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01485ceec3
* Adding Flax XLM-RoBERTa * Add Flax to __init__ * Adding doc and dummy objects * Add tests * Add Flax XLM-R models autodoc * Fix tests * Add Flask XLM-RoBERTa to TEST_FILES_WITH_NO_COMMON_TESTS * Update src/transformers/models/xlm_roberta/modeling_flax_xlm_roberta.py Co-authored-by: Suraj Patil <surajp815@gmail.com> * Update tests/xlm_roberta/test_modeling_flax_xlm_roberta.py Co-authored-by: Suraj Patil <surajp815@gmail.com> * Update tests/xlm_roberta/test_modeling_flax_xlm_roberta.py Co-authored-by: Suraj Patil <surajp815@gmail.com> * Remove test on large Flask XLM-RoBERTa * Add tokenizer to the test Co-authored-by: Suraj Patil <surajp815@gmail.com>
48 lines
1.8 KiB
Python
48 lines
1.8 KiB
Python
# coding=utf-8
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# Copyright 2022 The HuggingFace Team. All rights reserved.
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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 AutoTokenizer, is_flax_available
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from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
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if is_flax_available():
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import jax.numpy as jnp
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from transformers import FlaxXLMRobertaModel
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@require_sentencepiece
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@require_tokenizers
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@require_flax
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class FlaxXLMRobertaModelIntegrationTest(unittest.TestCase):
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@slow
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def test_flax_xlm_roberta_base(self):
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model = FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base")
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tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
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text = "The dog is cute and lives in the garden house"
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input_ids = jnp.array([tokenizer.encode(text)])
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expected_output_shape = (1, 12, 768) # batch_size, sequence_length, embedding_vector_dim
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expected_output_values_last_dim = jnp.array(
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[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]
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)
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output = model(input_ids)["last_hidden_state"]
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self.assertEqual(output.shape, expected_output_shape)
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# compare the actual values for a slice of last dim
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self.assertTrue(jnp.allclose(output[:, :, -1], expected_output_values_last_dim, atol=1e-3))
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