def main(): parser = argparse.ArgumentParser( description='Train a model on TriviaQA unfiltered') parser.add_argument( 'mode', choices=["confidence", "merge", "shared-norm", "sigmoid", "paragraph"]) parser.add_argument("name", help="Where to store the model") parser.add_argument("-t", "--n_tokens", default=400, type=int, help="Paragraph size") parser.add_argument( '-n', '--n_processes', type=int, default=2, help="Number of processes (i.e., select which paragraphs to train on) " "the data with") args = parser.parse_args() mode = args.mode out = args.name + "-" + datetime.now().strftime("%m%d-%H%M%S") model = get_model(100, 140, mode, WithIndicators()) extract = ExtractMultiParagraphsPerQuestion(MergeParagraphs(args.n_tokens), ShallowOpenWebRanker(16), model.preprocessor, intern=True) eval = [ LossEvaluator(), MultiParagraphSpanEvaluator(8, "triviaqa", mode != "merge", per_doc=False) ] oversample = [1] * 4 if mode == "paragraph": n_epochs = 120 test = RandomParagraphSetDatasetBuilder(120, "flatten", True, oversample) train = StratifyParagraphsBuilder(ClusteredBatcher( 60, ContextLenBucketedKey(3), True), oversample, only_answers=True) elif mode == "confidence" or mode == "sigmoid": if mode == "sigmoid": n_epochs = 640 else: n_epochs = 160 test = RandomParagraphSetDatasetBuilder(120, "flatten", True, oversample) train = StratifyParagraphsBuilder( ClusteredBatcher(60, ContextLenBucketedKey(3), True), oversample) else: n_epochs = 80 test = RandomParagraphSetDatasetBuilder( 120, "merge" if mode == "merge" else "group", True, oversample) train = StratifyParagraphSetsBuilder(30, mode == "merge", True, oversample) data = TriviaQaOpenDataset() params = TrainParams(SerializableOptimizer("Adadelta", dict(learning_rate=1)), num_epochs=n_epochs, ema=0.999, max_checkpoints_to_keep=2, async_encoding=10, log_period=30, eval_period=1800, save_period=1800, eval_samples=dict(dev=None, train=6000)) data = PreprocessedData(data, extract, train, test, eval_on_verified=False) data.preprocess(args.n_processes, 1000) with open(__file__, "r") as f: notes = f.read() notes = "Mode: " + args.mode + "\n" + notes trainer.start_training(data, model, params, eval, model_dir.ModelDir(out), notes)
def main(): parser = argparse.ArgumentParser( description='Train a model on TriviaQA unfiltered') parser.add_argument( 'mode', choices=["confidence", "merge", "shared-norm", "sigmoid", "paragraph"]) parser.add_argument("name", help="Where to store the model") parser.add_argument("-t", "--n_tokens", default=400, type=int, help="Paragraph size") parser.add_argument( '-n', '--n_processes', type=int, default=2, help="Number of processes (i.e., select which paragraphs to train on) " "the data with") parser.add_argument("-s", "--source_dir", type=str, default=None, help="where to take input files") parser.add_argument("--n_epochs", type=int, default=None, help="Max number of epoches to train on ") parser.add_argument("--char_th", type=int, default=None, help="char level embeddings") parser.add_argument("--hl_dim", type=int, default=None, help="hidden layer dim size") parser.add_argument("--regularization", type=int, default=None, help="hidden layer dim size") parser.add_argument("--LR", type=float, default=1.0, help="hidden layer dim size") parser.add_argument("--save_every", type=int, default=1800, help="save period") parser.add_argument("--init_from", type=str, default=None, help="model to init from") args = parser.parse_args() mode = args.mode #out = args.name + "-" + datetime.now().strftime("%m%d-%H%M%S") out = join('models', args.name) char_th = 100 hl_dim = 140 if args.char_th is not None: print(args.char_th) char_th = int(args.char_th) out += '--th' + str(char_th) if args.hl_dim is not None: print(args.hl_dim) hl_dim = int(args.hl_dim) out += '--hl' + str(hl_dim) if args.init_from is None: model = get_model(char_th, hl_dim, mode, WithIndicators()) else: md = model_dir.ModelDir(args.init_from) model = md.get_model() extract = ExtractMultiParagraphsPerQuestion(MergeParagraphs(args.n_tokens), ShallowOpenWebRanker(16), model.preprocessor, intern=True) eval = [ LossEvaluator(), MultiParagraphSpanEvaluator(8, "triviaqa", mode != "merge", per_doc=False) ] oversample = [1] * 4 if mode == "paragraph": n_epochs = 120 test = RandomParagraphSetDatasetBuilder(120, "flatten", True, oversample) train = StratifyParagraphsBuilder(ClusteredBatcher( 60, ContextLenBucketedKey(3), True), oversample, only_answers=True) elif mode == "confidence" or mode == "sigmoid": if mode == "sigmoid": n_epochs = 640 else: n_epochs = 160 test = RandomParagraphSetDatasetBuilder(120, "flatten", True, oversample) train = StratifyParagraphsBuilder( ClusteredBatcher(60, ContextLenBucketedKey(3), True), oversample) else: n_epochs = 80 test = RandomParagraphSetDatasetBuilder( 120, "merge" if mode == "merge" else "group", True, oversample) train = StratifyParagraphSetsBuilder(30, mode == "merge", True, oversample) if args.n_epochs is not None: n_epochs = args.n_epochs out += '--' + str(n_epochs) if args.LR != 1.0: out += '--' + str(args.LR) data = TriviaQaOpenDataset(args.source_dir) async_encoding = 10 #async_encoding = 0 params = TrainParams(SerializableOptimizer("Adadelta", dict(learning_rate=args.LR)), num_epochs=n_epochs, num_of_steps=250000, ema=0.999, max_checkpoints_to_keep=2, async_encoding=async_encoding, log_period=30, eval_period=1800, save_period=args.save_every, eval_samples=dict(dev=None, train=6000), regularization_weight=None) data = PreprocessedData(data, extract, train, test, eval_on_verified=False) data.preprocess(args.n_processes, 1000) with open(__file__, "r") as f: notes = f.read() notes = "Mode: " + args.mode + "\n" + notes if args.init_from is not None: init_from = model_dir.ModelDir(args.init_from).get_best_weights() if init_from is None: init_from = model_dir.ModelDir( args.init_from).get_latest_checkpoint() else: init_from = None trainer.start_training(data, model, params, eval, model_dir.ModelDir(out), notes, initialize_from=init_from)