HuggingFace镜像/bert-finetuned-ner-openmind
模型介绍文件和版本分析
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test-bert-finetuned-ner

该模型是 bert-base-cased 在 conll2003 数据集上的微调版本。 它在评估集上取得了以下结果:

  • 损失:0.0600
  • 精确率:0.9355
  • 召回率:0.9514
  • F1 值:0.9433
  • 准确率:0.9868

模型描述

需要更多信息

在 Openmind 中的使用

from openmind import pipeline, is_torch_npu_available
from openmind import AutoTokenizer, AutoModelForCausalLM
from openmind_hub import snapshot_download
import torch.nn.functional as F
from torch import Tensor
import openmind
import torch
import argparse
import time

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to model",
        default="jeffding/bert-finetuned-ner-openmind",
    )
    args = parser.parse_args()
    return args

def main():
    args = parse_args()
    model_path = args.model_name_or_path

    if is_torch_npu_available():
        device = "npu:0"
    else:
        device = "cpu"
    
    start_time = time.time()
    classifier = pipeline(task="token-classification", model=model_path, framework="pt", device=device)

    output = classifier("Apple Inc. was founded on April 1, 1976, by Steve Jobs, Steve Wozniak, and Ronald Wayne in the garage of Steve Jobs childhood home in Los Altos, California. It was registered on January 3, 1977, initially named Apple Computer, but to commemorate its 30th anniversary and reflect the diversity of its products, the term computer was removed on January 9, 2015.")
    print(output)
    
    end_time = time.time()
    print(f"硬件环境:{device},推理执行时间:{end_time - start_time}秒")
    
if __name__ == "__main__":
    main()

预期用途与限制

需要更多信息

训练与评估数据

需要更多信息

训练过程

训练超参数

训练过程中使用了以下超参数:

  • learning_rate: 2e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam,betas=(0.9,0.999),epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3

训练结果

训练损失轮次步数验证损失精确率召回率F1值准确率
0.08491.017560.07130.91440.93660.92530.9817
0.03592.035120.06580.93460.95000.94220.9860
0.02063.052680.06000.93550.95140.94330.9868

框架版本

  • Transformers 4.11.0.dev0
  • Pytorch 1.8.1+cu111
  • Datasets 1.12.1.dev0
  • Tokenizers 0.10.3