cannot reproduce the results reported in the Espresso paper

[Alex357853]

Hi, this is a really good and useful codebase. I tried to reproduce the results reported in the paper but failed. I used the code in README_ESE.md:

WANDB_MODE=disabled CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 --master_port=1234 -m angle_emb.angle_trainer \
--model_name_or_path WhereIsAI/UAE-Large-V1 \
--train_name_or_path SeanLee97/nli_for_simcse --save_dir ckpts/UAE-Large-Espresso \
--ibn_w 10.0 --cosine_w 0. --angle_w 1.0 --angle_tau 20.0 --learning_rate 1e-6 --maxlen 75 \
--workers 16 \
--pooling_strategy cls \
--epochs 1 \
--batch_size 128 \
--logging_steps 100 \
--warmup_steps 200 \
--save_steps 1000 \
--fp16 1 \
--gradient_accumulation_steps 4 \
--apply_ese 1 \
--ese_compression_size 128 \
--ese_kl_temperature 1.0

However, it only gave the following results:	sts12	sts13	sts14	sts15	sts16	STSB	SICKR	Avg.
79.25	88.63	84.15	89.61	85.99	87.79	79.59	85.00

I also change --cosine_w 0. to --cosine_w 1.0 and --ibn_w 10.0 to --ibn_w 35.0, but the results were even worse.

The results reported in your paper are:	sts12	sts13	sts14	sts15	sts16	STSB	SICKR	Avg.
79.64	90.40	85.76	90.33	86.64	88.54	81.09	86.06	,

If I purely evaluate the WhereIsAI/UAE-Large-V1 model, the results are:	sts12	sts13	sts14	sts15	sts16	STSB	SICKR	Avg.
79.09	89.62	85.02	89.51	86.61	89.06	82.09	85.86

This means fine-tuning gave me worse performance. In addition, I noticed that the more epochs I train, the worse the performance gets. Besides, I also tried the code in examples/NLI/README.md to train Qwen1.5-0.5B:

CUDA_VISIBLE_DEVICES=1,2,3,4 torchrun --nproc_per_node=4 --master_port=1234 train_angle.py \
--task NLI-STS --save_dir ckpts/NLI-STS-angle-Qwen1.5-0.5B \
--model_name Qwen/Qwen1.5-0.5B \
--w2 35 --learning_rate 1e-4 --maxlen 50 \
--lora_r 32 --lora_alpha 32 --lora_dropout 0.1 \
--save_steps 500 --batch_size 120 --seed 42 --do_eval 0 --load_kbit 4 --gradient_accumulation_steps 4 --epochs 1

It gave me an average score of 70.23, whereas the paper reports 82.82.

I wonder whether these scripts are the ones you used to train your model, especially regarding the parameter values. It would be really helpful if you could assist me in reproducing the results so I can use this codebase. I really appreciate your time and help! Thank you!

[SeanLee97]

hi @Alex357853 thanks for following our work. Since ESE is under review, we didn't provide many details.

1) For UAE, you can try to increase ibn_w to 20, and evaluate it with cls_avg pooling (training with cls).

2) For Qwen, we use bi-directional LLMs, i.e., removing the causal mask of LLMs. For more details, you can refer to this documentation: https://angle.readthedocs.io/en/latest/notes/training.html#angle-trainer-recommended (in 3. Examples / b.LLaMA-based) Specifically, we set --apply_billm 1, --billm_model_class Qwen2ForCausalLM, --load_kbit 8, and set --epochs 2. I've uploaded the evaluation script here and made the ese-qwen weight public. You can have a try to evaluate the public model and check whether the evaluation works as expected.

The evaluation script is as follows:

BiLLM_START_INDEX=0 CUDA_VISIBLE_DEVICES=0 python eval_ese_nli.py --pooling_strategy avg --model_name_or_path Qwen/Qwen1.5-0.5B  --lora_weight WhereIsAI/ese-qwen-0.5b-nli --billm_model_class Qwen2ForCausalLM

BTW, you can try to increase the gradient_accumulation_steps to x-times gpu_counts. It might help improve performance further.

[SeanLee97]

hi @Alex357853 thanks for following our work. Since ESE is under review, we didn't provided many details.

1. For UAE, you can try to increase ibn_w to 20, and evaluate it with cls_avg pooling (training with cls).
2. For Qwen, we use bi-directional LLMs, i.e., removing the causal mask of LLMs. For more details, you can refer to this documentation: https://angle.readthedocs.io/en/latest/notes/training.html#angle-trainer-recommended (in 3. Examples / b.LLaMA-based) Specifically, we set --apply_billm 1, --load_kbit 8, and set --epochs 2. I've uploaded the evaluation script here and made the ese-qwen weight public. You can have a try to evaluate the public model and check whether the evaluation works as expected.

The evaluation script is as follows:

BiLLM_START_INDEX=0 CUDA_VISIBLE_DEVICES=0 python eval_nli.py --pooling_strategy avg --model_name_or_path Qwen/Qwen1.5-0.5B  --lora_weight WhereIsAI/ese-qwen-0.5b-nli --billm_model_class Qwen2ForCausalLM

BTW, you can try to increase the gradient_accumulation_steps to x-times gpu_counts. It might help improve performance further.

BTW, you can have a try using the newly released Qwen/Qwen2-0.5B, it might boost the performance further.