| datasets = [ |
| [ |
| dict( |
| abbr='LongBench_passage_retrieval_en', |
| eval_cfg=dict( |
| evaluator=dict( |
| type='opencompass.datasets.LongBenchRetrievalEvaluator'), |
| pred_role='BOT'), |
| infer_cfg=dict( |
| inferencer=dict( |
| max_out_len=32, |
| type='opencompass.openicl.icl_inferencer.GenInferencer'), |
| prompt_template=dict( |
| template=dict(round=[ |
| dict( |
| prompt= |
| 'Here are 30 paragraphs from Wikipedia, along with an abstract. Please determine which paragraph the abstract is from.\n\n{context}\n\nThe following is an abstract.\n\n{input}\n\nPlease enter the number of the paragraph that the abstract is from. The answer format must be like "Paragraph 1", "Paragraph 2", etc.\n\nThe answer is: ', |
| role='HUMAN'), |
| ]), |
| type= |
| 'opencompass.openicl.icl_prompt_template.PromptTemplate'), |
| retriever=dict( |
| type='opencompass.openicl.icl_retriever.ZeroRetriever')), |
| name='passage_retrieval_en', |
| path='opencompass/Longbench', |
| reader_cfg=dict( |
| input_columns=[ |
| 'context', |
| 'input', |
| ], |
| output_column='answers', |
| test_split='test', |
| train_split='test'), |
| type='opencompass.datasets.LongBenchpassage_retrieval_enDataset'), |
| ], |
| ] |
| eval = dict(runner=dict(task=dict(dump_details=True))) |
| models = [ |
| dict( |
| abbr='mask_deltanet', |
| batch_size=128, |
| max_seq_len=2048, |
| model_kwargs=dict( |
| device_map='auto', |
| torch_dtype='torch.bfloat16', |
| trust_remote_code=True), |
| path='/mnt/jfzn/msj/train_exp/mask_deltanet_1B_rank4', |
| run_cfg=dict(num_gpus=1), |
| tokenizer_kwargs=dict(padding_side='left', truncation_side='left'), |
| tokenizer_path='/mnt/jfzn/msj/train_exp/mask_gdn_1B_hrr-rank4', |
| type='opencompass.models.HuggingFaceBaseModel'), |
| ] |
| work_dir = 'outputs/default/20251128_162747' |
|
|
