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# Script for HF autotrain space runner 🚀
# Expected environment variables:
# CONFIG: points to *.json configuration file
# HF_TOKEN: HF access token from https://huggingface.co/settings/tokens
# REPO_NAME: name of HF datasets repo
import os
import flair
import json
import importlib
from huggingface_hub import login, HfApi
fine_tuner = importlib.import_module("flair-fine-tuner")
config_file = os.environ.get("CONFIG")
hf_token = os.environ.get("HF_TOKEN")
repo_name = os.environ.get("REPO_NAME")
login(token=hf_token, add_to_git_credential=True)
api = HfApi()
with open(config_file, "rt") as f_p:
json_config = json.load(f_p)
seeds = json_config["seeds"]
batch_sizes = json_config["batch_sizes"]
epochs = json_config["epochs"]
learning_rates = json_config["learning_rates"]
subword_poolings = json_config["subword_poolings"]
hipe_datasets = json_config["hipe_datasets"] # Do not iterate over them
cuda = json_config["cuda"]
flair.device = f'cuda:{cuda}'
for seed in seeds:
for batch_size in batch_sizes:
for epoch in epochs:
for learning_rate in learning_rates:
for subword_pooling in subword_poolings:
fine_tuner.run_experiment(seed, batch_size, epoch, learning_rate, subword_pooling, hipe_datasets, json_config)
api.upload_folder(
folder_path="./",
path_in_repo="./",
repo_id=repo_name,
repo_type="dataset",
)
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