def __init__(
self,
model: str = "distilbert-base-uncased-distilled-squad",
tokenizer: str = "distilbert-base-uncased",
context_window_size: int = 30,
use_gpu: int = 0,
n_best_per_passage: int = 2,
no_answer: bool = True
):
"""
Load a QA model from Transformers.
Available models include:
- distilbert-base-uncased-distilled-squad
- bert-large-cased-whole-word-masking-finetuned-squad
- bert-large-uncased-whole-word-masking-finetuned-squad
See https://huggingface.co/models for full list of available QA models
:param model: name of the model
:param tokenizer: name of the tokenizer (usually the same as model)
:param context_window_size: num of chars (before and after the answer) to return as "context" for each answer.
The context usually helps users to understand if the answer really makes sense.
:param use_gpu: < 0 -> use cpu
>= 0 -> ordinal of the gpu to use
:param n_best_per_passage: num of best answers to take into account for each passage
:param no_answer: True -> Hugging Face model could return an "impossible"/"empty" answer (i.e. when there is an unanswerable question)
False -> otherwise
"""
self.model = pipeline('question-answering', model=model, tokenizer=tokenizer, device=use_gpu)
self.context_window_size = context_window_size
self.n_best_per_passage = n_best_per_passage
self.no_answer = no_answer
def __init__(self,
model: str = "distilbert-base-uncased-distilled-squad",
tokenizer: Optional[str] = None,
context_window_size: int = 70,
use_gpu: int = 0,
top_k_per_candidate: int = 4,
return_no_answers: bool = True,
max_seq_len: int = 256,
doc_stride: int = 128):
"""
Load a QA model from Transformers.
Available models include:
- distilbert-base-uncased-distilled-squad
- bert-large-cased-whole-word-masking-finetuned-squad
- bert-large-uncased-whole-word-masking-finetuned-squad
See https://huggingface.co/models for full list of available QA models
:param model: name of the model
:param tokenizer: name of the tokenizer (usually the same as model)
:param context_window_size: num of chars (before and after the answer) to return as "context" for each answer.
The context usually helps users to understand if the answer really makes sense.
:param use_gpu: < 0 -> use cpu
>= 0 -> ordinal of the gpu to use
:param top_k_per_candidate: How many answers to extract for each candidate doc that is coming from the retriever (might be a long text).
Note: - This is not the number of "final answers" you will receive
(see `top_k` in TransformersReader.predict() or Finder.get_answers() for that)
- Can includes no_answer in the sorted list of predictions
:param return_no_answers: True -> Hugging Face model could return an "impossible"/"empty" answer (i.e. when there is an unanswerable question)
False -> otherwise
no_answer_boost is unfortunately not available with TransformersReader. If you would like to
set no_answer_boost, use a FARMReader
:param max_seq_len: max sequence length of one input text for the model
:param doc_stride: length of striding window for splitting long texts (used if len(text) > max_seq_len)
"""
self.model = pipeline('question-answering',
model=model,
tokenizer=tokenizer,
device=use_gpu)
self.context_window_size = context_window_size
self.top_k_per_candidate = top_k_per_candidate
self.return_no_answers = return_no_answers
self.max_seq_len = max_seq_len
self.doc_stride = doc_stride
def __init__(self,
model: str = "distilbert-base-uncased-distilled-squad",
tokenizer: str = "distilbert-base-uncased",
context_window_size: int = 30,
use_gpu: int = 0,
top_k_per_candidate: int = 4,
no_answer: bool = True):
"""
Load a QA model from Transformers.
Available models include:
- distilbert-base-uncased-distilled-squad
- bert-large-cased-whole-word-masking-finetuned-squad
- bert-large-uncased-whole-word-masking-finetuned-squad
See https://huggingface.co/models for full list of available QA models
:param model: name of the model
:param tokenizer: name of the tokenizer (usually the same as model)
:param context_window_size: num of chars (before and after the answer) to return as "context" for each answer.
The context usually helps users to understand if the answer really makes sense.
:param use_gpu: < 0 -> use cpu
>= 0 -> ordinal of the gpu to use
:param top_k_per_candidate: How many answers to extract for each candidate doc that is coming from the retriever (might be a long text).
Note: - This is not the number of "final answers" you will receive
(see `top_k` in TransformersReader.predict() or Finder.get_answers() for that)
- Can includes no_answer in the sorted list of predictions
:param no_answer: True -> Hugging Face model could return an "impossible"/"empty" answer (i.e. when there is an unanswerable question)
False -> otherwise
"""
self.model = pipeline('question-answering',
model=model,
tokenizer=tokenizer,
device=use_gpu)
self.context_window_size = context_window_size
self.top_k_per_candidate = top_k_per_candidate
self.no_answer = no_answer