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BCMPIIRAB_MiniLM_ALLNewV2

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
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---
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
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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> |
## 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_ALLNewV2")
# Run inference
preds = model("Thank you for your email. Please go ahead and issue. Please invoice in KES")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## 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: (8, 8)
- num_epochs: (5, 5)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 68
- body_learning_rate: (1.44030579311381e-05, 1.44030579311381e-05)
- head_learning_rate: 0.01
- 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 | 0.2917 | - |
| 0.0088 | 50 | 0.2434 | - |
| 0.0175 | 100 | 0.2053 | - |
| 0.0263 | 150 | 0.1789 | - |
| 0.0350 | 200 | 0.2249 | - |
| 0.0438 | 250 | 0.1773 | - |
| 0.0525 | 300 | 0.1648 | - |
| 0.0613 | 350 | 0.2617 | - |
| 0.0700 | 400 | 0.1342 | - |
| 0.0788 | 450 | 0.1064 | - |
| 0.0875 | 500 | 0.1273 | - |
| 0.0963 | 550 | 0.1248 | - |
| 0.1050 | 600 | 0.2013 | - |
| 0.1138 | 650 | 0.1979 | - |
| 0.1225 | 700 | 0.1631 | - |
| 0.1313 | 750 | 0.1079 | - |
| 0.1401 | 800 | 0.0858 | - |
| 0.1488 | 850 | 0.0999 | - |
| 0.1576 | 900 | 0.0638 | - |
| 0.1663 | 950 | 0.1287 | - |
| 0.1751 | 1000 | 0.1408 | - |
| 0.1838 | 1050 | 0.1902 | - |
| 0.1926 | 1100 | 0.0648 | - |
| 0.2013 | 1150 | 0.1383 | - |
| 0.2101 | 1200 | 0.0609 | - |
| 0.2188 | 1250 | 0.0865 | - |
| 0.2276 | 1300 | 0.1069 | - |
| 0.2363 | 1350 | 0.051 | - |
| 0.2451 | 1400 | 0.0692 | - |
| 0.2539 | 1450 | 0.123 | - |
| 0.2626 | 1500 | 0.0758 | - |
| 0.2714 | 1550 | 0.0835 | - |
| 0.2801 | 1600 | 0.0523 | - |
| 0.2889 | 1650 | 0.0946 | - |
| 0.2976 | 1700 | 0.0445 | - |
| 0.3064 | 1750 | 0.0248 | - |
| 0.3151 | 1800 | 0.0373 | - |
| 0.3239 | 1850 | 0.0248 | - |
| 0.3326 | 1900 | 0.0446 | - |
| 0.3414 | 1950 | 0.0142 | - |
| 0.3501 | 2000 | 0.023 | - |
| 0.3589 | 2050 | 0.0119 | - |
| 0.3676 | 2100 | 0.0383 | - |
| 0.3764 | 2150 | 0.0188 | - |
| 0.3852 | 2200 | 0.0204 | - |
| 0.3939 | 2250 | 0.0109 | - |
| 0.4027 | 2300 | 0.0273 | - |
| 0.4114 | 2350 | 0.0216 | - |
| 0.4202 | 2400 | 0.0073 | - |
| 0.4289 | 2450 | 0.0338 | - |
| 0.4377 | 2500 | 0.0047 | - |
| 0.4464 | 2550 | 0.0096 | - |
| 0.4552 | 2600 | 0.0069 | - |
| 0.4639 | 2650 | 0.0078 | - |
| 0.4727 | 2700 | 0.0122 | - |
| 0.4814 | 2750 | 0.0578 | - |
| 0.4902 | 2800 | 0.0074 | - |
| 0.4989 | 2850 | 0.0103 | - |
| 0.5077 | 2900 | 0.0092 | - |
| 0.5165 | 2950 | 0.004 | - |
| 0.5252 | 3000 | 0.0061 | - |
| 0.5340 | 3050 | 0.0214 | - |
| 0.5427 | 3100 | 0.0048 | - |
| 0.5515 | 3150 | 0.0036 | - |
| 0.5602 | 3200 | 0.0041 | - |
| 0.5690 | 3250 | 0.0151 | - |
| 0.5777 | 3300 | 0.0042 | - |
| 0.5865 | 3350 | 0.0029 | - |
| 0.5952 | 3400 | 0.0021 | - |
| 0.6040 | 3450 | 0.0018 | - |
| 0.6127 | 3500 | 0.0058 | - |
| 0.6215 | 3550 | 0.0011 | - |
| 0.6303 | 3600 | 0.0078 | - |
| 0.6390 | 3650 | 0.0011 | - |
| 0.6478 | 3700 | 0.0017 | - |
| 0.6565 | 3750 | 0.0022 | - |
| 0.6653 | 3800 | 0.0016 | - |
| 0.6740 | 3850 | 0.002 | - |
| 0.6828 | 3900 | 0.0023 | - |
| 0.6915 | 3950 | 0.0011 | - |
| 0.7003 | 4000 | 0.0012 | - |
| 0.7090 | 4050 | 0.0007 | - |
| 0.7178 | 4100 | 0.0021 | - |
| 0.7265 | 4150 | 0.0019 | - |
| 0.7353 | 4200 | 0.002 | - |
| 0.7440 | 4250 | 0.0018 | - |
| 0.7528 | 4300 | 0.0029 | - |
| 0.7616 | 4350 | 0.0015 | - |
| 0.7703 | 4400 | 0.0022 | - |
| 0.7791 | 4450 | 0.0012 | - |
| 0.7878 | 4500 | 0.0007 | - |
| 0.7966 | 4550 | 0.0015 | - |
| 0.8053 | 4600 | 0.0011 | - |
| 0.8141 | 4650 | 0.0016 | - |
| 0.8228 | 4700 | 0.0009 | - |
| 0.8316 | 4750 | 0.0007 | - |
| 0.8403 | 4800 | 0.0011 | - |
| 0.8491 | 4850 | 0.001 | - |
| 0.8578 | 4900 | 0.0008 | - |
| 0.8666 | 4950 | 0.0014 | - |
| 0.8754 | 5000 | 0.0022 | - |
| 0.8841 | 5050 | 0.0012 | - |
| 0.8929 | 5100 | 0.0007 | - |
| 0.9016 | 5150 | 0.0014 | - |
| 0.9104 | 5200 | 0.0007 | - |
| 0.9191 | 5250 | 0.0012 | - |
| 0.9279 | 5300 | 0.0011 | - |
| 0.9366 | 5350 | 0.0012 | - |
| 0.9454 | 5400 | 0.0029 | - |
| 0.9541 | 5450 | 0.001 | - |
| 0.9629 | 5500 | 0.0011 | - |
| 0.9716 | 5550 | 0.0004 | - |
| 0.9804 | 5600 | 0.0009 | - |
| 0.9891 | 5650 | 0.0004 | - |
| 0.9979 | 5700 | 0.003 | - |
| 1.0 | 5712 | - | 0.0459 |
| 1.0067 | 5750 | 0.0014 | - |
| 1.0154 | 5800 | 0.0008 | - |
| 1.0242 | 5850 | 0.0009 | - |
| 1.0329 | 5900 | 0.0007 | - |
| 1.0417 | 5950 | 0.0007 | - |
| 1.0504 | 6000 | 0.0006 | - |
| 1.0592 | 6050 | 0.0008 | - |
| 1.0679 | 6100 | 0.0006 | - |
| 1.0767 | 6150 | 0.0006 | - |
| 1.0854 | 6200 | 0.0007 | - |
| 1.0942 | 6250 | 0.0025 | - |
| 1.1029 | 6300 | 0.0006 | - |
| 1.1117 | 6350 | 0.0009 | - |
| 1.1204 | 6400 | 0.0009 | - |
| 1.1292 | 6450 | 0.0009 | - |
| 1.1380 | 6500 | 0.0006 | - |
| 1.1467 | 6550 | 0.0004 | - |
| 1.1555 | 6600 | 0.0014 | - |
| 1.1642 | 6650 | 0.0029 | - |
| 1.1730 | 6700 | 0.0004 | - |
| 1.1817 | 6750 | 0.0027 | - |
| 1.1905 | 6800 | 0.0003 | - |
| 1.1992 | 6850 | 0.0003 | - |
| 1.2080 | 6900 | 0.0006 | - |
| 1.2167 | 6950 | 0.0015 | - |
| 1.2255 | 7000 | 0.0005 | - |
| 1.2342 | 7050 | 0.0005 | - |
| 1.2430 | 7100 | 0.0016 | - |
| 1.2518 | 7150 | 0.0005 | - |
| 1.2605 | 7200 | 0.0003 | - |
| 1.2693 | 7250 | 0.0006 | - |
| 1.2780 | 7300 | 0.0007 | - |
| 1.2868 | 7350 | 0.0004 | - |
| 1.2955 | 7400 | 0.0007 | - |
| 1.3043 | 7450 | 0.0007 | - |
| 1.3130 | 7500 | 0.0007 | - |
| 1.3218 | 7550 | 0.0003 | - |
| 1.3305 | 7600 | 0.0002 | - |
| 1.3393 | 7650 | 0.0002 | - |
| 1.3480 | 7700 | 0.0005 | - |
| 1.3568 | 7750 | 0.0014 | - |
| 1.3655 | 7800 | 0.0012 | - |
| 1.3743 | 7850 | 0.0002 | - |
| 1.3831 | 7900 | 0.0002 | - |
| 1.3918 | 7950 | 0.0003 | - |
| 1.4006 | 8000 | 0.0005 | - |
| 1.4093 | 8050 | 0.0006 | - |
| 1.4181 | 8100 | 0.0003 | - |
| 1.4268 | 8150 | 0.0009 | - |
| 1.4356 | 8200 | 0.0004 | - |
| 1.4443 | 8250 | 0.0002 | - |
| 1.4531 | 8300 | 0.0004 | - |
| 1.4618 | 8350 | 0.0008 | - |
| 1.4706 | 8400 | 0.0002 | - |
| 1.4793 | 8450 | 0.0004 | - |
| 1.4881 | 8500 | 0.0006 | - |
| 1.4968 | 8550 | 0.0011 | - |
| 1.5056 | 8600 | 0.0003 | - |
| 1.5144 | 8650 | 0.0003 | - |
| 1.5231 | 8700 | 0.0004 | - |
| 1.5319 | 8750 | 0.0004 | - |
| 1.5406 | 8800 | 0.0002 | - |
| 1.5494 | 8850 | 0.0007 | - |
| 1.5581 | 8900 | 0.0003 | - |
| 1.5669 | 8950 | 0.0002 | - |
| 1.5756 | 9000 | 0.0007 | - |
| 1.5844 | 9050 | 0.0005 | - |
| 1.5931 | 9100 | 0.0005 | - |
| 1.6019 | 9150 | 0.0011 | - |
| 1.6106 | 9200 | 0.0004 | - |
| 1.6194 | 9250 | 0.0004 | - |
| 1.6282 | 9300 | 0.0003 | - |
| 1.6369 | 9350 | 0.0002 | - |
| 1.6457 | 9400 | 0.0003 | - |
| 1.6544 | 9450 | 0.0006 | - |
| 1.6632 | 9500 | 0.0004 | - |
| 1.6719 | 9550 | 0.0004 | - |
| 1.6807 | 9600 | 0.0006 | - |
| 1.6894 | 9650 | 0.0001 | - |
| 1.6982 | 9700 | 0.0002 | - |
| 1.7069 | 9750 | 0.0004 | - |
| 1.7157 | 9800 | 0.0004 | - |
| 1.7244 | 9850 | 0.0001 | - |
| 1.7332 | 9900 | 0.0004 | - |
| 1.7419 | 9950 | 0.0004 | - |
| 1.7507 | 10000 | 0.0006 | - |
| 1.7595 | 10050 | 0.0003 | - |
| 1.7682 | 10100 | 0.0002 | - |
| 1.7770 | 10150 | 0.0004 | - |
| 1.7857 | 10200 | 0.0004 | - |
| 1.7945 | 10250 | 0.0002 | - |
| 1.8032 | 10300 | 0.0008 | - |
| 1.8120 | 10350 | 0.0004 | - |
| 1.8207 | 10400 | 0.0005 | - |
| 1.8295 | 10450 | 0.0004 | - |
| 1.8382 | 10500 | 0.0001 | - |
| 1.8470 | 10550 | 0.0003 | - |
| 1.8557 | 10600 | 0.0003 | - |
| 1.8645 | 10650 | 0.0005 | - |
| 1.8732 | 10700 | 0.0005 | - |
| 1.8820 | 10750 | 0.0003 | - |
| 1.8908 | 10800 | 0.0001 | - |
| 1.8995 | 10850 | 0.0002 | - |
| 1.9083 | 10900 | 0.0001 | - |
| 1.9170 | 10950 | 0.0003 | - |
| 1.9258 | 11000 | 0.0005 | - |
| 1.9345 | 11050 | 0.0003 | - |
| 1.9433 | 11100 | 0.0004 | - |
| 1.9520 | 11150 | 0.0007 | - |
| 1.9608 | 11200 | 0.0002 | - |
| 1.9695 | 11250 | 0.0003 | - |
| 1.9783 | 11300 | 0.0001 | - |
| 1.9870 | 11350 | 0.0001 | - |
| 1.9958 | 11400 | 0.0002 | - |
| 2.0 | 11424 | - | 0.042 |
| 2.0046 | 11450 | 0.0003 | - |
| 2.0133 | 11500 | 0.0002 | - |
| 2.0221 | 11550 | 0.0002 | - |
| 2.0308 | 11600 | 0.0002 | - |
| 2.0396 | 11650 | 0.0003 | - |
| 2.0483 | 11700 | 0.0003 | - |
| 2.0571 | 11750 | 0.0002 | - |
| 2.0658 | 11800 | 0.0002 | - |
| 2.0746 | 11850 | 0.0002 | - |
| 2.0833 | 11900 | 0.0002 | - |
| 2.0921 | 11950 | 0.0001 | - |
| 2.1008 | 12000 | 0.0003 | - |
| 2.1096 | 12050 | 0.0005 | - |
| 2.1183 | 12100 | 0.0002 | - |
| 2.1271 | 12150 | 0.0003 | - |
| 2.1359 | 12200 | 0.0002 | - |
| 2.1446 | 12250 | 0.0003 | - |
| 2.1534 | 12300 | 0.0003 | - |
| 2.1621 | 12350 | 0.0001 | - |
| 2.1709 | 12400 | 0.0002 | - |
| 2.1796 | 12450 | 0.0002 | - |
| 2.1884 | 12500 | 0.0002 | - |
| 2.1971 | 12550 | 0.0002 | - |
| 2.2059 | 12600 | 0.0001 | - |
| 2.2146 | 12650 | 0.0002 | - |
| 2.2234 | 12700 | 0.0003 | - |
| 2.2321 | 12750 | 0.0003 | - |
| 2.2409 | 12800 | 0.0004 | - |
| 2.2496 | 12850 | 0.0002 | - |
| 2.2584 | 12900 | 0.0002 | - |
| 2.2672 | 12950 | 0.0003 | - |
| 2.2759 | 13000 | 0.0002 | - |
| 2.2847 | 13050 | 0.0002 | - |
| 2.2934 | 13100 | 0.0002 | - |
| 2.3022 | 13150 | 0.0001 | - |
| 2.3109 | 13200 | 0.0002 | - |
| 2.3197 | 13250 | 0.0001 | - |
| 2.3284 | 13300 | 0.0002 | - |
| 2.3372 | 13350 | 0.0003 | - |
| 2.3459 | 13400 | 0.0002 | - |
| 2.3547 | 13450 | 0.0001 | - |
| 2.3634 | 13500 | 0.0002 | - |
| 2.3722 | 13550 | 0.0001 | - |
| 2.3810 | 13600 | 0.0006 | - |
| 2.3897 | 13650 | 0.0001 | - |
| 2.3985 | 13700 | 0.0002 | - |
| 2.4072 | 13750 | 0.0002 | - |
| 2.4160 | 13800 | 0.0004 | - |
| 2.4247 | 13850 | 0.0001 | - |
| 2.4335 | 13900 | 0.0003 | - |
| 2.4422 | 13950 | 0.0001 | - |
| 2.4510 | 14000 | 0.0001 | - |
| 2.4597 | 14050 | 0.0001 | - |
| 2.4685 | 14100 | 0.0005 | - |
| 2.4772 | 14150 | 0.0002 | - |
| 2.4860 | 14200 | 0.0001 | - |
| 2.4947 | 14250 | 0.0003 | - |
| 2.5035 | 14300 | 0.0005 | - |
| 2.5123 | 14350 | 0.0002 | - |
| 2.5210 | 14400 | 0.0002 | - |
| 2.5298 | 14450 | 0.0003 | - |
| 2.5385 | 14500 | 0.0001 | - |
| 2.5473 | 14550 | 0.0001 | - |
| 2.5560 | 14600 | 0.0002 | - |
| 2.5648 | 14650 | 0.0002 | - |
| 2.5735 | 14700 | 0.0001 | - |
| 2.5823 | 14750 | 0.0001 | - |
| 2.5910 | 14800 | 0.0001 | - |
| 2.5998 | 14850 | 0.0003 | - |
| 2.6085 | 14900 | 0.0002 | - |
| 2.6173 | 14950 | 0.0001 | - |
| 2.6261 | 15000 | 0.0001 | - |
| 2.6348 | 15050 | 0.0001 | - |
| 2.6436 | 15100 | 0.0001 | - |
| 2.6523 | 15150 | 0.0002 | - |
| 2.6611 | 15200 | 0.0001 | - |
| 2.6698 | 15250 | 0.0002 | - |
| 2.6786 | 15300 | 0.0002 | - |
| 2.6873 | 15350 | 0.0002 | - |
| 2.6961 | 15400 | 0.0002 | - |
| 2.7048 | 15450 | 0.0002 | - |
| 2.7136 | 15500 | 0.0001 | - |
| 2.7223 | 15550 | 0.0002 | - |
| 2.7311 | 15600 | 0.0002 | - |
| 2.7398 | 15650 | 0.0003 | - |
| 2.7486 | 15700 | 0.0002 | - |
| 2.7574 | 15750 | 0.0001 | - |
| 2.7661 | 15800 | 0.0002 | - |
| 2.7749 | 15850 | 0.0002 | - |
| 2.7836 | 15900 | 0.0003 | - |
| 2.7924 | 15950 | 0.0004 | - |
| 2.8011 | 16000 | 0.0007 | - |
| 2.8099 | 16050 | 0.0001 | - |
| 2.8186 | 16100 | 0.0001 | - |
| 2.8274 | 16150 | 0.0002 | - |
| 2.8361 | 16200 | 0.0002 | - |
| 2.8449 | 16250 | 0.0001 | - |
| 2.8536 | 16300 | 0.0001 | - |
| 2.8624 | 16350 | 0.0002 | - |
| 2.8711 | 16400 | 0.0002 | - |
| 2.8799 | 16450 | 0.0001 | - |
| 2.8887 | 16500 | 0.0002 | - |
| 2.8974 | 16550 | 0.0002 | - |
| 2.9062 | 16600 | 0.0001 | - |
| 2.9149 | 16650 | 0.0001 | - |
| 2.9237 | 16700 | 0.0001 | - |
| 2.9324 | 16750 | 0.0003 | - |
| 2.9412 | 16800 | 0.0002 | - |
| 2.9499 | 16850 | 0.0003 | - |
| 2.9587 | 16900 | 0.0001 | - |
| 2.9674 | 16950 | 0.0002 | - |
| 2.9762 | 17000 | 0.0001 | - |
| 2.9849 | 17050 | 0.0001 | - |
| 2.9937 | 17100 | 0.0001 | - |
| **3.0** | **17136** | **-** | **0.0419** |
| 3.0025 | 17150 | 0.0002 | - |
| 3.0112 | 17200 | 0.0002 | - |
| 3.0200 | 17250 | 0.0003 | - |
| 3.0287 | 17300 | 0.0001 | - |
| 3.0375 | 17350 | 0.0002 | - |
| 3.0462 | 17400 | 0.0001 | - |
| 3.0550 | 17450 | 0.0002 | - |
| 3.0637 | 17500 | 0.0002 | - |
| 3.0725 | 17550 | 0.0002 | - |
| 3.0812 | 17600 | 0.0001 | - |
| 3.0900 | 17650 | 0.0001 | - |
| 3.0987 | 17700 | 0.0001 | - |
| 3.1075 | 17750 | 0.0001 | - |
| 3.1162 | 17800 | 0.0001 | - |
| 3.125 | 17850 | 0.0001 | - |
| 3.1338 | 17900 | 0.0002 | - |
| 3.1425 | 17950 | 0.0001 | - |
| 3.1513 | 18000 | 0.0003 | - |
| 3.1600 | 18050 | 0.0001 | - |
| 3.1688 | 18100 | 0.0001 | - |
| 3.1775 | 18150 | 0.0001 | - |
| 3.1863 | 18200 | 0.0002 | - |
| 3.1950 | 18250 | 0.0002 | - |
| 3.2038 | 18300 | 0.0001 | - |
| 3.2125 | 18350 | 0.0001 | - |
| 3.2213 | 18400 | 0.0001 | - |
| 3.2300 | 18450 | 0.0002 | - |
| 3.2388 | 18500 | 0.0001 | - |
| 3.2475 | 18550 | 0.0002 | - |
| 3.2563 | 18600 | 0.0001 | - |
| 3.2651 | 18650 | 0.0002 | - |
| 3.2738 | 18700 | 0.0001 | - |
| 3.2826 | 18750 | 0.0001 | - |
| 3.2913 | 18800 | 0.0001 | - |
| 3.3001 | 18850 | 0.0001 | - |
| 3.3088 | 18900 | 0.0003 | - |
| 3.3176 | 18950 | 0.0002 | - |
| 3.3263 | 19000 | 0.0001 | - |
| 3.3351 | 19050 | 0.0003 | - |
| 3.3438 | 19100 | 0.0001 | - |
| 3.3526 | 19150 | 0.0001 | - |
| 3.3613 | 19200 | 0.0001 | - |
| 3.3701 | 19250 | 0.0001 | - |
| 3.3789 | 19300 | 0.0001 | - |
| 3.3876 | 19350 | 0.0002 | - |
| 3.3964 | 19400 | 0.0001 | - |
| 3.4051 | 19450 | 0.0001 | - |
| 3.4139 | 19500 | 0.0001 | - |
| 3.4226 | 19550 | 0.0001 | - |
| 3.4314 | 19600 | 0.0001 | - |
| 3.4401 | 19650 | 0.0001 | - |
| 3.4489 | 19700 | 0.0002 | - |
| 3.4576 | 19750 | 0.0001 | - |
| 3.4664 | 19800 | 0.0001 | - |
| 3.4751 | 19850 | 0.0001 | - |
| 3.4839 | 19900 | 0.0001 | - |
| 3.4926 | 19950 | 0.0001 | - |
| 3.5014 | 20000 | 0.0001 | - |
| 3.5102 | 20050 | 0.0002 | - |
| 3.5189 | 20100 | 0.0003 | - |
| 3.5277 | 20150 | 0.0001 | - |
| 3.5364 | 20200 | 0.0002 | - |
| 3.5452 | 20250 | 0.0001 | - |
| 3.5539 | 20300 | 0.0001 | - |
| 3.5627 | 20350 | 0.0001 | - |
| 3.5714 | 20400 | 0.0004 | - |
| 3.5802 | 20450 | 0.0001 | - |
| 3.5889 | 20500 | 0.0001 | - |
| 3.5977 | 20550 | 0.0001 | - |
| 3.6064 | 20600 | 0.0002 | - |
| 3.6152 | 20650 | 0.0001 | - |
| 3.6239 | 20700 | 0.0001 | - |
| 3.6327 | 20750 | 0.0 | - |
| 3.6415 | 20800 | 0.0002 | - |
| 3.6502 | 20850 | 0.0001 | - |
| 3.6590 | 20900 | 0.0001 | - |
| 3.6677 | 20950 | 0.0002 | - |
| 3.6765 | 21000 | 0.0001 | - |
| 3.6852 | 21050 | 0.0001 | - |
| 3.6940 | 21100 | 0.0001 | - |
| 3.7027 | 21150 | 0.0002 | - |
| 3.7115 | 21200 | 0.0004 | - |
| 3.7202 | 21250 | 0.0001 | - |
| 3.7290 | 21300 | 0.0002 | - |
| 3.7377 | 21350 | 0.0001 | - |
| 3.7465 | 21400 | 0.0004 | - |
| 3.7553 | 21450 | 0.0002 | - |
| 3.7640 | 21500 | 0.0001 | - |
| 3.7728 | 21550 | 0.0001 | - |
| 3.7815 | 21600 | 0.0001 | - |
| 3.7903 | 21650 | 0.0001 | - |
| 3.7990 | 21700 | 0.0001 | - |
| 3.8078 | 21750 | 0.0001 | - |
| 3.8165 | 21800 | 0.0 | - |
| 3.8253 | 21850 | 0.0 | - |
| 3.8340 | 21900 | 0.0001 | - |
| 3.8428 | 21950 | 0.0003 | - |
| 3.8515 | 22000 | 0.0001 | - |
| 3.8603 | 22050 | 0.0001 | - |
| 3.8690 | 22100 | 0.0002 | - |
| 3.8778 | 22150 | 0.0001 | - |
| 3.8866 | 22200 | 0.0003 | - |
| 3.8953 | 22250 | 0.0001 | - |
| 3.9041 | 22300 | 0.0 | - |
| 3.9128 | 22350 | 0.0001 | - |
| 3.9216 | 22400 | 0.0002 | - |
| 3.9303 | 22450 | 0.0001 | - |
| 3.9391 | 22500 | 0.0001 | - |
| 3.9478 | 22550 | 0.0 | - |
| 3.9566 | 22600 | 0.0003 | - |
| 3.9653 | 22650 | 0.0001 | - |
| 3.9741 | 22700 | 0.0001 | - |
| 3.9828 | 22750 | 0.0001 | - |
| 3.9916 | 22800 | 0.0002 | - |
| 4.0 | 22848 | - | 0.0419 |
| 4.0004 | 22850 | 0.0 | - |
| 4.0091 | 22900 | 0.0001 | - |
| 4.0179 | 22950 | 0.0001 | - |
| 4.0266 | 23000 | 0.0001 | - |
| 4.0354 | 23050 | 0.0001 | - |
| 4.0441 | 23100 | 0.0002 | - |
| 4.0529 | 23150 | 0.0001 | - |
| 4.0616 | 23200 | 0.0001 | - |
| 4.0704 | 23250 | 0.0002 | - |
| 4.0791 | 23300 | 0.0 | - |
| 4.0879 | 23350 | 0.0001 | - |
| 4.0966 | 23400 | 0.0001 | - |
| 4.1054 | 23450 | 0.0001 | - |
| 4.1141 | 23500 | 0.0001 | - |
| 4.1229 | 23550 | 0.0002 | - |
| 4.1317 | 23600 | 0.0001 | - |
| 4.1404 | 23650 | 0.0001 | - |
| 4.1492 | 23700 | 0.0001 | - |
| 4.1579 | 23750 | 0.0002 | - |
| 4.1667 | 23800 | 0.0002 | - |
| 4.1754 | 23850 | 0.0001 | - |
| 4.1842 | 23900 | 0.0001 | - |
| 4.1929 | 23950 | 0.0001 | - |
| 4.2017 | 24000 | 0.0001 | - |
| 4.2104 | 24050 | 0.0001 | - |
| 4.2192 | 24100 | 0.0001 | - |
| 4.2279 | 24150 | 0.0 | - |
| 4.2367 | 24200 | 0.0001 | - |
| 4.2454 | 24250 | 0.0001 | - |
| 4.2542 | 24300 | 0.0003 | - |
| 4.2630 | 24350 | 0.0 | - |
| 4.2717 | 24400 | 0.0001 | - |
| 4.2805 | 24450 | 0.0 | - |
| 4.2892 | 24500 | 0.0001 | - |
| 4.2980 | 24550 | 0.0001 | - |
| 4.3067 | 24600 | 0.0002 | - |
| 4.3155 | 24650 | 0.0 | - |
| 4.3242 | 24700 | 0.0001 | - |
| 4.3330 | 24750 | 0.0001 | - |
| 4.3417 | 24800 | 0.0001 | - |
| 4.3505 | 24850 | 0.0001 | - |
| 4.3592 | 24900 | 0.0001 | - |
| 4.3680 | 24950 | 0.0 | - |
| 4.3768 | 25000 | 0.0002 | - |
| 4.3855 | 25050 | 0.0001 | - |
| 4.3943 | 25100 | 0.0001 | - |
| 4.4030 | 25150 | 0.0001 | - |
| 4.4118 | 25200 | 0.0001 | - |
| 4.4205 | 25250 | 0.0001 | - |
| 4.4293 | 25300 | 0.0002 | - |
| 4.4380 | 25350 | 0.0002 | - |
| 4.4468 | 25400 | 0.0001 | - |
| 4.4555 | 25450 | 0.0001 | - |
| 4.4643 | 25500 | 0.0001 | - |
| 4.4730 | 25550 | 0.0001 | - |
| 4.4818 | 25600 | 0.0001 | - |
| 4.4905 | 25650 | 0.0001 | - |
| 4.4993 | 25700 | 0.0001 | - |
| 4.5081 | 25750 | 0.0001 | - |
| 4.5168 | 25800 | 0.0001 | - |
| 4.5256 | 25850 | 0.0001 | - |
| 4.5343 | 25900 | 0.0001 | - |
| 4.5431 | 25950 | 0.0001 | - |
| 4.5518 | 26000 | 0.0 | - |
| 4.5606 | 26050 | 0.0 | - |
| 4.5693 | 26100 | 0.0001 | - |
| 4.5781 | 26150 | 0.0001 | - |
| 4.5868 | 26200 | 0.0001 | - |
| 4.5956 | 26250 | 0.0001 | - |
| 4.6043 | 26300 | 0.0001 | - |
| 4.6131 | 26350 | 0.0001 | - |
| 4.6218 | 26400 | 0.0002 | - |
| 4.6306 | 26450 | 0.0001 | - |
| 4.6394 | 26500 | 0.0001 | - |
| 4.6481 | 26550 | 0.0001 | - |
| 4.6569 | 26600 | 0.0001 | - |
| 4.6656 | 26650 | 0.0 | - |
| 4.6744 | 26700 | 0.0002 | - |
| 4.6831 | 26750 | 0.0 | - |
| 4.6919 | 26800 | 0.0001 | - |
| 4.7006 | 26850 | 0.0002 | - |
| 4.7094 | 26900 | 0.0002 | - |
| 4.7181 | 26950 | 0.0001 | - |
| 4.7269 | 27000 | 0.0001 | - |
| 4.7356 | 27050 | 0.0001 | - |
| 4.7444 | 27100 | 0.0 | - |
| 4.7532 | 27150 | 0.0001 | - |
| 4.7619 | 27200 | 0.0001 | - |
| 4.7707 | 27250 | 0.0001 | - |
| 4.7794 | 27300 | 0.0 | - |
| 4.7882 | 27350 | 0.0001 | - |
| 4.7969 | 27400 | 0.0001 | - |
| 4.8057 | 27450 | 0.0002 | - |
| 4.8144 | 27500 | 0.0 | - |
| 4.8232 | 27550 | 0.0001 | - |
| 4.8319 | 27600 | 0.0001 | - |
| 4.8407 | 27650 | 0.0001 | - |
| 4.8494 | 27700 | 0.0 | - |
| 4.8582 | 27750 | 0.0001 | - |
| 4.8669 | 27800 | 0.0001 | - |
| 4.8757 | 27850 | 0.0001 | - |
| 4.8845 | 27900 | 0.0001 | - |
| 4.8932 | 27950 | 0.0001 | - |
| 4.9020 | 28000 | 0.0001 | - |
| 4.9107 | 28050 | 0.0001 | - |
| 4.9195 | 28100 | 0.0 | - |
| 4.9282 | 28150 | 0.0001 | - |
| 4.9370 | 28200 | 0.0001 | - |
| 4.9457 | 28250 | 0.0001 | - |
| 4.9545 | 28300 | 0.0001 | - |
| 4.9632 | 28350 | 0.0001 | - |
| 4.9720 | 28400 | 0.0001 | - |
| 4.9807 | 28450 | 0.0001 | - |
| 4.9895 | 28500 | 0.0002 | - |
| 4.9982 | 28550 | 0.0 | - |
| 5.0 | 28560 | - | 0.0425 |
* 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}
}
```
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