add rougel for dolly

federatedscope/llm/eval/eval_for_rougel/eval.py

@@ -0,0 +1,79 @@
+import os
+
+import numpy as np
+import transformers
+from tqdm import tqdm
+from rouge import Rouge
+
+from federatedscope.core.configs.config import global_cfg
+from federatedscope.core.cmd_args import parse_args, parse_client_cfg
+from federatedscope.core.auxiliaries.utils import setup_seed
+from federatedscope.core.auxiliaries.logging import update_logger
+from federatedscope.core.data.utils import download_url
+from federatedscope.llm.dataloader.dataloader import load_jsonl
+from federatedscope.llm.misc.fschat import FSChatBot
+
+transformers.logging.set_verbosity(40)
+
+DEBUG = False
+
+rouge = Rouge()
+
+
+def rouge_score(hyps, refs):
+    try:
+        rouge_score = rouge.get_scores(hyps, refs)[0]['rouge-l']['f']
+    except ValueError:
+        return 0.0
+    return rouge_score
+
+
+def main():
+    init_cfg = global_cfg.clone()
+    args = parse_args()
+
+    if args.cfg_file:
+        init_cfg.merge_from_file(args.cfg_file)
+    cfg_opt, client_cfg_opt = parse_client_cfg(args.opts)
+    init_cfg.merge_from_list(cfg_opt)
+
+    update_logger(init_cfg, clear_before_add=True)
+    setup_seed(init_cfg.seed)
+
+    # load your finetuned model (saved as xxx.ckpt)
+    #    in yaml file federate.save_to
+    fschatbot = FSChatBot(init_cfg)
+
+    # Get test file
+    fp = os.path.join(init_cfg.data.root, "databricks-dolly-15k.jsonl")
+    if not os.path.exists(fp):
+        download_url(
+            'https://raw.githubusercontent.com/databrickslabs'
+            '/dolly/d000e3030970379aabbf6d291f50ffdd3b715b64'
+            '/data/databricks-dolly-15k.jsonl', init_cfg.data.root)
+        os.rename(os.path.join(init_cfg.data.root, 'test.jsonl'), fp)
+
+    list_data_dict = load_jsonl(fp,
+                                instruction='instruction',
+                                input='context',
+                                output='response',
+                                category='category')
+    answers = []
+    for sample in tqdm(list_data_dict):
+        input_text = sample['instruction']
+        generate_kwargs = dict(max_new_tokens=256, top_p=0.95, temperature=0.8)
+        model_answer = fschatbot.generate(input_text, generate_kwargs)
+
+        rougel_cor = rouge_score(model_answer, sample['output'])
+        answers.append(rougel_cor)
+        if DEBUG:
+            print(f'Full input_text:\n{input_text}\n\n')
+        print(f'Question: {sample["instruction"]}\n\n'
+              f'Answers: {model_answer}\n\n')
+
+        print(f'Num of total question: {len(answers)}, '
+              f'Average score: {np.average(answers)}.')
+
+
+if __name__ == "__main__":
+    main()