Using the latest cached version of the dataset since squad couldn't be found on the Hugging Face Hub Found the latest cached dataset configuration 'plain_text' at /root/.cache/huggingface/datasets/squad/plain_text/0.0.0/7b6d24c440a36b6815f21b70d25016731768db1f (last modified on Fri Dec 27...
>>>fromdatasetsimportload_dataset >>>dataset = load_dataset('microsoft/orca-math-word-problems-200k', split='train') >>>defpreprocess_dataset(x:dict) ->dict: ...# to be implemented ... >>> >>>new_ds = dataset.map(preprocess_dataset) >>>print(new_ds) Dataset({ features: ['input...
首先，我们用 int8 加载模型，准备训练，然后加入 LoRA 微调器。# load model in 8bitmodel = AutoModelForCausalLM.from_pretrained( args.model_path, load_in_8bit=True, device_map={"": Accelerator().local_process_index} )model = prepare_model_for_int8_training(model)# add LoRA ...
鉴于nomic 的工作方式,每次运行atlas.map_data时,它都会在你的帐户下创建一个新的 Atlas 数据集。我想保持相同的数据集更新。目前最好的方法是删除旧数据集。 ac = AtlasClass() atlas_id = ac._get_dataset_by_slug_identifier("derek2/boru-subreddit-neural-search")['id'] ac._delete_project_by_id(...
datasets = load_dataset('wikitext', 'wikitext-2-raw-v1') 对于因果语言建模(CLM),我们将获取数据集中的所有文本,并在标记化后将它们连接起来。然后,我们将它们分成一定序列长度的样本。这样,模型将接收连续文本块。 from transformers import AutoTokenizer ...
eval_dataset=lm_datasets["validation"], ) trainer.train() 训练完成后,评估以如下方式进行: import math eval_results = trainer.evaluate() print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}") 监督微调 这个特定领域的预训练步骤的输出是一个可以识别输入文本的上下文并预测下一个单词/句...
hf_model_name = HF_MODEL_NAME_MAP[model_name] filename = f"{model_name}.pt" # First try normal download try: weights_path = hf_hub_download( final_weights_path = os.path.join(os.path.dirname(constants.HF_HUB_CACHE), filename) if os.path.exists(final_weights_path): print(f"Found...
train_data = dataset.map( chatml_format, num_proc=1, remove_columns=original_columns, load_from_cache_file=True ) train_data = train_data.train_test_split(test_size=2000) train_dataset = train_data["train"] eval_dataset = train_data["test"] ...
datasets = load_dataset('wikitext', 'wikitext-2-raw-v1') 对于因果语言建模(CLM),我们将获取数据集中的所有文本,并在标记化后将它们连接起来。然后,我们将它们分成一定序列长度的样本。这样,模型将接收连续文本块。 from transformers import AutoTokenizer ...
#Load the datasetfromdatasetsimportload_dataset datasets = load_dataset('wikitext','wikitext-2-raw-v1') 对于因果语言建模(CLM),我们将获取数据集中的所有文本,并在标记化后将它们连接起来。然后,我们将它们分成一定序列长度的样本。这样,模型将接收连续文本块。