Component to validate inputs to the distillation pipeline

Version: 0.0.4

View in Studio: https://ml.azure.com/registries/azureml/components/oss_distillation_validate_pipeline/version/0.0.4

Inputs

Name	Description	Type	Default	Optional	Enum
train_file_path	Path to the registered training data asset. The supported data formats are jsonl, json, csv, tsv and parquet.	uri_file
validation_file_path	Path to the registered validation data asset. The supported data formats are jsonl, json, csv, tsv and parquet.	uri_file		True
teacher_model_endpoint_name	Teacher model endpoint name	string		True
teacher_model_endpoint_url	Teacher model endpoint URL	string		True
teacher_model_endpoint_key	Teacher model endpoint key	string		True
teacher_model_max_new_tokens	Teacher model max_new_tokens inference parameter	integer	128
teacher_model_temperature	Teacher model temperature inference parameter	number	0.2
teacher_model_top_p	Teacher model top_p inference parameter	number	0.1
teacher_model_frequency_penalty	Teacher model frequency penalty inference parameter	number	0.0
teacher_model_presence_penalty	Teacher model presence penalty inference parameter	number	0.0
teacher_model_stop	Teacher model stop inference parameter	string		True
request_batch_size	No of data records to hit teacher model endpoint in one go	integer	10
min_endpoint_success_ratio	The minimum value of (successful_requests / total_requests) required for classifying inference as successful. If (successful_requests / total_requests) < min_endpoint_success_ratio, the experiment will be marked as failed. By default it is 0.7 (0 means all requests are allowed to fail while 1 means no request should fail.)	number	0.7
enable_chain_of_thought	Enable Chain of thought for data generation	string	false	True
enable_chain_of_density	Enable Chain of density for text summarization	string	false	True
max_len_summary	Maximum Length Summary for text summarization	integer	80	True
data_generation_task_type	Data generation task type. Supported values are: 1. NLI: Generate Natural Language Inference data 2. CONVERSATION: Generate conversational data (multi/single turn) 3. NLU_QA: Generate Natural Language Understanding data for Question Answering data 4. MATH: Generate Math data for numerical responses 5. SUMMARIZATION: Generate Key Summary for an Article	string			['NLI', 'CONVERSATION', 'NLU_QA', 'MATH', 'SUMMARIZATION']
num_train_epochs	training epochs	integer	1	True
per_device_train_batch_size	Train batch size	integer	1	True
learning_rate	Start learning rate.	number	0.0003	True

Name	Description	Type
validation_info	Validation status.	uri_file

azureml://registries/azureml/environments/acft-hf-nlp-gpu/versions/76