def create_and_check_squeezebert_model(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = SqueezeBertModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, input_mask)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def test_model_from_pretrained(self):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = SqueezeBertModel.from_pretrained(model_name)
self.assertIsNotNone(model)
from transformers import SqueezeBertTokenizer, SqueezeBertModel
import torch
tokenizer = SqueezeBertTokenizer.from_pretrained(
'squeezebert/squeezebert-mnli-headless')
model = SqueezeBertModel.from_pretrained(
'squeezebert/squeezebert-mnli-headless')
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state