lmflow.tokenization.hf_text_regression_model#

Attributes#

`logger`
`tok_logger`

`blocking_paired`(→ Dict)
`blocking`(→ Dict)
`blocking_text_to_textlist`(→ Dict)
`paired_conversation_tokenize_function`(→ Dict)
`conversation_tokenize_function`(→ Dict)	Handels conversation datasets tokenization
`tokenize_function`(→ Dict)	Handels text_only and text2text datasets tokenization
`text_to_textlist_tokenize_function`(→ Dict)	For rm inference, and don't need attn mask and labels.

lmflow.tokenization.hf_text_regression_model.blocking_paired(token_dict: Dict, column_names: List, block_size: int, model_max_length: int, pad_token_id: int, padding_side: str, truncation_side: str = 'right') → Dict[source]#

lmflow.tokenization.hf_text_regression_model.blocking(token_dict: Dict, block_size: int, model_max_length: int, pad_token_id: int, padding_side: str, truncation_side: str = 'right') → Dict[source]#

lmflow.tokenization.hf_text_regression_model.blocking_text_to_textlist(token_dict: Dict, block_size: int, model_max_length: int, pad_token_id: int, padding_side: str, truncation_side: str = 'right') → Dict[source]#

lmflow.tokenization.hf_text_regression_model.paired_conversation_tokenize_function(examples, data_args: lmflow.args.DatasetArguments, tokenizer: transformers.PreTrainedTokenizer | transformers.PreTrainedTokenizerFast, column_names, conversation_template: lmflow.utils.conversation_template.ConversationTemplate) → Dict[source]#

lmflow.tokenization.hf_text_regression_model.conversation_tokenize_function(examples, data_args: lmflow.args.DatasetArguments, tokenizer: transformers.PreTrainedTokenizer | transformers.PreTrainedTokenizerFast, column_names, conversation_template: lmflow.utils.conversation_template.ConversationTemplate) → Dict[source]#: Handels conversation datasets tokenization

lmflow.tokenization.hf_text_regression_model.tokenize_function(examples, data_args: lmflow.args.DatasetArguments, tokenizer: transformers.PreTrainedTokenizer | transformers.PreTrainedTokenizerFast, column_names, label_columns, tokenized_column_order, add_special_tokens, use_truncation) → Dict[source]#: Handels text_only and text2text datasets tokenization

lmflow.tokenization.hf_text_regression_model.text_to_textlist_tokenize_function(examples, data_args: lmflow.args.DatasetArguments, tokenizer: transformers.PreTrainedTokenizer | transformers.PreTrainedTokenizerFast, column_names, add_special_tokens, use_truncation) → Dict[source]#: For rm inference, and don’t need attn mask and labels. NOTE: input_ids here refers to the tokenized input_ids of the input and output