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mann2107
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BCMPIIRAB_MiniLM_HTTest

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
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- ---
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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: We are awaiting payment for the project completed in June. Please confirm
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- when this will be processed.
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- - text: Hello, Good morning, would you mind cancelling this rental car?
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- - text: 'Kindly book accommodation for Lindelani Mkhize as follows: Establishment:
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- City Lodge Lynwood Date checked in : 04 October 2023 Time checked in: 19h00pm
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- Date checked out: 06 October 2023 Time checked out: 07h00am'
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- - text: You've been selected for a free energy audit. Click here to schedule your
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- appointment.
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- - text: 'Please can you provide with the invoices for my stays this month as follows: 1.
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- Premier Splendid Inn Bayshore (07 Aug - 08 Aug) 2. Port Nolloth Beach Shack
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- (14 Aug - 17 Aug)'
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- metrics:
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- - silhouette_score
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- pipeline_tag: text-classification
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- library_name: setfit
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- inference: true
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- base_model: sentence-transformers/paraphrase-MiniLM-L6-v2
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- model-index:
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- - name: SetFit with sentence-transformers/paraphrase-MiniLM-L6-v2
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- results:
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- - task:
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- type: text-classification
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- name: Text Classification
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- dataset:
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- name: Unknown
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- type: unknown
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- split: test
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- metrics:
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- - type: silhouette_score
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- value: 0.6826105442176871
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- name: Silhouette_Score
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- ---
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-
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- # SetFit with sentence-transformers/paraphrase-MiniLM-L6-v2
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-
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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/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.
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-
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- The model has been trained using an efficient few-shot learning technique that involves:
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-
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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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-
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- ## Model Details
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-
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- ### Model Description
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- - **Model Type:** SetFit
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- - **Sentence Transformer body:** [sentence-transformers/paraphrase-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-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:** 128 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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-
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- ### Model Sources
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-
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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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-
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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 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> |
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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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-
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- ## Evaluation
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-
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- ### Metrics
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- | Label | Silhouette_Score |
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- |:--------|:-----------------|
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- | **all** | 0.6826 |
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-
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- ## Uses
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-
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- ### Direct Use for Inference
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-
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- First install the SetFit library:
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-
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- ```bash
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- pip install setfit
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- ```
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-
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- Then you can load this model and run inference.
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-
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- ```python
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- from setfit import SetFitModel
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-
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- # Download from the 🤗 Hub
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- model = SetFitModel.from_pretrained("mann2107/BCMPIIRAB_MiniLM_HTTest")
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- # Run inference
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- preds = model("Hello, Good morning, would you mind cancelling this rental car?")
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- ```
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-
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- <!--
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- ### Downstream Use
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-
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- *List how someone could finetune this model on their own dataset.*
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- -->
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-
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- <!--
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- ### Out-of-Scope Use
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-
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- *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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- -->
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-
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- <!--
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- ## Bias, Risks and Limitations
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-
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- *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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- -->
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-
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- <!--
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- ### Recommendations
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-
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- *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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- -->
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-
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- ## Training Details
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-
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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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-
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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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-
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- ### Training Hyperparameters
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- - batch_size: (8, 8)
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- - num_epochs: (3, 3)
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- - max_steps: -1
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- - sampling_strategy: oversampling
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- - num_iterations: 100
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- - body_learning_rate: (3e-05, 3e-05)
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- - head_learning_rate: 3e-05
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- - loss: MultipleNegativesRankingLoss
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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: True
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- - warmup_proportion: 0.1
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- - l2_weight: 0.01
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- - seed: 42
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- - eval_max_steps: -1
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- - load_best_model_at_end: False
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-
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- ### Training Results
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- | Epoch | Step | Training Loss | Validation Loss |
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- |:------:|:-----:|:-------------:|:---------------:|
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- | 0.0001 | 1 | 2.5259 | - |
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- | 0.0060 | 50 | 2.8997 | - |
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- | 0.0119 | 100 | 2.8192 | - |
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- | 0.0179 | 150 | 2.8803 | - |
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- | 0.0238 | 200 | 2.635 | - |
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- | 0.0298 | 250 | 2.5501 | - |
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- | 0.0357 | 300 | 2.4468 | - |
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- | 0.0417 | 350 | 2.1309 | - |
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- | 0.0476 | 400 | 2.0439 | - |
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- | 0.0536 | 450 | 1.9429 | - |
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- | 0.0595 | 500 | 1.9344 | - |
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- | 0.0655 | 550 | 1.8493 | - |
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- | 0.0714 | 600 | 1.7907 | - |
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- | 0.0774 | 650 | 1.7712 | - |
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- | 0.0833 | 700 | 1.7349 | - |
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- | 0.0893 | 750 | 1.7783 | - |
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- | 0.0952 | 800 | 1.7022 | - |
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- | 0.1012 | 850 | 1.6757 | - |
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- | 0.1071 | 900 | 1.709 | - |
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- | 0.1131 | 950 | 1.6231 | - |
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- | 0.1190 | 1000 | 1.6647 | - |
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- | 0.125 | 1050 | 1.7618 | - |
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- | 0.1310 | 1100 | 1.652 | - |
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- | 0.1369 | 1150 | 1.5564 | - |
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- | 0.1429 | 1200 | 1.7067 | - |
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- | 0.1488 | 1250 | 1.664 | - |
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- | 0.1548 | 1300 | 1.7426 | - |
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- | 0.1607 | 1350 | 1.6281 | - |
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- | 0.1667 | 1400 | 1.6375 | - |
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- | 0.1726 | 1450 | 1.6216 | - |
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- | 0.1786 | 1500 | 1.5998 | - |
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- | 0.1845 | 1550 | 1.4892 | - |
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- | 0.1905 | 1600 | 1.556 | - |
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- | 0.1964 | 1650 | 1.6657 | - |
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- | 0.2024 | 1700 | 1.6113 | - |
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- | 0.2083 | 1750 | 1.634 | - |
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- | 0.2143 | 1800 | 1.6615 | - |
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- | 0.2202 | 1850 | 1.5192 | - |
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- | 0.2262 | 1900 | 1.5846 | - |
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- | 0.2321 | 1950 | 1.5376 | - |
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- | 0.2381 | 2000 | 1.6028 | - |
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- | 0.2440 | 2050 | 1.5744 | - |
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- | 0.25 | 2100 | 1.645 | - |
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- | 0.2560 | 2150 | 1.5432 | - |
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- | 0.2619 | 2200 | 1.5922 | - |
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- | 0.2679 | 2250 | 1.612 | - |
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- | 0.2738 | 2300 | 1.6553 | - |
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- | 0.2798 | 2350 | 1.5797 | - |
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- | 0.2857 | 2400 | 1.5249 | - |
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- | 0.2917 | 2450 | 1.639 | - |
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- | 0.2976 | 2500 | 1.7246 | - |
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- | 0.3036 | 2550 | 1.6186 | - |
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- | 0.3095 | 2600 | 1.537 | - |
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- | 0.3155 | 2650 | 1.5701 | - |
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- | 0.3214 | 2700 | 1.6095 | - |
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- | 0.3274 | 2750 | 1.5344 | - |
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- | 0.3333 | 2800 | 1.6029 | - |
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- | 0.3393 | 2850 | 1.6141 | - |
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- | 0.3452 | 2900 | 1.5655 | - |
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- | 0.3512 | 2950 | 1.5892 | - |
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- | 0.3571 | 3000 | 1.595 | - |
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- | 0.3631 | 3050 | 1.5068 | - |
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- | 0.3690 | 3100 | 1.5826 | - |
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- | 0.375 | 3150 | 1.481 | - |
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- | 0.3810 | 3200 | 1.6001 | - |
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- | 0.3869 | 3250 | 1.4991 | - |
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- | 0.3929 | 3300 | 1.605 | - |
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- | 0.3988 | 3350 | 1.6154 | - |
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- | 0.4048 | 3400 | 1.5516 | - |
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- | 0.4107 | 3450 | 1.559 | - |
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- | 0.4167 | 3500 | 1.559 | - |
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- | 0.4226 | 3550 | 1.5725 | - |
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- | 0.4286 | 3600 | 1.5719 | - |
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- | 0.4345 | 3650 | 1.4918 | - |
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- | 0.4405 | 3700 | 1.5816 | - |
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- | 0.4464 | 3750 | 1.5017 | - |
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- | 0.4524 | 3800 | 1.5093 | - |
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- | 0.4583 | 3850 | 1.5705 | - |
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- | 0.4643 | 3900 | 1.5584 | - |
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- | 0.4702 | 3950 | 1.5328 | - |
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- | 0.4762 | 4000 | 1.4932 | - |
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- | 0.4821 | 4050 | 1.5907 | - |
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- | 0.4881 | 4100 | 1.5339 | - |
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- | 0.4940 | 4150 | 1.4954 | - |
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- | 0.5 | 4200 | 1.5256 | - |
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- | 0.5060 | 4250 | 1.5349 | - |
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- | 0.5119 | 4300 | 1.5238 | - |
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- | 0.5179 | 4350 | 1.5222 | - |
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- | 0.5238 | 4400 | 1.6318 | - |
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- | 0.5298 | 4450 | 1.5872 | - |
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- | 0.5357 | 4500 | 1.4892 | - |
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- | 0.5417 | 4550 | 1.5764 | - |
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- | 0.5476 | 4600 | 1.6123 | - |
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- | 0.5536 | 4650 | 1.4708 | - |
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- | 0.5595 | 4700 | 1.5201 | - |
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- | 0.5655 | 4750 | 1.4975 | - |
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- | 0.5714 | 4800 | 1.5402 | - |
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- | 0.5774 | 4850 | 1.5396 | - |
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- | 0.5833 | 4900 | 1.5325 | - |
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- | 0.5893 | 4950 | 1.5166 | - |
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- | 0.5952 | 5000 | 1.5216 | - |
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- | 0.6012 | 5050 | 1.5934 | - |
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- | 0.6071 | 5100 | 1.5118 | - |
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- | 0.6131 | 5150 | 1.6581 | - |
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- | 0.6190 | 5200 | 1.4251 | - |
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- | 0.625 | 5250 | 1.5259 | - |
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- | 0.6310 | 5300 | 1.4854 | - |
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- | 0.6369 | 5350 | 1.6242 | - |
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- | 0.6429 | 5400 | 1.5234 | - |
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- | 0.6488 | 5450 | 1.4594 | - |
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- | 0.6548 | 5500 | 1.5513 | - |
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- | 0.6607 | 5550 | 1.3946 | - |
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- | 0.6667 | 5600 | 1.4795 | - |
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- | 0.6726 | 5650 | 1.5203 | - |
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- | 0.6786 | 5700 | 1.5137 | - |
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- | 0.6845 | 5750 | 1.5305 | - |
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- | 0.6905 | 5800 | 1.4958 | - |
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- | 0.6964 | 5850 | 1.5028 | - |
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- | 0.7024 | 5900 | 1.419 | - |
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- | 0.7083 | 5950 | 1.5043 | - |
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- | 0.7143 | 6000 | 1.4512 | - |
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- | 0.7202 | 6050 | 1.5199 | - |
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- | 0.7262 | 6100 | 1.5097 | - |
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- | 0.7321 | 6150 | 1.4989 | - |
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- | 0.7381 | 6200 | 1.4632 | - |
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- | 0.7440 | 6250 | 1.4781 | - |
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- | 0.75 | 6300 | 1.4592 | - |
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- | 0.7560 | 6350 | 1.507 | - |
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- | 0.7619 | 6400 | 1.5535 | - |
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- | 0.7679 | 6450 | 1.3831 | - |
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- | 0.7738 | 6500 | 1.572 | - |
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- | 0.7798 | 6550 | 1.5461 | - |
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- | 0.7857 | 6600 | 1.5142 | - |
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- | 0.7917 | 6650 | 1.494 | - |
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- | 0.7976 | 6700 | 1.5487 | - |
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- | 0.8036 | 6750 | 1.4344 | - |
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- | 0.8095 | 6800 | 1.5262 | - |
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- | 0.8155 | 6850 | 1.4942 | - |
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- | 0.8214 | 6900 | 1.54 | - |
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- | 0.8274 | 6950 | 1.518 | - |
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- | 0.8333 | 7000 | 1.5765 | - |
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- | 0.8393 | 7050 | 1.5526 | - |
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- | 0.8452 | 7100 | 1.5548 | - |
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- | 0.8512 | 7150 | 1.3953 | - |
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- | 0.8571 | 7200 | 1.5273 | - |
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- | 0.8631 | 7250 | 1.4349 | - |
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- | 0.8690 | 7300 | 1.4176 | - |
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- | 0.875 | 7350 | 1.5242 | - |
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- | 0.8810 | 7400 | 1.5263 | - |
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- | 0.8869 | 7450 | 1.5435 | - |
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- | 0.8929 | 7500 | 1.4882 | - |
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- | 0.8988 | 7550 | 1.4965 | - |
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- | 0.9048 | 7600 | 1.5185 | - |
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- | 0.9107 | 7650 | 1.5739 | - |
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- | 0.9167 | 7700 | 1.5821 | - |
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- | 0.9226 | 7750 | 1.6197 | - |
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- | 0.9286 | 7800 | 1.5154 | - |
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- | 0.9345 | 7850 | 1.5844 | - |
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- | 0.9405 | 7900 | 1.5242 | - |
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- | 0.9464 | 7950 | 1.488 | - |
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- | 0.9524 | 8000 | 1.5414 | - |
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- | 0.9583 | 8050 | 1.4829 | - |
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- | 0.9643 | 8100 | 1.5162 | - |
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- | 0.9702 | 8150 | 1.4136 | - |
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- | 0.9762 | 8200 | 1.36 | - |
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- | 0.9821 | 8250 | 1.5511 | - |
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- | 0.9881 | 8300 | 1.4908 | - |
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- | 0.9940 | 8350 | 1.5312 | - |
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- | 1.0 | 8400 | 1.5008 | - |
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- | 1.0060 | 8450 | 1.4283 | - |
354
- | 1.0119 | 8500 | 1.5027 | - |
355
- | 1.0179 | 8550 | 1.48 | - |
356
- | 1.0238 | 8600 | 1.425 | - |
357
- | 1.0298 | 8650 | 1.5233 | - |
358
- | 1.0357 | 8700 | 1.4259 | - |
359
- | 1.0417 | 8750 | 1.4355 | - |
360
- | 1.0476 | 8800 | 1.5006 | - |
361
- | 1.0536 | 8850 | 1.511 | - |
362
- | 1.0595 | 8900 | 1.3043 | - |
363
- | 1.0655 | 8950 | 1.5039 | - |
364
- | 1.0714 | 9000 | 1.4909 | - |
365
- | 1.0774 | 9050 | 1.4493 | - |
366
- | 1.0833 | 9100 | 1.4877 | - |
367
- | 1.0893 | 9150 | 1.5232 | - |
368
- | 1.0952 | 9200 | 1.6282 | - |
369
- | 1.1012 | 9250 | 1.4438 | - |
370
- | 1.1071 | 9300 | 1.5234 | - |
371
- | 1.1131 | 9350 | 1.5368 | - |
372
- | 1.1190 | 9400 | 1.5029 | - |
373
- | 1.125 | 9450 | 1.4776 | - |
374
- | 1.1310 | 9500 | 1.4877 | - |
375
- | 1.1369 | 9550 | 1.4917 | - |
376
- | 1.1429 | 9600 | 1.4474 | - |
377
- | 1.1488 | 9650 | 1.3519 | - |
378
- | 1.1548 | 9700 | 1.5118 | - |
379
- | 1.1607 | 9750 | 1.5507 | - |
380
- | 1.1667 | 9800 | 1.4395 | - |
381
- | 1.1726 | 9850 | 1.4883 | - |
382
- | 1.1786 | 9900 | 1.4524 | - |
383
- | 1.1845 | 9950 | 1.4756 | - |
384
- | 1.1905 | 10000 | 1.5255 | - |
385
- | 1.1964 | 10050 | 1.4795 | - |
386
- | 1.2024 | 10100 | 1.5277 | - |
387
- | 1.2083 | 10150 | 1.477 | - |
388
- | 1.2143 | 10200 | 1.4438 | - |
389
- | 1.2202 | 10250 | 1.5517 | - |
390
- | 1.2262 | 10300 | 1.588 | - |
391
- | 1.2321 | 10350 | 1.5352 | - |
392
- | 1.2381 | 10400 | 1.3697 | - |
393
- | 1.2440 | 10450 | 1.4449 | - |
394
- | 1.25 | 10500 | 1.4473 | - |
395
- | 1.2560 | 10550 | 1.5566 | - |
396
- | 1.2619 | 10600 | 1.4502 | - |
397
- | 1.2679 | 10650 | 1.4821 | - |
398
- | 1.2738 | 10700 | 1.4296 | - |
399
- | 1.2798 | 10750 | 1.4801 | - |
400
- | 1.2857 | 10800 | 1.4542 | - |
401
- | 1.2917 | 10850 | 1.4258 | - |
402
- | 1.2976 | 10900 | 1.4142 | - |
403
- | 1.3036 | 10950 | 1.6023 | - |
404
- | 1.3095 | 11000 | 1.4291 | - |
405
- | 1.3155 | 11050 | 1.5386 | - |
406
- | 1.3214 | 11100 | 1.4433 | - |
407
- | 1.3274 | 11150 | 1.4218 | - |
408
- | 1.3333 | 11200 | 1.4345 | - |
409
- | 1.3393 | 11250 | 1.5321 | - |
410
- | 1.3452 | 11300 | 1.5001 | - |
411
- | 1.3512 | 11350 | 1.3381 | - |
412
- | 1.3571 | 11400 | 1.4819 | - |
413
- | 1.3631 | 11450 | 1.4676 | - |
414
- | 1.3690 | 11500 | 1.5056 | - |
415
- | 1.375 | 11550 | 1.5052 | - |
416
- | 1.3810 | 11600 | 1.5217 | - |
417
- | 1.3869 | 11650 | 1.391 | - |
418
- | 1.3929 | 11700 | 1.46 | - |
419
- | 1.3988 | 11750 | 1.5022 | - |
420
- | 1.4048 | 11800 | 1.4579 | - |
421
- | 1.4107 | 11850 | 1.5025 | - |
422
- | 1.4167 | 11900 | 1.5058 | - |
423
- | 1.4226 | 11950 | 1.5107 | - |
424
- | 1.4286 | 12000 | 1.5327 | - |
425
- | 1.4345 | 12050 | 1.4727 | - |
426
- | 1.4405 | 12100 | 1.4353 | - |
427
- | 1.4464 | 12150 | 1.42 | - |
428
- | 1.4524 | 12200 | 1.5349 | - |
429
- | 1.4583 | 12250 | 1.473 | - |
430
- | 1.4643 | 12300 | 1.5228 | - |
431
- | 1.4702 | 12350 | 1.498 | - |
432
- | 1.4762 | 12400 | 1.4321 | - |
433
- | 1.4821 | 12450 | 1.5058 | - |
434
- | 1.4881 | 12500 | 1.4601 | - |
435
- | 1.4940 | 12550 | 1.5346 | - |
436
- | 1.5 | 12600 | 1.5985 | - |
437
- | 1.5060 | 12650 | 1.4683 | - |
438
- | 1.5119 | 12700 | 1.5088 | - |
439
- | 1.5179 | 12750 | 1.5082 | - |
440
- | 1.5238 | 12800 | 1.5784 | - |
441
- | 1.5298 | 12850 | 1.5241 | - |
442
- | 1.5357 | 12900 | 1.434 | - |
443
- | 1.5417 | 12950 | 1.452 | - |
444
- | 1.5476 | 13000 | 1.4459 | - |
445
- | 1.5536 | 13050 | 1.4965 | - |
446
- | 1.5595 | 13100 | 1.5313 | - |
447
- | 1.5655 | 13150 | 1.4781 | - |
448
- | 1.5714 | 13200 | 1.5502 | - |
449
- | 1.5774 | 13250 | 1.4602 | - |
450
- | 1.5833 | 13300 | 1.4477 | - |
451
- | 1.5893 | 13350 | 1.4736 | - |
452
- | 1.5952 | 13400 | 1.5035 | - |
453
- | 1.6012 | 13450 | 1.4829 | - |
454
- | 1.6071 | 13500 | 1.4941 | - |
455
- | 1.6131 | 13550 | 1.5462 | - |
456
- | 1.6190 | 13600 | 1.4764 | - |
457
- | 1.625 | 13650 | 1.4838 | - |
458
- | 1.6310 | 13700 | 1.4264 | - |
459
- | 1.6369 | 13750 | 1.6312 | - |
460
- | 1.6429 | 13800 | 1.4323 | - |
461
- | 1.6488 | 13850 | 1.514 | - |
462
- | 1.6548 | 13900 | 1.3944 | - |
463
- | 1.6607 | 13950 | 1.4709 | - |
464
- | 1.6667 | 14000 | 1.4268 | - |
465
- | 1.6726 | 14050 | 1.5699 | - |
466
- | 1.6786 | 14100 | 1.5433 | - |
467
- | 1.6845 | 14150 | 1.431 | - |
468
- | 1.6905 | 14200 | 1.5421 | - |
469
- | 1.6964 | 14250 | 1.4854 | - |
470
- | 1.7024 | 14300 | 1.4341 | - |
471
- | 1.7083 | 14350 | 1.4321 | - |
472
- | 1.7143 | 14400 | 1.4284 | - |
473
- | 1.7202 | 14450 | 1.4725 | - |
474
- | 1.7262 | 14500 | 1.5744 | - |
475
- | 1.7321 | 14550 | 1.4892 | - |
476
- | 1.7381 | 14600 | 1.5357 | - |
477
- | 1.7440 | 14650 | 1.4536 | - |
478
- | 1.75 | 14700 | 1.4861 | - |
479
- | 1.7560 | 14750 | 1.5268 | - |
480
- | 1.7619 | 14800 | 1.4613 | - |
481
- | 1.7679 | 14850 | 1.4313 | - |
482
- | 1.7738 | 14900 | 1.4522 | - |
483
- | 1.7798 | 14950 | 1.4291 | - |
484
- | 1.7857 | 15000 | 1.5054 | - |
485
- | 1.7917 | 15050 | 1.495 | - |
486
- | 1.7976 | 15100 | 1.5352 | - |
487
- | 1.8036 | 15150 | 1.4803 | - |
488
- | 1.8095 | 15200 | 1.3922 | - |
489
- | 1.8155 | 15250 | 1.4879 | - |
490
- | 1.8214 | 15300 | 1.4752 | - |
491
- | 1.8274 | 15350 | 1.5102 | - |
492
- | 1.8333 | 15400 | 1.4474 | - |
493
- | 1.8393 | 15450 | 1.4939 | - |
494
- | 1.8452 | 15500 | 1.5216 | - |
495
- | 1.8512 | 15550 | 1.4656 | - |
496
- | 1.8571 | 15600 | 1.5171 | - |
497
- | 1.8631 | 15650 | 1.3437 | - |
498
- | 1.8690 | 15700 | 1.4875 | - |
499
- | 1.875 | 15750 | 1.4692 | - |
500
- | 1.8810 | 15800 | 1.4804 | - |
501
- | 1.8869 | 15850 | 1.4423 | - |
502
- | 1.8929 | 15900 | 1.4592 | - |
503
- | 1.8988 | 15950 | 1.5764 | - |
504
- | 1.9048 | 16000 | 1.4083 | - |
505
- | 1.9107 | 16050 | 1.4852 | - |
506
- | 1.9167 | 16100 | 1.5158 | - |
507
- | 1.9226 | 16150 | 1.4602 | - |
508
- | 1.9286 | 16200 | 1.4465 | - |
509
- | 1.9345 | 16250 | 1.412 | - |
510
- | 1.9405 | 16300 | 1.483 | - |
511
- | 1.9464 | 16350 | 1.5342 | - |
512
- | 1.9524 | 16400 | 1.3866 | - |
513
- | 1.9583 | 16450 | 1.4318 | - |
514
- | 1.9643 | 16500 | 1.6241 | - |
515
- | 1.9702 | 16550 | 1.5514 | - |
516
- | 1.9762 | 16600 | 1.46 | - |
517
- | 1.9821 | 16650 | 1.4069 | - |
518
- | 1.9881 | 16700 | 1.457 | - |
519
- | 1.9940 | 16750 | 1.4273 | - |
520
- | 2.0 | 16800 | 1.3673 | - |
521
- | 2.0060 | 16850 | 1.3753 | - |
522
- | 2.0119 | 16900 | 1.4279 | - |
523
- | 2.0179 | 16950 | 1.3897 | - |
524
- | 2.0238 | 17000 | 1.4659 | - |
525
- | 2.0298 | 17050 | 1.4494 | - |
526
- | 2.0357 | 17100 | 1.4533 | - |
527
- | 2.0417 | 17150 | 1.3735 | - |
528
- | 2.0476 | 17200 | 1.4232 | - |
529
- | 2.0536 | 17250 | 1.4229 | - |
530
- | 2.0595 | 17300 | 1.4597 | - |
531
- | 2.0655 | 17350 | 1.4825 | - |
532
- | 2.0714 | 17400 | 1.4661 | - |
533
- | 2.0774 | 17450 | 1.4332 | - |
534
- | 2.0833 | 17500 | 1.5895 | - |
535
- | 2.0893 | 17550 | 1.4824 | - |
536
- | 2.0952 | 17600 | 1.4472 | - |
537
- | 2.1012 | 17650 | 1.4001 | - |
538
- | 2.1071 | 17700 | 1.4638 | - |
539
- | 2.1131 | 17750 | 1.4651 | - |
540
- | 2.1190 | 17800 | 1.4711 | - |
541
- | 2.125 | 17850 | 1.4474 | - |
542
- | 2.1310 | 17900 | 1.4544 | - |
543
- | 2.1369 | 17950 | 1.3935 | - |
544
- | 2.1429 | 18000 | 1.4449 | - |
545
- | 2.1488 | 18050 | 1.4671 | - |
546
- | 2.1548 | 18100 | 1.4169 | - |
547
- | 2.1607 | 18150 | 1.5095 | - |
548
- | 2.1667 | 18200 | 1.4186 | - |
549
- | 2.1726 | 18250 | 1.4574 | - |
550
- | 2.1786 | 18300 | 1.4448 | - |
551
- | 2.1845 | 18350 | 1.5045 | - |
552
- | 2.1905 | 18400 | 1.4998 | - |
553
- | 2.1964 | 18450 | 1.3559 | - |
554
- | 2.2024 | 18500 | 1.4862 | - |
555
- | 2.2083 | 18550 | 1.4018 | - |
556
- | 2.2143 | 18600 | 1.4407 | - |
557
- | 2.2202 | 18650 | 1.5812 | - |
558
- | 2.2262 | 18700 | 1.4268 | - |
559
- | 2.2321 | 18750 | 1.4434 | - |
560
- | 2.2381 | 18800 | 1.5467 | - |
561
- | 2.2440 | 18850 | 1.4281 | - |
562
- | 2.25 | 18900 | 1.482 | - |
563
- | 2.2560 | 18950 | 1.5261 | - |
564
- | 2.2619 | 19000 | 1.4152 | - |
565
- | 2.2679 | 19050 | 1.5267 | - |
566
- | 2.2738 | 19100 | 1.4237 | - |
567
- | 2.2798 | 19150 | 1.5455 | - |
568
- | 2.2857 | 19200 | 1.4679 | - |
569
- | 2.2917 | 19250 | 1.3398 | - |
570
- | 2.2976 | 19300 | 1.4697 | - |
571
- | 2.3036 | 19350 | 1.4176 | - |
572
- | 2.3095 | 19400 | 1.4661 | - |
573
- | 2.3155 | 19450 | 1.4397 | - |
574
- | 2.3214 | 19500 | 1.5095 | - |
575
- | 2.3274 | 19550 | 1.4873 | - |
576
- | 2.3333 | 19600 | 1.4312 | - |
577
- | 2.3393 | 19650 | 1.441 | - |
578
- | 2.3452 | 19700 | 1.4341 | - |
579
- | 2.3512 | 19750 | 1.4229 | - |
580
- | 2.3571 | 19800 | 1.4917 | - |
581
- | 2.3631 | 19850 | 1.4397 | - |
582
- | 2.3690 | 19900 | 1.4027 | - |
583
- | 2.375 | 19950 | 1.5022 | - |
584
- | 2.3810 | 20000 | 1.441 | - |
585
- | 2.3869 | 20050 | 1.4392 | - |
586
- | 2.3929 | 20100 | 1.4454 | - |
587
- | 2.3988 | 20150 | 1.4886 | - |
588
- | 2.4048 | 20200 | 1.4776 | - |
589
- | 2.4107 | 20250 | 1.3946 | - |
590
- | 2.4167 | 20300 | 1.5492 | - |
591
- | 2.4226 | 20350 | 1.534 | - |
592
- | 2.4286 | 20400 | 1.4011 | - |
593
- | 2.4345 | 20450 | 1.5276 | - |
594
- | 2.4405 | 20500 | 1.4633 | - |
595
- | 2.4464 | 20550 | 1.4446 | - |
596
- | 2.4524 | 20600 | 1.5005 | - |
597
- | 2.4583 | 20650 | 1.4818 | - |
598
- | 2.4643 | 20700 | 1.4319 | - |
599
- | 2.4702 | 20750 | 1.4406 | - |
600
- | 2.4762 | 20800 | 1.4496 | - |
601
- | 2.4821 | 20850 | 1.4963 | - |
602
- | 2.4881 | 20900 | 1.4731 | - |
603
- | 2.4940 | 20950 | 1.4536 | - |
604
- | 2.5 | 21000 | 1.5153 | - |
605
- | 2.5060 | 21050 | 1.5522 | - |
606
- | 2.5119 | 21100 | 1.3759 | - |
607
- | 2.5179 | 21150 | 1.4285 | - |
608
- | 2.5238 | 21200 | 1.4162 | - |
609
- | 2.5298 | 21250 | 1.4383 | - |
610
- | 2.5357 | 21300 | 1.4408 | - |
611
- | 2.5417 | 21350 | 1.4009 | - |
612
- | 2.5476 | 21400 | 1.4589 | - |
613
- | 2.5536 | 21450 | 1.4478 | - |
614
- | 2.5595 | 21500 | 1.4876 | - |
615
- | 2.5655 | 21550 | 1.4206 | - |
616
- | 2.5714 | 21600 | 1.4927 | - |
617
- | 2.5774 | 21650 | 1.5047 | - |
618
- | 2.5833 | 21700 | 1.3988 | - |
619
- | 2.5893 | 21750 | 1.4714 | - |
620
- | 2.5952 | 21800 | 1.3605 | - |
621
- | 2.6012 | 21850 | 1.5635 | - |
622
- | 2.6071 | 21900 | 1.4678 | - |
623
- | 2.6131 | 21950 | 1.4618 | - |
624
- | 2.6190 | 22000 | 1.4407 | - |
625
- | 2.625 | 22050 | 1.5451 | - |
626
- | 2.6310 | 22100 | 1.4844 | - |
627
- | 2.6369 | 22150 | 1.4088 | - |
628
- | 2.6429 | 22200 | 1.5056 | - |
629
- | 2.6488 | 22250 | 1.4678 | - |
630
- | 2.6548 | 22300 | 1.4262 | - |
631
- | 2.6607 | 22350 | 1.4492 | - |
632
- | 2.6667 | 22400 | 1.4463 | - |
633
- | 2.6726 | 22450 | 1.3851 | - |
634
- | 2.6786 | 22500 | 1.513 | - |
635
- | 2.6845 | 22550 | 1.45 | - |
636
- | 2.6905 | 22600 | 1.4382 | - |
637
- | 2.6964 | 22650 | 1.4637 | - |
638
- | 2.7024 | 22700 | 1.4487 | - |
639
- | 2.7083 | 22750 | 1.4507 | - |
640
- | 2.7143 | 22800 | 1.5065 | - |
641
- | 2.7202 | 22850 | 1.4116 | - |
642
- | 2.7262 | 22900 | 1.479 | - |
643
- | 2.7321 | 22950 | 1.444 | - |
644
- | 2.7381 | 23000 | 1.4056 | - |
645
- | 2.7440 | 23050 | 1.3913 | - |
646
- | 2.75 | 23100 | 1.5108 | - |
647
- | 2.7560 | 23150 | 1.4092 | - |
648
- | 2.7619 | 23200 | 1.4341 | - |
649
- | 2.7679 | 23250 | 1.4274 | - |
650
- | 2.7738 | 23300 | 1.4748 | - |
651
- | 2.7798 | 23350 | 1.3819 | - |
652
- | 2.7857 | 23400 | 1.5012 | - |
653
- | 2.7917 | 23450 | 1.3594 | - |
654
- | 2.7976 | 23500 | 1.4708 | - |
655
- | 2.8036 | 23550 | 1.4425 | - |
656
- | 2.8095 | 23600 | 1.3566 | - |
657
- | 2.8155 | 23650 | 1.456 | - |
658
- | 2.8214 | 23700 | 1.5937 | - |
659
- | 2.8274 | 23750 | 1.3835 | - |
660
- | 2.8333 | 23800 | 1.4137 | - |
661
- | 2.8393 | 23850 | 1.3861 | - |
662
- | 2.8452 | 23900 | 1.4249 | - |
663
- | 2.8512 | 23950 | 1.3599 | - |
664
- | 2.8571 | 24000 | 1.4789 | - |
665
- | 2.8631 | 24050 | 1.4527 | - |
666
- | 2.8690 | 24100 | 1.4406 | - |
667
- | 2.875 | 24150 | 1.4301 | - |
668
- | 2.8810 | 24200 | 1.4059 | - |
669
- | 2.8869 | 24250 | 1.5052 | - |
670
- | 2.8929 | 24300 | 1.4429 | - |
671
- | 2.8988 | 24350 | 1.5183 | - |
672
- | 2.9048 | 24400 | 1.4288 | - |
673
- | 2.9107 | 24450 | 1.4673 | - |
674
- | 2.9167 | 24500 | 1.4582 | - |
675
- | 2.9226 | 24550 | 1.4792 | - |
676
- | 2.9286 | 24600 | 1.4598 | - |
677
- | 2.9345 | 24650 | 1.4785 | - |
678
- | 2.9405 | 24700 | 1.4259 | - |
679
- | 2.9464 | 24750 | 1.4877 | - |
680
- | 2.9524 | 24800 | 1.5162 | - |
681
- | 2.9583 | 24850 | 1.4854 | - |
682
- | 2.9643 | 24900 | 1.3679 | - |
683
- | 2.9702 | 24950 | 1.3985 | - |
684
- | 2.9762 | 25000 | 1.421 | - |
685
- | 2.9821 | 25050 | 1.5048 | - |
686
- | 2.9881 | 25100 | 1.4618 | - |
687
- | 2.9940 | 25150 | 1.5061 | - |
688
- | 3.0 | 25200 | 1.3634 | - |
689
-
690
- ### Framework Versions
691
- - Python: 3.12.0
692
- - SetFit: 1.2.0.dev0
693
- - Sentence Transformers: 3.2.1
694
- - Transformers: 4.45.2
695
- - PyTorch: 2.5.0+cpu
696
- - Datasets: 3.0.2
697
- - Tokenizers: 0.20.1
698
-
699
- ## Citation
700
-
701
- ### BibTeX
702
- ```bibtex
703
- @article{https://doi.org/10.48550/arxiv.2209.11055,
704
- doi = {10.48550/ARXIV.2209.11055},
705
- url = {https://arxiv.org/abs/2209.11055},
706
- author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
707
- keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
708
- title = {Efficient Few-Shot Learning Without Prompts},
709
- publisher = {arXiv},
710
- year = {2022},
711
- copyright = {Creative Commons Attribution 4.0 International}
712
- }
713
- ```
714
-
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- <!--
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- ## Glossary
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-
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- *Clearly define terms in order to be accessible across audiences.*
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- -->
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-
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- <!--
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- ## Model Card Authors
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-
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- *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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- -->
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-
727
- <!--
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- ## Model Card Contact
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-
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- *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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  -->
 
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+ - text: You've been selected for a free energy audit. Click here to schedule your
15
+ appointment.
16
+ - text: 'Please can you provide with the invoices for my stays this month as follows: 1.
17
+ Premier Splendid Inn Bayshore (07 Aug - 08 Aug) 2. Port Nolloth Beach Shack
18
+ (14 Aug - 17 Aug)'
19
+ metrics:
20
+ - silhouette_score
21
+ pipeline_tag: text-classification
22
+ library_name: setfit
23
+ inference: true
24
+ base_model: sentence-transformers/paraphrase-MiniLM-L6-v2
25
+ model-index:
26
+ - name: SetFit with sentence-transformers/paraphrase-MiniLM-L6-v2
27
+ results:
28
+ - task:
29
+ type: text-classification
30
+ name: Text Classification
31
+ dataset:
32
+ name: Unknown
33
+ type: unknown
34
+ split: test
35
+ metrics:
36
+ - type: silhouette_score
37
+ value: 0.6826105442176871
38
+ name: Silhouette_Score
39
+ ---
40
+
41
+ # SetFit with sentence-transformers/paraphrase-MiniLM-L6-v2
42
+
43
+ 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.
44
+
45
+ The model has been trained using an efficient few-shot learning technique that involves:
46
+
47
+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
48
+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
49
+
50
+ ## Model Details
51
+
52
+ ### Model Description
53
+ - **Model Type:** SetFit
54
+ - **Sentence Transformer body:** [sentence-transformers/paraphrase-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2)
55
+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
56
+ - **Maximum Sequence Length:** 128 tokens
57
+ - **Number of Classes:** 14 classes
58
+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
59
+ <!-- - **Language:** Unknown -->
60
+ <!-- - **License:** Unknown -->
61
+
62
+ ### Model Sources
63
+
64
+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
65
+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
66
+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
67
+
68
+ ## Evaluation
69
+
70
+ ### Metrics
71
+ | Label | Silhouette_Score |
72
+ |:--------|:-----------------|
73
+ | **all** | 0.6826 |
74
+
75
+ ## Uses
76
+
77
+ ### Direct Use for Inference
78
+
79
+ First install the SetFit library:
80
+
81
+ ```bash
82
+ pip install setfit
83
+ ```
84
+
85
+ Then you can load this model and run inference.
86
+
87
+ ```python
88
+ from setfit import SetFitModel
89
+
90
+ # Download from the 🤗 Hub
91
+ model = SetFitModel.from_pretrained("mann2107/BCMPIIRAB_MiniLM_HTTest")
92
+ # Run inference
93
+ preds = model("Hello, Good morning, would you mind cancelling this rental car?")
94
+ ```
95
+
96
+ <!--
97
+ ### Downstream Use
98
+
99
+ *List how someone could finetune this model on their own dataset.*
100
+ -->
101
+
102
+ <!--
103
+ ### Out-of-Scope Use
104
+
105
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
106
+ -->
107
+
108
+ <!--
109
+ ## Bias, Risks and Limitations
110
+
111
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
112
+ -->
113
+
114
+ <!--
115
+ ### Recommendations
116
+
117
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
118
+ -->
119
+
120
+ ## Training Details
121
+
122
+ ### Training Set Metrics
123
+ | Training set | Min | Median | Max |
124
+ |:-------------|:----|:--------|:----|
125
+ | Word count | 1 | 25.6577 | 136 |
126
+
127
+ | Label | Training Sample Count |
128
+ |:------|:----------------------|
129
+ | 0 | 24 |
130
+ | 1 | 24 |
131
+ | 2 | 24 |
132
+ | 3 | 24 |
133
+ | 4 | 24 |
134
+ | 5 | 24 |
135
+ | 6 | 24 |
136
+ | 7 | 24 |
137
+ | 8 | 24 |
138
+ | 9 | 24 |
139
+ | 10 | 24 |
140
+ | 11 | 24 |
141
+ | 12 | 24 |
142
+ | 13 | 24 |
143
+
144
+ ### Training Hyperparameters
145
+ - batch_size: (8, 8)
146
+ - num_epochs: (3, 3)
147
+ - max_steps: -1
148
+ - sampling_strategy: oversampling
149
+ - num_iterations: 100
150
+ - body_learning_rate: (3e-05, 3e-05)
151
+ - head_learning_rate: 3e-05
152
+ - loss: MultipleNegativesRankingLoss
153
+ - distance_metric: cosine_distance
154
+ - margin: 0.25
155
+ - end_to_end: False
156
+ - use_amp: True
157
+ - warmup_proportion: 0.1
158
+ - l2_weight: 0.01
159
+ - seed: 42
160
+ - eval_max_steps: -1
161
+ - load_best_model_at_end: False
162
+
163
+ ### Training Results
164
+ | Epoch | Step | Training Loss | Validation Loss |
165
+ |:------:|:-----:|:-------------:|:---------------:|
166
+ | 0.0001 | 1 | 2.5259 | - |
167
+ | 0.0060 | 50 | 2.8997 | - |
168
+ | 0.0119 | 100 | 2.8192 | - |
169
+ | 0.0179 | 150 | 2.8803 | - |
170
+ | 0.0238 | 200 | 2.635 | - |
171
+ | 0.0298 | 250 | 2.5501 | - |
172
+ | 0.0357 | 300 | 2.4468 | - |
173
+ | 0.0417 | 350 | 2.1309 | - |
174
+ | 0.0476 | 400 | 2.0439 | - |
175
+ | 0.0536 | 450 | 1.9429 | - |
176
+ | 0.0595 | 500 | 1.9344 | - |
177
+ | 0.0655 | 550 | 1.8493 | - |
178
+ | 0.0714 | 600 | 1.7907 | - |
179
+ | 0.0774 | 650 | 1.7712 | - |
180
+ | 0.0833 | 700 | 1.7349 | - |
181
+ | 0.0893 | 750 | 1.7783 | - |
182
+ | 0.0952 | 800 | 1.7022 | - |
183
+ | 0.1012 | 850 | 1.6757 | - |
184
+ | 0.1071 | 900 | 1.709 | - |
185
+ | 0.1131 | 950 | 1.6231 | - |
186
+ | 0.1190 | 1000 | 1.6647 | - |
187
+ | 0.125 | 1050 | 1.7618 | - |
188
+ | 0.1310 | 1100 | 1.652 | - |
189
+ | 0.1369 | 1150 | 1.5564 | - |
190
+ | 0.1429 | 1200 | 1.7067 | - |
191
+ | 0.1488 | 1250 | 1.664 | - |
192
+ | 0.1548 | 1300 | 1.7426 | - |
193
+ | 0.1607 | 1350 | 1.6281 | - |
194
+ | 0.1667 | 1400 | 1.6375 | - |
195
+ | 0.1726 | 1450 | 1.6216 | - |
196
+ | 0.1786 | 1500 | 1.5998 | - |
197
+ | 0.1845 | 1550 | 1.4892 | - |
198
+ | 0.1905 | 1600 | 1.556 | - |
199
+ | 0.1964 | 1650 | 1.6657 | - |
200
+ | 0.2024 | 1700 | 1.6113 | - |
201
+ | 0.2083 | 1750 | 1.634 | - |
202
+ | 0.2143 | 1800 | 1.6615 | - |
203
+ | 0.2202 | 1850 | 1.5192 | - |
204
+ | 0.2262 | 1900 | 1.5846 | - |
205
+ | 0.2321 | 1950 | 1.5376 | - |
206
+ | 0.2381 | 2000 | 1.6028 | - |
207
+ | 0.2440 | 2050 | 1.5744 | - |
208
+ | 0.25 | 2100 | 1.645 | - |
209
+ | 0.2560 | 2150 | 1.5432 | - |
210
+ | 0.2619 | 2200 | 1.5922 | - |
211
+ | 0.2679 | 2250 | 1.612 | - |
212
+ | 0.2738 | 2300 | 1.6553 | - |
213
+ | 0.2798 | 2350 | 1.5797 | - |
214
+ | 0.2857 | 2400 | 1.5249 | - |
215
+ | 0.2917 | 2450 | 1.639 | - |
216
+ | 0.2976 | 2500 | 1.7246 | - |
217
+ | 0.3036 | 2550 | 1.6186 | - |
218
+ | 0.3095 | 2600 | 1.537 | - |
219
+ | 0.3155 | 2650 | 1.5701 | - |
220
+ | 0.3214 | 2700 | 1.6095 | - |
221
+ | 0.3274 | 2750 | 1.5344 | - |
222
+ | 0.3333 | 2800 | 1.6029 | - |
223
+ | 0.3393 | 2850 | 1.6141 | - |
224
+ | 0.3452 | 2900 | 1.5655 | - |
225
+ | 0.3512 | 2950 | 1.5892 | - |
226
+ | 0.3571 | 3000 | 1.595 | - |
227
+ | 0.3631 | 3050 | 1.5068 | - |
228
+ | 0.3690 | 3100 | 1.5826 | - |
229
+ | 0.375 | 3150 | 1.481 | - |
230
+ | 0.3810 | 3200 | 1.6001 | - |
231
+ | 0.3869 | 3250 | 1.4991 | - |
232
+ | 0.3929 | 3300 | 1.605 | - |
233
+ | 0.3988 | 3350 | 1.6154 | - |
234
+ | 0.4048 | 3400 | 1.5516 | - |
235
+ | 0.4107 | 3450 | 1.559 | - |
236
+ | 0.4167 | 3500 | 1.559 | - |
237
+ | 0.4226 | 3550 | 1.5725 | - |
238
+ | 0.4286 | 3600 | 1.5719 | - |
239
+ | 0.4345 | 3650 | 1.4918 | - |
240
+ | 0.4405 | 3700 | 1.5816 | - |
241
+ | 0.4464 | 3750 | 1.5017 | - |
242
+ | 0.4524 | 3800 | 1.5093 | - |
243
+ | 0.4583 | 3850 | 1.5705 | - |
244
+ | 0.4643 | 3900 | 1.5584 | - |
245
+ | 0.4702 | 3950 | 1.5328 | - |
246
+ | 0.4762 | 4000 | 1.4932 | - |
247
+ | 0.4821 | 4050 | 1.5907 | - |
248
+ | 0.4881 | 4100 | 1.5339 | - |
249
+ | 0.4940 | 4150 | 1.4954 | - |
250
+ | 0.5 | 4200 | 1.5256 | - |
251
+ | 0.5060 | 4250 | 1.5349 | - |
252
+ | 0.5119 | 4300 | 1.5238 | - |
253
+ | 0.5179 | 4350 | 1.5222 | - |
254
+ | 0.5238 | 4400 | 1.6318 | - |
255
+ | 0.5298 | 4450 | 1.5872 | - |
256
+ | 0.5357 | 4500 | 1.4892 | - |
257
+ | 0.5417 | 4550 | 1.5764 | - |
258
+ | 0.5476 | 4600 | 1.6123 | - |
259
+ | 0.5536 | 4650 | 1.4708 | - |
260
+ | 0.5595 | 4700 | 1.5201 | - |
261
+ | 0.5655 | 4750 | 1.4975 | - |
262
+ | 0.5714 | 4800 | 1.5402 | - |
263
+ | 0.5774 | 4850 | 1.5396 | - |
264
+ | 0.5833 | 4900 | 1.5325 | - |
265
+ | 0.5893 | 4950 | 1.5166 | - |
266
+ | 0.5952 | 5000 | 1.5216 | - |
267
+ | 0.6012 | 5050 | 1.5934 | - |
268
+ | 0.6071 | 5100 | 1.5118 | - |
269
+ | 0.6131 | 5150 | 1.6581 | - |
270
+ | 0.6190 | 5200 | 1.4251 | - |
271
+ | 0.625 | 5250 | 1.5259 | - |
272
+ | 0.6310 | 5300 | 1.4854 | - |
273
+ | 0.6369 | 5350 | 1.6242 | - |
274
+ | 0.6429 | 5400 | 1.5234 | - |
275
+ | 0.6488 | 5450 | 1.4594 | - |
276
+ | 0.6548 | 5500 | 1.5513 | - |
277
+ | 0.6607 | 5550 | 1.3946 | - |
278
+ | 0.6667 | 5600 | 1.4795 | - |
279
+ | 0.6726 | 5650 | 1.5203 | - |
280
+ | 0.6786 | 5700 | 1.5137 | - |
281
+ | 0.6845 | 5750 | 1.5305 | - |
282
+ | 0.6905 | 5800 | 1.4958 | - |
283
+ | 0.6964 | 5850 | 1.5028 | - |
284
+ | 0.7024 | 5900 | 1.419 | - |
285
+ | 0.7083 | 5950 | 1.5043 | - |
286
+ | 0.7143 | 6000 | 1.4512 | - |
287
+ | 0.7202 | 6050 | 1.5199 | - |
288
+ | 0.7262 | 6100 | 1.5097 | - |
289
+ | 0.7321 | 6150 | 1.4989 | - |
290
+ | 0.7381 | 6200 | 1.4632 | - |
291
+ | 0.7440 | 6250 | 1.4781 | - |
292
+ | 0.75 | 6300 | 1.4592 | - |
293
+ | 0.7560 | 6350 | 1.507 | - |
294
+ | 0.7619 | 6400 | 1.5535 | - |
295
+ | 0.7679 | 6450 | 1.3831 | - |
296
+ | 0.7738 | 6500 | 1.572 | - |
297
+ | 0.7798 | 6550 | 1.5461 | - |
298
+ | 0.7857 | 6600 | 1.5142 | - |
299
+ | 0.7917 | 6650 | 1.494 | - |
300
+ | 0.7976 | 6700 | 1.5487 | - |
301
+ | 0.8036 | 6750 | 1.4344 | - |
302
+ | 0.8095 | 6800 | 1.5262 | - |
303
+ | 0.8155 | 6850 | 1.4942 | - |
304
+ | 0.8214 | 6900 | 1.54 | - |
305
+ | 0.8274 | 6950 | 1.518 | - |
306
+ | 0.8333 | 7000 | 1.5765 | - |
307
+ | 0.8393 | 7050 | 1.5526 | - |
308
+ | 0.8452 | 7100 | 1.5548 | - |
309
+ | 0.8512 | 7150 | 1.3953 | - |
310
+ | 0.8571 | 7200 | 1.5273 | - |
311
+ | 0.8631 | 7250 | 1.4349 | - |
312
+ | 0.8690 | 7300 | 1.4176 | - |
313
+ | 0.875 | 7350 | 1.5242 | - |
314
+ | 0.8810 | 7400 | 1.5263 | - |
315
+ | 0.8869 | 7450 | 1.5435 | - |
316
+ | 0.8929 | 7500 | 1.4882 | - |
317
+ | 0.8988 | 7550 | 1.4965 | - |
318
+ | 0.9048 | 7600 | 1.5185 | - |
319
+ | 0.9107 | 7650 | 1.5739 | - |
320
+ | 0.9167 | 7700 | 1.5821 | - |
321
+ | 0.9226 | 7750 | 1.6197 | - |
322
+ | 0.9286 | 7800 | 1.5154 | - |
323
+ | 0.9345 | 7850 | 1.5844 | - |
324
+ | 0.9405 | 7900 | 1.5242 | - |
325
+ | 0.9464 | 7950 | 1.488 | - |
326
+ | 0.9524 | 8000 | 1.5414 | - |
327
+ | 0.9583 | 8050 | 1.4829 | - |
328
+ | 0.9643 | 8100 | 1.5162 | - |
329
+ | 0.9702 | 8150 | 1.4136 | - |
330
+ | 0.9762 | 8200 | 1.36 | - |
331
+ | 0.9821 | 8250 | 1.5511 | - |
332
+ | 0.9881 | 8300 | 1.4908 | - |
333
+ | 0.9940 | 8350 | 1.5312 | - |
334
+ | 1.0 | 8400 | 1.5008 | - |
335
+ | 1.0060 | 8450 | 1.4283 | - |
336
+ | 1.0119 | 8500 | 1.5027 | - |
337
+ | 1.0179 | 8550 | 1.48 | - |
338
+ | 1.0238 | 8600 | 1.425 | - |
339
+ | 1.0298 | 8650 | 1.5233 | - |
340
+ | 1.0357 | 8700 | 1.4259 | - |
341
+ | 1.0417 | 8750 | 1.4355 | - |
342
+ | 1.0476 | 8800 | 1.5006 | - |
343
+ | 1.0536 | 8850 | 1.511 | - |
344
+ | 1.0595 | 8900 | 1.3043 | - |
345
+ | 1.0655 | 8950 | 1.5039 | - |
346
+ | 1.0714 | 9000 | 1.4909 | - |
347
+ | 1.0774 | 9050 | 1.4493 | - |
348
+ | 1.0833 | 9100 | 1.4877 | - |
349
+ | 1.0893 | 9150 | 1.5232 | - |
350
+ | 1.0952 | 9200 | 1.6282 | - |
351
+ | 1.1012 | 9250 | 1.4438 | - |
352
+ | 1.1071 | 9300 | 1.5234 | - |
353
+ | 1.1131 | 9350 | 1.5368 | - |
354
+ | 1.1190 | 9400 | 1.5029 | - |
355
+ | 1.125 | 9450 | 1.4776 | - |
356
+ | 1.1310 | 9500 | 1.4877 | - |
357
+ | 1.1369 | 9550 | 1.4917 | - |
358
+ | 1.1429 | 9600 | 1.4474 | - |
359
+ | 1.1488 | 9650 | 1.3519 | - |
360
+ | 1.1548 | 9700 | 1.5118 | - |
361
+ | 1.1607 | 9750 | 1.5507 | - |
362
+ | 1.1667 | 9800 | 1.4395 | - |
363
+ | 1.1726 | 9850 | 1.4883 | - |
364
+ | 1.1786 | 9900 | 1.4524 | - |
365
+ | 1.1845 | 9950 | 1.4756 | - |
366
+ | 1.1905 | 10000 | 1.5255 | - |
367
+ | 1.1964 | 10050 | 1.4795 | - |
368
+ | 1.2024 | 10100 | 1.5277 | - |
369
+ | 1.2083 | 10150 | 1.477 | - |
370
+ | 1.2143 | 10200 | 1.4438 | - |
371
+ | 1.2202 | 10250 | 1.5517 | - |
372
+ | 1.2262 | 10300 | 1.588 | - |
373
+ | 1.2321 | 10350 | 1.5352 | - |
374
+ | 1.2381 | 10400 | 1.3697 | - |
375
+ | 1.2440 | 10450 | 1.4449 | - |
376
+ | 1.25 | 10500 | 1.4473 | - |
377
+ | 1.2560 | 10550 | 1.5566 | - |
378
+ | 1.2619 | 10600 | 1.4502 | - |
379
+ | 1.2679 | 10650 | 1.4821 | - |
380
+ | 1.2738 | 10700 | 1.4296 | - |
381
+ | 1.2798 | 10750 | 1.4801 | - |
382
+ | 1.2857 | 10800 | 1.4542 | - |
383
+ | 1.2917 | 10850 | 1.4258 | - |
384
+ | 1.2976 | 10900 | 1.4142 | - |
385
+ | 1.3036 | 10950 | 1.6023 | - |
386
+ | 1.3095 | 11000 | 1.4291 | - |
387
+ | 1.3155 | 11050 | 1.5386 | - |
388
+ | 1.3214 | 11100 | 1.4433 | - |
389
+ | 1.3274 | 11150 | 1.4218 | - |
390
+ | 1.3333 | 11200 | 1.4345 | - |
391
+ | 1.3393 | 11250 | 1.5321 | - |
392
+ | 1.3452 | 11300 | 1.5001 | - |
393
+ | 1.3512 | 11350 | 1.3381 | - |
394
+ | 1.3571 | 11400 | 1.4819 | - |
395
+ | 1.3631 | 11450 | 1.4676 | - |
396
+ | 1.3690 | 11500 | 1.5056 | - |
397
+ | 1.375 | 11550 | 1.5052 | - |
398
+ | 1.3810 | 11600 | 1.5217 | - |
399
+ | 1.3869 | 11650 | 1.391 | - |
400
+ | 1.3929 | 11700 | 1.46 | - |
401
+ | 1.3988 | 11750 | 1.5022 | - |
402
+ | 1.4048 | 11800 | 1.4579 | - |
403
+ | 1.4107 | 11850 | 1.5025 | - |
404
+ | 1.4167 | 11900 | 1.5058 | - |
405
+ | 1.4226 | 11950 | 1.5107 | - |
406
+ | 1.4286 | 12000 | 1.5327 | - |
407
+ | 1.4345 | 12050 | 1.4727 | - |
408
+ | 1.4405 | 12100 | 1.4353 | - |
409
+ | 1.4464 | 12150 | 1.42 | - |
410
+ | 1.4524 | 12200 | 1.5349 | - |
411
+ | 1.4583 | 12250 | 1.473 | - |
412
+ | 1.4643 | 12300 | 1.5228 | - |
413
+ | 1.4702 | 12350 | 1.498 | - |
414
+ | 1.4762 | 12400 | 1.4321 | - |
415
+ | 1.4821 | 12450 | 1.5058 | - |
416
+ | 1.4881 | 12500 | 1.4601 | - |
417
+ | 1.4940 | 12550 | 1.5346 | - |
418
+ | 1.5 | 12600 | 1.5985 | - |
419
+ | 1.5060 | 12650 | 1.4683 | - |
420
+ | 1.5119 | 12700 | 1.5088 | - |
421
+ | 1.5179 | 12750 | 1.5082 | - |
422
+ | 1.5238 | 12800 | 1.5784 | - |
423
+ | 1.5298 | 12850 | 1.5241 | - |
424
+ | 1.5357 | 12900 | 1.434 | - |
425
+ | 1.5417 | 12950 | 1.452 | - |
426
+ | 1.5476 | 13000 | 1.4459 | - |
427
+ | 1.5536 | 13050 | 1.4965 | - |
428
+ | 1.5595 | 13100 | 1.5313 | - |
429
+ | 1.5655 | 13150 | 1.4781 | - |
430
+ | 1.5714 | 13200 | 1.5502 | - |
431
+ | 1.5774 | 13250 | 1.4602 | - |
432
+ | 1.5833 | 13300 | 1.4477 | - |
433
+ | 1.5893 | 13350 | 1.4736 | - |
434
+ | 1.5952 | 13400 | 1.5035 | - |
435
+ | 1.6012 | 13450 | 1.4829 | - |
436
+ | 1.6071 | 13500 | 1.4941 | - |
437
+ | 1.6131 | 13550 | 1.5462 | - |
438
+ | 1.6190 | 13600 | 1.4764 | - |
439
+ | 1.625 | 13650 | 1.4838 | - |
440
+ | 1.6310 | 13700 | 1.4264 | - |
441
+ | 1.6369 | 13750 | 1.6312 | - |
442
+ | 1.6429 | 13800 | 1.4323 | - |
443
+ | 1.6488 | 13850 | 1.514 | - |
444
+ | 1.6548 | 13900 | 1.3944 | - |
445
+ | 1.6607 | 13950 | 1.4709 | - |
446
+ | 1.6667 | 14000 | 1.4268 | - |
447
+ | 1.6726 | 14050 | 1.5699 | - |
448
+ | 1.6786 | 14100 | 1.5433 | - |
449
+ | 1.6845 | 14150 | 1.431 | - |
450
+ | 1.6905 | 14200 | 1.5421 | - |
451
+ | 1.6964 | 14250 | 1.4854 | - |
452
+ | 1.7024 | 14300 | 1.4341 | - |
453
+ | 1.7083 | 14350 | 1.4321 | - |
454
+ | 1.7143 | 14400 | 1.4284 | - |
455
+ | 1.7202 | 14450 | 1.4725 | - |
456
+ | 1.7262 | 14500 | 1.5744 | - |
457
+ | 1.7321 | 14550 | 1.4892 | - |
458
+ | 1.7381 | 14600 | 1.5357 | - |
459
+ | 1.7440 | 14650 | 1.4536 | - |
460
+ | 1.75 | 14700 | 1.4861 | - |
461
+ | 1.7560 | 14750 | 1.5268 | - |
462
+ | 1.7619 | 14800 | 1.4613 | - |
463
+ | 1.7679 | 14850 | 1.4313 | - |
464
+ | 1.7738 | 14900 | 1.4522 | - |
465
+ | 1.7798 | 14950 | 1.4291 | - |
466
+ | 1.7857 | 15000 | 1.5054 | - |
467
+ | 1.7917 | 15050 | 1.495 | - |
468
+ | 1.7976 | 15100 | 1.5352 | - |
469
+ | 1.8036 | 15150 | 1.4803 | - |
470
+ | 1.8095 | 15200 | 1.3922 | - |
471
+ | 1.8155 | 15250 | 1.4879 | - |
472
+ | 1.8214 | 15300 | 1.4752 | - |
473
+ | 1.8274 | 15350 | 1.5102 | - |
474
+ | 1.8333 | 15400 | 1.4474 | - |
475
+ | 1.8393 | 15450 | 1.4939 | - |
476
+ | 1.8452 | 15500 | 1.5216 | - |
477
+ | 1.8512 | 15550 | 1.4656 | - |
478
+ | 1.8571 | 15600 | 1.5171 | - |
479
+ | 1.8631 | 15650 | 1.3437 | - |
480
+ | 1.8690 | 15700 | 1.4875 | - |
481
+ | 1.875 | 15750 | 1.4692 | - |
482
+ | 1.8810 | 15800 | 1.4804 | - |
483
+ | 1.8869 | 15850 | 1.4423 | - |
484
+ | 1.8929 | 15900 | 1.4592 | - |
485
+ | 1.8988 | 15950 | 1.5764 | - |
486
+ | 1.9048 | 16000 | 1.4083 | - |
487
+ | 1.9107 | 16050 | 1.4852 | - |
488
+ | 1.9167 | 16100 | 1.5158 | - |
489
+ | 1.9226 | 16150 | 1.4602 | - |
490
+ | 1.9286 | 16200 | 1.4465 | - |
491
+ | 1.9345 | 16250 | 1.412 | - |
492
+ | 1.9405 | 16300 | 1.483 | - |
493
+ | 1.9464 | 16350 | 1.5342 | - |
494
+ | 1.9524 | 16400 | 1.3866 | - |
495
+ | 1.9583 | 16450 | 1.4318 | - |
496
+ | 1.9643 | 16500 | 1.6241 | - |
497
+ | 1.9702 | 16550 | 1.5514 | - |
498
+ | 1.9762 | 16600 | 1.46 | - |
499
+ | 1.9821 | 16650 | 1.4069 | - |
500
+ | 1.9881 | 16700 | 1.457 | - |
501
+ | 1.9940 | 16750 | 1.4273 | - |
502
+ | 2.0 | 16800 | 1.3673 | - |
503
+ | 2.0060 | 16850 | 1.3753 | - |
504
+ | 2.0119 | 16900 | 1.4279 | - |
505
+ | 2.0179 | 16950 | 1.3897 | - |
506
+ | 2.0238 | 17000 | 1.4659 | - |
507
+ | 2.0298 | 17050 | 1.4494 | - |
508
+ | 2.0357 | 17100 | 1.4533 | - |
509
+ | 2.0417 | 17150 | 1.3735 | - |
510
+ | 2.0476 | 17200 | 1.4232 | - |
511
+ | 2.0536 | 17250 | 1.4229 | - |
512
+ | 2.0595 | 17300 | 1.4597 | - |
513
+ | 2.0655 | 17350 | 1.4825 | - |
514
+ | 2.0714 | 17400 | 1.4661 | - |
515
+ | 2.0774 | 17450 | 1.4332 | - |
516
+ | 2.0833 | 17500 | 1.5895 | - |
517
+ | 2.0893 | 17550 | 1.4824 | - |
518
+ | 2.0952 | 17600 | 1.4472 | - |
519
+ | 2.1012 | 17650 | 1.4001 | - |
520
+ | 2.1071 | 17700 | 1.4638 | - |
521
+ | 2.1131 | 17750 | 1.4651 | - |
522
+ | 2.1190 | 17800 | 1.4711 | - |
523
+ | 2.125 | 17850 | 1.4474 | - |
524
+ | 2.1310 | 17900 | 1.4544 | - |
525
+ | 2.1369 | 17950 | 1.3935 | - |
526
+ | 2.1429 | 18000 | 1.4449 | - |
527
+ | 2.1488 | 18050 | 1.4671 | - |
528
+ | 2.1548 | 18100 | 1.4169 | - |
529
+ | 2.1607 | 18150 | 1.5095 | - |
530
+ | 2.1667 | 18200 | 1.4186 | - |
531
+ | 2.1726 | 18250 | 1.4574 | - |
532
+ | 2.1786 | 18300 | 1.4448 | - |
533
+ | 2.1845 | 18350 | 1.5045 | - |
534
+ | 2.1905 | 18400 | 1.4998 | - |
535
+ | 2.1964 | 18450 | 1.3559 | - |
536
+ | 2.2024 | 18500 | 1.4862 | - |
537
+ | 2.2083 | 18550 | 1.4018 | - |
538
+ | 2.2143 | 18600 | 1.4407 | - |
539
+ | 2.2202 | 18650 | 1.5812 | - |
540
+ | 2.2262 | 18700 | 1.4268 | - |
541
+ | 2.2321 | 18750 | 1.4434 | - |
542
+ | 2.2381 | 18800 | 1.5467 | - |
543
+ | 2.2440 | 18850 | 1.4281 | - |
544
+ | 2.25 | 18900 | 1.482 | - |
545
+ | 2.2560 | 18950 | 1.5261 | - |
546
+ | 2.2619 | 19000 | 1.4152 | - |
547
+ | 2.2679 | 19050 | 1.5267 | - |
548
+ | 2.2738 | 19100 | 1.4237 | - |
549
+ | 2.2798 | 19150 | 1.5455 | - |
550
+ | 2.2857 | 19200 | 1.4679 | - |
551
+ | 2.2917 | 19250 | 1.3398 | - |
552
+ | 2.2976 | 19300 | 1.4697 | - |
553
+ | 2.3036 | 19350 | 1.4176 | - |
554
+ | 2.3095 | 19400 | 1.4661 | - |
555
+ | 2.3155 | 19450 | 1.4397 | - |
556
+ | 2.3214 | 19500 | 1.5095 | - |
557
+ | 2.3274 | 19550 | 1.4873 | - |
558
+ | 2.3333 | 19600 | 1.4312 | - |
559
+ | 2.3393 | 19650 | 1.441 | - |
560
+ | 2.3452 | 19700 | 1.4341 | - |
561
+ | 2.3512 | 19750 | 1.4229 | - |
562
+ | 2.3571 | 19800 | 1.4917 | - |
563
+ | 2.3631 | 19850 | 1.4397 | - |
564
+ | 2.3690 | 19900 | 1.4027 | - |
565
+ | 2.375 | 19950 | 1.5022 | - |
566
+ | 2.3810 | 20000 | 1.441 | - |
567
+ | 2.3869 | 20050 | 1.4392 | - |
568
+ | 2.3929 | 20100 | 1.4454 | - |
569
+ | 2.3988 | 20150 | 1.4886 | - |
570
+ | 2.4048 | 20200 | 1.4776 | - |
571
+ | 2.4107 | 20250 | 1.3946 | - |
572
+ | 2.4167 | 20300 | 1.5492 | - |
573
+ | 2.4226 | 20350 | 1.534 | - |
574
+ | 2.4286 | 20400 | 1.4011 | - |
575
+ | 2.4345 | 20450 | 1.5276 | - |
576
+ | 2.4405 | 20500 | 1.4633 | - |
577
+ | 2.4464 | 20550 | 1.4446 | - |
578
+ | 2.4524 | 20600 | 1.5005 | - |
579
+ | 2.4583 | 20650 | 1.4818 | - |
580
+ | 2.4643 | 20700 | 1.4319 | - |
581
+ | 2.4702 | 20750 | 1.4406 | - |
582
+ | 2.4762 | 20800 | 1.4496 | - |
583
+ | 2.4821 | 20850 | 1.4963 | - |
584
+ | 2.4881 | 20900 | 1.4731 | - |
585
+ | 2.4940 | 20950 | 1.4536 | - |
586
+ | 2.5 | 21000 | 1.5153 | - |
587
+ | 2.5060 | 21050 | 1.5522 | - |
588
+ | 2.5119 | 21100 | 1.3759 | - |
589
+ | 2.5179 | 21150 | 1.4285 | - |
590
+ | 2.5238 | 21200 | 1.4162 | - |
591
+ | 2.5298 | 21250 | 1.4383 | - |
592
+ | 2.5357 | 21300 | 1.4408 | - |
593
+ | 2.5417 | 21350 | 1.4009 | - |
594
+ | 2.5476 | 21400 | 1.4589 | - |
595
+ | 2.5536 | 21450 | 1.4478 | - |
596
+ | 2.5595 | 21500 | 1.4876 | - |
597
+ | 2.5655 | 21550 | 1.4206 | - |
598
+ | 2.5714 | 21600 | 1.4927 | - |
599
+ | 2.5774 | 21650 | 1.5047 | - |
600
+ | 2.5833 | 21700 | 1.3988 | - |
601
+ | 2.5893 | 21750 | 1.4714 | - |
602
+ | 2.5952 | 21800 | 1.3605 | - |
603
+ | 2.6012 | 21850 | 1.5635 | - |
604
+ | 2.6071 | 21900 | 1.4678 | - |
605
+ | 2.6131 | 21950 | 1.4618 | - |
606
+ | 2.6190 | 22000 | 1.4407 | - |
607
+ | 2.625 | 22050 | 1.5451 | - |
608
+ | 2.6310 | 22100 | 1.4844 | - |
609
+ | 2.6369 | 22150 | 1.4088 | - |
610
+ | 2.6429 | 22200 | 1.5056 | - |
611
+ | 2.6488 | 22250 | 1.4678 | - |
612
+ | 2.6548 | 22300 | 1.4262 | - |
613
+ | 2.6607 | 22350 | 1.4492 | - |
614
+ | 2.6667 | 22400 | 1.4463 | - |
615
+ | 2.6726 | 22450 | 1.3851 | - |
616
+ | 2.6786 | 22500 | 1.513 | - |
617
+ | 2.6845 | 22550 | 1.45 | - |
618
+ | 2.6905 | 22600 | 1.4382 | - |
619
+ | 2.6964 | 22650 | 1.4637 | - |
620
+ | 2.7024 | 22700 | 1.4487 | - |
621
+ | 2.7083 | 22750 | 1.4507 | - |
622
+ | 2.7143 | 22800 | 1.5065 | - |
623
+ | 2.7202 | 22850 | 1.4116 | - |
624
+ | 2.7262 | 22900 | 1.479 | - |
625
+ | 2.7321 | 22950 | 1.444 | - |
626
+ | 2.7381 | 23000 | 1.4056 | - |
627
+ | 2.7440 | 23050 | 1.3913 | - |
628
+ | 2.75 | 23100 | 1.5108 | - |
629
+ | 2.7560 | 23150 | 1.4092 | - |
630
+ | 2.7619 | 23200 | 1.4341 | - |
631
+ | 2.7679 | 23250 | 1.4274 | - |
632
+ | 2.7738 | 23300 | 1.4748 | - |
633
+ | 2.7798 | 23350 | 1.3819 | - |
634
+ | 2.7857 | 23400 | 1.5012 | - |
635
+ | 2.7917 | 23450 | 1.3594 | - |
636
+ | 2.7976 | 23500 | 1.4708 | - |
637
+ | 2.8036 | 23550 | 1.4425 | - |
638
+ | 2.8095 | 23600 | 1.3566 | - |
639
+ | 2.8155 | 23650 | 1.456 | - |
640
+ | 2.8214 | 23700 | 1.5937 | - |
641
+ | 2.8274 | 23750 | 1.3835 | - |
642
+ | 2.8333 | 23800 | 1.4137 | - |
643
+ | 2.8393 | 23850 | 1.3861 | - |
644
+ | 2.8452 | 23900 | 1.4249 | - |
645
+ | 2.8512 | 23950 | 1.3599 | - |
646
+ | 2.8571 | 24000 | 1.4789 | - |
647
+ | 2.8631 | 24050 | 1.4527 | - |
648
+ | 2.8690 | 24100 | 1.4406 | - |
649
+ | 2.875 | 24150 | 1.4301 | - |
650
+ | 2.8810 | 24200 | 1.4059 | - |
651
+ | 2.8869 | 24250 | 1.5052 | - |
652
+ | 2.8929 | 24300 | 1.4429 | - |
653
+ | 2.8988 | 24350 | 1.5183 | - |
654
+ | 2.9048 | 24400 | 1.4288 | - |
655
+ | 2.9107 | 24450 | 1.4673 | - |
656
+ | 2.9167 | 24500 | 1.4582 | - |
657
+ | 2.9226 | 24550 | 1.4792 | - |
658
+ | 2.9286 | 24600 | 1.4598 | - |
659
+ | 2.9345 | 24650 | 1.4785 | - |
660
+ | 2.9405 | 24700 | 1.4259 | - |
661
+ | 2.9464 | 24750 | 1.4877 | - |
662
+ | 2.9524 | 24800 | 1.5162 | - |
663
+ | 2.9583 | 24850 | 1.4854 | - |
664
+ | 2.9643 | 24900 | 1.3679 | - |
665
+ | 2.9702 | 24950 | 1.3985 | - |
666
+ | 2.9762 | 25000 | 1.421 | - |
667
+ | 2.9821 | 25050 | 1.5048 | - |
668
+ | 2.9881 | 25100 | 1.4618 | - |
669
+ | 2.9940 | 25150 | 1.5061 | - |
670
+ | 3.0 | 25200 | 1.3634 | - |
671
+
672
+ ### Framework Versions
673
+ - Python: 3.12.0
674
+ - SetFit: 1.2.0.dev0
675
+ - Sentence Transformers: 3.2.1
676
+ - Transformers: 4.45.2
677
+ - PyTorch: 2.5.0+cpu
678
+ - Datasets: 3.0.2
679
+ - Tokenizers: 0.20.1
680
+
681
+ ## Citation
682
+
683
+ ### BibTeX
684
+ ```bibtex
685
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
686
+ doi = {10.48550/ARXIV.2209.11055},
687
+ url = {https://arxiv.org/abs/2209.11055},
688
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
689
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
690
+ title = {Efficient Few-Shot Learning Without Prompts},
691
+ publisher = {arXiv},
692
+ year = {2022},
693
+ copyright = {Creative Commons Attribution 4.0 International}
694
+ }
695
+ ```
696
+
697
+ <!--
698
+ ## Glossary
699
+
700
+ *Clearly define terms in order to be accessible across audiences.*
701
+ -->
702
+
703
+ <!--
704
+ ## Model Card Authors
705
+
706
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
707
+ -->
708
+
709
+ <!--
710
+ ## Model Card Contact
711
+
712
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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