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https://github.com/huggingface/transformers.git
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c67d1a0259
* TF outputs and test on BERT * Albert to DistilBert * All remaining TF models except T5 * Documentation * One file forgotten * TF outputs and test on BERT * Albert to DistilBert * All remaining TF models except T5 * Documentation * One file forgotten * Add new models and fix issues * Quality improvements * Add T5 * A bit of cleanup * Fix for slow tests * Style
50 lines
1.8 KiB
Python
50 lines
1.8 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from transformers import is_tf_available
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from transformers.testing_utils import require_tf, slow
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if is_tf_available():
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import tensorflow as tf
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import numpy as np
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from transformers import TFCamembertModel
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@require_tf
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class TFCamembertModelIntegrationTest(unittest.TestCase):
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@slow
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def test_output_embeds_base_model(self):
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model = TFCamembertModel.from_pretrained("jplu/tf-camembert-base")
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input_ids = tf.convert_to_tensor(
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[[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]], dtype=tf.int32,
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) # J'aime le camembert !"
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output = model(input_ids)["last_hidden_state"]
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expected_shape = tf.TensorShape((1, 10, 768))
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self.assertEqual(output.shape, expected_shape)
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# compare the actual values for a slice.
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expected_slice = tf.convert_to_tensor(
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[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]], dtype=tf.float32,
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)
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# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
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# camembert.eval()
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# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
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self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4))
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