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---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: We are awaiting payment for the project completed in June. Please confirm
when this will be processed.
- text: Hello, Good morning, would you mind cancelling this rental car?
- text: 'Kindly book accommodation for Lindelani Mkhize as follows: Establishment:
City Lodge Lynwood Date checked in : 04 October 2023 Time checked in: 19h00pm
Date checked out: 06 October 2023 Time checked out: 07h00am'
- text: You've been selected for a free energy audit. Click here to schedule your
appointment.
- text: 'Please can you provide with the invoices for my stays this month as follows: 1.
Premier Splendid Inn Bayshore (07 Aug - 08 Aug) 2. Port Nolloth Beach Shack
(14 Aug - 17 Aug)'
metrics:
- silhouette_score
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/paraphrase-MiniLM-L6-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-MiniLM-L6-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: silhouette_score
value: 0.4196937375508804
name: Silhouette_Score
---
# SetFit with sentence-transformers/paraphrase-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/paraphrase-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-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/paraphrase-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 14 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
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### 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 | <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."</li><li>'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'</li></ul> |
| 1 | <ul><li>'I need to cancel my flight booking from London Heathrow to JFK, New York, scheduled for August 15th, 2024. The booking reference is XJ12345.'</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> |
| 2 | <ul><li>'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?'</li><li>'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.'</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> |
| 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> |
| 4 | <ul><li>'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.'</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> |
| 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> |
| 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> |
| 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> |
| 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> |
| 9 | <ul><li>'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?'</li><li>"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?"</li><li>"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?"</li></ul> |
| 10 | <ul><li>'Please assist with payment for the conference room booking at Hilton last week.'</li><li>'Kindly process the invoice for the catering services provided during the annual company meeting.'</li><li>"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."</li></ul> |
| 11 | <ul><li>"Congratulations! You've been selected to win a brand new iPhone 14. Click here to claim your prize now!"</li><li>'Get rich quick! Invest in our exclusive cryptocurrency and watch your money grow 10x in just a month. Limited time offer!'</li><li>'Your PayPal account has been compromised. Please click here to verify your information and secure your account.'</li></ul> |
| 12 | <ul><li>'Your flight booking has been confirmed. Flight details: Flight #BA283 from LHR to LAX on November 10th, departure at 12:30 PM.'</li><li>'We regret to inform you that your hotel reservation at The Plaza, New York, was unsuccessful due to unavailability. Please try booking another date.'</li><li>'Your car rental reservation with Hertz has been confirmed. Pickup location: JFK Airport, Date: October 20th, Time: 10:00 AM.'</li></ul> |
| 13 | <ul><li>'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.'</li><li>'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.'</li><li>'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.'</li></ul> |
## Evaluation
### Metrics
| Label | Silhouette_Score |
|:--------|:-----------------|
| **all** | 0.4197 |
## 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_HTTest")
# Run inference
preds = model("Hello, Good morning, would you mind cancelling this rental car?")
```
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## 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: (32, 32)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 1
- body_learning_rate: (3e-05, 3e-05)
- head_learning_rate: 3e-05
- loss: MultipleNegativesRankingLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: True
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0476 | 1 | 5.5459 | - |
### Framework Versions
- Python: 3.12.0
- SetFit: 1.2.0.dev0
- Sentence Transformers: 3.2.1
- Transformers: 4.45.2
- PyTorch: 2.5.0+cpu
- Datasets: 3.0.2
- Tokenizers: 0.20.1
## 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}
}
```
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