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Redo changing of inti

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timoeller 2020-02-24 16:23:13 +01:00
parent f681026a56
commit d15448f60a
1 changed files with 43 additions and 43 deletions
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86
haystack/reader/farm.py
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@ -23,6 +23,49 @@ class FARMReader:
- fine-tune the model on QA data via train()
"""
def __init__(
self,
model_name_or_path,
context_window_size=30,
batch_size=50,
use_gpu=True,
no_ans_boost=None,
n_candidates_per_paragraph=1):
"""
:param model_name_or_path: directory of a saved model or the name of a public model:
- 'bert-base-cased'
- 'deepset/bert-base-cased-squad2'
- 'deepset/bert-base-cased-squad2'
- 'distilbert-base-uncased-distilled-squad'
....
See https://huggingface.co/models for full list of available models.
:param context_window_size: The size, in characters, of the window around the answer span that is used when displaying the context around the answer.
:param batch_size: Number of samples the model receives in one batch for inference
Memory consumption is much lower in inference mode. Recommendation: increase the batch size to a value so only a single batch is used.
:param use_gpu: Whether to use GPU (if available)
:param no_ans_boost: How much the no_answer logit is boosted/increased.
Possible values: None (default) = disable returning "no answer" predictions
Negative = lower chance of "no answer" being predicted
Positive = increase chance of "no answer"
:param n_candidates_per_paragraph: How many candidate answers are extracted per text sequence that the model can process at once (depends on `max_seq_len`).
Note: - This is not the number of "final answers" you will receive
(see `top_k` in FARMReader.predict() or Finder.get_answers() for that)
- FARM includes no_answer in the sorted list of predictions
"""
if no_ans_boost is None:
no_ans_boost = 0
self.return_no_answers = False
else:
self.return_no_answers = True
self.n_candidates_per_paragraph = n_candidates_per_paragraph
self.inferencer = Inferencer.load(model_name_or_path, batch_size=batch_size, gpu=use_gpu, task_type="question_answering")
self.inferencer.model.prediction_heads[0].context_window_size = context_window_size
self.inferencer.model.prediction_heads[0].no_ans_boost = no_ans_boost
self.inferencer.model.prediction_heads[0].n_best = n_candidates_per_paragraph + 1 # including possible no_answer
def train(self, data_dir, train_filename, dev_filename=None, test_file_name=None,
use_gpu=True, batch_size=10, n_epochs=2, learning_rate=1e-5,
max_seq_len=256, warmup_proportion=0.2, dev_split=0.1, evaluate_every=300, save_dir=None):
@ -104,49 +147,6 @@ class FARMReader:
self.inferencer.model = trainer.train()
self.save(save_dir)
def __init__(
self,
model_name_or_path,
context_window_size=30,
batch_size=50,
use_gpu=True,
no_ans_boost=None,
n_candidates_per_paragraph=1):
"""
:param model_name_or_path: directory of a saved model or the name of a public model:
- 'bert-base-cased'
- 'deepset/bert-base-cased-squad2'
- 'deepset/bert-base-cased-squad2'
- 'distilbert-base-uncased-distilled-squad'
....
See https://huggingface.co/models for full list of available models.
:param context_window_size: The size, in characters, of the window around the answer span that is used when displaying the context around the answer.
:param batch_size: Number of samples the model receives in one batch for inference
Memory consumption is much lower in inference mode. Recommendation: increase the batch size to a value so only a single batch is used.
:param use_gpu: Whether to use GPU (if available)
:param no_ans_boost: How much the no_answer logit is boosted/increased.
Possible values: None (default) = disable returning "no answer" predictions
Negative = lower chance of "no answer" being predicted
Positive = increase chance of "no answer"
:param n_candidates_per_paragraph: How many candidate answers are extracted per text sequence that the model can process at once (depends on `max_seq_len`).
Note: - This is not the number of "final answers" you will receive
(see `top_k` in FARMReader.predict() or Finder.get_answers() for that)
- FARM includes no_answer in the sorted list of predictions
"""
if no_ans_boost is None:
no_ans_boost = 0
self.return_no_answers = False
else:
self.return_no_answers = True
self.n_candidates_per_paragraph = n_candidates_per_paragraph
self.inferencer = Inferencer.load(model_name_or_path, batch_size=batch_size, gpu=use_gpu, task_type="question_answering")
self.inferencer.model.prediction_heads[0].context_window_size = context_window_size
self.inferencer.model.prediction_heads[0].no_ans_boost = no_ans_boost
self.inferencer.model.prediction_heads[0].n_best = n_candidates_per_paragraph + 1 # including possible no_answer
def save(self, directory):
logger.info(f"Saving reader model to {directory}")
self.inferencer.model.save(directory)