Push model using huggingface_hub.
Browse files- 1_Pooling/config.json +10 -0
- README.md +210 -0
- config.json +26 -0
- config_sentence_transformers.json +10 -0
- config_setfit.json +4 -0
- model.safetensors +3 -0
- model_head.pkl +3 -0
- modules.json +20 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +64 -0
- vocab.txt +0 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 384,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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README.md
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---
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base_model: sentence-transformers/all-MiniLM-L6-v2
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library_name: setfit
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metrics:
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- accuracy
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pipeline_tag: text-classification
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tags:
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- setfit
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- sentence-transformers
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- text-classification
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- generated_from_setfit_trainer
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widget:
|
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+
- text: Thank you for your email. Please go ahead and issue. Please invoice in KES
|
14 |
+
- text: Hi, We are missing some invoices, can you please provide it. 02 - 12 - 2020
|
15 |
+
AGENT FEE 8900784339018 $21.00 02 - 19 - 2020 AGENT FEE 0017417554160 $22.00 02
|
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+
- 19 - 2020 AGENT FEE 0017417554143 $22.00 02 - 19 - 2020 AGENT FEE 8900783383420
|
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+
$21.00
|
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+
- text: We need your assistance with the payment for the recent office supplies order.
|
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+
Let us know once it's done.
|
20 |
+
- text: I have reported this in November and not only was the trip supposed to be
|
21 |
+
cancelled and credited I was double billed and the billing has not been corrected.
|
22 |
+
The total credit should be $667.20. Please confirm this will be done.
|
23 |
+
- text: The invoice for the travel arrangements needs to be settled. Kindly provide
|
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+
payment confirmation.
|
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inference: true
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---
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+
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+
# SetFit with sentence-transformers/all-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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+
|
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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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### Model Description
|
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- **Model Type:** SetFit
|
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+
- **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
|
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+
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
|
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+
- **Maximum Sequence Length:** 256 tokens
|
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+
- **Number of Classes:** 14 classes
|
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+
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
|
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+
<!-- - **Language:** Unknown -->
|
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+
<!-- - **License:** Unknown -->
|
48 |
+
|
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+
### Model Sources
|
50 |
+
|
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+
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
|
52 |
+
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
|
53 |
+
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
|
54 |
+
|
55 |
+
### Model Labels
|
56 |
+
| Label | Examples |
|
57 |
+
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
58 |
+
| 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> |
|
59 |
+
| 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> |
|
60 |
+
| 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> |
|
61 |
+
| 3 | <ul><li>'Please add the below employee to our Concur system. In addition, make sure the Ghost Card is added into their profile. Lindsay Griffin [email protected]'</li><li>"Good afternoon - CAEP has 4 new staff members that we'd like to set - up new user profiles for. Please see the below information and let me know should anything additional be required. Last First Middle Travel Class Email Gender DOB Graham Rose - Helen Xiuqing Staff rose - [email protected] Female 6/14/1995 Gumbs Mary - Frances Akua Staff [email protected] Female 10/18/1995 Lee Elizabeth Andie Staff [email protected] Female 4/23/1991 Gilchrist Gabriel Jake Staff [email protected] Male"</li><li>'Good Morning, Please create a profile for Amelia West: Name: Amelia Jean - Danielle West DOB: 05/21/1987 PH: 202 - 997 - 6592 Email: [email protected]'</li></ul> |
|
62 |
+
| 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> |
|
63 |
+
| 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> |
|
64 |
+
| 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> |
|
65 |
+
| 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> |
|
66 |
+
| 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> |
|
67 |
+
| 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> |
|
68 |
+
| 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> |
|
69 |
+
| 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> |
|
70 |
+
| 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> |
|
71 |
+
| 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> |
|
72 |
+
|
73 |
+
## Uses
|
74 |
+
|
75 |
+
### Direct Use for Inference
|
76 |
+
|
77 |
+
First install the SetFit library:
|
78 |
+
|
79 |
+
```bash
|
80 |
+
pip install setfit
|
81 |
+
```
|
82 |
+
|
83 |
+
Then you can load this model and run inference.
|
84 |
+
|
85 |
+
```python
|
86 |
+
from setfit import SetFitModel
|
87 |
+
|
88 |
+
# Download from the 🤗 Hub
|
89 |
+
model = SetFitModel.from_pretrained("mann2107/BCMPIIRAB_MiniLM_ALLNew")
|
90 |
+
# Run inference
|
91 |
+
preds = model("Thank you for your email. Please go ahead and issue. Please invoice in KES")
|
92 |
+
```
|
93 |
+
|
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+
<!--
|
95 |
+
### Downstream Use
|
96 |
+
|
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+
*List how someone could finetune this model on their own dataset.*
|
98 |
+
-->
|
99 |
+
|
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+
<!--
|
101 |
+
### Out-of-Scope Use
|
102 |
+
|
103 |
+
*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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+
<!--
|
107 |
+
## Bias, Risks and Limitations
|
108 |
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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.*
|
110 |
+
-->
|
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|
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<!--
|
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+
### Recommendations
|
114 |
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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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| 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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### Training Hyperparameters
|
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- batch_size: (64, 64)
|
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- num_epochs: (2, 2)
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- max_steps: -1
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- sampling_strategy: oversampling
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- num_iterations: 1
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- body_learning_rate: (0.00014039790878227676, 0.00014039790878227676)
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- head_learning_rate: 0.0005686856164979006
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- loss: CosineSimilarityLoss
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- distance_metric: cosine_distance
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- margin: 0.25
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- end_to_end: False
|
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- use_amp: False
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- warmup_proportion: 0.1
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- max_length: 512
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- seed: 42
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- eval_max_steps: -1
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- load_best_model_at_end: True
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|
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### Training Results
|
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| Epoch | Step | Training Loss | Validation Loss |
|
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+
|:-------:|:------:|:-------------:|:---------------:|
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| 0.0909 | 1 | 0.2491 | - |
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| 1.0 | 11 | - | 0.1241 |
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| **2.0** | **22** | **-** | **0.086** |
|
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+
|
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* The bold row denotes the saved checkpoint.
|
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+
### Framework Versions
|
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- Python: 3.10.12
|
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+
- SetFit: 1.1.0.dev0
|
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+
- Sentence Transformers: 3.0.1
|
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+
- Transformers: 4.42.4
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- PyTorch: 2.3.1+cu121
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- Datasets: 2.20.0
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- Tokenizers: 0.19.1
|
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+
|
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## Citation
|
179 |
+
|
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### BibTeX
|
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```bibtex
|
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+
@article{https://doi.org/10.48550/arxiv.2209.11055,
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doi = {10.48550/ARXIV.2209.11055},
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url = {https://arxiv.org/abs/2209.11055},
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
|
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
|
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title = {Efficient Few-Shot Learning Without Prompts},
|
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+
publisher = {arXiv},
|
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+
year = {2022},
|
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copyright = {Creative Commons Attribution 4.0 International}
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}
|
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```
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<!--
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## Glossary
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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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*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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|
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<!--
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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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-->
|
config.json
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{
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"_name_or_path": "checkpoints/step_22",
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"initializer_range": 0.02,
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13 |
+
"intermediate_size": 1536,
|
14 |
+
"layer_norm_eps": 1e-12,
|
15 |
+
"max_position_embeddings": 512,
|
16 |
+
"model_type": "bert",
|
17 |
+
"num_attention_heads": 12,
|
18 |
+
"num_hidden_layers": 6,
|
19 |
+
"pad_token_id": 0,
|
20 |
+
"position_embedding_type": "absolute",
|
21 |
+
"torch_dtype": "float32",
|
22 |
+
"transformers_version": "4.42.4",
|
23 |
+
"type_vocab_size": 2,
|
24 |
+
"use_cache": true,
|
25 |
+
"vocab_size": 30522
|
26 |
+
}
|
config_sentence_transformers.json
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
1 |
+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "3.0.1",
|
4 |
+
"transformers": "4.42.4",
|
5 |
+
"pytorch": "2.3.1+cu121"
|
6 |
+
},
|
7 |
+
"prompts": {},
|
8 |
+
"default_prompt_name": null,
|
9 |
+
"similarity_fn_name": null
|
10 |
+
}
|
config_setfit.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"normalize_embeddings": false,
|
3 |
+
"labels": null
|
4 |
+
}
|
model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:66c49c5c2e94da4d0dc3a52a80b24d4f706279db5dbb4cb2652f1efb37d691a4
|
3 |
+
size 90864192
|
model_head.pkl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0164d0e39c0d0cf8f7597cee8b614192a0356cd5dc4a1b971960c5878a6f2544
|
3 |
+
size 44071
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modules.json
ADDED
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
[
|
2 |
+
{
|
3 |
+
"idx": 0,
|
4 |
+
"name": "0",
|
5 |
+
"path": "",
|
6 |
+
"type": "sentence_transformers.models.Transformer"
|
7 |
+
},
|
8 |
+
{
|
9 |
+
"idx": 1,
|
10 |
+
"name": "1",
|
11 |
+
"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
},
|
14 |
+
{
|
15 |
+
"idx": 2,
|
16 |
+
"name": "2",
|
17 |
+
"path": "2_Normalize",
|
18 |
+
"type": "sentence_transformers.models.Normalize"
|
19 |
+
}
|
20 |
+
]
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 256,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
special_tokens_map.json
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
1 |
+
{
|
2 |
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"cls_token": {
|
3 |
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"content": "[CLS]",
|
4 |
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"lstrip": false,
|
5 |
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"normalized": false,
|
6 |
+
"rstrip": false,
|
7 |
+
"single_word": false
|
8 |
+
},
|
9 |
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"mask_token": {
|
10 |
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"content": "[MASK]",
|
11 |
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"lstrip": false,
|
12 |
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"normalized": false,
|
13 |
+
"rstrip": false,
|
14 |
+
"single_word": false
|
15 |
+
},
|
16 |
+
"pad_token": {
|
17 |
+
"content": "[PAD]",
|
18 |
+
"lstrip": false,
|
19 |
+
"normalized": false,
|
20 |
+
"rstrip": false,
|
21 |
+
"single_word": false
|
22 |
+
},
|
23 |
+
"sep_token": {
|
24 |
+
"content": "[SEP]",
|
25 |
+
"lstrip": false,
|
26 |
+
"normalized": false,
|
27 |
+
"rstrip": false,
|
28 |
+
"single_word": false
|
29 |
+
},
|
30 |
+
"unk_token": {
|
31 |
+
"content": "[UNK]",
|
32 |
+
"lstrip": false,
|
33 |
+
"normalized": false,
|
34 |
+
"rstrip": false,
|
35 |
+
"single_word": false
|
36 |
+
}
|
37 |
+
}
|
tokenizer.json
ADDED
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|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,64 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
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|
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|
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|
|
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|
|
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|
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|
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|
|
|
|
|
1 |
+
{
|
2 |
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"added_tokens_decoder": {
|
3 |
+
"0": {
|
4 |
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"content": "[PAD]",
|
5 |
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"lstrip": false,
|
6 |
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"normalized": false,
|
7 |
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"rstrip": false,
|
8 |
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"single_word": false,
|
9 |
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"special": true
|
10 |
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},
|
11 |
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"100": {
|
12 |
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"content": "[UNK]",
|
13 |
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"lstrip": false,
|
14 |
+
"normalized": false,
|
15 |
+
"rstrip": false,
|
16 |
+
"single_word": false,
|
17 |
+
"special": true
|
18 |
+
},
|
19 |
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"101": {
|
20 |
+
"content": "[CLS]",
|
21 |
+
"lstrip": false,
|
22 |
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"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": true
|
26 |
+
},
|
27 |
+
"102": {
|
28 |
+
"content": "[SEP]",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false,
|
33 |
+
"special": true
|
34 |
+
},
|
35 |
+
"103": {
|
36 |
+
"content": "[MASK]",
|
37 |
+
"lstrip": false,
|
38 |
+
"normalized": false,
|
39 |
+
"rstrip": false,
|
40 |
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"single_word": false,
|
41 |
+
"special": true
|
42 |
+
}
|
43 |
+
},
|
44 |
+
"clean_up_tokenization_spaces": true,
|
45 |
+
"cls_token": "[CLS]",
|
46 |
+
"do_basic_tokenize": true,
|
47 |
+
"do_lower_case": true,
|
48 |
+
"mask_token": "[MASK]",
|
49 |
+
"max_length": 128,
|
50 |
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"model_max_length": 256,
|
51 |
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"never_split": null,
|
52 |
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"pad_to_multiple_of": null,
|
53 |
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"pad_token": "[PAD]",
|
54 |
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"pad_token_type_id": 0,
|
55 |
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"padding_side": "right",
|
56 |
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"sep_token": "[SEP]",
|
57 |
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"stride": 0,
|
58 |
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"strip_accents": null,
|
59 |
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"tokenize_chinese_chars": true,
|
60 |
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"tokenizer_class": "BertTokenizer",
|
61 |
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"truncation_side": "right",
|
62 |
+
"truncation_strategy": "longest_first",
|
63 |
+
"unk_token": "[UNK]"
|
64 |
+
}
|
vocab.txt
ADDED
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See raw diff
|
|