| # Evaluation |
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| SWIFT supports eval (evaluation) capabilities to provide standardized evaluation metrics for both raw models and trained models. |
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| ## Capability Introduction |
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| SWIFT's eval capability utilizes the EvalScope evaluation framework from the Magic Tower community, which has been advanced in its encapsulation to support the evaluation needs of various models. |
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| > Note: EvalScope supports many other complex capabilities, such as [model performance evaluation](https://evalscope.readthedocs.io/en/latest/user_guides/stress_test/quick_start.html), so please use the EvalScope framework directly. |
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| Currently, we support the evaluation process of **standard evaluation datasets** as well as the evaluation process of **user-defined** evaluation datasets. The **standard evaluation datasets** are supported by three evaluation backends: |
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| Below are the names of the supported datasets. For detailed information on the datasets, please refer to [all supported datasets](https://evalscope.readthedocs.io/en/latest/get_started/supported_dataset/index.html). |
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| 1. Native (default): |
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| Primarily supports pure text evaluation, while **supporting** visualization of evaluation results. |
| ```text |
| 'arc', 'bbh', 'ceval', 'cmmlu', 'competition_math', |
| 'general_qa', 'gpqa', 'gsm8k', 'hellaswag', 'humaneval', |
| 'ifeval', 'iquiz', 'mmlu', 'mmlu_pro', |
| 'race', 'trivia_qa', 'truthful_qa' |
| ``` |
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| 2. OpenCompass: |
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| Primarily supports pure text evaluation, currently **does not support** visualization of evaluation results. |
| ```text |
| 'obqa', 'cmb', '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' |
| ``` |
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| 3. VLMEvalKit: |
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| Primarily supports multimodal evaluation and currently **does not support** visualization of evaluation results. |
| ```text |
| 'COCO_VAL', 'MME', 'HallusionBench', 'POPE', 'MMBench_DEV_EN', 'MMBench_TEST_EN', 'MMBench_DEV_CN', 'MMBench_TEST_CN', |
| 'MMBench', 'MMBench_CN', 'MMBench_DEV_EN_V11', 'MMBench_TEST_EN_V11', 'MMBench_DEV_CN_V11', |
| 'MMBench_TEST_CN_V11', 'MMBench_V11', 'MMBench_CN_V11', 'SEEDBench_IMG', 'SEEDBench2', |
| 'SEEDBench2_Plus', 'ScienceQA_VAL', 'ScienceQA_TEST', 'MMT-Bench_ALL_MI', 'MMT-Bench_ALL', |
| 'MMT-Bench_VAL_MI', 'MMT-Bench_VAL', 'AesBench_VAL', 'AesBench_TEST', 'CCBench', 'AI2D_TEST', 'MMStar', |
| 'RealWorldQA', 'MLLMGuard_DS', 'BLINK', 'OCRVQA_TEST', 'OCRVQA_TESTCORE', 'TextVQA_VAL', 'DocVQA_VAL', |
| 'DocVQA_TEST', 'InfoVQA_VAL', 'InfoVQA_TEST', 'ChartQA_TEST', 'MathVision', 'MathVision_MINI', |
| 'MMMU_DEV_VAL', 'MMMU_TEST', 'OCRBench', 'MathVista_MINI', 'LLaVABench', 'MMVet', 'MTVQA_TEST', |
| 'MMLongBench_DOC', 'VCR_EN_EASY_500', 'VCR_EN_EASY_100', 'VCR_EN_EASY_ALL', 'VCR_EN_HARD_500', |
| 'VCR_EN_HARD_100', 'VCR_EN_HARD_ALL', 'VCR_ZH_EASY_500', 'VCR_ZH_EASY_100', 'VCR_ZH_EASY_ALL', |
| 'VCR_ZH_HARD_500', 'VCR_ZH_HARD_100', 'VCR_ZH_HARD_ALL', 'MMDU', 'MMBench-Video', 'Video-MME' |
| ``` |
| |
| ## Environment Preparation |
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| ```shell |
| pip install ms-swift[eval] -U |
| ``` |
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| Or install from source: |
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| ```shell |
| git clone https://github.com/modelscope/ms-swift.git |
| cd ms-swift |
| pip install -e '.[eval]' |
| ``` |
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| ## Evaluation |
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| Supports four methods of evaluation: pure text evaluation, multimodal evaluation, URL evaluation, and custom dataset evaluation. |
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| **Basic Example** |
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| ```shell |
| CUDA_VISIBLE_DEVICES=0 \ |
| swift eval \ |
| --model Qwen/Qwen2.5-0.5B-Instruct \ |
| --eval_backend Native \ |
| --infer_backend transformers \ |
| --eval_limit 10 \ |
| --eval_dataset gsm8k |
| ``` |
| Where: |
| - model: Can specify a local model path or a model ID on modelscope |
| - eval_backend: Options are Native, OpenCompass, VLMEvalKit; default is Native |
| - infer_backend: Options are transformers, vllm, sglang, lmdeploy; default is transformers |
| - eval_limit: Sample size for each evaluation set; default is None, which means using all data; can be used for quick validation |
| - eval_dataset: Evaluation dataset(s); multiple datasets can be set, separated by spaces |
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| **Complex Evaluation Example** |
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| ```shell |
| CUDA_VISIBLE_DEVICES=0 \ |
| swift eval \ |
| --model Qwen/Qwen2.5-0.5B-Instruct \ |
| --eval_backend Native \ |
| --infer_backend transformers \ |
| --eval_limit 10 \ |
| --eval_dataset gsm8k \ |
| --eval_dataset_args '{"gsm8k": {"few_shot_num": 0, "filters": {"remove_until": "</think>"}}}' \ |
| --eval_generation_config '{"max_tokens": 512, "temperature": 0}' \ |
| --extra_eval_args '{"ignore_errors": true, "debug": true}' |
| ``` |
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| For a specific list of evaluation parameters, please refer to [here](./Command-line-parameters.md#evaluation-arguments). |
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| ## Evaluation During Training |
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| SWIFT supports using EvalScope to evaluate the current model during the training process, allowing for timely understanding of the model's training effectiveness. |
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| **Basic Example** |
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| ```shell |
| CUDA_VISIBLE_DEVICES=0 \ |
| swift sft \ |
| --model "Qwen/Qwen2.5-0.5B-Instruct" \ |
| --tuner_type "lora" \ |
| --dataset "AI-ModelScope/alpaca-gpt4-data-zh#100" \ |
| --torch_dtype "bfloat16" \ |
| --num_train_epochs "1" \ |
| --per_device_train_batch_size "1" \ |
| --learning_rate "1e-4" \ |
| --lora_rank "8" \ |
| --lora_alpha "32" \ |
| --target_modules "all-linear" \ |
| --gradient_accumulation_steps "16" \ |
| --save_steps "50" \ |
| --save_total_limit "5" \ |
| --logging_steps "5" \ |
| --max_length "2048" \ |
| --eval_strategy "steps" \ |
| --eval_steps "5" \ |
| --per_device_eval_batch_size "5" \ |
| --eval_use_evalscope \ |
| --eval_dataset "gsm8k" \ |
| --eval_dataset_args '{"gsm8k": {"few_shot_num": 0}}' \ |
| --eval_limit "10" |
| ``` |
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| Note that the launch command is `sft`, and the evaluation-related parameters include: |
| - eval_strategy: Evaluation strategy. Defaults to None, following the `save_strategy` policy |
| - eval_steps: Defaults to None. If an evaluation dataset exists, it follows the `save_steps` policy |
| - eval_use_evalscope: Whether to use evalscope for evaluation, this parameter needs to be set to enable evaluation |
| - eval_dataset: Evaluation datasets, multiple datasets can be set, separated by spaces |
| - eval_dataset_args: Evaluation dataset parameters in JSON format, parameters for multiple datasets can be set |
| - eval_limit: Number of samples from the evaluation dataset |
| - eval_generation_config: Model inference configuration during evaluation, in JSON format, default is `{'max_tokens': 512}` |
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| More evaluation examples can be found in [examples](https://github.com/modelscope/ms-swift/tree/main/examples/eval). |
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| ## Custom Evaluation Datasets |
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| This framework supports two predefined dataset formats: multiple-choice questions (MCQ) and question-and-answer (QA). The usage process is as follows: |
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| *Note: When using a custom evaluation, the `eval_backend` parameter must be set to `Native`.* |
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| ### Multiple-Choice Question Format (MCQ) |
| This format is suitable for scenarios involving multiple-choice questions, and the evaluation metric is accuracy. |
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| **Data Preparation** |
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| Prepare a CSV file in the multiple-choice question format, structured as follows: |
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| ```text |
| mcq/ |
| ├── example_dev.csv # (Optional) The filename should follow the format `{subset_name}_dev.csv` for few-shot evaluation |
| └── example_val.csv # The filename should follow the format `{subset_name}_val.csv` for the actual evaluation data |
| ``` |
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| The CSV file should follow this format: |
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| ```text |
| id,question,A,B,C,D,answer |
| 1,Generally speaking, the amino acids that make up animal proteins are____,4 types,22 types,20 types,19 types,C |
| 2,Among the substances present in the blood, which is not a metabolic end product?____,Urea,Uric acid,Pyruvate,Carbon dioxide,C |
| ``` |
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| Where: |
| - `id` is an optional index |
| - `question` is the question |
| - `A`, `B`, `C`, `D`, etc. are the options, with a maximum of 10 options |
| - `answer` is the correct option |
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| **Launching Evaluation** |
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| Run the following command: |
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| ```bash |
| CUDA_VISIBLE_DEVICES=0 \ |
| swift eval \ |
| --model Qwen/Qwen2.5-0.5B-Instruct \ |
| --eval_backend Native \ |
| --infer_backend transformers \ |
| --eval_dataset general_mcq \ |
| --eval_dataset_args '{"general_mcq": {"local_path": "/path/to/mcq", "subset_list": ["example"]}}' |
| ``` |
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| Where: |
| - `eval_dataset` should be set to `general_mcq` |
| - `eval_dataset_args` should be set with: |
| - `local_path` as the path to the custom dataset folder |
| - `subset_list` as the name of the evaluation dataset, taken from the `*_dev.csv` mentioned above |
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| **Running Results** |
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| ```text |
| +---------------------+-------------+-----------------+----------+-------+---------+---------+ |
| | Model | Dataset | Metric | Subset | Num | Score | Cat.0 | |
| +=====================+=============+=================+==========+=======+=========+=========+ |
| | Qwen2-0.5B-Instruct | general_mcq | AverageAccuracy | example | 12 | 0.5833 | default | |
| +---------------------+-------------+-----------------+----------+-------+---------+---------+ |
| ``` |
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| ## Question-and-Answer Format (QA) |
| This format is suitable for scenarios involving question-and-answer, and the evaluation metrics are `ROUGE` and `BLEU`. |
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| **Data Preparation** |
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| Prepare a JSON Lines file in the question-and-answer format, containing one file in the following structure: |
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| ```text |
| qa/ |
| └── example.jsonl |
| ``` |
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| The JSON Lines file should follow this format: |
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| ```json |
| {"query": "What is the capital of China?", "response": "The capital of China is Beijing"} |
| {"query": "What is the highest mountain in the world?", "response": "It is Mount Everest"} |
| {"query": "Why can't penguins be seen in the Arctic?", "response": "Because most penguins live in Antarctica"} |
| ``` |
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| **Launching Evaluation** |
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| Run the following command: |
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| ```bash |
| CUDA_VISIBLE_DEVICES=0 \ |
| swift eval \ |
| --model Qwen/Qwen2.5-0.5B-Instruct \ |
| --eval_backend Native \ |
| --infer_backend transformers \ |
| --eval_dataset general_qa \ |
| --eval_dataset_args '{"general_qa": {"local_path": "/path/to/qa", "subset_list": ["example"]}}' |
| ``` |
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| Where: |
| - `eval_dataset` should be set to `general_qa` |
| - `eval_dataset_args` is a JSON string that needs to be set with: |
| - `local_path` as the path to the custom dataset folder |
| - `subset_list` as the name of the evaluation dataset, taken from the `*.jsonl` mentioned above |
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| **Running Results** |
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| ```text |
| +---------------------+-------------+-----------------+----------+-------+---------+---------+ |
| | Model | Dataset | Metric | Subset | Num | Score | Cat.0 | |
| +=====================+=============+=================+==========+=======+=========+=========+ |
| | Qwen2-0.5B-Instruct | general_qa | bleu-1 | default | 12 | 0.2324 | default | |
| +---------------------+-------------+-----------------+----------+-------+---------+---------+ |
| | Qwen2-0.5B-Instruct | general_qa | bleu-2 | default | 12 | 0.1451 | default | |
| +---------------------+-------------+-----------------+----------+-------+---------+---------+ |
| | Qwen2-0.5B-Instruct | general_qa | bleu-3 | default | 12 | 0.0625 | default | |
| +---------------------+-------------+-----------------+----------+-------+---------+---------+ |
| | Qwen2-0.5B-Instruct | general_qa | bleu-4 | default | 12 | 0.0556 | default | |
| +---------------------+-------------+-----------------+----------+-------+---------+---------+ |
| | Qwen2-0.5B-Instruct | general_qa | rouge-1-f | default | 12 | 0.3441 | default | |
| +---------------------+-------------+-----------------+----------+-------+---------+---------+ |
| | Qwen2-0.5B-Instruct | general_qa | rouge-1-p | default | 12 | 0.2393 | default | |
| +---------------------+-------------+-----------------+----------+-------+---------+---------+ |
| | Qwen2-0.5B-Instruct | general_qa | rouge-1-r | default | 12 | 0.8889 | default | |
| +---------------------+-------------+-----------------+----------+-------+---------+---------+ |
| | Qwen2-0.5B-Instruct | general_qa | rouge-2-f | default | 12 | 0.2062 | default | |
| +---------------------+-------------+-----------------+----------+-------+---------+---------+ |
| | Qwen2-0.5B-Instruct | general_qa | rouge-2-p | default | 12 | 0.1453 | default | |
| +---------------------+-------------+-----------------+----------+-------+---------+---------+ |
| | Qwen2-0.5B-Instruct | general_qa | rouge-2-r | default | 12 | 0.6167 | default | |
| +---------------------+-------------+-----------------+----------+-------+---------+---------+ |
| | Qwen2-0.5B-Instruct | general_qa | rouge-l-f | default | 12 | 0.333 | default | |
| +---------------------+-------------+-----------------+----------+-------+---------+---------+ |
| | Qwen2-0.5B-Instruct | general_qa | rouge-l-p | default | 12 | 0.2324 | default | |
| +---------------------+-------------+-----------------+----------+-------+---------+---------+ |
| | Qwen2-0.5B-Instruct | general_qa | rouge-l-r | default | 12 | 0.8889 | default | |
| +---------------------+-------------+-----------------+----------+-------+---------+---------+ |
| ``` |
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