{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/load-embeddings","entry":"load_embeddings","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":33,"n_papers_ran":12,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":34,"n_samples_ran":12,"n_samples_fingerprinted":0,"n_places":35,"n_places_pointer_only":11,"by_status":{"ran_honours":3,"ran_violates":0,"ran_draft_wrong":5,"ran_fixture":0,"ran":4,"unverified":22},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2607.19376","paper":"/paper/arxiv-2607-19376","title":"Refnd: Preventing Data Leakage in Relational Datasets","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"JacobCote/refnd_experimental_code","path":"code.py","file_url":"https://github.com/JacobCote/refnd_experimental_code/blob/HEAD/code.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b389563dc1ac25a0","mcp_get_code":{"code_sha256":"b389563dc1ac25a0"}},{"arxiv_id":"2603.24150","paper":"/paper/arxiv-2603-24150","title":"A visual observation on the geometry of UMAP projections of the difference vectors of antonym and synonym word pair embeddings","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"ramiluisto/CuriousSwirl","path":"semsim/embeddings.py","file_url":"https://github.com/ramiluisto/CuriousSwirl/blob/HEAD/semsim/embeddings.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e298f4b865e41113","mcp_get_code":{"code_sha256":"e298f4b865e41113"}},{"arxiv_id":"2512.02206","paper":"/paper/arxiv-2512-02206","title":"WhAM: Towards A Translative Model of Sperm Whale Vocalization","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"Project-CETI/wham","path":"wham/generation/eval/calculate_custom_fad.py","file_url":"https://github.com/Project-CETI/wham/blob/HEAD/wham/generation/eval/calculate_custom_fad.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4346ebecc7c60973","mcp_get_code":{"code_sha256":"4346ebecc7c60973"}},{"arxiv_id":"2505.13910","paper":"/paper/shortcutprobe-probing-prediction-shortcuts","title":"ShortcutProbe: Probing Prediction Shortcuts for Learning Robust Models","date":"2025-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gtzheng/ShortcutProbe","path":"algorithms/shortcut_probe.py","file_url":"https://github.com/gtzheng/ShortcutProbe/blob/HEAD/algorithms/shortcut_probe.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2a04614f7104524c","mcp_get_code":{"code_sha256":"2a04614f7104524c"}},{"arxiv_id":"2412.05430","paper":"/paper/dart-eval-a-comprehensive-dna-language-model","title":"DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA","date":"2024-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kundajelab/dart-eval","path":"src/dnalm_bench/task_2_5_single/experiments/task_2_transcription_factor_binding/footprint_eval_embeddings.py","file_url":"https://github.com/kundajelab/dart-eval/blob/HEAD/src/dnalm_bench/task_2_5_single/experiments/task_2_transcription_factor_binding/footprint_eval_embeddings.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b05bd25300b743b3","mcp_get_code":{"code_sha256":"b05bd25300b743b3"}},{"arxiv_id":"2411.06646","paper":"/paper/understanding-scaling-laws-with-statistical","title":"Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data","date":"2024-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dahoas/transformer_manifolds_learning","path":"embeddings.py","file_url":"https://github.com/dahoas/transformer_manifolds_learning/blob/HEAD/embeddings.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"39104783d817f46e","mcp_get_code":{"code_sha256":"39104783d817f46e"}},{"arxiv_id":"2410.22971","paper":"/paper/private-synthetic-text-generation-with","title":"Private Synthetic Text Generation with Diffusion Models","date":"2024-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"trusthlt/private-synthetic-text-generation","path":"SeqDiffuSeq/inference_main.py","file_url":"https://github.com/trusthlt/private-synthetic-text-generation/blob/HEAD/SeqDiffuSeq/inference_main.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"91ba51cc5a12c5e9","mcp_get_code":{"code_sha256":"91ba51cc5a12c5e9"}},{"arxiv_id":"2407.16658","paper":"/paper/egocvr-an-egocentric-benchmark-for-fine","title":"EgoCVR: An Egocentric Benchmark for Fine-Grained Composed Video Retrieval","date":"2024-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"explainableml/egocvr","path":"egocvr_retrieval.py","file_url":"https://github.com/explainableml/egocvr/blob/HEAD/egocvr_retrieval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0bdbe9b1b43f5498","mcp_get_code":{"code_sha256":"0bdbe9b1b43f5498"}},{"arxiv_id":"2407.06947","paper":"/paper/audio-language-datasets-of-scenes-and-events","title":"Audio-Language Datasets of Scenes and Events: A Survey","date":null,"month_inferred_from_arxiv_id":"2024-07","title_source":"archive","repo":"gljs/audio-datasets","path":"visualization/clap_evaluation_heatmap.py","file_url":"https://github.com/gljs/audio-datasets/blob/HEAD/visualization/clap_evaluation_heatmap.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e0512ec2d59ef318","mcp_get_code":{"code_sha256":"e0512ec2d59ef318"}},{"arxiv_id":"2407.06947","paper":"/paper/audio-language-datasets-of-scenes-and-events","title":"Audio-Language Datasets of Scenes and Events: A Survey","date":null,"month_inferred_from_arxiv_id":"2024-07","title_source":"archive","repo":"gljs/audio-datasets","path":"get_diff.py","file_url":"https://github.com/gljs/audio-datasets/blob/HEAD/get_diff.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b8e022027ecb13b5","mcp_get_code":{"code_sha256":"b8e022027ecb13b5"}},{"arxiv_id":"2406.16330","paper":"/paper/pruning-via-merging-compressing-llms-via","title":"Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging","date":"2024-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sempraety/pruning-via-merging","path":"pipeline.py","file_url":"https://github.com/sempraety/pruning-via-merging/blob/HEAD/pipeline.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ad08a638e2808f27","mcp_get_code":{"code_sha256":"ad08a638e2808f27"}},{"arxiv_id":"2405.20363","paper":"/paper/llmgeo-benchmarking-large-language-models-on","title":"LLMGeo: Benchmarking Large Language Models on Image Geolocation In-the-wild","date":"2024-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yeyimilk/llmgeo","path":"src/knn_for_embedings.py","file_url":"https://github.com/yeyimilk/llmgeo/blob/HEAD/src/knn_for_embedings.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"73d1a3fb5929f121","mcp_get_code":{"code_sha256":"73d1a3fb5929f121"}},{"arxiv_id":"2403.19600","paper":"/paper/enhance-image-classification-via-inter-class","title":"Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model","date":"2024-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhicaiwww/diff-mix","path":"augmentation/ti_mix.py","file_url":"https://github.com/zhicaiwww/diff-mix/blob/HEAD/augmentation/ti_mix.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1826c47b14d934be","mcp_get_code":{"code_sha256":"1826c47b14d934be"}},{"arxiv_id":"2403.01749","paper":"/paper/differentially-private-synthetic-data-via-1","title":"Differentially Private Synthetic Data via Foundation Model APIs 2: Text","date":"2024-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AI-secure/aug-pe","path":"dpsda/logging.py","file_url":"https://github.com/AI-secure/aug-pe/blob/HEAD/dpsda/logging.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2404f2b745222315","mcp_get_code":{"code_sha256":"2404f2b745222315"}},{"arxiv_id":"2402.14526","paper":"/paper/balanced-data-sampling-for-language-model","title":"Balanced Data Sampling for Language Model Training with Clustering","date":"2024-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"choosewhatulike/cluster-clip","path":"kmeans.py","file_url":"https://github.com/choosewhatulike/cluster-clip/blob/HEAD/kmeans.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0fb325eb5b6a375b","mcp_get_code":{"code_sha256":"0fb325eb5b6a375b"}},{"arxiv_id":"2401.05792","paper":"/paper/discovering-low-rank-subspaces-for-language","title":"Discovering Low-rank Subspaces for Language-agnostic Multilingual Representations","date":"2024-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fffffarmer/lsar","path":"src/utils_extract.py","file_url":"https://github.com/fffffarmer/lsar/blob/HEAD/src/utils_extract.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bbf163a91793e6ca","mcp_get_code":{"code_sha256":"bbf163a91793e6ca"}},{"arxiv_id":"2311.10774","paper":"/paper/mmc-advancing-multimodal-chart-understanding","title":"MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning","date":"2023-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FuxiaoLiu/VisualNews-Repository","path":"model/utils.py","file_url":"https://github.com/FuxiaoLiu/VisualNews-Repository/blob/HEAD/model/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d85bcc4f59fe10b4","mcp_get_code":{"code_sha256":"d85bcc4f59fe10b4"}},{"arxiv_id":"2311.08849","paper":"/paper/ofa-a-framework-of-initializing-unseen","title":"OFA: A Framework of Initializing Unseen Subword Embeddings for Efficient Large-scale Multilingual Continued Pretraining","date":"2023-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cisnlp/ofa","path":"evaluation/retrieval/evaluate_retrieval_bible.py","file_url":"https://github.com/cisnlp/ofa/blob/HEAD/evaluation/retrieval/evaluate_retrieval_bible.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0c8227fd1e0b86be","mcp_get_code":{"code_sha256":"0c8227fd1e0b86be"}},{"arxiv_id":"2305.19435","paper":"/paper/adanns-a-framework-for-adaptive-semantic","title":"AdANNS: A Framework for Adaptive Semantic Search","date":"2023-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RAIVNLab/AdANNS","path":"adanns/utils.py","file_url":"https://github.com/RAIVNLab/AdANNS/blob/HEAD/adanns/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"11a69b869864eb2e","mcp_get_code":{"code_sha256":"11a69b869864eb2e"}},{"arxiv_id":"2106.14282","paper":"/paper/a-closer-look-at-how-fine-tuning-changes-bert","title":"A Closer Look at How Fine-tuning Changes BERT","date":"2021-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"utahnlp/BERT-fine-tuning-analysis","path":"probing/utils.py","file_url":"https://github.com/utahnlp/BERT-fine-tuning-analysis/blob/HEAD/probing/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bf4b967c85115c8b","mcp_get_code":{"code_sha256":"bf4b967c85115c8b"}},{"arxiv_id":"2104.05904","paper":"/paper/directprobe-studying-representations-without","title":"DirectProbe: Studying Representations without Classifiers","date":"2021-04-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"utahnlp/DirectProbe","path":"directprobe/utils.py","file_url":"https://github.com/utahnlp/DirectProbe/blob/HEAD/directprobe/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5bcb43d32734848e","mcp_get_code":{"code_sha256":"5bcb43d32734848e"}},{"arxiv_id":"1912.04853","paper":"/paper/embedding-comparator-visualizing-differences","title":"Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples","date":"2019-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mitvis/embedding-comparator","path":"preprocess_data.py","file_url":"https://github.com/mitvis/embedding-comparator/blob/HEAD/preprocess_data.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2be7686b484da4eb","mcp_get_code":{"code_sha256":"2be7686b484da4eb"}},{"arxiv_id":"1911.03070","paper":"/paper/interactive-refinement-of-cross-lingual-word","title":"Interactive Refinement of Cross-Lingual Word Embeddings","date":"2019-11-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"forest-snow/clime-ui","path":"prepare_ui.py","file_url":"https://github.com/forest-snow/clime-ui/blob/HEAD/prepare_ui.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"109cd48e485aae7a","mcp_get_code":{"code_sha256":"109cd48e485aae7a"}},{"arxiv_id":"1909.02151","paper":"/paper/kagnet-knowledge-aware-graph-networks-for","title":"KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning","date":"2019-09-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"INK-USC/KagNet","path":"models/csqa_dataset.py","file_url":"https://github.com/INK-USC/KagNet/blob/HEAD/models/csqa_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9b0d6be3854d5d6d","mcp_get_code":{"code_sha256":"9b0d6be3854d5d6d"}},{"arxiv_id":"1906.05017","paper":"/paper/graph-embedding-on-biomedical-networks","title":"Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations","date":"2019-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiangyue9607/BioNEV","path":"src/bionev/OpenNE/classify.py","file_url":"https://github.com/xiangyue9607/BioNEV/blob/HEAD/src/bionev/OpenNE/classify.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"515a9464367d7d2b","mcp_get_code":{"code_sha256":"515a9464367d7d2b"}},{"arxiv_id":"1904.02882","paper":"/paper/libritts-a-corpus-derived-from-librispeech","title":"LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech","date":null,"month_inferred_from_arxiv_id":"2019-04","title_source":"archive","repo":"Helsinki-NLP/prosody","path":"prosody_dataset.py","file_url":"https://github.com/Helsinki-NLP/prosody/blob/HEAD/prosody_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c3b2065399bb567e","mcp_get_code":{"code_sha256":"c3b2065399bb567e"}},{"arxiv_id":"1904.02882","paper":"/paper/libritts-a-corpus-derived-from-librispeech","title":"LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech","date":null,"month_inferred_from_arxiv_id":"2019-04","title_source":"archive","repo":"mamachengcheng/prosody","path":"prosody_dataset.py","file_url":"https://github.com/mamachengcheng/prosody/blob/HEAD/prosody_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b6f866c5e91fb701","mcp_get_code":{"code_sha256":"b6f866c5e91fb701"}},{"arxiv_id":"1903.03862","paper":"/paper/lipstick-on-a-pig-debiasing-methods-cover-up","title":"Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them","date":"2019-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gonenhila/gender_bias_lipstick","path":"source/save_embeds.py","file_url":"https://github.com/gonenhila/gender_bias_lipstick/blob/HEAD/source/save_embeds.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f91aba169ad03e06","mcp_get_code":{"code_sha256":"f91aba169ad03e06"}},{"arxiv_id":"1804.05685","paper":"/paper/a-discourse-aware-attention-model-for","title":"A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents","date":"2018-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"acohan/long-summarization","path":"util.py","file_url":"https://github.com/acohan/long-summarization/blob/HEAD/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ab9862cbeb4502b8","mcp_get_code":{"code_sha256":"ab9862cbeb4502b8"}},{"arxiv_id":"1702.02181","paper":"/paper/deep-learning-with-dynamic-computation-graphs","title":"Deep Learning with Dynamic Computation Graphs","date":"2017-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tensorflow/fold","path":"tensorflow_fold/blocks/examples/sentiment/sentiment.py","file_url":"https://github.com/tensorflow/fold/blob/HEAD/tensorflow_fold/blocks/examples/sentiment/sentiment.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b1e9ed773635122b","mcp_get_code":{"code_sha256":"b1e9ed773635122b"}},{"arxiv_id":"1611.07810","paper":"/paper/a-dataset-and-exploration-of-models-for","title":"A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering","date":"2016-11-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"totalgood/viddesc","path":"src/viddesc/keras_lstm.py","file_url":"https://github.com/totalgood/viddesc/blob/HEAD/src/viddesc/keras_lstm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5933f2701ea63955","mcp_get_code":{"code_sha256":"5933f2701ea63955"}},{"arxiv_id":"1611.01734","paper":"/paper/deep-biaffine-attention-for-neural-dependency","title":"Deep Biaffine Attention for Neural Dependency Parsing","date":"2016-11-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chantera/biaffineparser","path":"src/utils/data.py","file_url":"https://github.com/chantera/biaffineparser/blob/HEAD/src/utils/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d9f663d6651746c4","mcp_get_code":{"code_sha256":"d9f663d6651746c4"}},{"arxiv_id":"1606.02960","paper":"/paper/sequence-to-sequence-learning-as-beam-search","title":"Sequence-to-Sequence Learning as Beam-Search Optimization","date":"2016-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning","path":"utils.py","file_url":"https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4cc9097b30b644e0","mcp_get_code":{"code_sha256":"4cc9097b30b644e0"}},{"arxiv_id":"1502.03044","paper":"/paper/show-attend-and-tell-neural-image-caption","title":"Show, Attend and Tell: Neural Image Caption Generation with Visual Attention","date":"2015-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Japanese-Image-Captioning/SAT-for-Japanese","path":"utils.py","file_url":"https://github.com/Japanese-Image-Captioning/SAT-for-Japanese/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4cc9097b30b644e0","mcp_get_code":{"code_sha256":"4cc9097b30b644e0"}},{"arxiv_id":"2025.findings-emnlp.1375","paper":null,"title":"arXiv:2025.findings-emnlp.1375","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"natashamariejohnson330/FicSim","path":"calculate_cosine_similarity.py","file_url":"https://github.com/natashamariejohnson330/FicSim/blob/HEAD/calculate_cosine_similarity.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d692a0f8f21443ff","mcp_get_code":{"code_sha256":"d692a0f8f21443ff"}}]}