Push model using huggingface_hub.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,438 @@
+---
+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)
+<!-- - **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)
+
+## 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.")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### 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   | 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

added_tokens.json

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+{
+  "[MASK]": 128000
+}

config.json

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+{
+  "_name_or_path": "microsoft/deberta-v3-base",
+  "architectures": [
+    "DebertaV2Model"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.2",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.45.2",
+    "pytorch": "2.4.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

@@ -0,0 +1,11 @@
+{
+  "normalize_embeddings": false,
+  "labels": [
+    "Computer Science",
+    "Physics",
+    "Mathematics",
+    "Statistics",
+    "Quantitative Biology",
+    "Quantitative Finance"
+  ]
+}

model.safetensors

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modules.json

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+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "bos_token": "[CLS]",
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+  "eos_token": "[SEP]",
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+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

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tokenizer_config.json

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+    "0": {
+      "content": "[PAD]",
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+      "normalized": false,
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+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "sp_model_kwargs": {},
+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}