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add roberta support #1503

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3bac785
add roberta support
BunnyNoBugs Nov 11, 2021
2684253
Update .gitignore
BunnyNoBugs Nov 19, 2021
978509b
Revert "Update .gitignore"
BunnyNoBugs Nov 19, 2021
aa39a0a
add other encoders support
BunnyNoBugs Nov 19, 2021
e4ad3ba
add other models support for training
BunnyNoBugs Nov 25, 2021
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175 changes: 175 additions & 0 deletions deeppavlov/configs/squad/refactor_squad_torch_bert.json
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Original file line number Diff line number Diff line change
@@ -0,0 +1,175 @@
{
"dataset_reader": {
"class_name": "squad_dataset_reader",
"data_path": "{DOWNLOADS_PATH}/squad/"
},
"dataset_iterator": {
"class_name": "squad_iterator",
"seed": 1337,
"shuffle": true
},
"chainer": {
"in": [
"context_raw",
"question_raw"
],
"in_y": [
"ans_raw",
"ans_raw_start"
],
"pipe": [
{
"class_name": "torch_squad_transformers_preprocessor",
"vocab_file": "{TRANSFORMER}",
"do_lower_case": "{LOWERCASE}",
"max_seq_length": 384,
"return_tokens": true,
"in": [
"question_raw",
"context_raw"
],
"out": [
"bert_features",
"subtokens"
]
},
{
"class_name": "squad_bert_mapping",
"do_lower_case": "{LOWERCASE}",
"in": [
"context_raw",
"bert_features",
"subtokens"
],
"out": [
"subtok2chars",
"char2subtoks"
]
},
{
"class_name": "squad_bert_ans_preprocessor",
"do_lower_case": "{LOWERCASE}",
"in": [
"ans_raw",
"ans_raw_start",
"char2subtoks"
],
"out": [
"ans",
"ans_start",
"ans_end"
]
},
{
"class_name": "torch_transformers_squad",
"pretrained_bert": "{TRANSFORMER}",
"save_path": "{MODEL_PATH}/model",
"load_path": "{MODEL_PATH}/model",
"optimizer": "AdamW",
"optimizer_parameters": {
"lr": 2e-05,
"weight_decay": 0.01,
"betas": [
0.9,
0.999
],
"eps": 1e-06
},
"learning_rate_drop_patience": 2,
"learning_rate_drop_div": 2.0,
"in": [
"bert_features"
],
"in_y": [
"ans_start",
"ans_end"
],
"out": [
"ans_start_predicted",
"ans_end_predicted",
"logits"
]
},
{
"class_name": "squad_bert_ans_postprocessor",
"in": [
"ans_start_predicted",
"ans_end_predicted",
"context_raw",
"bert_features",
"subtok2chars",
"subtokens"
],
"out": [
"ans_predicted",
"ans_start_predicted",
"ans_end_predicted"
]
}
],
"out": [
"ans_predicted",
"ans_start_predicted",
"logits"
]
},
"train": {
"show_examples": false,
"evaluation_targets": [
"valid"
],
"log_every_n_batches": 250,
"val_every_n_batches": 500,
"batch_size": 10,
"pytest_max_batches": 2,
"pytest_batch_size": 5,
"validation_patience": 10,
"metrics": [
{
"name": "squad_v1_f1",
"inputs": [
"ans",
"ans_predicted"
]
},
{
"name": "squad_v1_em",
"inputs": [
"ans",
"ans_predicted"
]
},
{
"name": "squad_v2_f1",
"inputs": [
"ans",
"ans_predicted"
]
},
{
"name": "squad_v2_em",
"inputs": [
"ans",
"ans_predicted"
]
}
],
"class_name": "torch_trainer"
},
"metadata": {
"variables": {
"LOWERCASE": true,
"TRANSFORMER": "allenai/longformer-base-4096",
"ROOT_PATH": "~/.deeppavlov",
"DOWNLOADS_PATH": "{ROOT_PATH}/downloads",
"MODELS_PATH": "{ROOT_PATH}/models",
"MODEL_PATH": "{MODELS_PATH}/squad_torch_bert/{TRANSFORMER}"
},
"download": [
{
"url": "http://files.deeppavlov.ai/v1/squad/squad_torch_bert.tar.gz",
"subdir": "{ROOT_PATH}/models"
}
]
}
}
5 changes: 4 additions & 1 deletion deeppavlov/models/preprocessors/squad_preprocessor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -404,7 +404,10 @@ def __call__(self, contexts, bert_features, *args, **kwargs):
subtokens = args[0][batch_counter]
else:
subtokens = features.tokens
context_start = subtokens.index('[SEP]') + 1
if '[SEP]' in subtokens:
context_start = subtokens.index('[SEP]') + 1
else:
context_start = subtokens.index('<s>') + 1
idx = 0
subtok2char: Dict[int, int] = {}
char2subtok: Dict[int, int] = {}
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8 changes: 6 additions & 2 deletions deeppavlov/models/torch_bert/torch_transformers_squad.py
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Original file line number Diff line number Diff line change
Expand Up @@ -123,12 +123,14 @@ def train_on_batch(self, features: List[InputFeatures], y_st: List[List[int]], y
b_input_ids = torch.cat(input_ids, dim=0).to(self.device)
b_input_masks = torch.cat(input_masks, dim=0).to(self.device)
b_input_type_ids = torch.cat(input_type_ids, dim=0).to(self.device)
if any(x in self.pretrained_bert for x in ['roberta', 'distilbert', 'bart', 'longformer']):
b_input_type_ids = b_input_type_ids.unsqueeze(1).expand(-1, b_input_ids.shape[-1])

y_st = [x[0] for x in y_st]
y_end = [x[0] for x in y_end]
b_y_st = torch.from_numpy(np.array(y_st)).to(self.device)
b_y_end = torch.from_numpy(np.array(y_end)).to(self.device)

input_ = {
'input_ids': b_input_ids,
'attention_mask': b_input_masks,
Expand Down Expand Up @@ -184,7 +186,9 @@ def __call__(self, features: List[InputFeatures]) -> Tuple[List[int], List[int],
b_input_ids = torch.cat(input_ids, dim=0).to(self.device)
b_input_masks = torch.cat(input_masks, dim=0).to(self.device)
b_input_type_ids = torch.cat(input_type_ids, dim=0).to(self.device)

if any(x in self.pretrained_bert for x in ['roberta', 'distilbert', 'bart', 'longformer']):
b_input_type_ids = b_input_type_ids.unsqueeze(1).expand(-1, b_input_ids.shape[-1])

input_ = {
'input_ids': b_input_ids,
'attention_mask': b_input_masks,
Expand Down