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argilla/
pc_components_classifier

Name: pc_components_classifier
Rating: 2.5 (11 reviews)

setfit

safetensors

bert

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

TaylorAI/bge-micro-v2

finetune

model-index

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SetFit with TaylorAI/bge-micro-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses TaylorAI/bge-micro-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: TaylorAI/bge-micro-v2
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
neutral	'Hey, I just got the Ryzen 5 3600 and it’s actually pretty good for the price, even though I’m still learning how to set up everything. The installation was a bit tricky, but the guides on YouTube helped a lot. I’m from Australia, so shipping took a bit longer, but it’s working fine so far.' 'I recently upgraded my PC with the AMD Ryzen 5 5600X and an NVMe SSD from Samsung, and the performance boost is noticeable, especially when running multiple applications. The only downside is that the case fans are a bit noisy at full load, but it’s a small price to pay for the speed.' 'Just got the new Intel Core i9-13900K, and I must say, it’s a beast for multithreaded tasks, but the thermal management is something to keep an eye on, especially if you’re pushing it to the limits. The MSI MPG Z690 Carbon Wi-Fi motherboard pairs well, though I had to tweak the BIOS settings for optimal performance.'
negative	'I just got the new Ryzen 5 5600X and it’s not living up to the hype at all. My old i5 4670K was doing better in some games, and the power consumption is way too high. Plus, the stock cooler is a joke, barely keeps it under 80°C under load. Really disappointed, feels like a waste of money.' 'I just built my rig with the Ryzen 5 3600 and MSI B450 Tomahawk, and the cooling system is a joke. Had to swap out the stock cooler for a Noctua NH-U12S just to keep it from throttling during League of Legends. Not cool, man, not cool.' 'I just installed the new Ryzen 7 5800X and the cooling system is a joke, even with a Corsair H100i. Temps are through the roof, and the performance is nowhere near what I expected. Really disappointed, and I’m not sure if it’s the CPU or the cooling setup, but this build is a mess.'
positive	"I finally got my hands on the Ryzen 9 7950X and paired it with an X670E motherboard, and it's a beast! The performance gains over my old 5900X are insane, especially in multi-threaded tasks. The power efficiency is also a game changer. Can't wait to see how it handles my next 3D rendering project." "I just assembled a system with the Ryzen 9 5950X and an ASUS ROG Strix X570-E Gaming motherboard, and it's running smoother than a gelato in Florence. The thermals are excellent, and the performance is simply outstanding, especially under heavy loads." "Just built my new rig with the Ryzen 5 5600X and RTX 3060, and it's a game changer! The performance is super smooth, even with all the latest games on ultra settings. Also, the B550 Aorus Master motherboard is a beast, really stable and easy to overclock. Highly recommend this setup for anyone looking to build a solid mid-range gaming PC."

Evaluation

Metrics

Label	Accuracy
all	0.7375

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("setfit_model_id")
# Run inference
preds = model("I just built my first PC and got the AMD Ryzen 5 3600 with a GTX 1660 Super, and it's running pretty smooth for the price. Had some trouble with the case fans but managed to figure it out with some help from YouTube.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	38	52.1765	65

Label	Training Sample Count
negative	5
neutral	6
positive	6

Training Hyperparameters

batch_size: (16, 2)
num_epochs: (1, 16)
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
l2_weight: 0.01
seed: 42
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0833	1	0.1986	-

Framework Versions

Python: 3.11.9
SetFit: 1.1.0
Sentence Transformers: 3.1.1
Transformers: 4.45.2
PyTorch: 2.4.1
Datasets: 3.0.1
Tokenizers: 0.20.1

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}
}

argilla/pc_components_classifier