训练24小时了还是只有7%，是什么原因

[AI-Mart]

百度关系抽取数据集，需要大佬看看是不是参数配置有问题，数据生成都是参考的大佬的dev分支

import string
import random

common = {
    "exp_name": "duie2",
    "rel2id": "rel2id.json",
    "device_num": 0,
#     "encoder": "BiLSTM",
    "encoder": "BERT", 
    "hyper_parameters": {
        "shaking_type": "cat", # cat, cat_plus, cln, cln_plus; Experiments show that cat/cat_plus work better with BiLSTM, while cln/cln_plus work better with BERT. The results in the paper are produced by "cat". So, if you want to reproduce the results, "cat" is enough, no matter for BERT or BiLSTM.
        "inner_enc_type": "lstm", # valid only if cat_plus or cln_plus is set. It is the way how to encode inner tokens between each token pairs. If you only want to reproduce the results, just leave it alone.
        "dist_emb_size": -1, # -1: do not use distance embedding; other number: need to be larger than the max_seq_len of the inputs. set -1 if you only want to reproduce the results in the paper.
        "ent_add_dist": False, # set true if you want add distance embeddings for each token pairs. (for entity decoder)
        "rel_add_dist": False, # the same as above (for relation decoder)
        "match_pattern": "whole_span", # only_head_text (nyt_star, webnlg_star), whole_text (nyt, webnlg), only_head_index, whole_span
    },
}
common["run_name"] = "{}+{}+{}".format("TP1", common["hyper_parameters"]["shaking_type"], common["encoder"]) + ""

run_id = ''.join(random.sample(string.ascii_letters + string.digits, 8))
train_config = {
    "train_data": "train_data.json",
    "valid_data": "valid_data.json",
    "rel2id": "rel2id.json",
    # "logger": "wandb", # if wandb, comment the following four lines

#     # if logger is set as default, uncomment the following four lines
    "logger": "default",
    "run_id": run_id,
    "log_path": "./default_log_dir/default.log",
    "path_to_save_model": "./default_log_dir/{}".format(run_id),

    # only save the model state dict if F1 score surpasses <f1_2_save>
    "f1_2_save": 0, 
    # whether train_config from scratch
    "fr_scratch": True,
    # write down notes here if you want, it will be logged 
    "note": "start from scratch",
    # if not fr scratch, set a model_state_dict
    "model_state_dict_path": "",
    "hyper_parameters": {
        "batch_size": 2,
        "epochs": 100,
        "seed": 2333,
        "log_interval": 10,
        "max_seq_len": 128,
        "sliding_len": 20,
        "loss_weight_recover_steps": 6000, # to speed up the training process, the loss of EH-to-ET sequence is set higher than other sequences at the beginning, but it will recover in <loss_weight_recover_steps> steps.
        "scheduler": "CAWR", # Step
    },
}

eval_config = {
    "model_state_dict_dir": "./wandb", # if use wandb, set "./wandb", or set "./default_log_dir" if you use default logger
    "run_ids": ["10suiyrf", ],
    "last_k_model": 1,
    "test_data": "*test*.json", # "*test*.json"

    # where to save results
    "save_res": False,
    "save_res_dir": "../results",

    # score: set true only if test set is annotated with ground truth
    "score": True,

    "hyper_parameters": {
        "batch_size": 2,
        "force_split": False,
        "max_test_seq_len": 512,
        "sliding_len": 50,
    },
}

bert_config = {
    "data_home": "../data4bert",
    "bert_path": "../pretrained_models/bert-base-cased",
    "hyper_parameters": {
        "lr": 5e-5,
    },
}
bilstm_config = {
    "data_home": "../data4bilstm",
    "token2idx": "token2idx.json",
    "pretrained_word_embedding_path": "../../pretrained_emb/glove_300_nyt.emb",
    "hyper_parameters": {
         "lr": 1e-3,
         "enc_hidden_size": 300,
         "dec_hidden_size": 600,
         "emb_dropout": 0.1,
         "rnn_dropout": 0.1,
         "word_embedding_dim": 300,
    },
}

cawr_scheduler = {
    # CosineAnnealingWarmRestarts
    "T_mult": 1,
    "rewarm_epoch_num": 2,
}
step_scheduler = {
    # StepLR
    "decay_rate": 0.999,
    "decay_steps": 100,
}

# ---------------------------dicts above is all you need to set---------------------------------------------------
if common["encoder"] == "BERT":
    hyper_params = {**common["hyper_parameters"], **bert_config["hyper_parameters"]}
    common = {**common, **bert_config}
    common["hyper_parameters"] = hyper_params
elif common["encoder"] == "BiLSTM":
    hyper_params = {**common["hyper_parameters"], **bilstm_config["hyper_parameters"]}
    common = {**common, **bilstm_config}
    common["hyper_parameters"] = hyper_params

hyper_params = {**common["hyper_parameters"], **train_config["hyper_parameters"]}
train_config = {**train_config, **common}
train_config["hyper_parameters"] = hyper_params
if train_config["hyper_parameters"]["scheduler"] == "CAWR":
    train_config["hyper_parameters"] = {**train_config["hyper_parameters"], **cawr_scheduler}
elif train_config["hyper_parameters"]["scheduler"] == "Step":
    train_config["hyper_parameters"] = {**train_config["hyper_parameters"], **step_scheduler}

hyper_params = {**common["hyper_parameters"], **eval_config["hyper_parameters"]}
eval_config = {**eval_config, **common}
eval_config["hyper_parameters"] = hyper_params

[AI-Mart]

以下是utils新增的代码

 elif ori_format == "duie2":
                if dataset_type != 'test':
                    text = sample["text"]
                    rel_list = sample["relation_list"]
                    subj_key, pred_key, obj_key = "subject", "predicate", "object"
                else:
                    text = sample["text"]
                    rel_list = []

[131250208]

"bert_path": "../pretrained_models/bert-base-cased", 你用英文的BERT做中文数据怎么会有效果呢...

[AI-Mart]

是中文的模型呢，特地去hugging face下载的，只是改了个文件夹名字，这样config代码配置就不用改了

[AI-Mart]

现在训练快60小时了，现在到55%了，相比作者的24小时可能慢了点

[131250208]

这个效果肯定是有问题的，检查数据预处理过程，正常百度的数据集能到75%左右

[nlper01]

是中文的模型呢，特地去hugging face下载的，只是改了个文件夹名字，这样config代码配置就不用改了

你好，大佬，能否分享一份处理好的百度数据集，我处理的一直有问题，我的邮箱2674053421@qq.com