The first step is to save embeddings of corpus. For LM tasks, we didn't include title in generating embeddings. Download the Wikipedia files from:
wget https://dl.fbaipublicfiles.com/dpr/wikipedia_split/psgs_w100.tsv.gz
Generate embeddings&index for the corpus
OUTPUT_DIR=/path/to/output/embeddings
python generate_passage_embeddings.py \
--model_name_or_path facebook/contriever \
--output_dir $OUTPUT_DIR \
--passages psgs_w100.tsv \
--shard_id 0 --num_shards 1 \
Please also add the API key in the qa_final.py file.
MODEL=code-davinci-002
DATASET=nq
QA_DATA=/path/to/qa/downloaded/nq_or_tqa/data
python -u downstream_eval/qa_final.py \
--engine $MODEL \
--data_dir $QA_DATA \
--task $DATASET \
--prompt_method open-book \
--save_prob \
--maxlen 10 \
--do_retrieval 1 \
--re_model_name_or_path facebook/contriever \
--passages psgs_w100.tsv \
--save_or_load_index \
--passages_embeddings "$OUTPUT_DIR/*"
The first step is to save embeddings of corpus. For LM tasks, we didn't include title in generating embeddings. Download the Wikipedia files from:
wget https://dl.fbaipublicfiles.com/dpr/wikipedia_split/psgs_w100.tsv.gz
Generate embeddings&index for the corpus
OUTPUT_DIR=/path/to/output/embeddings
python generate_passage_embeddings.py \
--model_name_or_path facebook/contriever \
--output_dir $OUTPUT_DIR \
--passages psgs_w100.tsv \
--shard_id 0 --num_shards 1 \
Download MMLU data from https://github.com/hendrycks/test
Please also add the API key in the mmlu_final.py file.
MODEL=code-davinci-002
DATASET=mmlu
MMLU_DATA=/path/to/mmlu/downloaded/data
python -u downstream_eval/mmlu_final.py \
--engine $MODEL \
--data_dir $MMLU_DATA \
--task $DATASET \
--prompt_method open-book \
--save_prob \
--split test \
--maxlen 2 \
--do_retrieval 1 \
--re_model_name_or_path facebook/contriever \
--passages psgs_w100.tsv \
--save_or_load_index \
--passages_embeddings "$OUTPUT_DIR/*"
The first step is to save embeddings of corpus. For LM tasks, we didn't include title in generating embeddings.
ENCODE_PATH="./data/text.jsonl" # It could also be ENCODE_PATH=wikitext-2-v1
python3 generate_passage_embeddings.py \
--model_name_or_path "facebook/contriever" \
--passages $ENCODE_PATH \
--output_dir data/embeddings \
--shard_id 0 \
--num_shards 1 \
--per_gpu_batch_size 500 \
--passage_maxlength 128 \
--no_title \
--chunk_size 64 \
--passage_maxlength 128
First create a model config in preset_configs. One example model config:
"model_type": "gpt2", # Use "gpt2" for all models that can be downloaded from Huggingface.
"model_name": "bigscience/bloom-1b7", # specify the model_name you want to use
"context_len": 128, # any number is fine as it will be overwritten later
"max_seq_len": 896, # any number is fine as it will be overwritten later
"device": "cuda:0"
Second, save log probabilites of your data using the following commands
ENCODE_PATH="./data/text.jsonl"
EMB_PATH="./embeddings/passages_00"
RETRIEVER="facebook/contriever"
MODEL_CONFIG="preset_configs/bloom-7b1.json"
ENSEMBLE_DOCS=10
python save_logprob_data.py
--model_config_path $MODEL_CONFIG \
--passages $ENCODE_PATH # the path to the raw corpus from step 1 \
--passages_embeddings $EMB_PATH # the path to encoded corpus from step 1 \
--re_model_name_or_path $RETRIEVER \
--data wikitext-2-v1 # dataset you want to use. Change the dataloading in line82/92 in save_logprob_data.py \
--retrieved_max_length 128 \ # max length of each retrieved documents.
--context_len 128 \ # Prior context used as the retrieval query
--pred_len 768 \ # length of the next sentence following the prior context. This next sentence will be used to compute the log probability
--output_path outputs/ppl.data \
--ensemble $ENSEMBLE_DOCS \
--n_docs $ENSEMBLE_DOCS \
--save_or_load_index
python LSR_finetune/replug_lsr.py
--model_config_path $MODEL_CONFIG \
--passages $ENCODE_PATH # the path to the raw corpus from step 1 \
--passages_embeddings $EMB_PATH # the path to encoded corpus from step 1 \
--re_model_name_or_path $RETRIEVER \
--data wikitext-2-v1 # dataset you want to use. Change the dataloading in line82/92 in save_logprob_data.py \
--retrieved_max_length 128 \ # max length of each retrieved documents.
--context_len 128 \ # Prior context used as the retrieval query
--pred_len 768 \ # length of the next sentence following the prior context. This next sentence will be used to compute the log probability
--output_path outputs/ppl.data \
--ensemble $ENSEMBLE_DOCS \
--n_docs $ENSEMBLE_DOCS \
--save_or_load_index