from transformers.pipelines.pt_utils import KeyDataset from datasets import load_dataset pipe = pipeline(model="hf-internal-testing/tiny-random-wav2vec2", device=0) dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation[:10]") for out in pipe(KeyDatas...
一个完整的transformer模型主要包含三部分:Config、Tokenizer、Model。 Config 用于配置模型的名称、最终输出的样式、隐藏层宽度和深度、激活函数的类别等。 示例: {"architectures":["BertForMaskedLM"],"attention_probs_dropout_prob":0.1,"gradient_checkpointing":false,"hidden_act":"gelu","hidden_dropout_prob...
Generation config I know it has just been added so it is normal! But the following are missing (and are pretty intuitive w.r.t our other objects such as configs, processors etc): GenerationConfig.from_pretrained("openai/whisper-tiny.en" ...
from transformers import AutoTokenizer, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained(llama3_path) tokenizer = AutoTokenizer.from_pretrained(llama3_path) 1. 2. 3. text_generation用法chat模型用法 参数介绍GenerationConfig 以text_generation为例。 huggingface GenerationConfig参数介绍 控制...
Feature request 👋 The request is for a way to pass a GenerationConfig to a Seq2SeqTrainer (through Seq2SeqTrainingArguments). Motivation ATOW, Seq2SeqTrainer only supports a few arguments for generation: max_length / max_new_tokens, num_...
一个完整的transformer模型主要包含三部分:Config、Tokenizer、Model。 Config 用于配置模型的名称、最终输出的样式、隐藏层宽度和深度、激活函数的类别等。 示例: 代码语言:javascript 复制 {"architectures":["BertForMaskedLM"],"attention_probs_dropout_prob":0.1,"gradient_checkpointing":false,"hidden_act":"gel...
forked fromGitee 极速下载/huggingface-transformers 代码Wiki统计流水线 服务 我知道了,不再自动展开 main 克隆/下载 git config --global user.name userName git config --global user.email userEmail 分支1456 标签141 贡献代码 同步代码 Joao GanteGenerate:GenerationConfigthrows an exc...510270a1年前 ...
self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, pooled_output): seq_relationship_score = self.seq_relationship(pooled_output) return seq_relationship_score 这个教程部分基本就完成了。学习完了,后基本就可以用transformers来训练模型了。
简介:本部分首先介绍如何使用pipeline()进行快速推理,然后介绍AutoClass:用AutoModel加载预训练模型、用tokenizer将文本转化为模型的数字输入、用AutoConfig来改变模型超参、用AutoFeatureExtractor加载预训练的feature extractor、用AutoProcessor加载预训练的processor。本文将仅关注PyTorch语言,但对TensorFlow语言的适配在本部分...
sampling_rate = model.generation_config.sample_rate Audio(speech_output[0].cpu().numpy(), rate=sampling_rate) 访问阅读原文试听或下载该音频文件。 重要说明 上例中运行次数较少。为了测量和后续对比的准确性,运行次数需要增加到至少 100。 增加nb_loops一个主要原因是,同一输入的多次运行所生成的语音长度差...