Datasets:

placingholocaust
/

spacy-project

	title: "Demo spancat in a new pipeline (Span Categorization)"
	description: "A minimal demo spancat project for spaCy v3"

	# Variables can be referenced across the project.yml using ${vars.var_name}
	vars:
	name: "placing_holocaust"
	lang: "en"
	annotations_file: "annotated_data_spans.jsonl"
	train: "train"
	dev: "dev"
	test: "test"
	version: "0.0.1"
	# Set a random seed
	seed: 0
	# Set your GPU ID, -1 is CPU
	gpu_id: -1
	vectors_model_md: "en_core_web_md"
	vectors_model_lg: "en_core_web_lg"

	# These are the directories that the project needs. The project CLI will make
	# sure that they always exist.
	directories: ["assets", "corpus", "configs", "training", "scripts", "packages"]

	# Assets that should be downloaded or available in the directory. We're shipping
	# them with the project, so they won't have to be downloaded.
	assets:
	- dest: "assets/train.jsonl"
	description: "Training data. For this project, they were chunked into sentences."
	- dest: "assets/dev.jsonl"
	description: "Validation data. For this project, they were chunked into sentences."
	- dest: "assets/test.jsonl"
	description: "Testing data. For this project, they were chunked into sentences."

	- dest: "assets/annotated_data.json/"
	description: "All data, including those without annotations because they are negative examples."

	- dest: "assets/annotated_data_spans.jsonl"
	description: "This is just the data that contained examples of span annotations."

	- dest: "corpus/train.spacy"
	description: "Training data in serialized format."
	- dest: "corpus/dev.spacy"
	description: "Validation data in serialized format."
	- dest: "corpus/test.spacy"
	description: "Testing data in serialized format."

	- dest: "gold-training-data/*"
	description: "The original outputs from Prodigy, the annotation software used."

	- dest: "notebooks/*"
	description: "A collection of notebooks for testing different features of the project."

	- dest: "configs/*"
	description: "A collection of config files used for training the spaCy models."
	# Workflows are sequences of commands (see below) executed in order. You can
	# run them via "spacy project run [workflow]". If a commands's inputs/outputs
	# haven't changed, it won't be re-run.
	workflows:
	all-sm-sents:
	- convert-sents
	- split
	- create-config-sm
	- train-sm
	- evaluate-sm
	# all-trf:
	# - download
	# - convert
	# - create-config
	# - train-with-vectors
	# - evaluate

	# Project commands, specified in a style similar to CI config files (e.g. Azure
	# pipelines). The name is the command name that lets you trigger the command
	# via "spacy project run [command] [path]". The help message is optional and
	# shown when executing "spacy project run [optional command] [path] --help".
	commands:

	#### DOWNLOADING VECTORS #####
	- name: "download-lg"
	help: "Download a spaCy model with pretrained vectors"
	script:
	- "python -m spacy download ${vars.vectors_model_lg}"

	- name: "download-md"
	help: "Download a spaCy model with pretrained vectors"
	script:
	- "python -m spacy download ${vars.vectors_model_md}"

	#### PREPROCESSING #####
	- name: "convert"
	help: "Convert the data to spaCy's binary format"
	script:
	- "python scripts/convert.py ${vars.lang} assets/${vars.train}.jsonl corpus"
	- "python scripts/convert.py ${vars.lang} assets/${vars.dev}.jsonl corpus"
	- "python scripts/convert.py ${vars.lang} assets/${vars.test}.jsonl corpus"
	deps:
	- "assets/${vars.train}.jsonl"
	- "assets/${vars.dev}.jsonl"
	- "assets/${vars.test}.jsonl"
	- "scripts/convert.py"
	outputs:
	- "corpus/train.spacy"
	- "corpus/dev.spacy"
	- "corpus/test.spacy"

	- name: "convert-sents"
	help: "Convert the data to to sentences before converting to spaCy's binary format"
	script:
	- "python scripts/convert_sents.py ${vars.lang} assets/${vars.train}.jsonl corpus"
	- "python scripts/convert_sents.py ${vars.lang} assets/${vars.dev}.jsonl corpus"
	- "python scripts/convert_sents.py ${vars.lang} assets/${vars.test}.jsonl corpus"
	deps:
	- "assets/${vars.train}.jsonl"
	- "assets/${vars.dev}.jsonl"
	- "assets/${vars.test}.jsonl"
	- "scripts/convert.py"
	outputs:
	- "corpus/train.spacy"
	- "corpus/dev.spacy"
	- "corpus/test.spacy"

	- name: "split"
	help: "Split data into train/dev/test sets"
	script:
	- "python scripts/split.py assets/${vars.annotations_file}"
	deps:
	- "scripts/split.py"
	outputs:
	- "assets/train.jsonl"
	- "assets/dev.jsonl"
	- "assets/test.jsonl"



	#### CONFIG CREATIONS #####

	- name: "create-config-sm"
	help: "Create a new config with a spancat pipeline component"
	script:
	- "python -m spacy init fill-config configs/base_config_sm.cfg configs/config_sm.cfg"
	deps:
	- configs/base_config_sm.cfg
	outputs:
	- "configs/config.cfg"


	#### TRAINING #####

	### small ###
	- name: "train-sm"
	help: "Train the spancat model"
	script:
	- >-
	python -m spacy train configs/config_sm.cfg --output training/sm/
	--paths.train corpus/train.spacy --paths.dev corpus/dev.spacy
	--training.eval_frequency 50
	--training.patience 0
	--gpu-id ${vars.gpu_id}
	--system.seed ${vars.seed}
	deps:
	- "configs/config_lg.cfg"
	- "corpus/train.spacy"
	- "corpus/dev.spacy"
	outputs:
	- "training/model-best"


	### medium ###
	- name: "train-md"
	help: "Train the spancat model with vectors"
	script:
	- >-
	python -m spacy train configs/config_md.cfg --output training/md/
	--paths.train corpus/train.spacy --paths.dev corpus/dev.spacy
	--training.eval_frequency 50
	--training.patience 0
	--gpu-id ${vars.gpu_id}
	--initialize.vectors ${vars.vectors_model_md}
	--system.seed ${vars.seed}
	--components.tok2vec.model.embed.include_static_vectors true
	deps:
	- "configs/config_md.cfg"
	- "corpus/train.spacy"
	- "corpus/dev.spacy"
	outputs:
	- "training/model-best"


	### large ###
	- name: "train-lg"
	help: "Train the spancat model with vectors"
	script:
	- >-
	python -m spacy train configs/config_lg.cfg --output training/lg/
	--paths.train corpus/train.spacy --paths.dev corpus/dev.spacy
	--training.eval_frequency 50
	--training.patience 0
	--gpu-id ${vars.gpu_id}
	--initialize.vectors ${vars.vectors_model_lg}
	--system.seed ${vars.seed}
	--components.tok2vec.model.embed.include_static_vectors true
	deps:
	- "configs/config_lg.cfg"
	- "corpus/train.spacy"
	- "corpus/dev.spacy"
	outputs:
	- "training/model-best"


	### transformer ###
	- name: "train-trf"
	help: "Train the spancat model"
	script:
	- >-
	python -m spacy train configs/config_trf.cfg --output training/trf/
	--paths.train corpus/train.spacy --paths.dev corpus/dev.spacy
	--training.patience 100
	--gpu-id ${vars.gpu_id}
	--system.seed ${vars.seed}
	deps:
	- "configs/config.cfg"
	- "corpus/train.spacy"
	- "corpus/dev.spacy"
	outputs:
	- "training/model-best"


	#### EVALUATION #####

	### small ###
	- name: "evaluate-sm"
	help: "Evaluate the model and export metrics"
	script:
	- "python -m spacy evaluate training/sm/model-best corpus/test.spacy --output training/sm/metrics.json"
	deps:
	- "corpus/test.spacy"
	- "training/sm/model-best"
	outputs:
	- "training/sm/metrics.json"

	### medium ###

	- name: "evaluate-md"
	help: "Evaluate the model and export metrics"
	script:
	- "python -m spacy evaluate training/md/model-best corpus/test.spacy --output training/md/metrics.json"
	deps:
	- "corpus/test.spacy"
	- "training/md/model-best"
	outputs:
	- "training/md/metrics.json"

	### large ###
	- name: "evaluate-lg"
	help: "Evaluate the model and export metrics"
	script:
	- "python -m spacy evaluate training/lg/model-best corpus/test.spacy --output training/lg/metrics.json"
	deps:
	- "corpus/test.spacy"
	- "training/lg/model-best"
	outputs:
	- "training/lg/metrics.json"


	#### PACKAGING #####

	- name: "build-table"
	help: "builds a nice table from the metrics for README.md"
	script:
	- "python scripts/build-table.py"

	- name: "readme"
	help: "builds a nice table from the metrics for README.md"
	script:
	- "python scripts/readme.py"

	- name: package
	help: "Package the trained model as a pip package"
	script:
	- "python -m spacy package training/model-best packages --name ${vars.name} --version ${vars.version} --force"
	deps:
	- "training/model-best"
	outputs_no_cache:
	- "packages/${vars.lang}_${vars.name}-${vars.version}/dist/${vars.lang}_${vars.name}-${vars.version}.tar.gz"

	- name: clean
	help: "Remove intermediary directories"
	script:
	- "rm -rf corpus/*"
	- "rm -rf training/*"
	- "rm -rf metrics/*"