GPT2ForSequenceClassification是Hugging Face Transformers库中的一个类,用于基于GPT-2模型的序列分类任务。 标准的初始化方法通常涉及从预训练的GPT-2模型中加载权重,并可能根据任务需求对模型的输出层进行调整(例如,添加或修改分类层)。 分析为何这些权重没有按照标准方法被初始化: 可能的原因包括:预训练模型与当前...
(tokenize_function, batched=True) # 创建DataCollator data_collator = DataCollatorWithPadding(tokenizer=tokenizer, padding=True) # 初始化GPT-2模型,并添加分类头 model = GPT2ForSequenceClassification.from_pretrained('gpt2', num_labels=2) #完全正确地通过使用提供的代码设置其 pad token 来解决您描述...
myclass = importlib.import_module(gena_module_name) cls = getattr(importlib.import_module(gena_module_name), 'GPT2ForSequenceClassification') model = cls.from_pretrained('dnagpt/human_gpt2-v1', num_labels=2) model.config.pad_token_id = model.config.eos_token_id 接着就是准备数据: from ...
python benchmarks/dynamo/huggingface.py --accuracy --amp -dcpu -n50 --no-skip --dashboard --batch-size 1 --threads 1 --timeout 9000 --backend=inductor --inference --only GPT2ForSequenceClassification When AMX is used (default case), this issue is reproducible. With environment variable...
from transformers import GPT2Tokenizer, GPT2ForSequenceClassification, Trainer, TrainingArguments import torch # 加载预训练模型和分词器 model_name = "gpt2" tokenizer = GPT2Tokenizer.from_pretrained(model_name) model = GPT2ForSequenceClassification.from_pretrained(model_name, num_labels=2) # 准备数据...
import logging import tempfile from datasets import load_dataset from transformers import ( set_seed, TrainingArguments, Trainer, DataCollatorWithPadding, AutoTokenizer, AutoModelForSequenceClassification, ) logger = logging.getLogger() def train(output_dir: str): set_seed(42) imdb = load_dataset(...
super(GPT2ForSequenceClassification, self).__init__(self.config, auto_prefix=True) self.seq_length = self.config.seq_length self.num_labels = self.config.num_labels self.hidden_size = self.config.hidden_size parallel_config = self.config.parallel_config dp = parallel_config.data_par...
Universal language model fine-tuning for text classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pp. 328–339, 2018. Jelinek, F. and Mercer, R. L. Interpolated estimation of markov source parameters from...
converting the model’s probabilistic output(vocab size classification) to text iteratively,迭代似的。意味着更多的计算量 quality & diversity GPT是单向的,Bert是双向的 """fromtransformersimportAutoModelForCausalLM# 'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'# 'gpt2': https://huggingface....
开放神经网络交换（Open Neural Network Exchange，简称 ONNX）是一个开放的生态系统，它提供了基于人工...