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LLM Evaluation Documentation

SWIFT supports the eval (evaluation) capability to provide standardized evaluation metrics for the original model and the fine-tuned model.

Table of Contents

Introduction

SWIFT's eval capability utilizes the EvalScope evaluation framework from the ModelScope community and Open-Compass and provides advanced encapsulation to support evaluation needs for various models. Currently, we support the evaluation process for standard evaluation sets and user-defined evaluation sets. The standard evaluation sets include:

'obqa', 'AX_b', 'siqa', 'nq', 'mbpp', 'winogrande', 'mmlu', 'BoolQ', 'cluewsc', 'ocnli', 'lambada', 'CMRC', 'ceval', 'csl', 'cmnli', 'bbh', 'ReCoRD', 'math', 'humaneval', 'eprstmt', 'WSC', 'storycloze', 'MultiRC', 'RTE', 'chid', 'gsm8k', 'AX_g', 'bustm', 'afqmc', 'piqa', 'lcsts', 'strategyqa', 'Xsum', 'agieval', 'ocnli_fc', 'C3', 'tnews', 'race', 'triviaqa', 'CB', 'WiC', 'hellaswag', 'summedits', 'GaokaoBench', 'ARC_e', 'COPA', 'ARC_c', 'DRCD'

Check out the detail descriptions of these datasets: https://hub.opencompass.org.cn/home

At the first time of running eval, a resource dataset will be downloaded: https://www.modelscope.cn/datasets/swift/evalscope_resource/files If downloading fails, you can manually download the dataset to your local disk, please pay attention to the log of the eval command.

Environment Setup

pip install ms-swift[eval] -U

or install from source code:

git clone https://github.com/modelscope/swift.git
cd swift
pip install -e '.[eval]'

Evaluation

Evaluation supports the use of vLLM for acceleration. Here we demonstrate the evaluation of the original model and the LoRA fine-tuned qwen2-7b-instruct.

# Original model (approximately half an hour on a single A100)
CUDA_VISIBLE_DEVCIES=0 swift eval --model_type qwen2-7b-instruct \
    --eval_dataset ARC_e --infer_backend vllm

# After LoRA fine-tuning
CUDA_VISIBLE_DEVICES=0 swift eval --ckpt_dir qwen2-7b-instruct/vx-xxx/checkpoint-xxx \
    --eval_dataset ARC_e --infer_backend vllm \
    --merge_lora true \

You can refer to here for the list of evaluation parameters.

Please pay attention: The eval result will be saved in {--eval_output_dir}/{--name}/{some-timestamp}, if you changed nothing, the default dir will be:

the current folder(`pwd` folder)/eval_outputs/default/20240628_190000/xxx

Evaluation using the deployed method

# Start deployment using the OpenAI API method
CUDA_VISIBLE_DEVICES=0 swift deploy --model_type qwen2-7b-instruct

# Evaluate using the API
# If it is not a Swift deployment, you need to additionally pass in `--eval_is_chat_model true --model_type qwen2-7b-instruct`.
swift eval --eval_url http://127.0.0.1:8000/v1 --eval_dataset ARC_e

# The same applies to the model after LoRA fine-tuning.

Custom Evaluation Sets

In addition, we support users in customizing their own evaluation sets. Custom evaluation sets must be consistent with the data format (pattern) of an official evaluation set. Below, we explain step-by-step how to use your own evaluation set for evaluation.

Preparing Your Own Evaluation Set

Currently, we support two patterns of evaluation sets: multiple-choice format (CEval) and question-answer format (General-QA).

Multiple-choice: CEval Format

The CEval format is suitable for multiple-choice scenarios, where you select the correct answer from four options, and the evaluation metric is accuracy. It is recommended to directly modify the CEval scaffold directory. This directory contains two files:

default_dev.csv # Used for few-shot evaluation, must contain at least the number of entries specified by eval_few_shot. If it is 0-shot evaluation, this CSV can be empty.
default_val.csv # Data used for actual evaluation.

The CEval CSV file should be in the following format:

id,question,A,B,C,D,answer,explanation
1,Typically, how many amino acids make up animal proteins? ,4,22,20,19,C,1. Currently, it is known that 20 amino acids make up animal proteins.
2,Among the following substances present in blood, which one is not a metabolic end product? ,urea,uric acid,pyruvic acid,carbon dioxide,C,"A metabolic end product is a substance that cannot be further utilized in the body's metabolism and needs to be excreted. Pyruvic acid is a product of carbohydrate metabolism and can be further metabolized for energy or synthesis of other substances, so it is not a metabolic end product."

In this format, id is the evaluation sequence number, question is the question, A, B, C, D are options (if there are fewer than four options, leave the corresponding fields empty), answer is the correct option, and explanation is the explanation.

The default file name is the sub-dataset name for the CEval evaluation, which can be changed and will be used in the configuration below.

Question-Answer: General-QA

The General-QA format is suitable for question-answer scenarios, and the evaluation metrics are rouge and bleu. It is recommended to directly modify the General-QA scaffold directory. This directory contains one file:

default.jsonl

This JSONL file should be in the following format:

{"history": [], "query": "What is the capital of China?", "response": "The capital of China is Beijing."}
{"history": [], "query": "What is the highest mountain in the world?", "response": "It is Mount Everest."}
{"history": [], "query": "Why can't you see penguins in the Arctic?", "response": "Because most penguins live in the Antarctic."}

Note that history is currently a reserved field and is not yet supported.

Defining a Configuration File for the Evaluation Command

After preparing the files above, you need to write a JSON file to pass into the evaluation command. It is recommended to directly modify the official configuration scaffold file. The content of this file is as follows:

[
    {
        "name": "custom_general_qa", # Name of the evaluation item, can be freely specified
        "pattern": "general_qa", # Pattern of this evaluation set
        "dataset": "eval_example/custom_general_qa", # Directory of this evaluation set, it is strongly recommended to use an absolute path to prevent read failures
        "subset_list": ["default"] # Sub-datasets to be evaluated, i.e., the `default_x` file name above
    },
    {
        "name": "custom_ceval",
        "pattern": "ceval",
        "dataset": "eval_example/custom_ceval", # Directory of this evaluation set, it is strongly recommended to use an absolute path to prevent read failures
        "subset_list": ["default"]
    }
]

You can then pass this configuration file for evaluation:

# Use arc evaluation, limit evaluation to 10 entries per sub-dataset, inference backend using pt
# cd examples/pytorch/llm
# eval_dataset can also be set, running both official and custom datasets together
swift eval \
    --model_type "qwen-7b-chat" \
    --eval_dataset no \
    --```shell
    --infer_backend pt \
    --custom_eval_config eval_example/custom_config.json

The results will be output as follows:

2024-04-10 17:21:33,275 - llmuses - INFO - *** Report table ***
+------------------------------+----------------+---------------------------------+
| Model                        | custom_ceval   | custom_general_qa               |
+==============================+================+=================================+
| qa-custom_ceval_qwen-7b-chat | 1.0 (acc)      | 0.8888888888888888 (rouge-1-r)  |
|                              |                | 0.33607503607503614 (rouge-1-p) |
|                              |                | 0.40616618868713145 (rouge-1-f) |
|                              |                | 0.39999999999999997 (rouge-2-r) |
|                              |                | 0.27261904761904765 (rouge-2-p) |
|                              |                | 0.30722525589718247 (rouge-2-f) |
|                              |                | 0.8333333333333334 (rouge-l-r)  |
|                              |                | 0.30742204655248134 (rouge-l-p) |
|                              |                | 0.3586824745225346 (rouge-l-f)  |
|                              |                | 0.3122529644268775 (bleu-1)     |
|                              |                | 0.27156862745098037 (bleu-2)    |
|                              |                | 0.25 (bleu-3)                   |
|                              |                | 0.2222222222222222 (bleu-4)     |
+------------------------------+----------------+---------------------------------+
Final report: {'report': [{'name': 'custom_general_qa', 'metric': 'WeightedAverageBLEU', 'score': {'rouge-1-r': 0.8888888888888888, 'rouge-1-p': 0.33607503607503614, 'rouge-1-f': 0.40616618868713145, 'rouge-2-r': 0.39999999999999997, 'rouge-2-p': 0.27261904761904765, 'rouge-2-f': 0.30722525589718247, 'rouge-l-r': 0.8333333333333334, 'rouge-l-p': 0.30742204655248134, 'rouge-l-f': 0.3586824745225346, 'bleu-1': 0.3122529644268775, 'bleu-2': 0.27156862745098037, 'bleu-3': 0.25, 'bleu-4': 0.2222222222222222}, 'category': [{'name': 'DEFAULT', 'score': {'rouge-1-r': 0.8888888888888888, 'rouge-1-p': 0.33607503607503614, 'rouge-1-f': 0.40616618868713145, 'rouge-2-r': 0.39999999999999997, 'rouge-2-p': 0.27261904761904765, 'rouge-2-f': 0.30722525589718247, 'rouge-l-r': 0.8333333333333334, 'rouge-l-p': 0.30742204655248134, 'rouge-l-f': 0.3586824745225346, 'bleu-1': 0.3122529644268775, 'bleu-2': 0.27156862745098037, 'bleu-3': 0.25, 'bleu-4': 0.2222222222222222}, 'subset': [{'name': 'default', 'score': {'rouge-1-r': 0.8888888888888888, 'rouge-1-p': 0.33607503607503614, 'rouge-1-f': 0.40616618868713145, 'rouge-2-r': 0.39999999999999997, 'rouge-2-p': 0.27261904761904765, 'rouge-2-f': 0.30722525589718247, 'rouge-l-r': 0.8333333333333334, 'rouge-l-p': 0.30742204655248134, 'rouge-l-f': 0.3586824745225346, 'bleu-1': 0