Your flight booking has been confirmed. Flight details: Flight #BA283 from LHR to LAX on November 10th, departure at 12:30 PM.'
- 'We regret to inform you that your hotel reservation at The Plaza, New York, was unsuccessful due to unavailability. Please try booking another date.'
- 'Your car rental reservation with Hertz has been confirmed. Pickup location: JFK Airport, Date: October 20th, Time: 10:00 AM.'
|
| 13 | - 'We have received a request to charge the attached invoice to the corporate credit card on file for Jane Doe. Please confirm the payment details at your earliest convenience.'
- 'Dear Travel Agency, we regret to inform you that the room booked for Mr. John Smith is unavailable due to overbooking. We have arranged an alternative accommodation at a nearby hotel. Please advise if this is acceptable.'
- 'Regarding the recent stay of Mr. Alan Harper, we noticed a discrepancy in the billing. The minibar charges were not included in the initial invoice. Kindly review the attached revised bill.'
|
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mann2107/BCMPIIRAB_MiniLM_ALLNew")
# Run inference
preds = model("Thank you for your email. Please go ahead and issue. Please invoice in KES")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 25.6577 | 136 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 24 |
| 1 | 24 |
| 2 | 24 |
| 3 | 24 |
| 4 | 24 |
| 5 | 24 |
| 6 | 24 |
| 7 | 24 |
| 8 | 24 |
| 9 | 24 |
| 10 | 24 |
| 11 | 24 |
| 12 | 24 |
| 13 | 24 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 99
- body_learning_rate: (0.0002733656643765287, 0.0002733656643765287)
- head_learning_rate: 2.7029049129688732e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- max_length: 512
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0002 | 1 | 0.2546 | - |
| 0.0120 | 50 | 0.1667 | - |
| 0.0241 | 100 | 0.1165 | - |
| 0.0361 | 150 | 0.0799 | - |
| 0.0481 | 200 | 0.0212 | - |
| 0.0601 | 250 | 0.0188 | - |
| 0.0722 | 300 | 0.0531 | - |
| 0.0842 | 350 | 0.0273 | - |
| 0.0962 | 400 | 0.0111 | - |
| 0.1082 | 450 | 0.0203 | - |
| 0.1203 | 500 | 0.0397 | - |
| 0.1323 | 550 | 0.0164 | - |
| 0.1443 | 600 | 0.0045 | - |
| 0.1563 | 650 | 0.0032 | - |
| 0.1684 | 700 | 0.001 | - |
| 0.1804 | 750 | 0.0011 | - |
| 0.1924 | 800 | 0.0004 | - |
| 0.2044 | 850 | 0.0009 | - |
| 0.2165 | 900 | 0.0006 | - |
| 0.2285 | 950 | 0.0008 | - |
| 0.2405 | 1000 | 0.0004 | - |
| 0.2525 | 1050 | 0.0008 | - |
| 0.2646 | 1100 | 0.0005 | - |
| 0.2766 | 1150 | 0.0006 | - |
| 0.2886 | 1200 | 0.0007 | - |
| 0.3006 | 1250 | 0.0043 | - |
| 0.3127 | 1300 | 0.0004 | - |
| 0.3247 | 1350 | 0.0005 | - |
| 0.3367 | 1400 | 0.0005 | - |
| 0.3487 | 1450 | 0.0004 | - |
| 0.3608 | 1500 | 0.0004 | - |
| 0.3728 | 1550 | 0.0005 | - |
| 0.3848 | 1600 | 0.0007 | - |
| 0.3968 | 1650 | 0.0006 | - |
| 0.4089 | 1700 | 0.0002 | - |
| 0.4209 | 1750 | 0.0006 | - |
| 0.4329 | 1800 | 0.0008 | - |
| 0.4449 | 1850 | 0.0003 | - |
| 0.4570 | 1900 | 0.0005 | - |
| 0.4690 | 1950 | 0.0003 | - |
| 0.4810 | 2000 | 0.0003 | - |
| 0.4930 | 2050 | 0.0003 | - |
| 0.5051 | 2100 | 0.0006 | - |
| 0.5171 | 2150 | 0.0003 | - |
| 0.5291 | 2200 | 0.0002 | - |
| 0.5411 | 2250 | 0.0002 | - |
| 0.5532 | 2300 | 0.0002 | - |
| 0.5652 | 2350 | 0.0004 | - |
| 0.5772 | 2400 | 0.0003 | - |
| 0.5892 | 2450 | 0.0003 | - |
| 0.6013 | 2500 | 0.0002 | - |
| 0.6133 | 2550 | 0.0002 | - |
| 0.6253 | 2600 | 0.0013 | - |
| 0.6373 | 2650 | 0.0002 | - |
| 0.6494 | 2700 | 0.0007 | - |
| 0.6614 | 2750 | 0.0004 | - |
| 0.6734 | 2800 | 0.0007 | - |
| 0.6854 | 2850 | 0.0018 | - |
| 0.6975 | 2900 | 0.0002 | - |
| 0.7095 | 2950 | 0.0003 | - |
| 0.7215 | 3000 | 0.0006 | - |
| 0.7335 | 3050 | 0.0003 | - |
| 0.7456 | 3100 | 0.0002 | - |
| 0.7576 | 3150 | 0.0002 | - |
| 0.7696 | 3200 | 0.0002 | - |
| 0.7816 | 3250 | 0.0002 | - |
| 0.7937 | 3300 | 0.0002 | - |
| 0.8057 | 3350 | 0.0001 | - |
| 0.8177 | 3400 | 0.0003 | - |
| 0.8297 | 3450 | 0.0002 | - |
| 0.8418 | 3500 | 0.0002 | - |
| 0.8538 | 3550 | 0.0002 | - |
| 0.8658 | 3600 | 0.0002 | - |
| 0.8778 | 3650 | 0.0002 | - |
| 0.8899 | 3700 | 0.0002 | - |
| 0.9019 | 3750 | 0.0005 | - |
| 0.9139 | 3800 | 0.0002 | - |
| 0.9259 | 3850 | 0.0001 | - |
| 0.9380 | 3900 | 0.0004 | - |
| 0.9500 | 3950 | 0.0001 | - |
| 0.9620 | 4000 | 0.0005 | - |
| 0.9740 | 4050 | 0.0002 | - |
| 0.9861 | 4100 | 0.0002 | - |
| 0.9981 | 4150 | 0.0001 | - |
| **1.0** | **4158** | **-** | **0.0302** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Datasets: 2.20.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```