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aisuko/
st-mpnet-v2-amazon-mi

Name: st-mpnet-v2-amazon-mi
Availability: InStock

setfit

safetensors

mpnet

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

sentence-transformers/paraphrase-mpnet-base-v2

finetune

model-index

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SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

Fine-tuning a Sentence Transformer with contrastive learning.
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
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 35 classes

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
alarm_query	'do i have any alarms set for six am tomorrow' 'what is the wake up time for my alarm i have set for the flight this weekend' 'please tell me what alarms are on'
alarm_set	'set an alarm for six thirty am' 'add an alarm for tomorrow morning at six am' 'wake me up at five am'
audio_volume_mute	'can you please stop speaking' 'turn off sound' 'shut down the sound'
calendar_query	'how long will my lunch meeting be on tuesday' 'what time is my doctor appointment on march thirty first' 'what days do i have booked'
calendar_remove	'clear everything off my calendar for the rest of the year' 'please clear my calendar' 'remove from my calendar meeting at nine am'
calendar_set	'new event' 'remind me of the event in my calendar' "mark april twenty as my brother's birthday"
cooking_recipe	'tell me the recipe of' 'how is rice prepared' 'what ingredient can be used instead of saffron'
datetime_query	'what is the time in canada now' "what's the time in australia" 'display the local time of london at this moment'
email_query	'do i have any unread emails' 'what about new mail' 'olly do i have any new emails'
email_sendemail	'dictate email' 'reply an email to jason that i will not come tonight' 'please send an email to cassy who is there on my family and friend list'
general_quirky	'where was will ferrell seen last night' 'do you think i should go to the theater today' 'what is the best chocolate chip cookies recipe'
iot_coffee	'i need a drink' 'please activate my coffee pot for me' 'prepare a cup of coffee for me'
iot_hue_lightchange	'please make the lights natural' 'make the room light blue' 'hey olly chance the current light settings'
iot_hue_lightoff	'siri please turn the lights off in the bathroom' 'turn my bedroom lights off' 'no lights in the kitchen'
lists_createoradd	'add business contacts to contact list' 'please create a new list for me' "i want to make this week's shopping list"
lists_query	'give me all available lists' 'give me the details on purchase order' 'find the list'
lists_remove	'replace' "delete my to do's for this week" 'get rid of tax list from nineteen ninety'
music_likeness	'store opinion on song' 'are there any upcoming concerts by' 'enter song suggestion'
music_query	'is the song by shakira' 'which film the music comes from what is the name of the music' 'which song is this one'
news_query	'news articles on a particular subject' 'get me match highlights' 'show me the latest news from the guardian'
play_audiobook	'continue the last chapter of the audio book i was listening to' 'open davinci code audiobook' 'resume the playback of a child called it'
play_game	'bring up papa pear saga' 'play ping pong' 'play racing'
play_music	'play mf doom anything' 'play only all music released between the year one thousand nine hundred and ninety and two thousand' 'nobody knows'
play_podcasts	'play all order of the green hand from previous week' 'i want to see the next podcast available' "search for podcasts that cover men's issues"
play_radio	'can you turn on the radio' 'play country radio' 'tune to classic hits'
qa_currency	'let me know about the exchange rate of rupee to dirham' 'how much is one dollar in pounds' 'what is the most current exchange rate in china'
qa_definition	'define elaborate' 'look up the definition of blunder' 'give details of rock sand'
qa_factoid	'where are the rocky mountains' 'what is the population of new york' 'where is new zealand located on a map'
recommendation_events	'are there any fun events in la today' "what's happening around me" 'are there any crafts fairs happening in this area'
recommendation_locations	'what is the nearest pizza shop' 'please look up local restaurants that are open now' 'tell me what clothing stores are within five miles of me'
social_post	"tweet at united airlines i'm angry you lost my bags" 'send a funny message to all of my friends' 'tweet my current location'
takeaway_query	'could you please confirm if paradise does takeaway' "i've canceled the order placed at mcd did it go through" "please find out of charley's steakhouse delivers"
transport_query	'directions please' 'what time does the train to place leave' 'look up the map to stores near me'
transport_ticket	'find me a train ticket to boston' 'can you please book train tickets for two for this friday' 'order a train ticket to boston'
weather_query	'will i need to shovel my driveway this morning' 'does the weather call for rain saturday' 'is there any rain in the forecast for the next week'

Evaluation

Metrics

Label	Accuracy
all	0.7743

Uses

Direct Use for Inference

First install the SetFit library:

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")

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}
}