unitorch.models.t5¤
T5Processor¤
Bases: HfTextGenerationProcessor
Processor for T5-based text generation models.
Initializes the T5Processor.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
vocab_path |
str
|
Path to the vocabulary file. |
required |
special_input_ids |
Optional[Dict]
|
Special input IDs. Defaults to an empty dictionary. |
dict()
|
max_seq_length |
Optional[int]
|
Maximum sequence length. Defaults to 128. |
128
|
max_gen_seq_length |
Optional[int]
|
Maximum generated sequence length. Defaults to 48. |
48
|
Source code in src/unitorch/models/t5/processing.py
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|
T5ForGeneration¤
Bases: GenericModel
T5 model for text generation tasks.
Initializes the T5ForGeneration model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
config_path |
str
|
Path to the configuration file. |
required |
gradient_checkpointing |
Optional[bool]
|
Whether to use gradient checkpointing. Defaults to False. |
False
|
Source code in src/unitorch/models/t5/modeling.py
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|
forward ¤
forward(
input_ids: Tensor,
attention_mask: Tensor,
decoder_input_ids: Tensor,
decoder_attention_mask: Tensor,
)
Performs forward pass of the T5ForGeneration model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
input_ids |
Tensor
|
Tensor of input token IDs. |
required |
attention_mask |
Tensor
|
Tensor of attention mask. |
required |
decoder_input_ids |
Tensor
|
Tensor of decoder input token IDs. |
required |
decoder_attention_mask |
Tensor
|
Tensor of decoder attention mask. |
required |
Returns:
Type | Description |
---|---|
Tensor
|
The model's logits. |
Source code in src/unitorch/models/t5/modeling.py
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|
generate ¤
generate(
input_ids: Tensor,
num_beams: Optional[int] = 5,
decoder_start_token_id: Optional[int] = 0,
decoder_end_token_id: Optional[
Union[int, List[int]]
] = 1,
num_return_sequences: Optional[int] = 1,
min_gen_seq_length: Optional[int] = 0,
max_gen_seq_length: Optional[int] = 48,
repetition_penalty: Optional[float] = 1.0,
no_repeat_ngram_size: Optional[int] = 0,
early_stopping: Optional[bool] = True,
length_penalty: Optional[float] = 1.0,
num_beam_groups: Optional[int] = 1,
diversity_penalty: Optional[float] = 0.0,
do_sample: Optional[bool] = False,
temperature: Optional[float] = 1.0,
top_k: Optional[int] = 50,
top_p: Optional[float] = 1.0,
)
Generates sequences using the T5ForGeneration model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
input_ids |
Tensor
|
The input token IDs. |
required |
num_beams |
int
|
The number of beams for beam search. Defaults to 5. |
5
|
decoder_start_token_id |
int
|
The decoder's start token ID. Defaults to 2. |
0
|
decoder_end_token_id |
int or List[int]
|
The decoder's end token ID. Defaults to 2. |
1
|
num_return_sequences |
int
|
The number of generated sequences to return. Defaults to 1. |
1
|
min_gen_seq_length |
int
|
The minimum length of the generated sequences. Defaults to 0. |
0
|
max_gen_seq_length |
int
|
The maximum length of the generated sequences. Defaults to 48. |
48
|
repetition_penalty |
float
|
The repetition penalty. Defaults to 1.0. |
1.0
|
no_repeat_ngram_size |
int
|
The size of n-grams to avoid repeating. Defaults to 0. |
0
|
early_stopping |
bool
|
Whether to stop generation early. Defaults to True. |
True
|
length_penalty |
float
|
The length penalty. Defaults to 1.0. |
1.0
|
num_beam_groups |
int
|
The number of beam groups for diverse beam search. Defaults to 1. |
1
|
diversity_penalty |
float
|
The diversity penalty. Defaults to 0.0. |
0.0
|
do_sample |
bool
|
Whether to use sampling for generation. Defaults to False. |
False
|
temperature |
float
|
The temperature for sampling. Defaults to 1.0. |
1.0
|
top_k |
int
|
The value for top-k sampling. Defaults to 50. |
50
|
top_p |
float
|
The value for top-p (nucleus) sampling. Defaults to 1.0. |
1.0
|
Returns:
Name | Type | Description |
---|---|---|
GenericOutputs |
The generated sequences and their scores. |
Source code in src/unitorch/models/t5/modeling.py
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|