Code for benchmarking language models with the Pile.
Evaluating on GPT-2 (uses GPU):
# Compute intermediate outputs for calculating perplexity (e.g. logprobs)
python lm_perplexity/save_lm_perplexity_data.py \
--model_config_path preset_configs/gpt2_medium.json \
--data_path /path/to/mydata.jsonl.zst \
--output_path /path/to/perplexity_data.p
# Use intermediate outputs to compute perplexity
python lm_perplexity/compute_perplexity.py \
--perplexity_data_path /path/to/perplexity_data.p \
--output_path /path/to/perplexity.jsonEvaluating on GPT-3 (requires OpenAI API key):
# Compute intermediate outputs for calculating perplexity (e.g. logprobs)
export OPENAI_API_SECRET_KEY=YOUR_KEY_HERE
python lm_perplexity/run_lm_perplexity.py \
--model_config_path preset_configs/gpt3_curie.json \
--data_path /path/to/mydata.jsonl.zst \
--output_path /path/to/perplexity_data.p
# Use intermediate outputs to compute perplexity
python lm_perplexity/compute_perplexity.py \
--perplexity_data_path /path/to/perplexity_data.p \
--output_path /path/to/perplexity.jsonJSON files in assets/${DATASET}/group${GROUP_ID}.json contain the document indices for the canonical one-tenth split of the test set. Evaluation in the paper were performed on group0.
- numpy
- torch
- transformers
- openai
- lm_dataformat
- tqdm
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent