| # Process data from paperswithcode | |
| See https://huggingface.co/datasets/pwc-archive/files/tree/main. | |
| Download and unzip evaluation tables: | |
| ```bash | |
| curl -L -O "https://huggingface.co/datasets/pwc-archive/files/resolve/main/jul-28-evaluation-tables.json.gz" | |
| gunzip jul-28-evaluation-tables.json.gz | |
| ``` | |
| Install jq. | |
| See https://jqlang.org/. | |
| If on Debian/Ubuntu, install with `sudo apt-get install jq`. | |
| Example jq to extract: | |
| ```bash | |
| jq -r ' | |
| def process(parent): | |
| .task as $current_task | | |
| (if parent then parent + " > " + $current_task else $current_task end) as $full_path | | |
| (.datasets[]? | | |
| .dataset as $dataset | | |
| .sota.rows[]? | | |
| { | |
| task_path: $full_path, | |
| dataset: $dataset, | |
| model_name: .model_name, | |
| paper_url: .paper_url, | |
| metrics: .metrics | |
| } | |
| ), | |
| (.subtasks[]? | process($full_path)); | |
| ["task_path", "dataset", "model_name", "paper_url", "metric_name", "metric_value"], | |
| ( | |
| [.[] | process(null)] | | |
| .[] | | |
| [.task_path, .dataset, .model_name, .paper_url] + | |
| (.metrics | to_entries[] | [.key, .value]) | | |
| flatten | |
| ) | | |
| @csv | |
| ' jul-28-evaluation-tables.json > results.csv | |
| ``` | |
| Should get 326,393 rows in results.csv and looks like this: | |
| ```bash | |
| ~/paperswithcode-data> nu -c "open results.csv | length" | |
| # 326393 | |
| ~/paperswithcode-data> nu -c "open results.csv | skip 100 | take 10" | |
| # ╭───┬────────────────────────────────────────────────────────────────────┬─────────────────┬───────────────┬────────────────────────────────────┬─────────────┬──────────────╮ | |
| # │ # │ task_path │ dataset │ model_name │ paper_url │ metric_name │ metric_value │ | |
| # ├───┼────────────────────────────────────────────────────────────────────┼─────────────────┼───────────────┼────────────────────────────────────┼─────────────┼──────────────┤ | |
| # │ 0 │ Optical Character Recognition (OCR) > Handwritten Text Recognition │ LAM(line-level) │ HTR-VT │ https://arxiv.org/abs/2409.08573v1 │ Test CER │ 2.80 │ | |
| # │ 1 │ Optical Character Recognition (OCR) > Handwritten Text Recognition │ LAM(line-level) │ HTR-VT │ https://arxiv.org/abs/2409.08573v1 │ Test WER │ 7.40 │ | |
| # │ 2 │ Optical Character Recognition (OCR) > Handwritten Text Recognition │ LAM(line-level) │ OrigamiNet-24 │ https://arxiv.org/abs/2006.07491v1 │ Test CER │ 3.00 │ | |
| # │ 3 │ Optical Character Recognition (OCR) > Handwritten Text Recognition │ LAM(line-level) │ OrigamiNet-24 │ https://arxiv.org/abs/2006.07491v1 │ Test WER │ 11.00 │ | |
| # │ 4 │ Optical Character Recognition (OCR) > Handwritten Text Recognition │ LAM(line-level) │ OrigamiNet-18 │ https://arxiv.org/abs/2006.07491v1 │ Test CER │ 3.10 │ | |
| # │ 5 │ Optical Character Recognition (OCR) > Handwritten Text Recognition │ LAM(line-level) │ OrigamiNet-18 │ https://arxiv.org/abs/2006.07491v1 │ Test WER │ 11.10 │ | |
| # │ 6 │ Optical Character Recognition (OCR) > Handwritten Text Recognition │ LAM(line-level) │ OrigamiNet-12 │ https://arxiv.org/abs/2006.07491v1 │ Test CER │ 3.10 │ | |
| # │ 7 │ Optical Character Recognition (OCR) > Handwritten Text Recognition │ LAM(line-level) │ OrigamiNet-12 │ https://arxiv.org/abs/2006.07491v1 │ Test WER │ 11.20 │ | |
| # │ 8 │ Optical Character Recognition (OCR) > Handwritten Text Recognition │ LAM(line-level) │ TrOCR │ https://arxiv.org/abs/2109.10282v5 │ Test CER │ 3.60 │ | |
| # │ 9 │ Optical Character Recognition (OCR) > Handwritten Text Recognition │ LAM(line-level) │ TrOCR │ https://arxiv.org/abs/2109.10282v5 │ Test WER │ 11.60 │ | |
| # ╰───┴────────────────────────────────────────────────────────────────────┴─────────────────┴───────────────┴────────────────────────────────────┴─────────────┴──────────────╯ | |
| ``` | |