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Making Text classification template similar to Image Classification [skip ci] #92

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Merged
merged 2 commits into from
Apr 19, 2021
Merged

Making Text classification template similar to Image Classification [skip ci] #92

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4dedd37
Fix template sidebar
trsvchn Apr 19, 2021
4f8dfcf
Code style
trsvchn Apr 19, 2021
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107 changes: 55 additions & 52 deletions templates/text_classification/_sidebar.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,63 +18,66 @@ def get_configs() -> dict:
config["eval_epoch_length"] = None
default_none_options(config)

st.header("Transformer")
with st.beta_expander("Text Classification Template Configurations", expanded=True):
st.info("Names in the parenthesis are variable names used in the generated code.")

st.subheader("Model Options")
config["model"] = st.selectbox(
"Model name (from transformers) to setup model, tokenize and config to train (model)",
options=["bert-base-uncased"],
)
config["model_dir"] = st.text_input("Cache directory to download the pretrained model (model_dir)", value="./")
config["tokenizer_dir"] = st.text_input("Tokenizer cache directory (tokenizer_dir)", value="./tokenizer")
config["num_classes"] = st.number_input(
"Number of target classes. Default, 1 (binary classification) (num_classes)", min_value=0, value=1
)
config["max_length"] = st.number_input(
"Maximum number of tokens for the inputs to the transformer model (max_length)", min_value=1, value=256
)
config["dropout"] = st.number_input(
"Dropout probability (dropout)", min_value=0.0, max_value=1.0, value=0.3, format="%f"
)
config["n_fc"] = st.number_input(
"Number of neurons in the last fully connected layer (n_fc)", min_value=1, value=768
)
st.markdown("---")
st.subheader("Model Options")
config["model"] = st.selectbox(
"Model name (from transformers) to setup model, tokenize and config to train (model)",
options=["bert-base-uncased"],
)
config["model_dir"] = st.text_input("Cache directory to download the pretrained model (model_dir)", value="./")
config["tokenizer_dir"] = st.text_input("Tokenizer cache directory (tokenizer_dir)", value="./tokenizer")
config["num_classes"] = st.number_input(
"Number of target classes. Default, 1 (binary classification) (num_classes)", min_value=0, value=1
)
config["max_length"] = st.number_input(
"Maximum number of tokens for the inputs to the transformer model (max_length)", min_value=1, value=256
)
config["dropout"] = st.number_input(
"Dropout probability (dropout)", min_value=0.0, max_value=1.0, value=0.3, format="%f"
)
config["n_fc"] = st.number_input(
"Number of neurons in the last fully connected layer (n_fc)", min_value=1, value=768
)
st.markdown("---")

st.subheader("Dataset Options")
config["data_dir"] = st.text_input("Dataset cache directory (data_dir)", value="./")
st.markdown("---")
st.subheader("Dataset Options")
config["data_dir"] = st.text_input("Dataset cache directory (data_dir)", value="./")
st.markdown("---")

st.subheader("DataLoader Options")
config["batch_size"] = st.number_input("Total batch size (batch_size)", min_value=1, value=4)
config["num_workers"] = st.number_input("Number of workers in the data loader (num_workers)", min_value=1, value=2)
st.markdown("---")
st.subheader("DataLoader Options")
config["batch_size"] = st.number_input("Total batch size (batch_size)", min_value=1, value=4)
config["num_workers"] = st.number_input(
"Number of workers in the data loader (num_workers)", min_value=1, value=2
)
st.markdown("---")

st.subheader("Optimizer Options")
config["learning_rate"] = st.number_input(
"Peak of piecewise linear learning rate scheduler", min_value=0.0, value=5e-5, format="%e"
)
config["weight_decay"] = st.number_input("Weight decay", min_value=0.0, value=0.01, format="%f")
st.markdown("---")
st.subheader("Optimizer Options")
config["learning_rate"] = st.number_input(
"Peak of piecewise linear learning rate scheduler", min_value=0.0, value=5e-5, format="%e"
)
config["weight_decay"] = st.number_input("Weight decay", min_value=0.0, value=0.01, format="%f")
st.markdown("---")

st.subheader("Training Options")
config["max_epochs"] = st.number_input("Number of epochs to train the model", min_value=1, value=3)
config["num_warmup_epochs"] = st.number_input(
"Number of warm-up epochs before learning rate decay", min_value=0, value=0
)
config["validate_every"] = st.number_input(
"Run model's validation every validate_every epochs", min_value=0, value=1
)
config["checkpoint_every"] = st.number_input(
"Store training checkpoint every checkpoint_every iterations", min_value=0, value=1000
)
config["log_every_iters"] = st.number_input(
"Argument to log batch loss every log_every_iters iterations. 0 to disable it", min_value=0, value=15
)
st.markdown("---")
st.subheader("Training Options")
config["max_epochs"] = st.number_input("Number of epochs to train the model", min_value=1, value=3)
config["num_warmup_epochs"] = st.number_input(
"Number of warm-up epochs before learning rate decay", min_value=0, value=0
)
config["validate_every"] = st.number_input(
"Run model's validation every validate_every epochs", min_value=0, value=1
)
config["checkpoint_every"] = st.number_input(
"Store training checkpoint every checkpoint_every iterations", min_value=0, value=1000
)
config["log_every_iters"] = st.number_input(
"Argument to log batch loss every log_every_iters iterations. 0 to disable it", min_value=0, value=15
)
st.markdown("---")

distributed_options(config)
ignite_handlers_options(config)
ignite_loggers_options(config)
distributed_options(config)
ignite_handlers_options(config)
ignite_loggers_options(config)

return config