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--- |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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library_name: setfit |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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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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widget: |
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- text: Thank you for your email. Please go ahead and issue. Please invoice in KES |
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- text: Hi, We are missing some invoices, can you please provide it. 02 - 12 - 2020 |
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AGENT FEE 8900784339018 $21.00 02 - 19 - 2020 AGENT FEE 0017417554160 $22.00 02 |
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- 19 - 2020 AGENT FEE 0017417554143 $22.00 02 - 19 - 2020 AGENT FEE 8900783383420 |
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$21.00 |
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- text: We need your assistance with the payment for the recent office supplies order. |
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Let us know once it's done. |
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- text: I have reported this in November and not only was the trip supposed to be |
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cancelled and credited I was double billed and the billing has not been corrected. |
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The total credit should be $667.20. Please confirm this will be done. |
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- text: The invoice for the travel arrangements needs to be settled. Kindly provide |
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payment confirmation. |
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inference: true |
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--- |
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# SetFit with sentence-transformers/all-MiniLM-L6-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/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. |
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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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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:** 256 tokens |
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- **Number of Classes:** 14 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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| 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> | |
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| 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> | |
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| 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> | |
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| 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 [email protected]'</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 - [email protected] Female 6/14/1995 Gumbs Mary - Frances Akua Staff [email protected] Female 10/18/1995 Lee Elizabeth Andie Staff [email protected] Female 4/23/1991 Gilchrist Gabriel Jake Staff [email protected] 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: [email protected]'</li></ul> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("mann2107/BCMPIIRAB_MiniLM_ALL") |
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# Run inference |
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preds = model("Thank you for your email. Please go ahead and issue. Please invoice in KES") |
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``` |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:--------|:----| |
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| Word count | 1 | 25.6577 | 136 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 24 | |
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| 1 | 24 | |
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| 2 | 24 | |
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| 3 | 24 | |
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| 4 | 24 | |
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| 5 | 24 | |
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| 6 | 24 | |
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| 7 | 24 | |
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| 8 | 24 | |
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| 9 | 24 | |
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| 10 | 24 | |
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| 11 | 24 | |
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| 12 | 24 | |
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| 13 | 24 | |
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### Training Hyperparameters |
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- batch_size: (64, 64) |
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- num_epochs: (2, 2) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 2 |
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- body_learning_rate: (0.0005201181161511404, 0.0005201181161511404) |
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- head_learning_rate: 0.00021200244124154418 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- max_length: 512 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: True |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:-------:|:------:|:-------------:|:---------------:| |
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| 0.0476 | 1 | 0.2504 | - | |
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| 1.0 | 21 | - | 0.0691 | |
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| **2.0** | **42** | **-** | **0.0445** | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.1.0.dev0 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.42.4 |
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- PyTorch: 2.3.1+cu121 |
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- Datasets: 2.20.0 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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