SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Add multi-class, multi-label and regression to transformers (#11012)

Browse Source 
* add to  bert

* review comments

* Update src/transformers/configuration_utils.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/configuration_utils.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* self.config.problem_type

* fix style

* fix

* fin

* fix

* update doc

* fix

* test

* Test more problem types

* Update src/transformers/configuration_utils.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix

* remove

* fix

* quality

* make fix-copies

* remove test

Co-authored-by: abhishek thakur <abhishekkrthakur@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
This commit is contained in:
abhishek thakur
2021-05-04 08:23:40 +02:00
committed by GitHub
parent 7c622482e8
commit c40c7e213b
30 changed files with 298 additions and 76 deletions
Download Patch File Download Diff File Expand all files Collapse all files

37
tests/test_modeling_common.py
View File

@@ -89,6 +89,7 @@ class ModelTesterMixin:
test_missing_keys = True
test_model_parallel = False
is_encoder_decoder = False
test_sequence_classification_problem_types = False
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = copy.deepcopy(inputs_dict)
@@ -1238,6 +1239,42 @@ class ModelTesterMixin:
model.parallelize()
model.generate(**cast_to_device(inputs_dict, "cuda:0"), num_beams=2)
def test_problem_types(self):
if not self.test_sequence_classification_problem_types:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
problem_types = [
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
{"title": "regression", "num_labels": 1, "dtype": torch.float},
]
for model_class in self.all_model_classes:
if model_class not in get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
continue
for problem_type in problem_types:
with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"):
config.problem_type = problem_type["title"]
config.num_labels = problem_type["num_labels"]
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
if problem_type["num_labels"] > 1:
inputs["labels"] = inputs["labels"].unsqueeze(1).repeat(1, problem_type["num_labels"])
inputs["labels"] = inputs["labels"].to(problem_type["dtype"])
loss = model(**inputs).loss
loss.backward()
global_rng = random.Random()