Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: a high-level explanation of the dataset characteristics explain motivations and summary of its content potential use cases of the dataset
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step 2. 准备data 和 trainer 并进行训练 data = load_dataset(data_path) trainer = transformers.Trainer( model=model, train_dataset=train_data, eval_dataset=val_data, args=transformers.TrainingArguments( per_device_train_batch_size=micro_batch_size, gradient_accumulation_steps=gradient_accumulation_st...
Self-Instruct paper:https://arxiv.org/abs/2212.10560 data generation:https://github.com/LianjiaTech/BELLEandhttps://guanaco-model.github.io/ the first work:https://github.com/tatsu-lab/stanford_alpaca We currently select the combination of BELLE and Guanaco data as our main training dataset. ...
dataset=train_data, eval_dataset=eval_data, peft_config=peft_config, dataset_text_...
If None, we use the same model as the one we are -e, --evaluation_dataset=EVALUATION_DATASET Type: Union Defaul... Path to the evaluation dataset or a function that returns a dataframe. If None, we use the default evaluat ion -a, --annotators_config=ANNOTATORS_CONFIG Type: Union ...
eval_set = datasets.load_dataset("tatsu-lab/alpaca_eval","alpaca_eval")["eval"] forexampleineval_set: # generate here is a placeholder for your models generations example["output"] = generate(example["instruction"]) 2)计算 golden 输出 reference_outputs。默认情况下,在 AlpacaEval 上使用 text...
Alpacademonstrated that with maybe 20% of the effort you get 80% of the result, if you have the right dataset. Which gets us to the second important thing they did, which was to distill ChatGPT’s knowledge into a dataset. If you have access to a good model, you can distill it to...
Trainer( model=model, train_dataset=train_data, eval_dataset=val_data, args=transformers.TrainingArguments( ... This is pretty simple. At the end, the script produces a model folder with checkpoints, adapter weights, and adapter configuration. Next, let's look at the main flow of generate...
?This is empirical task performance on the evaluation dataset for this experiment. Table 1gives our ?ndings in the ‘New Bracket’ rows. Boundless DAS is able to ?nd a good alignment for the training bracket with an IIAmax that is about the same as the task performance at 94%. For ...