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jamiehudson/
725_model_v2

Name: 725_model_v2
Availability: InStock

setfit

safetensors

bert

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

BAAI/bge-base-en-v1.5

finetune

model-index

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SetFit with BAAI/bge-base-en-v1.5

This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-base-en-v1.5 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: BAAI/bge-base-en-v1.5
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 3 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
neither	'it might sound strange, but in my opinion, sams intelligence intimidates him from expressing himself and creating personal art. for example, since product is a masterpiece in the sense, the bar is set very high, so he might even subconsciously be unable to put anything out less' 'lately, i really enjoy the genre of joke that makes you say the punchline in your head.' 'any idea in regard to the product product not being seen? i have 1 device with it, the rest are missing it. same wufb policies.'
pit	"brand or brand are behaving like lazy interns. when you need something useful from them like researching and consolidating a large bunch of information they'll just tell you to look it up yourself or right away refuse to do the work. useless." 'the moment i found out what exactly product does i just uninstalled product and went back to 10' "at least 80% of the product stuff posted here has produced erroneous results, and many have utilized ip theft/copyright infringement in informing the model. we're not going to spend community time on it at this point."
peak	"man, product/whatever is my new best friend. i like product but the integration of product into office and product is a lot of fun. i just spent the day feeding it my training presentation i'm preparing in my day job and it was very helpful. almost better than humans." "excited to share my experience with product, an incredible language model by brand! from answering questions to creative writing, it's a powerful tool that amazes me every time." 'product in product is a game changer!! here is a list of things it can do: it can answer your questions in natural language. it can summarize content to give you a brief overview it can adjust your pcs settings it can help troubleshoot issues. 1/2'

Evaluation

Metrics

Label	Accuracy	F1	Precision	Recall
all	0.8996	[0.5217391304347826, 0.5142857142857142, 0.9478260869565217]	[0.42857142857142855, 0.4090909090909091, 0.9775784753363229]	[0.6666666666666666, 0.6923076923076923, 0.919831223628692]

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("jamiehudson/725_model_v2")
# Run inference
preds = model("product the way it shows the sources is so fucking cool, this new ai is amazing")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	5	29.1484	90

Label	Training Sample Count
pit	44
peak	62
neither	150

Training Hyperparameters

batch_size: (32, 32)
num_epochs: (3, 3)
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: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0000	1	0.2383	-
0.0119	50	0.2395	-
0.0237	100	0.2129	-
0.0356	150	0.1317	-
0.0474	200	0.0695	-
0.0593	250	0.01	-
0.0711	300	0.0063	-
0.0830	350	0.0028	-
0.0948	400	0.0026	-
0.1067	450	0.0021	-
0.1185	500	0.0018	-
0.1304	550	0.0016	-
0.1422	600	0.0014	-
0.1541	650	0.0015	-
0.1659	700	0.0013	-
0.1778	750	0.0012	-
0.1896	800	0.0012	-
0.2015	850	0.0012	-
0.2133	900	0.0011	-
0.2252	950	0.0011	-
0.2370	1000	0.0009	-
0.2489	1050	0.001	-
0.2607	1100	0.0009	-
0.2726	1150	0.0008	-
0.2844	1200	0.0008	-
0.2963	1250	0.0009	-
0.3081	1300	0.0008	-
0.3200	1350	0.0007	-
0.3318	1400	0.0007	-
0.3437	1450	0.0007	-
0.3555	1500	0.0006	-
0.3674	1550	0.0007	-
0.3792	1600	0.0007	-
0.3911	1650	0.0008	-
0.4029	1700	0.0006	-
0.4148	1750	0.0006	-
0.4266	1800	0.0006	-
0.4385	1850	0.0006	-
0.4503	1900	0.0006	-
0.4622	1950	0.0006	-
0.4740	2000	0.0006	-
0.4859	2050	0.0005	-
0.4977	2100	0.0006	-
0.5096	2150	0.0006	-
0.5215	2200	0.0005	-
0.5333	2250	0.0005	-
0.5452	2300	0.0005	-
0.5570	2350	0.0006	-
0.5689	2400	0.0005	-
0.5807	2450	0.0005	-
0.5926	2500	0.0006	-
0.6044	2550	0.0006	-
0.6163	2600	0.0005	-
0.6281	2650	0.0005	-
0.6400	2700	0.0005	-
0.6518	2750	0.0005	-
0.6637	2800	0.0005	-
0.6755	2850	0.0005	-
0.6874	2900	0.0005	-
0.6992	2950	0.0004	-
0.7111	3000	0.0004	-
0.7229	3050	0.0004	-
0.7348	3100	0.0005	-
0.7466	3150	0.0005	-
0.7585	3200	0.0005	-
0.7703	3250	0.0004	-
0.7822	3300	0.0004	-
0.7940	3350	0.0004	-
0.8059	3400	0.0004	-
0.8177	3450	0.0004	-
0.8296	3500	0.0004	-
0.8414	3550	0.0004	-
0.8533	3600	0.0004	-
0.8651	3650	0.0004	-
0.8770	3700	0.0004	-
0.8888	3750	0.0004	-
0.9007	3800	0.0004	-
0.9125	3850	0.0004	-
0.9244	3900	0.0005	-
0.9362	3950	0.0004	-
0.9481	4000	0.0004	-
0.9599	4050	0.0004	-
0.9718	4100	0.0004	-
0.9836	4150	0.0004	-
0.9955	4200	0.0004	-
0.0000	1	0.2717	-
0.0013	50	0.0686	-
0.0026	100	0.088	-
0.0000	1	0.1796	-
0.0013	50	0.0584	-
0.0026	100	0.1018	-
0.0039	150	0.128	-
0.0052	200	0.0761	-
0.0065	250	0.0216	-
0.0078	300	0.1652	-
0.0091	350	0.0384	-
0.0104	400	0.0062	-
0.0117	450	0.0442	-
0.0130	500	0.0452	-
0.0143	550	0.0081	-
0.0156	600	0.0205	-
0.0169	650	0.0125	-
0.0182	700	0.0012	-
0.0195	750	0.0011	-
0.0208	800	0.0315	-
0.0221	850	0.0009	-
0.0009	1	0.0006	-
0.0429	50	0.0008	-
0.0858	100	0.0005	-
0.1288	150	0.0015	-
0.1717	200	0.0013	-
0.2146	250	0.0237	-
0.2575	300	0.0304	-
0.3004	350	0.0005	-
0.3433	400	0.0013	-
0.3863	450	0.03	-
0.4292	500	0.0005	-
0.4721	550	0.0006	-
0.5150	600	0.0005	-
0.5579	650	0.0005	-
0.6009	700	0.0004	-
0.6438	750	0.0004	-
0.6867	800	0.0004	-
0.7296	850	0.0004	-
0.7725	900	0.0004	-
0.8155	950	0.0003	-
0.8584	1000	0.0004	-
0.9013	1050	0.0003	-
0.9442	1100	0.0004	-
0.9871	1150	0.0003	-
1.0300	1200	0.0003	-
1.0730	1250	0.0004	-
1.1159	1300	0.0003	-
1.1588	1350	0.0005	-
1.2017	1400	0.0003	-
1.2446	1450	0.0003	-
1.2876	1500	0.0003	-
1.3305	1550	0.0003	-
1.3734	1600	0.0003	-
1.4163	1650	0.0003	-
1.4592	1700	0.0003	-
1.5021	1750	0.0005	-
1.5451	1800	0.0003	-
1.5880	1850	0.0003	-
1.6309	1900	0.0003	-
1.6738	1950	0.0005	-
1.7167	2000	0.0003	-
1.7597	2050	0.0007	-
1.8026	2100	0.0003	-
1.8455	2150	0.0003	-
1.8884	2200	0.0003	-
1.9313	2250	0.0003	-
1.9742	2300	0.0003	-
2.0172	2350	0.0003	-
2.0601	2400	0.0003	-
2.1030	2450	0.0003	-
2.1459	2500	0.0003	-
2.1888	2550	0.0002	-
2.2318	2600	0.0003	-
2.2747	2650	0.0004	-
2.3176	2700	0.0002	-
2.3605	2750	0.0003	-
2.4034	2800	0.0002	-
2.4464	2850	0.0002	-
2.4893	2900	0.0002	-
2.5322	2950	0.0002	-
2.5751	3000	0.0002	-
2.6180	3050	0.0004	-
2.6609	3100	0.0004	-
2.7039	3150	0.0003	-
2.7468	3200	0.0003	-
2.7897	3250	0.0003	-
2.8326	3300	0.0003	-
2.8755	3350	0.0003	-
2.9185	3400	0.0003	-
2.9614	3450	0.0005	-

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.5.1
Transformers: 4.38.1
PyTorch: 2.1.0+cu121
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}
}