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st-mpnet-v2-amazon-mi

Text Classification
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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
metrics:
- accuracy
widget:
- text: travel book a train ticket
- text: how much is the average house
- text: do i need a jacket
- text: i like the songs of yeshudas please play it
- text: tell me the current time
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.7743480574773816
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 35 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:-------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| alarm_query | <ul><li>'do i have any alarms set for six am tomorrow'</li><li>'what is the wake up time for my alarm i have set for the flight this weekend'</li><li>'please tell me what alarms are on'</li></ul> |
| alarm_set | <ul><li>'set an alarm for six thirty am'</li><li>'add an alarm for tomorrow morning at six am'</li><li>'wake me up at five am'</li></ul> |
| audio_volume_mute | <ul><li>'can you please stop speaking'</li><li>'turn off sound'</li><li>'shut down the sound'</li></ul> |
| calendar_query | <ul><li>'how long will my lunch meeting be on tuesday'</li><li>'what time is my doctor appointment on march thirty first'</li><li>'what days do i have booked'</li></ul> |
| calendar_remove | <ul><li>'clear everything off my calendar for the rest of the year'</li><li>'please clear my calendar'</li><li>'remove from my calendar meeting at nine am'</li></ul> |
| calendar_set | <ul><li>'new event'</li><li>'remind me of the event in my calendar'</li><li>"mark april twenty as my brother's birthday"</li></ul> |
| cooking_recipe | <ul><li>'tell me the recipe of'</li><li>'how is rice prepared'</li><li>'what ingredient can be used instead of saffron'</li></ul> |
| datetime_query | <ul><li>'what is the time in canada now'</li><li>"what's the time in australia"</li><li>'display the local time of london at this moment'</li></ul> |
| email_query | <ul><li>'do i have any unread emails'</li><li>'what about new mail'</li><li>'olly do i have any new emails'</li></ul> |
| email_sendemail | <ul><li>'dictate email'</li><li>'reply an email to jason that i will not come tonight'</li><li>'please send an email to cassy who is there on my family and friend list'</li></ul> |
| general_quirky | <ul><li>'where was will ferrell seen last night'</li><li>'do you think i should go to the theater today'</li><li>'what is the best chocolate chip cookies recipe'</li></ul> |
| iot_coffee | <ul><li>'i need a drink'</li><li>'please activate my coffee pot for me'</li><li>'prepare a cup of coffee for me'</li></ul> |
| iot_hue_lightchange | <ul><li>'please make the lights natural'</li><li>'make the room light blue'</li><li>'hey olly chance the current light settings'</li></ul> |
| iot_hue_lightoff | <ul><li>'siri please turn the lights off in the bathroom'</li><li>'turn my bedroom lights off'</li><li>'no lights in the kitchen'</li></ul> |
| lists_createoradd | <ul><li>'add business contacts to contact list'</li><li>'please create a new list for me'</li><li>"i want to make this week's shopping list"</li></ul> |
| lists_query | <ul><li>'give me all available lists'</li><li>'give me the details on purchase order'</li><li>'find the list'</li></ul> |
| lists_remove | <ul><li>'replace'</li><li>"delete my to do's for this week"</li><li>'get rid of tax list from nineteen ninety'</li></ul> |
| music_likeness | <ul><li>'store opinion on song'</li><li>'are there any upcoming concerts by'</li><li>'enter song suggestion'</li></ul> |
| music_query | <ul><li>'is the song by shakira'</li><li>'which film the music comes from what is the name of the music'</li><li>'which song is this one'</li></ul> |
| news_query | <ul><li>'news articles on a particular subject'</li><li>'get me match highlights'</li><li>'show me the latest news from the guardian'</li></ul> |
| play_audiobook | <ul><li>'continue the last chapter of the audio book i was listening to'</li><li>'open davinci code audiobook'</li><li>'resume the playback of a child called it'</li></ul> |
| play_game | <ul><li>'bring up papa pear saga'</li><li>'play ping pong'</li><li>'play racing'</li></ul> |
| play_music | <ul><li>'play mf doom anything'</li><li>'play only all music released between the year one thousand nine hundred and ninety and two thousand'</li><li>'nobody knows'</li></ul> |
| play_podcasts | <ul><li>'play all order of the green hand from previous week'</li><li>'i want to see the next podcast available'</li><li>"search for podcasts that cover men's issues"</li></ul> |
| play_radio | <ul><li>'can you turn on the radio'</li><li>'play country radio'</li><li>'tune to classic hits'</li></ul> |
| qa_currency | <ul><li>'let me know about the exchange rate of rupee to dirham'</li><li>'how much is one dollar in pounds'</li><li>'what is the most current exchange rate in china'</li></ul> |
| qa_definition | <ul><li>'define elaborate'</li><li>'look up the definition of blunder'</li><li>'give details of rock sand'</li></ul> |
| qa_factoid | <ul><li>'where are the rocky mountains'</li><li>'what is the population of new york'</li><li>'where is new zealand located on a map'</li></ul> |
| recommendation_events | <ul><li>'are there any fun events in la today'</li><li>"what's happening around me"</li><li>'are there any crafts fairs happening in this area'</li></ul> |
| recommendation_locations | <ul><li>'what is the nearest pizza shop'</li><li>'please look up local restaurants that are open now'</li><li>'tell me what clothing stores are within five miles of me'</li></ul> |
| social_post | <ul><li>"tweet at united airlines i'm angry you lost my bags"</li><li>'send a funny message to all of my friends'</li><li>'tweet my current location'</li></ul> |
| takeaway_query | <ul><li>'could you please confirm if paradise does takeaway'</li><li>"i've canceled the order placed at mcd did it go through"</li><li>"please find out of charley's steakhouse delivers"</li></ul> |
| transport_query | <ul><li>'directions please'</li><li>'what time does the train to place leave'</li><li>'look up the map to stores near me'</li></ul> |
| transport_ticket | <ul><li>'find me a train ticket to boston'</li><li>'can you please book train tickets for two for this friday'</li><li>'order a train ticket to boston'</li></ul> |
| weather_query | <ul><li>'will i need to shovel my driveway this morning'</li><li>'does the weather call for rain saturday'</li><li>'is there any rain in the forecast for the next week'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.7743 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("aisuko/st-mpnet-v2-amazon-mi")
# Run inference
preds = model("do i need a jacket")
```
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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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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 1 | 6.7114 | 19 |
| Label | Training Sample Count |
|:-------------------------|:----------------------|
| alarm_query | 10 |
| alarm_set | 10 |
| audio_volume_mute | 10 |
| calendar_query | 10 |
| calendar_remove | 10 |
| calendar_set | 10 |
| cooking_recipe | 10 |
| datetime_query | 10 |
| email_query | 10 |
| email_sendemail | 10 |
| general_quirky | 10 |
| iot_coffee | 10 |
| iot_hue_lightchange | 10 |
| iot_hue_lightoff | 10 |
| lists_createoradd | 10 |
| lists_query | 10 |
| lists_remove | 10 |
| music_likeness | 10 |
| music_query | 10 |
| news_query | 10 |
| play_audiobook | 10 |
| play_game | 10 |
| play_music | 10 |
| play_podcasts | 10 |
| play_radio | 10 |
| qa_currency | 10 |
| qa_definition | 10 |
| qa_factoid | 10 |
| recommendation_events | 10 |
| recommendation_locations | 10 |
| social_post | 10 |
| takeaway_query | 10 |
| transport_query | 10 |
| transport_ticket | 10 |
| weather_query | 10 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0001 | 1 | 0.1814 | - |
| 0.0067 | 50 | 0.1542 | - |
| 0.0134 | 100 | 0.0953 | - |
| 0.0202 | 150 | 0.0991 | - |
| 0.0269 | 200 | 0.0717 | - |
| 0.0336 | 250 | 0.0653 | - |
| 0.0403 | 300 | 0.0412 | - |
| 0.0471 | 350 | 0.0534 | - |
| 0.0538 | 400 | 0.013 | - |
| 0.0605 | 450 | 0.0567 | - |
| 0.0672 | 500 | 0.0235 | - |
| 0.0739 | 550 | 0.0086 | - |
| 0.0807 | 600 | 0.0086 | - |
| 0.0874 | 650 | 0.0786 | - |
| 0.0941 | 700 | 0.0092 | - |
| 0.1008 | 750 | 0.0081 | - |
| 0.1076 | 800 | 0.0196 | - |
| 0.1143 | 850 | 0.0138 | - |
| 0.1210 | 900 | 0.0081 | - |
| 0.1277 | 950 | 0.0295 | - |
| 0.1344 | 1000 | 0.0074 | - |
| 0.1412 | 1050 | 0.0025 | - |
| 0.1479 | 1100 | 0.0036 | - |
| 0.1546 | 1150 | 0.0021 | - |
| 0.1613 | 1200 | 0.0168 | - |
| 0.1681 | 1250 | 0.0024 | - |
| 0.1748 | 1300 | 0.0039 | - |
| 0.1815 | 1350 | 0.0155 | - |
| 0.1882 | 1400 | 0.0057 | - |
| 0.1949 | 1450 | 0.0027 | - |
| 0.2017 | 1500 | 0.0018 | - |
| 0.2084 | 1550 | 0.0012 | - |
| 0.2151 | 1600 | 0.0032 | - |
| 0.2218 | 1650 | 0.0017 | - |
| 0.2286 | 1700 | 0.0012 | - |
| 0.2353 | 1750 | 0.002 | - |
| 0.2420 | 1800 | 0.0025 | - |
| 0.2487 | 1850 | 0.0014 | - |
| 0.2554 | 1900 | 0.0033 | - |
| 0.2622 | 1950 | 0.0007 | - |
| 0.2689 | 2000 | 0.0006 | - |
| 0.2756 | 2050 | 0.001 | - |
| 0.2823 | 2100 | 0.001 | - |
| 0.2891 | 2150 | 0.0007 | - |
| 0.2958 | 2200 | 0.0011 | - |
| 0.3025 | 2250 | 0.0009 | - |
| 0.3092 | 2300 | 0.0006 | - |
| 0.3159 | 2350 | 0.001 | - |
| 0.3227 | 2400 | 0.0005 | - |
| 0.3294 | 2450 | 0.0012 | - |
| 0.3361 | 2500 | 0.0005 | - |
| 0.3428 | 2550 | 0.0007 | - |
| 0.3496 | 2600 | 0.0018 | - |
| 0.3563 | 2650 | 0.0008 | - |
| 0.3630 | 2700 | 0.0009 | - |
| 0.3697 | 2750 | 0.0007 | - |
| 0.3764 | 2800 | 0.0013 | - |
| 0.3832 | 2850 | 0.0004 | - |
| 0.3899 | 2900 | 0.0005 | - |
| 0.3966 | 2950 | 0.0005 | - |
| 0.4033 | 3000 | 0.0006 | - |
| 0.4101 | 3050 | 0.0005 | - |
| 0.4168 | 3100 | 0.0004 | - |
| 0.4235 | 3150 | 0.0007 | - |
| 0.4302 | 3200 | 0.0009 | - |
| 0.4369 | 3250 | 0.0007 | - |
| 0.4437 | 3300 | 0.0007 | - |
| 0.4504 | 3350 | 0.0004 | - |
| 0.4571 | 3400 | 0.0004 | - |
| 0.4638 | 3450 | 0.0009 | - |
| 0.4706 | 3500 | 0.0006 | - |
| 0.4773 | 3550 | 0.0006 | - |
| 0.4840 | 3600 | 0.0005 | - |
| 0.4907 | 3650 | 0.0005 | - |
| 0.4974 | 3700 | 0.0003 | - |
| 0.5042 | 3750 | 0.0004 | - |
| 0.5109 | 3800 | 0.0004 | - |
| 0.5176 | 3850 | 0.0005 | - |
| 0.5243 | 3900 | 0.0007 | - |
| 0.5311 | 3950 | 0.0005 | - |
| 0.5378 | 4000 | 0.0006 | - |
| 0.5445 | 4050 | 0.0004 | - |
| 0.5512 | 4100 | 0.0006 | - |
| 0.5579 | 4150 | 0.0005 | - |
| 0.5647 | 4200 | 0.0004 | - |
| 0.5714 | 4250 | 0.0003 | - |
| 0.5781 | 4300 | 0.0003 | - |
| 0.5848 | 4350 | 0.0005 | - |
| 0.5916 | 4400 | 0.0002 | - |
| 0.5983 | 4450 | 0.0006 | - |
| 0.6050 | 4500 | 0.0004 | - |
| 0.6117 | 4550 | 0.0005 | - |
| 0.6184 | 4600 | 0.0003 | - |
| 0.6252 | 4650 | 0.0005 | - |
| 0.6319 | 4700 | 0.0007 | - |
| 0.6386 | 4750 | 0.0003 | - |
| 0.6453 | 4800 | 0.0004 | - |
| 0.6521 | 4850 | 0.0004 | - |
| 0.6588 | 4900 | 0.0004 | - |
| 0.6655 | 4950 | 0.0003 | - |
| 0.6722 | 5000 | 0.0003 | - |
| 0.6789 | 5050 | 0.0004 | - |
| 0.6857 | 5100 | 0.0003 | - |
| 0.6924 | 5150 | 0.0005 | - |
| 0.6991 | 5200 | 0.0002 | - |
| 0.7058 | 5250 | 0.0004 | - |
| 0.7126 | 5300 | 0.0003 | - |
| 0.7193 | 5350 | 0.0007 | - |
| 0.7260 | 5400 | 0.0002 | - |
| 0.7327 | 5450 | 0.0002 | - |
| 0.7394 | 5500 | 0.0005 | - |
| 0.7462 | 5550 | 0.0003 | - |
| 0.7529 | 5600 | 0.0003 | - |
| 0.7596 | 5650 | 0.0003 | - |
| 0.7663 | 5700 | 0.0004 | - |
| 0.7731 | 5750 | 0.0004 | - |
| 0.7798 | 5800 | 0.0004 | - |
| 0.7865 | 5850 | 0.0003 | - |
| 0.7932 | 5900 | 0.0003 | - |
| 0.7999 | 5950 | 0.0004 | - |
| 0.8067 | 6000 | 0.0004 | - |
| 0.8134 | 6050 | 0.0004 | - |
| 0.8201 | 6100 | 0.0003 | - |
| 0.8268 | 6150 | 0.0002 | - |
| 0.8336 | 6200 | 0.0005 | - |
| 0.8403 | 6250 | 0.0003 | - |
| 0.8470 | 6300 | 0.0003 | - |
| 0.8537 | 6350 | 0.0002 | - |
| 0.8604 | 6400 | 0.0003 | - |
| 0.8672 | 6450 | 0.0004 | - |
| 0.8739 | 6500 | 0.0002 | - |
| 0.8806 | 6550 | 0.0003 | - |
| 0.8873 | 6600 | 0.0003 | - |
| 0.8941 | 6650 | 0.0002 | - |
| 0.9008 | 6700 | 0.0002 | - |
| 0.9075 | 6750 | 0.0002 | - |
| 0.9142 | 6800 | 0.0002 | - |
| 0.9209 | 6850 | 0.0003 | - |
| 0.9277 | 6900 | 0.0002 | - |
| 0.9344 | 6950 | 0.0002 | - |
| 0.9411 | 7000 | 0.0002 | - |
| 0.9478 | 7050 | 0.0002 | - |
| 0.9546 | 7100 | 0.0002 | - |
| 0.9613 | 7150 | 0.0003 | - |
| 0.9680 | 7200 | 0.0002 | - |
| 0.9747 | 7250 | 0.0003 | - |
| 0.9814 | 7300 | 0.0002 | - |
| 0.9882 | 7350 | 0.0003 | - |
| 0.9949 | 7400 | 0.0003 | - |
| **1.0** | **7438** | **-** | **0.0755** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.39.3
- PyTorch: 2.1.2
- Datasets: 2.18.0
- Tokenizers: 0.15.2
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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