def _get_pretrained_model(self): config = { "checkpoint": self._get_checkpoint(), "seed": 42, "data_dir": "./data", "glove_max_vectors": 10000, "glove_dim": 300, "batch_size": 64, "cuda": True, "encoder": self.encoder, "max_epochs": None, "lstm_hidden_dim": 2048 if self.encoder != "awe" else 300, "classifier_hidden_dim": 512, "debug": False } config = argparse.Namespace(**config) pl.seed_everything(config.seed) data_module = SNLIDataModule(config) data_module.setup() encoder = get_encoder(config) model = InferSent.load_from_checkpoint( config.checkpoint, embeddings=data_module.glove_embeddings(), encoder=encoder, config=config) model.to("cuda") return model, data_module
parser.add_argument("--classifier_hidden_dim", type=int, default=512) parser.add_argument("--debug", action="store_true", help="Only use 1%% of the training data.") config = parser.parse_args() pl.seed_everything(config.seed) data_module = SNLIDataModule(config) data_module.setup() encoder = get_encoder(config) model = InferSent.load_from_checkpoint( config.checkpoint, embeddings=data_module.glove_embeddings(), encoder=encoder, config=config) model.to("cuda") model.eval() PATH_TO_SENTEVAL = '../SentEval' PATH_TO_DATA = '../SentEval/data' sys.path.insert(0, PATH_TO_SENTEVAL) params_senteval = { 'task_path': PATH_TO_DATA, 'usepytorch': True, 'kfold': 2 } params_senteval['classifier'] = {