| { | |
| "_name_or_path": "bert-base-uncased", | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "secrecy rate", | |
| "1": "markov geographic model", | |
| "2": "graph convolution networks", | |
| "3": "convolutional neural network", | |
| "4": "computed tomography", | |
| "5": "betweenness centrality", | |
| "6": "forward error correction", | |
| "7": "fusion center", | |
| "8": "random vaccination", | |
| "9": "adversarial risk analysis", | |
| "10": "nash equilibrium", | |
| "11": "maximum likelihood", | |
| "12": "synthetic aperture radar", | |
| "13": "sound pressure level", | |
| "14": "support vector machine", | |
| "15": "high performance computing", | |
| "16": "access point", | |
| "17": "downlink", | |
| "18": "strictly piecewise", | |
| "19": "atomic , independent , declarative , and absolute", | |
| "20": "shortest dependency path", | |
| "21": "multi - layer same - resolution compressed", | |
| "22": "marginal contribution", | |
| "23": "spectral angle distance", | |
| "24": "information retrieval", | |
| "25": "resource description framework", | |
| "26": "atomic function computation", | |
| "27": "part of speech", | |
| "28": "long term evolution", | |
| "29": "mean squared error", | |
| "30": "permutation invariant training", | |
| "31": "minimum generation error", | |
| "32": "alternating least squares", | |
| "33": "reinforcement learning", | |
| "34": "machine learning", | |
| "35": "recurrent neural network", | |
| "36": "recurrent weighted average", | |
| "37": "question answering", | |
| "38": "multiple parallel instances", | |
| "39": "gaussian process", | |
| "40": "base station", | |
| "41": "receiver operating characteristic", | |
| "42": "threshold algorithm", | |
| "43": "click through rates", | |
| "44": "virtual machine", | |
| "45": "test case prioritization", | |
| "46": "neural network", | |
| "47": "belief propagation", | |
| "48": "contention adaptions", | |
| "49": "dynamic induction control", | |
| "50": "information embedding cost", | |
| "51": "lifelong metric learning", | |
| "52": "linear programming", | |
| "53": "multiple description coding", | |
| "54": "latent dirichlet allocation", | |
| "55": "collaborative filtering", | |
| "56": "medium access control", | |
| "57": "description logics", | |
| "58": "radio frequency", | |
| "59": "adaptive radix tree", | |
| "60": "integer linear programming", | |
| "61": "minimum risk training", | |
| "62": "constructive interference", | |
| "63": "line of sight", | |
| "64": "deep belief network", | |
| "65": "average precision", | |
| "66": "dropped pronoun", | |
| "67": "rate distortion function", | |
| "68": "intellectual property", | |
| "69": "geometric programming", | |
| "70": "gaussian mixture model", | |
| "71": "language model", | |
| "72": "adversarially robust distillation", | |
| "73": "controlled natural language", | |
| "74": "federated learning", | |
| "75": "augmented reality", | |
| "76": "matrix factorization", | |
| "77": "principal component analysis", | |
| "78": "node classification", | |
| "79": "smart object", | |
| "80": "poisson point process", | |
| "81": "attention network", | |
| "82": "constrained least squares", | |
| "83": "global positioning system", | |
| "84": "prepositional phrase", | |
| "85": "artificial neural network", | |
| "86": "directed belief net", | |
| "87": "false positive rate", | |
| "88": "latent semantic analysis", | |
| "89": "artificial intelligence", | |
| "90": "model predictive control", | |
| "91": "genetic algorithm", | |
| "92": "access part'", | |
| "93": "sensing application recently", | |
| "94": "mutual information", | |
| "95": "universal dependencies", | |
| "96": "secrecy outage probability", | |
| "97": "statistical compressed sensing", | |
| "98": "information bottleneck", | |
| "99": "ergodic sum capacity", | |
| "100": "image signal processor", | |
| "101": "particle swarm optimization", | |
| "102": "differential rectifier", | |
| "103": "technical debt", | |
| "104": "deep learning", | |
| "105": "hybrid monte carlo", | |
| "106": "ordinary differential equation", | |
| "107": "scalar multiplication", | |
| "108": "inductive logic programming", | |
| "109": "simulated annealing", | |
| "110": "entity set expansion", | |
| "111": "autism spectrum disorders", | |
| "112": "artificial bee colony", | |
| "113": "property graph", | |
| "114": "centralized solution", | |
| "115": "social status", | |
| "116": "taint dependency sequences", | |
| "117": "expectation maximization", | |
| "118": "machine translation", | |
| "119": "dynamic vision sensor", | |
| "120": "automatic speech recognition", | |
| "121": "user equipment", | |
| "122": "random neural networks", | |
| "123": "mean absolute error", | |
| "124": "bayesian network", | |
| "125": "singular value decomposition", | |
| "126": "multimedia event detection", | |
| "127": "median recovery error", | |
| "128": "nearest neighbor", | |
| "129": "friendly jamming", | |
| "130": "formal methods", | |
| "131": "intraclass correlation coefficient", | |
| "132": "central cloud", | |
| "133": "cumulative activation", | |
| "134": "mitral valve", | |
| "135": "discriminative correlation filter", | |
| "136": "transformation error", | |
| "137": "relation extraction", | |
| "138": "linear discriminant analysis", | |
| "139": "integrated circuit", | |
| "140": "stochastic block model", | |
| "141": "information extraction", | |
| "142": "socially assistive robots", | |
| "143": "hierarchical attention network", | |
| "144": "deep reinforcement learning", | |
| "145": "logistic regression", | |
| "146": "message passing interface", | |
| "147": "bug reports", | |
| "148": "alzheimer 's disease", | |
| "149": "data science and analytics", | |
| "150": "automatic differentiation", | |
| "151": "conditional random field", | |
| "152": "false negatives", | |
| "153": "sequential monte carlo", | |
| "154": "basic question", | |
| "155": "physical access", | |
| "156": "point multiplication", | |
| "157": "leicester scientific corpus", | |
| "158": "transformation encoder", | |
| "159": "deep convolutional neural network", | |
| "160": "thompson sampling", | |
| "161": "orthogonal least square", | |
| "162": "acquaintance vaccination", | |
| "163": "rate - selective", | |
| "164": "dynamic assignment ratio", | |
| "165": "multiple description", | |
| "166": "million song dataset", | |
| "167": "machine type communications", | |
| "168": "self attention network", | |
| "169": "term frequency", | |
| "170": "portable document format", | |
| "171": "parameter server", | |
| "172": "physical machines", | |
| "173": "exponential moving average", | |
| "174": "matrix pair beamformer", | |
| "175": "optimal transport", | |
| "176": "finite element method", | |
| "177": "differential evolution", | |
| "178": "product - based neural network", | |
| "179": "mean average conceptual similarity", | |
| "180": "power splitting", | |
| "181": "parkinson 's disease", | |
| "182": "new persian", | |
| "183": "artifact disentanglement network", | |
| "184": "statistical machine translation", | |
| "185": "manifold geometry matching", | |
| "186": "batch normalization", | |
| "187": "rank residual constraint", | |
| "188": "oblivious transfer", | |
| "189": "positive pointwise mutual information", | |
| "190": "triad significance profile", | |
| "191": "reverse classification accuracy", | |
| "192": "fully connected", | |
| "193": "corresponding arcs", | |
| "194": "maximum a posteriori", | |
| "195": "false positive", | |
| "196": "certain natural language", | |
| "197": "strategic dependency", | |
| "198": "strictly local", | |
| "199": "internet protocol", | |
| "200": "foveal tilt effects", | |
| "201": "dynamic cluster", | |
| "202": "domain name system", | |
| "203": "mean average precision", | |
| "204": "semantic role labeling", | |
| "205": "recurrent convolution", | |
| "206": "optical character recognition", | |
| "207": "charging current", | |
| "208": "low resolution", | |
| "209": "power system operations", | |
| "210": "compressive sensing", | |
| "211": "optimal power flow", | |
| "212": "deep context prediction", | |
| "213": "secondary users", | |
| "214": "o - d demand estimation", | |
| "215": "fully convolutional neural network", | |
| "216": "maximal ratio combining", | |
| "217": "quantile random forest", | |
| "218": "adaptive threshold", | |
| "219": "situation entity", | |
| "220": "relay station", | |
| "221": "discrete choice models", | |
| "222": "random forest", | |
| "223": "left ventricle", | |
| "224": "artificial noise" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
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| "access point": 16, | |
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| "adaptive radix tree": 59, | |
| "adaptive threshold": 218, | |
| "adversarial risk analysis": 9, | |
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| "alternating least squares": 32, | |
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| "artifact disentanglement network": 183, | |
| "artificial bee colony": 112, | |
| "artificial intelligence": 89, | |
| "artificial neural network": 85, | |
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| "atomic function computation": 26, | |
| "attention network": 81, | |
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| "bayesian network": 124, | |
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| "click through rates": 43, | |
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| "dynamic cluster": 201, | |
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| } | |