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components text_classification_pipeline

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Text Classification Pipeline

text_classification_pipeline

Overview

Pipeline component to finetune Hugging Face pretrained models for text classification task. The component supports optimizations such as LoRA, Deepspeed and ONNXRuntime for performance enhancement. See docs to learn more.

Version: 0.0.67

View in Studio: https://ml.azure.com/registries/azureml/components/text_classification_pipeline/version/0.0.67

Inputs

Compute parameters

Name Description Type Default Optional Enum
instance_type_model_import Instance type to be used for model_import component in case of serverless compute, eg. standard_d12_v2. The parameter compute_model_import must be set to 'serverless' for instance_type to be used string Standard_d12_v2 True
instance_type_preprocess Instance type to be used for preprocess component in case of serverless compute, eg. standard_d12_v2. The parameter compute_preprocess must be set to 'serverless' for instance_type to be used string Standard_d12_v2 True
instance_type_finetune Instance type to be used for finetune component in case of serverless compute, eg. standard_nc24rs_v3. The parameter compute_finetune must be set to 'serverless' for instance_type to be used string Standard_nc24rs_v3 True
instance_type_model_evaluation Instance type to be used for model_evaluation components in case of serverless compute, eg. standard_nc24rs_v3. The parameter compute_model_evaluation must be set to 'serverless' for instance_type to be used string Standard_nc24rs_v3 True
shm_size_finetune Shared memory size to be used for finetune component. It is useful while using Nebula (via DeepSpeed) which uses shared memory to save model and optimizer states. string 5g True
num_nodes_finetune number of nodes to be used for finetuning (used for distributed training) integer 1 True
number_of_gpu_to_use_finetuning number of gpus to be used per node for finetuning, should be equal to number of gpu per node in the compute SKU used for finetune integer 1 True

Model Import parameters (See docs to learn more)

Name Description Type Default Optional Enum
huggingface_id The string can be any valid Hugging Face id from the Hugging Face models webpage. Models from Hugging Face are subject to third party license terms available on the Hugging Face model details page. It is your responsibility to comply with the model's license terms. Special characters like \ and ' are invalid in the parameter value. string True
pytorch_model_path Pytorch model asset path. Special characters like \ and ' are invalid in the parameter value. custom_model True
mlflow_model_path MLflow model asset path. Special characters like \ and ' are invalid in the parameter value. mlflow_model True

Data PreProcess parameters (See docs to learn more)

Name Description Type Default Optional Enum
task_name Text Classification task type string SingleLabelClassification False ['SingleLabelClassification']
sentence1_key Key for sentence1_key in each example line. Special characters like \ and ' are invalid in the parameter value. string False
sentence2_key Key for sentence2_key in each example line. Special characters like \ and ' are invalid in the parameter value. string True
label_key label key in each example line. Special characters like \ and ' are invalid in the parameter value. string False
batch_size Number of examples to batch before calling the tokenization function integer 1000 True
pad_to_max_length If set to True, the returned sequences will be padded according to the model's padding side and padding index, up to their max_seq_length. If no max_seq_length is specified, the padding is done up to the model's max length. string false True ['true', 'false']
max_seq_length Controls the maximum length to use when pad_to_max_length parameter is set to true. Default is -1 which means the padding is done up to the model's max length. Else will be padded to max_seq_length. integer -1 True
train_file_path Path to the registered training data asset. The supported data formats are jsonl, json, csv, tsv and parquet. Special characters like \ and ' are invalid in the parameter value. uri_file True
validation_file_path Path to the registered validation data asset. The supported data formats are jsonl, json, csv, tsv and parquet. Special characters like \ and ' are invalid in the parameter value. uri_file True
test_file_path Path to the registered test data asset. The supported data formats are jsonl, json, csv, tsv and parquet. Special characters like \ and ' are invalid in the parameter value. uri_file True
train_mltable_path Path to the registered training data asset in mltable format. Special characters like \ and ' are invalid in the parameter value. mltable True
validation_mltable_path Path to the registered validation data asset in mltable format. Special characters like \ and ' are invalid in the parameter value. mltable True
test_mltable_path Path to the registered test data asset in mltable format. Special characters like \ and ' are invalid in the parameter value. mltable True

Finetune parameters (See docs to learn more)

Name Description Type Default Optional Enum
apply_lora If "true" enables lora. string false True ['true', 'false']
merge_lora_weights If "true", the lora weights are merged with the base Hugging Face model weights before saving. string true True ['true', 'false']
lora_alpha alpha attention parameter for lora. integer 128 True
lora_r lora dimension integer 8 True
lora_dropout lora dropout value number 0.0 True
num_train_epochs Number of epochs to run for finetune. integer 1 True
max_steps If set to a positive number, the total number of training steps to perform. Overrides 'epochs'. In case of using a finite iterable dataset the training may stop before reaching the set number of steps when all data is exhausted. integer -1 True
per_device_train_batch_size Per gpu batch size used for training. The effective training batch size is per_device_train_batch_size * num_gpus * num_nodes. integer 1 True
per_device_eval_batch_size Per gpu batch size used for validation. The default value is 1. The effective validation batch size is per_device_eval_batch_size * num_gpus * num_nodes. integer 1 True
auto_find_batch_size If set to "true" and if the provided 'per_device_train_batch_size' goes into Out Of Memory (OOM) auto_find_batch_size will find the correct batch size by iteratively reducing batch size by a factor of 2 till the OOM is fixed string false True ['true', 'false']
optim Optimizer to be used while training string adamw_hf True ['adamw_hf', 'adamw_torch', 'adafactor']
learning_rate Start learning rate used for training. number 2e-05 True
warmup_steps Number of steps for the learning rate scheduler warmup phase. integer 0 True
weight_decay Weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in AdamW optimizer number 0.0 True
adam_beta1 beta1 hyperparameter for the AdamW optimizer number 0.9 True
adam_beta2 beta2 hyperparameter for the AdamW optimizer number 0.999 True
adam_epsilon epsilon hyperparameter for the AdamW optimizer number 1e-08 True
gradient_accumulation_steps Number of updates steps to accumulate the gradients for, before performing a backward/update pass integer 1 True
eval_accumulation_steps Number of predictions steps to accumulate before moving the tensors to the CPU, will be passed as None if set to -1 integer -1 True
lr_scheduler_type learning rate scheduler to use. string linear True ['linear', 'cosine', 'cosine_with_restarts', 'polynomial', 'constant', 'constant_with_warmup']
precision Apply mixed precision training. This can reduce memory footprint by performing operations in half-precision. string 32 True ['32', '16']
seed Random seed that will be set at the beginning of training integer 42 True
enable_full_determinism Ensure reproducible behavior during distributed training. Check this link https://pytorch.org/docs/stable/notes/randomness.html for more details. string false True ['true', 'false']
dataloader_num_workers Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process. integer 0 True
ignore_mismatched_sizes Not setting this flag will raise an error if some of the weights from the checkpoint do not have the same size as the weights of the model. string true True ['true', 'false']
max_grad_norm Maximum gradient norm (for gradient clipping) number 1.0 True
evaluation_strategy The evaluation strategy to adopt during training. If set to "steps", either the evaluation_steps_interval or eval_steps needs to be specified, which helps to determine the step at which the model evaluation needs to be computed else evaluation happens at end of each epoch. string epoch True ['epoch', 'steps']
evaluation_steps_interval The evaluation steps in fraction of an epoch steps to adopt during training. Overwrites eval_steps if not 0. number 0.0 True
eval_steps Number of update steps between two evals if evaluation_strategy='steps' integer 500 True
logging_strategy The logging strategy to adopt during training. If set to "steps", the logging_steps will decide the frequency of logging else logging happens at the end of epoch.. string steps True ['epoch', 'steps']
logging_steps Number of update steps between two logs if logging_strategy='steps' integer 10 True
metric_for_best_model metric to use to compare two different model checkpoints string loss True ['loss', 'f1_macro', 'mcc', 'accuracy', 'precision_macro', 'recall_macro']
resume_from_checkpoint If set to "true", resumes the training from last saved checkpoint. Along with loading the saved weights, saved optimizer, scheduler and random states will be loaded if exist. The default value is "false" string false True ['true', 'false']
save_total_limit If a positive value is passed, it will limit the total number of checkpoints saved. The value of -1 saves all the checkpoints, otherwise if the number of checkpoints exceed the save_total_limit, the older checkpoints gets deleted. integer -1 True
apply_early_stopping If set to "true", early stopping is enabled. string false True ['true', 'false']
early_stopping_patience Stop training when the metric specified through metric_for_best_model worsens for early_stopping_patience evaluation calls.This value is only valid if apply_early_stopping is set to true. integer 1 True
early_stopping_threshold Denotes how much the specified metric must improve to satisfy early stopping conditions. This value is only valid if apply_early_stopping is set to true. number 0.0 True
apply_deepspeed If set to true, will enable deepspeed for training string false True ['true', 'false']
deepspeed Deepspeed config to be used for finetuning. Special characters like \ and ' are invalid in the parameter value. uri_file True
deepspeed_stage This parameter configures which DEFAULT deepspeed config to be used - stage2 or stage3. The default choice is stage2. Note that, this parameter is ONLY applicable when user doesn't pass any config information via deepspeed port. string 2 True ['2', '3']
apply_ort If set to true, will use the ONNXRunTime training string false True ['true', 'false']

Model Evaluation parameters

Name Description Type Default Optional Enum
evaluation_config Additional parameters for Computing Metrics. Special characters like \ and ' are invalid in the parameter value. uri_file True
evaluation_config_params Additional parameters as JSON serielized string string True

Compute parameters

Name Description Type Default Optional Enum
compute_model_import compute to be used for model_import eg. provide 'FT-Cluster' if your compute is named 'FT-Cluster'. Special characters like \ and ' are invalid in the parameter value. If compute cluster name is provided, instance_type field will be ignored and the respective cluster will be used string serverless True
compute_preprocess compute to be used for preprocess eg. provide 'FT-Cluster' if your compute is named 'FT-Cluster'. Special characters like \ and ' are invalid in the parameter value. If compute cluster name is provided, instance_type field will be ignored and the respective cluster will be used string serverless True
compute_finetune compute to be used for finetune eg. provide 'FT-Cluster' if your compute is named 'FT-Cluster'. Special characters like \ and ' are invalid in the parameter value. If compute cluster name is provided, instance_type field will be ignored and the respective cluster will be used string serverless True
compute_model_evaluation compute to be used for model_eavaluation eg. provide 'FT-Cluster' if your compute is named 'FT-Cluster'. Special characters like \ and ' are invalid in the parameter value. If compute cluster name is provided, instance_type field will be ignored and the respective cluster will be used string serverless True

Outputs

Name Description Type
pytorch_model_folder output folder containing best model as defined by metric_for_best_model. Along with the best model, output folder contains checkpoints saved after every evaluation which is defined by the evaluation_strategy. Each checkpoint contains the model weight(s), config, tokenizer, optimzer, scheduler and random number states. uri_folder
mlflow_model_folder output folder containing best finetuned model in mlflow format. mlflow_model
evaluation_result Test Data Evaluation Results uri_folder
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