--- base_model: microsoft/deberta-v3-base datasets: - bhujith10/multi_class_classification_dataset library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 'Title: Detecting Adversarial Samples Using Density Ratio Estimates, Abstract: Machine learning models, especially based on deep architectures are used in everyday applications ranging from self driving cars to medical diagnostics. It has been shown that such models are dangerously susceptible to adversarial samples, indistinguishable from real samples to human eye, adversarial samples lead to incorrect classifications with high confidence. Impact of adversarial samples is far-reaching and their efficient detection remains an open problem. We propose to use direct density ratio estimation as an efficient model agnostic measure to detect adversarial samples. Our proposed method works equally well with single and multi-channel samples, and with different adversarial sample generation methods. We also propose a method to use density ratio estimates for generating adversarial samples with an added constraint of preserving density ratio.' - text: 'Title: Dynamics of exciton magnetic polarons in CdMnSe/CdMgSe quantum wells: the effect of self-localization, Abstract: We study the exciton magnetic polaron (EMP) formation in (Cd,Mn)Se/(Cd,Mg)Se diluted-magnetic-semiconductor quantum wells using time-resolved photoluminescence (PL). The magnetic field and temperature dependencies of this dynamics allow us to separate the non-magnetic and magnetic contributions to the exciton localization. We deduce the EMP energy of 14 meV, which is in agreement with time-integrated measurements based on selective excitation and the magnetic field dependence of the PL circular polarization degree. The polaron formation time of 500 ps is significantly longer than the corresponding values reported earlier. We propose that this behavior is related to strong self-localization of the EMP, accompanied with a squeezing of the heavy-hole envelope wavefunction. This conclusion is also supported by the decrease of the exciton lifetime from 600 ps to 200 - 400 ps with increasing magnetic field and temperature.' - text: 'Title: Exponential Sums and Riesz energies, Abstract: We bound an exponential sum that appears in the study of irregularities of distribution (the low-frequency Fourier energy of the sum of several Dirac measures) by geometric quantities: a special case is that for all $\left\{ x_1, \dots, x_N\right\} \subset \mathbb{T}^2$, $X \geq 1$ and a universal $c>0$ $$ \sum_{i,j=1}^{N}{ \frac{X^2}{1 + X^4 \|x_i -x_j\|^4}} \lesssim \sum_{k \in \mathbb{Z}^2 \atop \|k\| \leq X}{ \left| \sum_{n=1}^{N}{ e^{2 \pi i \left\langle k, x_n \right\rangle}}\right|^2} \lesssim \sum_{i,j=1}^{N}{ X^2 e^{-c X^2\|x_i -x_j\|^2}}.$$ Since this exponential sum is intimately tied to rather subtle distribution properties of the points, we obtain nonlocal structural statements for near-minimizers of the Riesz-type energy. In the regime $X \gtrsim N^{1/2}$ both upper and lower bound match for maximally-separated point sets satisfying $\|x_i -x_j\| \gtrsim N^{-1/2}$.' - text: 'Title: Influence of Spin Orbit Coupling in the Iron-Based Superconductors, Abstract: We report on the influence of spin-orbit coupling (SOC) in the Fe-based superconductors (FeSCs) via application of circularly-polarized spin and angle-resolved photoemission spectroscopy. We combine this technique in representative members of both the Fe-pnictides and Fe-chalcogenides with ab initio density functional theory and tight-binding calculations to establish an ubiquitous modification of the electronic structure in these materials imbued by SOC. The influence of SOC is found to be concentrated on the hole pockets where the superconducting gap is generally found to be largest. This result contests descriptions of superconductivity in these materials in terms of pure spin-singlet eigenstates, raising questions regarding the possible pairing mechanisms and role of SOC therein.' - text: 'Title: Zero-point spin-fluctuations of single adatoms, Abstract: Stabilizing the magnetic signal of single adatoms is a crucial step towards their successful usage in widespread technological applications such as high-density magnetic data storage devices. The quantum mechanical nature of these tiny objects, however, introduces intrinsic zero-point spin-fluctuations that tend to destabilize the local magnetic moment of interest by dwindling the magnetic anisotropy potential barrier even at absolute zero temperature. Here, we elucidate the origins and quantify the effect of the fundamental ingredients determining the magnitude of the fluctuations, namely the ($i$) local magnetic moment, ($ii$) spin-orbit coupling and ($iii$) electron-hole Stoner excitations. Based on a systematic first-principles study of 3d and 4d adatoms, we demonstrate that the transverse contribution of the fluctuations is comparable in size to the magnetic moment itself, leading to a remarkable $\gtrsim$50$\%$ reduction of the magnetic anisotropy energy. Our analysis gives rise to a comprehensible diagram relating the fluctuation magnitude to characteristic features of adatoms, providing practical guidelines for designing magnetically stable nanomagnets with minimal quantum fluctuations.' inference: false --- # SetFit with microsoft/deberta-v3-base This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [bhujith10/multi_class_classification_dataset](https://huggingface.co/datasets/bhujith10/multi_class_classification_dataset) dataset that can be used for Text Classification. This SetFit model uses [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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:** [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) - **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 6 classes - **Training Dataset:** [bhujith10/multi_class_classification_dataset](https://huggingface.co/datasets/bhujith10/multi_class_classification_dataset) ### 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) ## 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("bhujith10/deberta-v3-base-setfit_finetuned") # Run inference preds = model("Title: Influence of Spin Orbit Coupling in the Iron-Based Superconductors, Abstract: We report on the influence of spin-orbit coupling (SOC) in the Fe-based superconductors (FeSCs) via application of circularly-polarized spin and angle-resolved photoemission spectroscopy. We combine this technique in representative members of both the Fe-pnictides and Fe-chalcogenides with ab initio density functional theory and tight-binding calculations to establish an ubiquitous modification of the electronic structure in these materials imbued by SOC. The influence of SOC is found to be concentrated on the hole pockets where the superconducting gap is generally found to be largest. This result contests descriptions of superconductivity in these materials in terms of pure spin-singlet eigenstates, raising questions regarding the possible pairing mechanisms and role of SOC therein.") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 23 | 148.1 | 303 | ### Training Hyperparameters - batch_size: (4, 4) - 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 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: True ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0002 | 1 | 0.4731 | - | | 0.0078 | 50 | 0.4561 | - | | 0.0155 | 100 | 0.4156 | - | | 0.0233 | 150 | 0.2469 | - | | 0.0311 | 200 | 0.2396 | - | | 0.0388 | 250 | 0.2376 | - | | 0.0466 | 300 | 0.2519 | - | | 0.0543 | 350 | 0.1987 | - | | 0.0621 | 400 | 0.1908 | - | | 0.0699 | 450 | 0.161 | - | | 0.0776 | 500 | 0.1532 | - | | 0.0854 | 550 | 0.17 | - | | 0.0932 | 600 | 0.139 | - | | 0.1009 | 650 | 0.1406 | - | | 0.1087 | 700 | 0.1239 | - | | 0.1165 | 750 | 0.1332 | - | | 0.1242 | 800 | 0.1566 | - | | 0.1320 | 850 | 0.0932 | - | | 0.1398 | 900 | 0.1101 | - | | 0.1475 | 950 | 0.1153 | - | | 0.1553 | 1000 | 0.0979 | - | | 0.1630 | 1050 | 0.0741 | - | | 0.1708 | 1100 | 0.0603 | - | | 0.1786 | 1150 | 0.1027 | - | | 0.1863 | 1200 | 0.0948 | - | | 0.1941 | 1250 | 0.0968 | - | | 0.2019 | 1300 | 0.085 | - | | 0.2096 | 1350 | 0.0883 | - | | 0.2174 | 1400 | 0.0792 | - | | 0.2252 | 1450 | 0.1054 | - | | 0.2329 | 1500 | 0.0556 | - | | 0.2407 | 1550 | 0.0777 | - | | 0.2484 | 1600 | 0.0922 | - | | 0.2562 | 1650 | 0.076 | - | | 0.2640 | 1700 | 0.0693 | - | | 0.2717 | 1750 | 0.0857 | - | | 0.2795 | 1800 | 0.0907 | - | | 0.2873 | 1850 | 0.0621 | - | | 0.2950 | 1900 | 0.0792 | - | | 0.3028 | 1950 | 0.0608 | - | | 0.3106 | 2000 | 0.052 | - | | 0.3183 | 2050 | 0.056 | - | | 0.3261 | 2100 | 0.0501 | - | | 0.3339 | 2150 | 0.0559 | - | | 0.3416 | 2200 | 0.0526 | - | | 0.3494 | 2250 | 0.0546 | - | | 0.3571 | 2300 | 0.0398 | - | | 0.3649 | 2350 | 0.0527 | - | | 0.3727 | 2400 | 0.0522 | - | | 0.3804 | 2450 | 0.0468 | - | | 0.3882 | 2500 | 0.0465 | - | | 0.3960 | 2550 | 0.0393 | - | | 0.4037 | 2600 | 0.0583 | - | | 0.4115 | 2650 | 0.0278 | - | | 0.4193 | 2700 | 0.0502 | - | | 0.4270 | 2750 | 0.0413 | - | | 0.4348 | 2800 | 0.0538 | - | | 0.4425 | 2850 | 0.0361 | - | | 0.4503 | 2900 | 0.0648 | - | | 0.4581 | 2950 | 0.0459 | - | | 0.4658 | 3000 | 0.0521 | - | | 0.4736 | 3050 | 0.0288 | - | | 0.4814 | 3100 | 0.0323 | - | | 0.4891 | 3150 | 0.0335 | - | | 0.4969 | 3200 | 0.0472 | - | | 0.5047 | 3250 | 0.0553 | - | | 0.5124 | 3300 | 0.0426 | - | | 0.5202 | 3350 | 0.0276 | - | | 0.5280 | 3400 | 0.0395 | - | | 0.5357 | 3450 | 0.042 | - | | 0.5435 | 3500 | 0.0343 | - | | 0.5512 | 3550 | 0.0314 | - | | 0.5590 | 3600 | 0.0266 | - | | 0.5668 | 3650 | 0.0314 | - | | 0.5745 | 3700 | 0.0379 | - | | 0.5823 | 3750 | 0.0485 | - | | 0.5901 | 3800 | 0.0311 | - | | 0.5978 | 3850 | 0.0415 | - | | 0.6056 | 3900 | 0.0266 | - | | 0.6134 | 3950 | 0.0384 | - | | 0.6211 | 4000 | 0.0348 | - | | 0.6289 | 4050 | 0.0298 | - | | 0.6366 | 4100 | 0.032 | - | | 0.6444 | 4150 | 0.031 | - | | 0.6522 | 4200 | 0.0367 | - | | 0.6599 | 4250 | 0.0289 | - | | 0.6677 | 4300 | 0.0333 | - | | 0.6755 | 4350 | 0.0281 | - | | 0.6832 | 4400 | 0.0307 | - | | 0.6910 | 4450 | 0.0312 | - | | 0.6988 | 4500 | 0.0488 | - | | 0.7065 | 4550 | 0.03 | - | | 0.7143 | 4600 | 0.0309 | - | | 0.7220 | 4650 | 0.031 | - | | 0.7298 | 4700 | 0.0268 | - | | 0.7376 | 4750 | 0.0324 | - | | 0.7453 | 4800 | 0.041 | - | | 0.7531 | 4850 | 0.0349 | - | | 0.7609 | 4900 | 0.0349 | - | | 0.7686 | 4950 | 0.0291 | - | | 0.7764 | 5000 | 0.025 | - | | 0.7842 | 5050 | 0.0249 | - | | 0.7919 | 5100 | 0.0272 | - | | 0.7997 | 5150 | 0.0302 | - | | 0.8075 | 5200 | 0.0414 | - | | 0.8152 | 5250 | 0.0295 | - | | 0.8230 | 5300 | 0.033 | - | | 0.8307 | 5350 | 0.0203 | - | | 0.8385 | 5400 | 0.0275 | - | | 0.8463 | 5450 | 0.0354 | - | | 0.8540 | 5500 | 0.0254 | - | | 0.8618 | 5550 | 0.0313 | - | | 0.8696 | 5600 | 0.0296 | - | | 0.8773 | 5650 | 0.0248 | - | | 0.8851 | 5700 | 0.036 | - | | 0.8929 | 5750 | 0.025 | - | | 0.9006 | 5800 | 0.0234 | - | | 0.9084 | 5850 | 0.0221 | - | | 0.9161 | 5900 | 0.0314 | - | | 0.9239 | 5950 | 0.0273 | - | | 0.9317 | 6000 | 0.0299 | - | | 0.9394 | 6050 | 0.0262 | - | | 0.9472 | 6100 | 0.0285 | - | | 0.9550 | 6150 | 0.021 | - | | 0.9627 | 6200 | 0.0215 | - | | 0.9705 | 6250 | 0.0312 | - | | 0.9783 | 6300 | 0.0259 | - | | 0.9860 | 6350 | 0.0234 | - | | 0.9938 | 6400 | 0.0222 | - | | 1.0 | 6440 | - | 0.1609 | ### Framework Versions - Python: 3.10.14 - SetFit: 1.1.0 - Sentence Transformers: 3.3.1 - Transformers: 4.45.2 - PyTorch: 2.4.0 - Datasets: 3.0.1 - Tokenizers: 0.20.0 ## 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} } ```