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Deberta_v2 tf (huggingface#13120)
* Deberta_v2 tf * added new line at the end of file, make style * +V2, typo * remove never executed branch of code * rm cmnt and fixed typo in url filter * cleanup according to review comments * added #Copied from
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‎docs/source/index.rst

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@@ -351,7 +351,7 @@ Flax), PyTorch, and/or TensorFlow.
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+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
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| DeBERTa | ✅ | ✅ | ✅ | ✅ | ❌ |
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+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
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| DeBERTa-v2 | ✅ | ❌ | ✅ | ❌ | ❌ |
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| DeBERTa-v2 | ✅ | ❌ | ✅ | ✅ | ❌ |
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+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
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| DeiT | ❌ | ❌ | ✅ | ❌ | ❌ |
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+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+

‎docs/source/model_doc/deberta_v2.rst

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@@ -58,7 +58,8 @@ New in v2:
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- **900M model & 1.5B model** Two additional model sizes are available: 900M and 1.5B, which significantly improves the
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performance of downstream tasks.
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This model was contributed by `DeBERTa <https://huggingface.co/DeBERTa>`__. The original code can be found `here
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This model was contributed by `DeBERTa <https://huggingface.co/DeBERTa>`__. This model TF 2.0 implementation was
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contributed by `kamalkraj <https://huggingface.co/kamalkraj>`__. The original code can be found `here
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<https://github.com/microsoft/DeBERTa>`__.
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@@ -117,3 +118,45 @@ DebertaV2ForQuestionAnswering
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.. autoclass:: transformers.DebertaV2ForQuestionAnswering
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:members: forward
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TFDebertaV2Model
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFDebertaV2Model
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:members: call
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TFDebertaV2PreTrainedModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFDebertaV2PreTrainedModel
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:members: call
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TFDebertaV2ForMaskedLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFDebertaV2ForMaskedLM
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:members: call
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TFDebertaV2ForSequenceClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFDebertaV2ForSequenceClassification
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:members: call
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TFDebertaV2ForTokenClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFDebertaV2ForTokenClassification
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:members: call
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TFDebertaV2ForQuestionAnswering
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFDebertaV2ForQuestionAnswering
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:members: call

‎src/transformers/__init__.py

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"TFDebertaPreTrainedModel",
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]
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)
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_import_structure["models.deberta_v2"].extend(
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[
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"TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
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"TFDebertaV2ForMaskedLM",
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"TFDebertaV2ForQuestionAnswering",
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"TFDebertaV2ForSequenceClassification",
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"TFDebertaV2ForTokenClassification",
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"TFDebertaV2Model",
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"TFDebertaV2PreTrainedModel",
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]
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)
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_import_structure["models.distilbert"].extend(
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[
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"TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
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TFDebertaModel,
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TFDebertaPreTrainedModel,
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)
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from .models.deberta_v2 import (
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TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
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TFDebertaV2ForMaskedLM,
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TFDebertaV2ForQuestionAnswering,
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TFDebertaV2ForSequenceClassification,
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TFDebertaV2ForTokenClassification,
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TFDebertaV2Model,
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TFDebertaV2PreTrainedModel,
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)
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from .models.distilbert import (
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TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
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TFDistilBertForMaskedLM,

‎src/transformers/models/auto/modeling_tf_auto.py

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TF_MODEL_MAPPING_NAMES = OrderedDict(
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[
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# Base model mapping
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("deberta-v2", "TFDebertaV2Model"),
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("deberta", "TFDebertaModel"),
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("rembert", "TFRemBertModel"),
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("roformer", "TFRoFormerModel"),
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TF_MODEL_FOR_MASKED_LM_MAPPING_NAMES = OrderedDict(
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[
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# Model for Masked LM mapping
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("deberta-v2", "TFDebertaV2ForMaskedLM"),
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("deberta", "TFDebertaForMaskedLM"),
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("rembert", "TFRemBertForMaskedLM"),
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("roformer", "TFRoFormerForMaskedLM"),
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TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
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[
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# Model for Sequence Classification mapping
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("deberta-v2", "TFDebertaV2ForSequenceClassification"),
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("deberta", "TFDebertaForSequenceClassification"),
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("rembert", "TFRemBertForSequenceClassification"),
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("roformer", "TFRoFormerForSequenceClassification"),
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TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
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[
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# Model for Question Answering mapping
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("deberta-v2", "TFDebertaV2ForQuestionAnswering"),
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("deberta", "TFDebertaForQuestionAnswering"),
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("rembert", "TFRemBertForQuestionAnswering"),
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("roformer", "TFRoFormerForQuestionAnswering"),
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TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
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[
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# Model for Token Classification mapping
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("deberta-v2", "TFDebertaV2ForTokenClassification"),
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("deberta", "TFDebertaForTokenClassification"),
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("rembert", "TFRemBertForTokenClassification"),
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("roformer", "TFRoFormerForTokenClassification"),

‎src/transformers/models/deberta_v2/__init__.py

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from typing import TYPE_CHECKING
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from ...file_utils import _LazyModule, is_torch_available
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from ...file_utils import _LazyModule, is_tf_available, is_torch_available
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_import_structure = {
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"configuration_deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config"],
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"tokenization_deberta_v2": ["DebertaV2Tokenizer"],
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}
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if is_tf_available():
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_import_structure["modeling_tf_deberta_v2"] = [
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"TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
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"TFDebertaV2ForMaskedLM",
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"TFDebertaV2ForQuestionAnswering",
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"TFDebertaV2ForSequenceClassification",
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"TFDebertaV2ForTokenClassification",
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"TFDebertaV2Model",
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"TFDebertaV2PreTrainedModel",
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]
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if is_torch_available():
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_import_structure["modeling_deberta_v2"] = [
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"DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
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from .configuration_deberta_v2 import DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaV2Config
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from .tokenization_deberta_v2 import DebertaV2Tokenizer
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if is_tf_available():
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from .modeling_tf_deberta_v2 import (
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TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
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TFDebertaV2ForMaskedLM,
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TFDebertaV2ForQuestionAnswering,
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TFDebertaV2ForSequenceClassification,
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TFDebertaV2ForTokenClassification,
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TFDebertaV2Model,
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TFDebertaV2PreTrainedModel,
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)
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if is_torch_available():
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from .modeling_deberta_v2 import (
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DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,

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