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Commit 727a79b3 authored by thomwolf's avatar thomwolf
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added TF2 model and tests - updated templates

parent 8fda532c
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templates/adding_a_new_model/modeling_tf_xxx.py
View file @ 727a79b3
......@@ -26,6 +26,8 @@ import logging
import math
import os
import sys
import copy
import itertools
from io import open
import numpy as np
......
templates/adding_a_new_model/modeling_xxx.py
View file @ 727a79b3
......@@ -25,6 +25,8 @@ import logging
import math
import os
import sys
import copy
import itertools
from io import open
import torch
......
transformers/__init__.py
View file @ 727a79b3
......@@ -158,6 +158,9 @@ if is_tf_available():
TFCTRLLMHeadModel,
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_t5 import (TFT5PreTrainedModel, TFT5Model, TFT5WithLMHeadModel,
TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP)
# TF 2.0 <=> PyTorch conversion utilities
from .modeling_tf_pytorch_utils import (convert_tf_weight_name_to_pt_weight_name,
load_pytorch_checkpoint_in_tf2_model,
......
transformers/configuration_auto.py
View file @ 727a79b3
......@@ -27,6 +27,7 @@ from .configuration_xlm import XLMConfig
from .configuration_roberta import RobertaConfig
from .configuration_distilbert import DistilBertConfig
from .configuration_ctrl import CTRLConfig
from .configuration_t5 import T5Config
logger = logging.getLogger(__name__)
......@@ -64,6 +65,7 @@ class AutoConfig(object):
The configuration class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `t5`: T5Config (T5 model)
- contains `distilbert`: DistilBertConfig (DistilBERT model)
- contains `bert`: BertConfig (Bert model)
- contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
......@@ -114,7 +116,9 @@ class AutoConfig(object):
assert unused_kwargs == {'foo': False}
"""
if 'distilbert' in pretrained_model_name_or_path:
if 't5' in pretrained_model_name_or_path:
return T5Config.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'distilbert' in pretrained_model_name_or_path:
return DistilBertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'roberta' in pretrained_model_name_or_path:
return RobertaConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
......
transformers/configuration_t5.py
View file @ 727a79b3
......@@ -27,8 +27,7 @@ from .configuration_utils import PretrainedConfig
logger = logging.getLogger(__name__)
T5_PRETRAINED_CONFIG_ARCHIVE_MAP = {
't5-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-uncased-config.json",
't5-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-uncased-config.json",
't5-small': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-config.json",
}
......
transformers/modeling_t5.py
View file @ 727a79b3
......@@ -41,8 +41,7 @@ logger = logging.getLogger(__name__)
# for the pretrained weights provided with the models
####################################################
T5_PRETRAINED_MODEL_ARCHIVE_MAP = {
't5-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-uncased-pytorch_model.bin",
't5-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-uncased-pytorch_model.bin",
't5-small': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-pytorch_model.bin",
}
####################################################
......@@ -442,7 +441,7 @@ class T5PreTrainedModel(PreTrainedModel):
if isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(factor*1.0)
elif isinstance(module, T5Model):
elif isinstance(module, (T5Model, T5WithLMHeadModel)):
# Mesh TensorFlow embeddings initialization
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624
module.shared.weight.data.normal_(mean=0.0, std=factor*1.0)
......@@ -502,11 +501,10 @@ class T5Stack(T5PreTrainedModel):
# ourselves in which case we just need to make it broadcastable to all heads.
if attention_mask.dim() == 3:
extended_attention_mask = attention_mask[:, None, :, :]
elif attention_mask.dim() == 2:
# Provided a padding mask of dimensions [batch_size, seq_length]
# - if the model is a decoder, apply a causal mask in addition to the padding mask
# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
if attention_mask.dim() == 2:
if self.config.is_decoder:
seq_ids = torch.arange(seq_length, device=hidden_states.device)
causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
......@@ -593,7 +591,7 @@ class T5Stack(T5PreTrainedModel):
T5_START_DOCSTRING = r""" The T5 model was proposed in
`Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer`_
by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.
It's an encoder decoder pre-trained transformer.
It's an encoder decoder transformer pre-trained in a text-to-text denoising generative setting.
This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and
refer to the PyTorch documentation for all matter related to general usage and behavior.
......@@ -634,16 +632,13 @@ T5_INPUTS_DOCSTRING = r"""
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1]``.
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
Mask to nullify selected heads of the self-attention modules.
Mask values selected in ``[0, 1]``:
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
"""
@add_start_docstrings("The bare single stack (encoder or decoder) of a T5 Model transformer outputting raw hidden-states"
@add_start_docstrings("The bare T5 Model transformer outputting raw hidden-states"
"without any specific head on top.",
T5_START_DOCSTRING, T5_INPUTS_DOCSTRING)
class T5Model(T5PreTrainedModel):
......@@ -661,8 +656,8 @@ class T5Model(T5PreTrainedModel):
Examples::
tokenizer = T5Tokenizer.from_pretrained('t5-base-uncased')
model = T5Model.from_pretrained('t5-base-uncased')
tokenizer = T5Tokenizer.from_pretrained('t5-small')
model = T5Model.from_pretrained('t5-small')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
......@@ -752,8 +747,8 @@ class T5WithLMHeadModel(T5PreTrainedModel):
Examples::
tokenizer = T5Tokenizer.from_pretrained('t5-base-uncased')
model = T5WithLMHeadModel.from_pretrained('t5-base-uncased')
tokenizer = T5Tokenizer.from_pretrained('t5-small')
model = T5WithLMHeadModel.from_pretrained('t5-small')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids, lm_labels=input_ids)
loss, prediction_scores = outputs[:2]
......@@ -763,31 +758,73 @@ class T5WithLMHeadModel(T5PreTrainedModel):
super(T5WithLMHeadModel, self).__init__(config)
self.model_dim = config.d_model
self.transformer = T5Model(config)
self.shared = nn.Embedding(config.vocab_size, config.d_model)
encoder_config = copy.deepcopy(config)
self.encoder = T5Stack(encoder_config)
decoder_config = copy.deepcopy(config)
decoder_config.is_decoder = True
self.decoder = T5Stack(decoder_config)
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
self.init_weights()
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, new_embeddings):
self.shared = new_embeddings
def get_output_embeddings(self):
return self.lm_head
def forward(self, **kwargs):
# keyword arguments come in 3 flavors: encoder-specific (prefixed by
# `encoder_`), decoder-specific (prefixed by `decoder_`) and those
# that apply to the model as whole.
# We let the specific kwargs override the common ones in case of conflict.
lm_labels = kwargs.pop('decoder_lm_labels', None)
outputs = self.transformer(**kwargs)
sequence_output = outputs[0]
kwargs_common = dict((k, v) for k, v in kwargs.items()
if not k.startswith("encoder_") and not k.startswith("decoder_"))
kwargs_encoder = kwargs_common.copy()
kwargs_decoder = kwargs_common.copy()
kwargs_encoder.update(dict((k[len("encoder_"):], v) for k, v in kwargs.items() if k.startswith("encoder_")))
kwargs_decoder.update(dict((k[len("decoder_"):], v) for k, v in kwargs.items() if k.startswith("decoder_")))
# Encode if needed (training, first prediction pass)
encoder_hidden_states = kwargs_encoder.pop("hidden_states", None)
if encoder_hidden_states is None:
encoder_inputs_ids = kwargs_encoder.pop("input_ids")
hidden_states = self.shared(encoder_inputs_ids) # Convert inputs in embeddings
encoder_outputs = self.encoder(hidden_states, **kwargs_encoder)
encoder_hidden_states = encoder_outputs[0]
else:
encoder_outputs = ()
# Decode
decoder_inputs_ids = kwargs_decoder.pop("input_ids")
hidden_states = self.shared(decoder_inputs_ids) # Convert inputs in embeddings
kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states
kwargs_decoder["encoder_attention_mask"] = kwargs_encoder.get("attention_mask", None)
decoder_outputs = self.decoder(hidden_states, **kwargs_decoder)
sequence_output = decoder_outputs[0]
# Rescale output before projecting on vocab
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
sequence_output = sequence_output * (self.model_dim ** -0.5)
lm_logits = self.lm_head(sequence_output)
outputs = (lm_logits,) + outputs[1:] # Add hidden states and attention if they are here
decoder_outputs = (lm_logits,) + decoder_outputs[1:] # Add hidden states and attention if they are here
if lm_labels is not None:
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = lm_labels[..., 1:].contiguous()
loss_fct = CrossEntropyLoss(ignore_index=-1)
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1))
outputs = (loss,) + outputs # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
decoder_outputs = (loss,) + decoder_outputs # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
return outputs # (lm_loss), lm_logits, (hidden_states), (attentions)
return decoder_outputs + encoder_outputs
transformers/modeling_tf_pytorch_utils.py
View file @ 727a79b3
......@@ -156,7 +156,7 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
e.args += (symbolic_weight.shape, array.shape)
raise e
logger.info("Initialize TF weight {}".format(symbolic_weight.name))
# logger.warning("Initialize TF weight {}".format(symbolic_weight.name))
weight_value_tuples.append((symbolic_weight, array))
all_pytorch_weights.discard(name)
......@@ -269,7 +269,7 @@ def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=F
e.args += (pt_weight.shape, array.shape)
raise e
logger.info("Initialize PyTorch weight {}".format(pt_weight_name))
# logger.warning("Initialize PyTorch weight {}".format(pt_weight_name))
new_pt_params_dict[pt_weight_name] = torch.from_numpy(array)
loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array)
......
transformers/modeling_tf_t5.py
View file @ 727a79b3
This diff is collapsed.
transformers/modeling_utils.py
View file @ 727a79b3
......@@ -160,7 +160,6 @@ class PreTrainedModel(nn.Module):
base_model.vocab_size = new_num_tokens
# Tie weights again if needed
if hasattr(self, 'tie_weights'):
self.tie_weights()
return model_embeds
......@@ -458,8 +457,7 @@ class PreTrainedModel(nn.Module):
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
model.__class__.__name__, "\n\t".join(error_msgs)))
if hasattr(model, 'tie_weights'):
model.tie_weights() # make sure word embedding weights are still tied
model.tie_weights() # make sure word embedding weights are still tied if needed
# Set model in evaluation mode to desactivate DropOut modules by default
model.eval()
......
transformers/tests/modeling_tf_common_test.py
View file @ 727a79b3
......@@ -69,6 +69,7 @@ class TFCommonTestCases:
test_torchscript = True
test_pruning = True
test_resize_embeddings = True
is_encoder_decoder = False
def test_initialization(self):
pass
......@@ -156,6 +157,10 @@ class TFCommonTestCases:
def test_compile_tf_model(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if self.is_encoder_decoder:
input_ids = {'decoder_input_ids': tf.keras.Input(batch_shape=(2, 2000), name='decoder_input_ids', dtype='int32'),
'encoder_input_ids': tf.keras.Input(batch_shape=(2, 2000), name='encoder_input_ids', dtype='int32')}
else:
input_ids = tf.keras.Input(batch_shape=(2, 2000), name='input_ids', dtype='int32')
optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
......@@ -189,7 +194,7 @@ class TFCommonTestCases:
outputs_dict = model(inputs_dict)
inputs_keywords = copy.deepcopy(inputs_dict)
input_ids = inputs_keywords.pop('input_ids')
input_ids = inputs_keywords.pop('input_ids', inputs_keywords.pop('decoder_input_ids'))
outputs_keywords = model(input_ids, **inputs_keywords)
output_dict = outputs_dict[0].numpy()
......@@ -216,12 +221,24 @@ class TFCommonTestCases:
self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
out_len = len(outputs)
if self.is_encoder_decoder:
self.assertEqual(out_len % 2, 0)
decoder_attentions = outputs[(out_len // 2)-1]
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, False)
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads,
self.model_tester.seq_length,
self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
# Check attention is always last and order is fine
config.output_attentions = True
config.output_hidden_states = True
model = model_class(config)
outputs = model(inputs_dict)
self.assertEqual(out_len+1, len(outputs))
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, True)
......
transformers/tests/modeling_tf_t5_test.py
View file @ 727a79b3
......@@ -26,7 +26,7 @@ from .configuration_common_test import ConfigTester
from transformers import T5Config, is_tf_available
if False: # is_tf_available():
if is_tf_available():
import tensorflow as tf
from transformers.modeling_tf_t5 import (TFT5Model, TFT5WithLMHeadModel,TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
......@@ -35,7 +35,8 @@ else:
class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFT5Model, TFT5WithLMHeadModel) if False else () # is_tf_available() else ()
is_encoder_decoder = True
all_model_classes = (TFT5Model, TFT5WithLMHeadModel) if is_tf_available() else ()
class TFT5ModelTester(object):
......@@ -45,22 +46,16 @@ class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
n_positions=14,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
d_ff=37,
relative_attention_num_buckets=8,
dropout_rate=0.1,
initializer_factor=0.002,
scope=None,
):
self.parent = parent
......@@ -68,22 +63,16 @@ class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.n_positions = n_positions
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.d_ff = d_ff
self.relative_attention_num_buckets = relative_attention_num_buckets
self.dropout_rate = dropout_rate
self.initializer_factor = initializer_factor
self.scope = scope
def prepare_config_and_inputs(self):
......@@ -93,61 +82,53 @@ class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
if self.use_input_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = T5Config(
vocab_size_or_config_json_file=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def create_and_check_t5_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
n_positions=self.n_positions,
d_model=self.hidden_size,
d_ff=self.d_ff,
d_kv=self.hidden_size // self.num_attention_heads,
num_layers=self.num_hidden_layers,
num_heads=self.num_attention_heads,
relative_attention_num_buckets=self.relative_attention_num_buckets,
dropout_rate=self.dropout_rate,
initializer_factor=self.initializer_factor)
return (config, input_ids, input_mask, token_labels)
def create_and_check_t5_model(self, config, input_ids, input_mask, token_labels):
model = TFT5Model(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
sequence_output, pooled_output = model(inputs)
inputs = [input_ids, input_mask]
sequence_output, pooled_output = model(inputs)
inputs = {'encoder_input_ids': input_ids,
'decoder_input_ids': input_ids,
'decoder_attention_mask': input_mask}
encoder_output, decoder_output = model(inputs)
sequence_output, pooled_output = model(input_ids)
encoder_output, decoder_output = model(input_ids,
decoder_attention_mask=input_mask,
encoder_input_ids=input_ids)
result = {
"sequence_output": sequence_output.numpy(),
"pooled_output": pooled_output.numpy(),
"encoder_output": encoder_output.numpy(),
"decoder_output": decoder_output.numpy(),
}
self.parent.assertListEqual(
list(result["sequence_output"].shape),
list(result["encoder_output"].shape),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(
list(result["decoder_output"].shape),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(list(result["pooled_output"].shape), [self.batch_size, self.hidden_size])
def create_and_check_t5_with_lm_head(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
def create_and_check_t5_with_lm_head(self, config, input_ids, input_mask, token_labels):
model = TFT5WithLMHeadModel(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
prediction_scores, = model(inputs)
inputs = {'encoder_input_ids': input_ids,
'decoder_input_ids': input_ids,
'decoder_attention_mask': input_mask}
prediction_scores, decoder_output = model(inputs)
result = {
"prediction_scores": prediction_scores.numpy(),
}
......@@ -158,14 +139,15 @@ class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, token_type_ids, input_mask,
sequence_labels, token_labels, choice_labels) = config_and_inputs
inputs_dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
(config, input_ids, input_mask, token_labels) = config_and_inputs
inputs_dict = {'encoder_input_ids': input_ids,
'decoder_input_ids': input_ids,
'decoder_attention_mask': input_mask}
return config, inputs_dict
def setUp(self):
self.model_tester = TFT5ModelTest.TFT5ModelTester(self)
self.config_tester = ConfigTester(self, config_class=T5Config, hidden_size=37)
self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37)
def test_config(self):
self.config_tester.run_common_tests()
......@@ -181,7 +163,7 @@ class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in ['t5-base']:
for model_name in ['t5-small']:
model = TFT5Model.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
......
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