Claude Skills by qhjqhj00

Evaluates sentiment polarity classification across diverse informal text genres, including tweets, sarcasm-marked tweets, and LiveJournal posts. It distinguishes between phrase-level contextual polarity and message-level sentiment, testing robustness to informal language, sarcasm-induced polarity inversion, and cross-platform generalization. Use when the user wants to benchmark on SemEval-2014 Task 9 Test Sets, or asks about evaluating this task. Reports macro- and micro-averaged F1.

Evaluates language models' ability to detect whether a multi-word expression (MWE) in a sentence is used idiomatically or literally, and to model the semantic similarity of idiomatic expressions. It tests compositionality understanding and contextual semantic representation across English, Portuguese, and Galician. Use when the user wants to benchmark on SemEval-2022 Task 2, or asks about evaluating this task. Reports macro F1 score.

Sentiment classification across twelve low-resource African languages and Creoles, evaluating model robustness to code-switching and varying degrees of lexical similarity to pretraining data. Use when the user wants to benchmark on SemEval-2023 Task 12, or asks about evaluating this task. Reports macro-F1.

Evaluates a model's ability to rank question-answer pairs by relevance. It probes the system's capacity to understand semantic similarity and perform information retrieval tasks using language-independent features and multi-task learning. Use when the user wants to benchmark on SemEval-2016 Task 3, or asks about evaluating this task. Reports MAP.

Evaluates the ability of models to classify sentiment in social media posts (tweets) across different languages (English and Arabic) and granularities (overall polarity, topic-specific polarity, and ordinal scales). Use when the user wants to benchmark on SemEval-2017 Task 4, or asks about evaluating this task. Reports macro-average recall.

Evaluates a model's ability to detect and rank lexical semantic change over time across multiple languages. It probes both binary classification of whether a word's meaning has changed and graded ranking of the magnitude of that change. Use when the user wants to benchmark on SemEval 2020 Unsupervised Lexical Semantic Change Detection, or asks about evaluating this task. Reports Spearman's rank correlation.

Detects persuasion techniques in unimodal text by classifying which techniques are present in a given text snippet. It probes the model's ability to perform multilabel classification on propaganda and persuasive content. Use when the user wants to benchmark on SemEval-2021 Task 6 Subtask 1, or asks about evaluating this task. Reports F1-Micro.

Identifies and classifies spans of persuasion techniques within unimodal text using sequence tagging. It probes the model's ability to localize and categorize persuasive spans at the token level. Use when the user wants to benchmark on SemEval-2021 Task 6 Subtask 2, or asks about evaluating this task. Reports F1.

Detects persuasion techniques in multimodal memes by jointly analyzing text and image content. It probes cross-modal alignment and interaction modeling for persuasive content detection. Use when the user wants to benchmark on SemEval-2021 Task 6 Subtask 3, or asks about evaluating this task. Reports F1-Micro.

This benchmark evaluates structured sentiment analysis by testing a model's ability to extract sentiment targets, opinions, and their relational dependencies from text. It probes cross-lingual generalization and the capacity to repurpose semantic dependency parsers for sentiment graph generation. Use when the user wants to benchmark on SemEval-2022 Task 10, or asks about evaluating this task. Reports F1.

Multilingual sentiment classification across low-resource African languages, including zero-shot generalization to unseen languages. Use when the user wants to benchmark on SemEval-2023 Task 12, or asks about evaluating this task. Reports F1.

Evaluates the ability of context compression methods to preserve information density for downstream reading comprehension tasks. It probes whether adaptive, density-aware compression can maintain answer accuracy while significantly reducing context length compared to static baselines. Use when the user wants to benchmark on HotpotQA, SQuAD, Natural Questions, AdversarialQA, or asks about evaluating this task. Reports substring accuracy.

Evaluates a model's ability to distinguish in-distribution (ID) samples from out-of-distribution (OOD) samples in a semi-supervised setting, where only labeled ID data and unlabeled mixed data are available during training. Use when the user wants to benchmark on MNIST, FashionMNIST, SVHN, CIFAR10, CIFAR100, ImageNet, or asks about evaluating this task. Reports AUROC.

Evaluates semi-supervised medical image segmentation performance under limited labeled data ratios (10% and 30%). It measures segmentation accuracy and boundary precision across multiple medical domains including colonoscopy, dermoscopy, pathology, and ultrasound. Use when the user wants to benchmark on Colonoscopy (CVC-ClinicDB, Kvasir, CVC-300), ISIC-2018, BCSS, BUSI, or asks about evaluating this task. Reports Dice coefficient (Dice).

Evaluates a semi-supervised graph neural network's ability to predict user loan defaults and classify user occupations using multiview graph data (social ties, app usage, nicks, addresses) on a large-scale financial platform dataset. Use when the user wants to benchmark on Alipay, or asks about evaluating this task. Reports AUC.

Evaluates speech processing models on authentic, real-world conversations among super-aged Chinese speakers (75+). It probes capabilities in speaker verification, diarization, automatic speech recognition, and speech editing under conditions of age-related vocal degradation, dialectal variation, and presbyphonia. Use when the user wants to benchmark on SeniorTalk, or asks about evaluating this task. Reports EER.

Compute the SensitivityAtSpecificity metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute SensitivityAtSpecificity, or asks how to score with SensitivityAtSpecificity.

Evaluates unsupervised lossy compression of irregular sensor data on radiation-hardened edge ASICs. It probes the ability to reconstruct high-granularity calorimeter images under extreme bandwidth and latency constraints. Use when the user wants to benchmark on CMS HGCal Trigger Data, or asks about evaluating this task. Reports Energy Mover's Distance (EMD).

Evaluates the zero-shot transferability of tactile representations across different physical sensors for shape reconstruction, object classification, and 3D pose estimation. It probes whether learned features generalize across varying optical designs and manufacturing differences without retraining. Use when the user wants to benchmark on Real-world tactile contact dataset, or asks about evaluating this task. Reports top-1 accuracy.

Predicts single-neuron responses in mouse primary visual cortex from dynamic video stimuli and behavioral covariates, probing spatio-temporal neural decoding and out-of-distribution generalization. Use when the user wants to benchmark on SENSORIUM 2023, or asks about evaluating this task. Reports R^2.

Evaluates a sentiment-aware reinforcement learning agent's ability to generate profitable and stable trading strategies across diverse market conditions, transaction cost regimes, and varying levels of financial news coverage. It benchmarks performance against a sentiment-free RL ablation and a buy-and-hold strategy using standard financial return and risk metrics. Use when the user wants to benchmark on 20-Asset Financial Time Series & News Corpus (2018-2020), or asks about evaluating this t...

Evaluates the recall and efficiency of continuous active learning systems for information retrieval when using sentence-level versus document-level relevance feedback. It measures how quickly a simulated reviewer can identify all relevant documents under varying effort models that account for assessment count and sentence reading time. Use when the user wants to benchmark on TREC Total Recall 2015 Track, HARD 2004 Track, or asks about evaluating this task. Reports Recall@E.

Evaluates the quality of sentence-level representations learned by a transformer-based autoencoder across semantic similarity, single- and multi-sentence classification, and controlled text generation. It probes the model's ability to capture semantic meaning, classify sentiment/acceptability, and reconstruct or modify text via vector arithmetic. Use when the user wants to benchmark on Semantic Textual Similarity (STS), GLUE benchmark, Yelp reviews, or asks about evaluating this task. Reports...

Probes a model's ability to detect sentence-level prosodic stress at the word/token level using only audio input. It evaluates zero-shot generalization and alignment-free stress identification across diverse speech styles and synthetic/human datasets. Use when the user wants to benchmark on TinyStress-15K, Aix-MARSEC, Expresso, EmphAssess, or asks about evaluating this task. Reports F1 score.

Evaluates abstractive summarization models on condensing source sentences into title-like summaries. It measures summary quality via lexical/semantic overlap and human judgments, while explicitly quantifying the degree of verbatim copying from the source text. Use when the user wants to benchmark on Gigaword, Newsroom, or asks about evaluating this task. Reports ROUGE-2.

This benchmark evaluates Persian grapheme-to-phoneme (G2P) systems on sentence-level text, specifically probing their ability to correctly map characters to phonemes and disambiguate homographs using contextual information. It measures both phonetic accuracy and contextual word-sense resolution capabilities. Use when the user wants to benchmark on SentenceBench, or asks about evaluating this task. Reports Homograph Acc. (%).

Evaluates the transferability and quality of universal sentence embeddings across a standardized suite of downstream tasks. It probes capabilities in sentiment classification, natural language inference, semantic textual similarity, and cross-modal image-caption retrieval using fixed hyperparameters and consistent preprocessing. Use when the user wants to benchmark on MR, CR, SUBJ, MPQA, TREC, SST-2, SST-5, SNLI, SICK-E, SICK-R, STS14, MRPC, COCO, or asks about evaluating this task. Reports a...

Evaluates large language models on fine-grained emotion classification across English and Chinese dialogues and social media text. It probes the model's ability to handle complex, multilingual contexts, long sequences, and imbalanced emotion categories using a self-analytical negotiation mechanism. Use when the user wants to benchmark on MELD, EmoryNLP, IEMOCAP, CPED, CH-SIMS, Twitter2015, Twitter2017, or asks about evaluating this task. Reports Accuracy.

Evaluates sentiment classification and justification generation capabilities for the low-resource Maithili language. It probes a model's ability to accurately predict sentence-level sentiment labels and generate culturally grounded, linguistically correct explanations in Maithili. Use when the user wants to benchmark on SentiMaithili, or asks about evaluating this task. Reports F1-score.

Evaluates a model's ability to classify text sentiment into binary or fine-grained polarity categories. Specifically probes how well the model handles negation scope and polarity disentanglement through multi-task learning. Use when the user wants to benchmark on SST-binary, SST-fine, SemEval-binary, SemEval-fine, or asks about evaluating this task. Reports accuracy.

Evaluates a model's ability to jointly classify sentiment (negative, neutral, positive) from healthcare transcripts and generate semantically coherent rationales explaining the classification. It probes multimodal sentiment analysis, explainable AI, and chain-of-thought reasoning in a clinical dialogue setting. Use when the user wants to benchmark on Sentiment Reasoning dataset, or asks about evaluating this task. Reports accuracy.

Evaluates a sentence-level early intervention framework for reducing object hallucinations in multimodal large language models (MLLMs) while preserving or enhancing general vision-language capabilities across multiple standard benchmarks. Use when the user wants to benchmark on Object HalBench, AMBER, HallusionBench, VQAv2, TextVQA, ScienceQA, MM-Vet, or asks about evaluating this task. Reports response-level hallucination rate (Resp.).

Evaluates deep learning models for forecasting solar energetic particle (SEP) events using solar magnetic field parameters and historical eruptive features. It probes the model's ability to classify general and operational SEP occurrences under different feature sets and temporal conditions. Use when the user wants to benchmark on SEPVAL, CLEAR, or asks about evaluating this task. Reports F1.

This survey evaluates and categorizes methodologies for sequence-aware recommender systems, focusing on offline evaluation protocols, data partitioning strategies, and ranking metrics used to assess prediction accuracy and list quality. It highlights how temporal dependencies and session boundaries require specialized splitting and target definition compared to traditional matrix completion. Use when the user wants to benchmark on Amazon, RecSys Chall. 2015, Delicious, or asks about evaluatin...

This evaluation protocol benchmarks sequential recommendation models by comparing sequence-level data augmentation strategies against contrastive learning baselines. It probes a model's ability to capture user intent from interaction sequences and generate accurate item rankings under varying data sparsity, sequence lengths, and cold-start conditions. Use when the user wants to benchmark on Amazon Beauty, Amazon Sports, Yelp, ML-1m, or asks about evaluating this task. Reports Recall@K / NDCG@...

Evaluates sequence modeling capabilities, specifically long-term memory retention and contextual understanding across synthetic stress tests and real-world benchmarks. It compares generic temporal convolutional networks against canonical recurrent architectures (LSTM, GRU, RNN) on tasks requiring prediction of sequential data. Use when the user wants to benchmark on Adding problem, Sequential MNIST, P-MNIST, Copy memory, Nottingham, JSB Chorales, PTB, Wikitext-103, LAMBADA, text8, or asks abo...

Evaluates the stability and performance of sequential knowledge editing methods on large language models over long horizons. It probes whether editing techniques can maintain factual accuracy, preserve general capabilities, and avoid norm blow-up or catastrophic forgetting across thousands of atomic updates. Use when the user wants to benchmark on CounterFact, ZsRE, WikiBigEdit, GLUE-style tasks (SST, MRPC, RTE, CoLA, MNLI), MMLU, or asks about evaluating this task. Reports Efficacy.

Evaluates a model's ability to predict the next item in a user's sequential interaction history. It probes how well the system captures temporal user preferences and handles discrete recommendation data under a strict chronological split. Use when the user wants to benchmark on Amazon Beauty, Amazon Toys, MovieLens-1M, or asks about evaluating this task. Reports NDCG@K.

Evaluates the ability of sequential recommendation models to predict the next item in a user's interaction history by distilling semantic user profiles from pre-trained LLMs into the recommender's internal representations. The protocol tests whether knowledge distillation improves recommendation accuracy while maintaining inference efficiency without requiring real-time LLM calls. Use when the user wants to benchmark on Beauty, ML20M, Kion, Amazon M2, or asks about evaluating this task. Repor...

Evaluates a model's ability to predict the next item in a user's sequential interaction history by leveraging both temporal proximity across users and within-user sequence dynamics. Use when the user wants to benchmark on Amazon (Beauty, Book, Video), Steam, or asks about evaluating this task. Reports NDCG@10.

Evaluates the performance and reproducibility of sequential recommender system (SRS) models across multiple user-item interaction datasets. It probes how architectural choices, hyperparameter settings, and training configurations affect ranking metrics and computational emissions. Use when the user wants to benchmark on Beauty, FS-NYC, FS-TKY, ML-100k, ML-1M, ML-20M, or asks about evaluating this task. Reports NDCG@10.

Evaluates the capability of speech emotion recognition models to classify emotional states from audio recordings using spectral features and attention mechanisms. The protocol measures classification performance across multiple standard SER benchmarks to assess robustness and generalization. Use when the user wants to benchmark on SAVEE, RAVDESS, CREMA-D, TESS, EMO-DB, EMOVO, or asks about evaluating this task. Reports accuracy.

Evaluates pre-trained speech models' ability to recognize emotions in audio across multiple languages. It specifically tests how internal layer representations and feature aggregation strategies impact classification performance. Use when the user wants to benchmark on AESDD, CaFE, EmoDB, EMOVO, IEMOCAP, RAVDESS, ShEMO, or asks about evaluating this task. Reports accuracy.

Evaluates deep learning models for speech emotion recognition (SER) using a multi-window data augmentation strategy. It probes the model's ability to classify categorical emotions from speech audio under varying feature extraction window sizes and class configurations. Use when the user wants to benchmark on IEMOCAP, RAVDESS, SAVEE, or asks about evaluating this task. Reports Unweighted Accuracy (UA).

Evaluates the accuracy and robustness of Sérsic profile fitting algorithms (GIM2D and GALFIT) on simulated HST/ACS galaxy images. It probes how well these codes recover true structural parameters under varying signal-to-noise ratios and surface brightness levels. Use when the user wants to benchmark on GEMS Bulge0001, GEMS Disk0001, or asks about evaluating this task. Reports magnitude_residual.

Evaluates the commercial viability and economic performance of text-to-image and image editing models in real-world design workflows. It measures how well generated images meet baseline requirements, visual quality standards, and commercial intent, linking outputs directly to human payment decisions and platform revenue. Use when the user wants to benchmark on ServImage, or asks about evaluating this task. Reports Task Acceptance (%).

Evaluates the predictive performance of session-based and session-aware recommendation models in ranking the next item a user will interact with. It benchmarks both neural and non-neural approaches across multiple real-world interaction datasets to assess accuracy, coverage, and popularity bias. Use when the user wants to benchmark on RETAIL, XING, COSMETICS, LASTFM, or asks about evaluating this task. Reports MAP@20.

Evaluates a model's ability to predict the next item in a user's shopping session based on sequential item interactions and cross-session collaborative signals. It probes how well the model captures dynamic user interests and leverages historical session data for accurate recommendations. Use when the user wants to benchmark on Diginetica, Tmall, Yoochoose1_64, or asks about evaluating this task. Reports P@20.

This evaluation probes a model's ability to predict the next item in a user session based on historical interactions. It measures ranking quality across multiple benchmark datasets, testing how well the model captures temporal patterns and prospective user preferences without relying on fixed recency heuristics. Use when the user wants to benchmark on Six session-based recommendation benchmarks (DG, GA, YC, TM, LF, NP), or asks about evaluating this task. Reports recall@k, MRR@k, NDCG@k.

This evaluation probes a model's ability to perform personalized session-based sequential recommendation by predicting the next item a user will interact with. It measures how well the model leverages both intra-session behavior and cross-session user history to rank relevant items in a top-5 list. Use when the user wants to benchmark on XING, VIDEO, or asks about evaluating this task. Reports MRR@5.