Add FastTokenizer to REALM (#15211)
* Remove BertTokenizer abstraction * Add FastTokenizer to REALM * Fix config archive map * Fix copies * Update realm.mdx * Apply suggestions from code review
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Li-Huai (Allan) Lin
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841d979190
@@ -246,7 +246,7 @@ Flax), PyTorch, and/or TensorFlow.
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| ProphetNet | ✅ | ❌ | ✅ | ❌ | ❌ |
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| ProphetNet | ✅ | ❌ | ✅ | ❌ | ❌ |
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| QDQBert | ❌ | ❌ | ✅ | ❌ | ❌ |
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| QDQBert | ❌ | ❌ | ✅ | ❌ | ❌ |
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| RAG | ✅ | ❌ | ✅ | ✅ | ❌ |
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| RAG | ✅ | ❌ | ✅ | ✅ | ❌ |
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| Realm | ✅ | ❌ | ✅ | ❌ | ❌ |
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| Realm | ✅ | ✅ | ✅ | ❌ | ❌ |
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| Reformer | ✅ | ✅ | ✅ | ❌ | ❌ |
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| Reformer | ✅ | ✅ | ✅ | ❌ | ❌ |
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| RemBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
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| RemBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
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| RetriBERT | ✅ | ✅ | ✅ | ❌ | ❌ |
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| RetriBERT | ✅ | ✅ | ✅ | ❌ | ❌ |
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@@ -49,6 +49,11 @@ This model was contributed by [qqaatw](https://huggingface.co/qqaatw). The origi
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- save_vocabulary
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- save_vocabulary
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- batch_encode_candidates
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- batch_encode_candidates
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## RealmTokenizerFast
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[[autodoc]] RealmTokenizerFast
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- batch_encode_candidates
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## RealmRetriever
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## RealmRetriever
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[[autodoc]] RealmRetriever
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[[autodoc]] RealmRetriever
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@@ -419,6 +419,7 @@ else:
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# tokenizers-backed objects
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# tokenizers-backed objects
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if is_tokenizers_available():
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if is_tokenizers_available():
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# Fast tokenizers
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# Fast tokenizers
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_import_structure["models.realm"].append("RealmTokenizerFast")
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_import_structure["models.fnet"].append("FNetTokenizerFast")
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_import_structure["models.fnet"].append("FNetTokenizerFast")
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_import_structure["models.roformer"].append("RoFormerTokenizerFast")
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_import_structure["models.roformer"].append("RoFormerTokenizerFast")
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_import_structure["models.clip"].append("CLIPTokenizerFast")
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_import_structure["models.clip"].append("CLIPTokenizerFast")
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@@ -2542,6 +2543,7 @@ if TYPE_CHECKING:
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from .models.mt5 import MT5TokenizerFast
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from .models.mt5 import MT5TokenizerFast
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from .models.openai import OpenAIGPTTokenizerFast
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from .models.openai import OpenAIGPTTokenizerFast
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from .models.pegasus import PegasusTokenizerFast
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from .models.pegasus import PegasusTokenizerFast
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from .models.realm import RealmTokenizerFast
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from .models.reformer import ReformerTokenizerFast
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from .models.reformer import ReformerTokenizerFast
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from .models.rembert import RemBertTokenizerFast
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from .models.rembert import RemBertTokenizerFast
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from .models.retribert import RetriBertTokenizerFast
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from .models.retribert import RetriBertTokenizerFast
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@@ -942,6 +942,7 @@ SLOW_TO_FAST_CONVERTERS = {
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"MobileBertTokenizer": BertConverter,
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"MobileBertTokenizer": BertConverter,
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"OpenAIGPTTokenizer": OpenAIGPTConverter,
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"OpenAIGPTTokenizer": OpenAIGPTConverter,
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"PegasusTokenizer": PegasusConverter,
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"PegasusTokenizer": PegasusConverter,
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"RealmTokenizer": BertConverter,
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"ReformerTokenizer": ReformerConverter,
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"ReformerTokenizer": ReformerConverter,
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"RemBertTokenizer": RemBertConverter,
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"RemBertTokenizer": RemBertConverter,
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"RetriBertTokenizer": BertConverter,
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"RetriBertTokenizer": BertConverter,
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@@ -25,6 +25,8 @@ _import_structure = {
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"tokenization_realm": ["RealmTokenizer"],
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"tokenization_realm": ["RealmTokenizer"],
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}
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}
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if is_tokenizers_available():
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_import_structure["tokenization_realm_fast"] = ["RealmTokenizerFast"]
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if is_torch_available():
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if is_torch_available():
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_import_structure["modeling_realm"] = [
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_import_structure["modeling_realm"] = [
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@@ -44,6 +46,9 @@ if TYPE_CHECKING:
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from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
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from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
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from .tokenization_realm import RealmTokenizer
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from .tokenization_realm import RealmTokenizer
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if is_tokenizers_available():
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from .tokenization_realm import RealmTokenizerFast
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if is_torch_available():
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if is_torch_available():
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from .modeling_realm import (
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from .modeling_realm import (
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REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
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REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
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@@ -21,14 +21,14 @@ from ...utils import logging
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logger = logging.get_logger(__name__)
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logger = logging.get_logger(__name__)
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REALM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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REALM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"realm-cc-news-pretrained-embedder": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-embedder/resolve/main/config.json",
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"qqaatw/realm-cc-news-pretrained-embedder": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-embedder/resolve/main/config.json",
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"realm-cc-news-pretrained-encoder": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-encoder/resolve/main/config.json",
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"qqaatw/realm-cc-news-pretrained-encoder": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-encoder/resolve/main/config.json",
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"realm-cc-news-pretrained-scorer": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-scorer/resolve/main/config.json",
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"qqaatw/realm-cc-news-pretrained-scorer": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-scorer/resolve/main/config.json",
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"realm-cc-news-pretrained-openqa": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-openqa/aresolve/main/config.json",
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"qqaatw/realm-cc-news-pretrained-openqa": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-openqa/aresolve/main/config.json",
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"realm-orqa-nq-openqa": "https://huggingface.co/qqaatw/realm-orqa-nq-openqa/resolve/main/config.json",
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"qqaatw/realm-orqa-nq-openqa": "https://huggingface.co/qqaatw/realm-orqa-nq-openqa/resolve/main/config.json",
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"realm-orqa-nq-reader": "https://huggingface.co/qqaatw/realm-orqa-nq-reader/resolve/main/config.json",
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"qqaatw/realm-orqa-nq-reader": "https://huggingface.co/qqaatw/realm-orqa-nq-reader/resolve/main/config.json",
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"realm-orqa-wq-openqa": "https://huggingface.co/qqaatw/realm-orqa-wq-openqa/resolve/main/config.json",
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"qqaatw/realm-orqa-wq-openqa": "https://huggingface.co/qqaatw/realm-orqa-wq-openqa/resolve/main/config.json",
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"realm-orqa-wq-reader": "https://huggingface.co/qqaatw/realm-orqa-wq-reader/resolve/main/config.json",
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"qqaatw/realm-orqa-wq-reader": "https://huggingface.co/qqaatw/realm-orqa-wq-reader/resolve/main/config.json",
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# See all REALM models at https://huggingface.co/models?filter=realm
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# See all REALM models at https://huggingface.co/models?filter=realm
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}
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}
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@@ -14,10 +14,15 @@
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# limitations under the License.
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# limitations under the License.
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"""Tokenization classes for REALM."""
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"""Tokenization classes for REALM."""
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import collections
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import os
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import unicodedata
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from typing import List, Optional, Tuple
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from ...file_utils import PaddingStrategy
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from ...file_utils import PaddingStrategy
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from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
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from ...tokenization_utils_base import BatchEncoding
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from ...tokenization_utils_base import BatchEncoding
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from ...utils import logging
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from ...utils import logging
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from ..bert.tokenization_bert import BertTokenizer
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logger = logging.get_logger(__name__)
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logger = logging.get_logger(__name__)
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@@ -26,54 +31,193 @@ VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
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PRETRAINED_VOCAB_FILES_MAP = {
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PRETRAINED_VOCAB_FILES_MAP = {
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"vocab_file": {
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"vocab_file": {
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"realm-cc-news-pretrained-embedder": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt",
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"qqaatw/realm-cc-news-pretrained-embedder": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt",
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"realm-cc-news-pretrained-encoder": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt",
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"qqaatw/realm-cc-news-pretrained-encoder": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt",
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"realm-cc-news-pretrained-scorer": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt",
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"qqaatw/realm-cc-news-pretrained-scorer": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt",
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"realm-cc-news-pretrained-openqa": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt",
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"qqaatw/realm-cc-news-pretrained-openqa": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt",
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"realm-orqa-nq-openqa": "https://huggingface.co/qqaatw/realm-orqa-nq-openqa/resolve/main/vocab.txt",
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"qqaatw/realm-orqa-nq-openqa": "https://huggingface.co/qqaatw/realm-orqa-nq-openqa/resolve/main/vocab.txt",
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"realm-orqa-nq-reader": "https://huggingface.co/qqaatw/realm-orqa-nq-reader/resolve/main/vocab.txt",
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"qqaatw/realm-orqa-nq-reader": "https://huggingface.co/qqaatw/realm-orqa-nq-reader/resolve/main/vocab.txt",
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"realm-orqa-wq-openqa": "https://huggingface.co/qqaatw/realm-orqa-wq-openqa/resolve/main/vocab.txt",
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"qqaatw/realm-orqa-wq-openqa": "https://huggingface.co/qqaatw/realm-orqa-wq-openqa/resolve/main/vocab.txt",
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"realm-orqa-wq-reader": "https://huggingface.co/qqaatw/realm-orqa-wq-reader/resolve/main/vocab.txt",
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"qqaatw/realm-orqa-wq-reader": "https://huggingface.co/qqaatw/realm-orqa-wq-reader/resolve/main/vocab.txt",
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}
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}
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}
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}
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
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"realm-cc-news-pretrained-embedder": 512,
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"qqaatw/realm-cc-news-pretrained-embedder": 512,
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"realm-cc-news-pretrained-encoder": 512,
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"qqaatw/realm-cc-news-pretrained-encoder": 512,
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"realm-cc-news-pretrained-scorer": 512,
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"qqaatw/realm-cc-news-pretrained-scorer": 512,
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"realm-cc-news-pretrained-openqa": 512,
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"qqaatw/realm-cc-news-pretrained-openqa": 512,
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"realm-orqa-nq-openqa": 512,
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"qqaatw/realm-orqa-nq-openqa": 512,
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"realm-orqa-nq-reader": 512,
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"qqaatw/realm-orqa-nq-reader": 512,
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"realm-orqa-wq-openqa": 512,
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"qqaatw/realm-orqa-wq-openqa": 512,
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"realm-orqa-wq-reader": 512,
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"qqaatw/realm-orqa-wq-reader": 512,
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}
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}
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PRETRAINED_INIT_CONFIGURATION = {
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PRETRAINED_INIT_CONFIGURATION = {
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"realm-cc-news-pretrained-embedder": {"do_lower_case": True},
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"qqaatw/realm-cc-news-pretrained-embedder": {"do_lower_case": True},
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"realm-cc-news-pretrained-encoder": {"do_lower_case": True},
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"qqaatw/realm-cc-news-pretrained-encoder": {"do_lower_case": True},
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"realm-cc-news-pretrained-scorer": {"do_lower_case": True},
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"qqaatw/realm-cc-news-pretrained-scorer": {"do_lower_case": True},
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"realm-cc-news-pretrained-openqa": {"do_lower_case": True},
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"qqaatw/realm-cc-news-pretrained-openqa": {"do_lower_case": True},
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"realm-orqa-nq-openqa": {"do_lower_case": True},
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"qqaatw/realm-orqa-nq-openqa": {"do_lower_case": True},
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"realm-orqa-nq-reader": {"do_lower_case": True},
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"qqaatw/realm-orqa-nq-reader": {"do_lower_case": True},
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"realm-orqa-wq-openqa": {"do_lower_case": True},
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"qqaatw/realm-orqa-wq-openqa": {"do_lower_case": True},
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"realm-orqa-wq-reader": {"do_lower_case": True},
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"qqaatw/realm-orqa-wq-reader": {"do_lower_case": True},
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}
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}
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class RealmTokenizer(BertTokenizer):
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def load_vocab(vocab_file):
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"""Loads a vocabulary file into a dictionary."""
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vocab = collections.OrderedDict()
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with open(vocab_file, "r", encoding="utf-8") as reader:
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tokens = reader.readlines()
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for index, token in enumerate(tokens):
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token = token.rstrip("\n")
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vocab[token] = index
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return vocab
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def whitespace_tokenize(text):
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"""Runs basic whitespace cleaning and splitting on a piece of text."""
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text = text.strip()
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if not text:
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return []
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tokens = text.split()
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return tokens
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class RealmTokenizer(PreTrainedTokenizer):
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r"""
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r"""
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Construct a REALM tokenizer.
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Construct a REALM tokenizer.
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[`RealmTokenizer`] is identical to [`BertTokenizer`] and runs end-to-end tokenization: punctuation splitting and
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[`RealmTokenizer`] is identical to [`BertTokenizer`] and runs end-to-end tokenization: punctuation splitting and
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wordpiece.
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wordpiece.
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Refer to superclass [`BertTokenizer`] for usage examples and documentation concerning parameters.
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
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this superclass for more information regarding those methods.
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Args:
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vocab_file (`str`):
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File containing the vocabulary.
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do_lower_case (`bool`, *optional*, defaults to `True`):
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Whether or not to lowercase the input when tokenizing.
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do_basic_tokenize (`bool`, *optional*, defaults to `True`):
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Whether or not to do basic tokenization before WordPiece.
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never_split (`Iterable`, *optional*):
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Collection of tokens which will never be split during tokenization. Only has an effect when
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`do_basic_tokenize=True`
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unk_token (`str`, *optional*, defaults to `"[UNK]"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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sep_token (`str`, *optional*, defaults to `"[SEP]"`):
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The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
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sequence classification or for a text and a question for question answering. It is also used as the last
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token of a sequence built with special tokens.
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pad_token (`str`, *optional*, defaults to `"[PAD]"`):
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The token used for padding, for example when batching sequences of different lengths.
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cls_token (`str`, *optional*, defaults to `"[CLS]"`):
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The classifier token which is used when doing sequence classification (classification of the whole sequence
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instead of per-token classification). It is the first token of the sequence when built with special tokens.
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mask_token (`str`, *optional*, defaults to `"[MASK]"`):
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The token used for masking values. This is the token used when training this model with masked language
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modeling. This is the token which the model will try to predict.
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tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
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Whether or not to tokenize Chinese characters.
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This should likely be deactivated for Japanese (see this
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[issue](https://github.com/huggingface/transformers/issues/328)).
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strip_accents: (`bool`, *optional*):
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Whether or not to strip all accents. If this option is not specified, then it will be determined by the
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value for `lowercase` (as in the original BERT).
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"""
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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vocab_files_names = VOCAB_FILES_NAMES
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pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
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pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
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pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
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pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
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def __init__(
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self,
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vocab_file,
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do_lower_case=True,
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do_basic_tokenize=True,
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never_split=None,
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unk_token="[UNK]",
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sep_token="[SEP]",
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pad_token="[PAD]",
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cls_token="[CLS]",
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mask_token="[MASK]",
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tokenize_chinese_chars=True,
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strip_accents=None,
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|
**kwargs
|
||||||
|
):
|
||||||
|
super().__init__(
|
||||||
|
do_lower_case=do_lower_case,
|
||||||
|
do_basic_tokenize=do_basic_tokenize,
|
||||||
|
never_split=never_split,
|
||||||
|
unk_token=unk_token,
|
||||||
|
sep_token=sep_token,
|
||||||
|
pad_token=pad_token,
|
||||||
|
cls_token=cls_token,
|
||||||
|
mask_token=mask_token,
|
||||||
|
tokenize_chinese_chars=tokenize_chinese_chars,
|
||||||
|
strip_accents=strip_accents,
|
||||||
|
**kwargs,
|
||||||
|
)
|
||||||
|
|
||||||
|
if not os.path.isfile(vocab_file):
|
||||||
|
raise ValueError(
|
||||||
|
f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained "
|
||||||
|
"model use `tokenizer = RealmTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
|
||||||
|
)
|
||||||
|
self.vocab = load_vocab(vocab_file)
|
||||||
|
self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
|
||||||
|
self.do_basic_tokenize = do_basic_tokenize
|
||||||
|
if do_basic_tokenize:
|
||||||
|
self.basic_tokenizer = BasicTokenizer(
|
||||||
|
do_lower_case=do_lower_case,
|
||||||
|
never_split=never_split,
|
||||||
|
tokenize_chinese_chars=tokenize_chinese_chars,
|
||||||
|
strip_accents=strip_accents,
|
||||||
|
)
|
||||||
|
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=self.unk_token)
|
||||||
|
|
||||||
|
@property
|
||||||
|
def do_lower_case(self):
|
||||||
|
return self.basic_tokenizer.do_lower_case
|
||||||
|
|
||||||
|
@property
|
||||||
|
def vocab_size(self):
|
||||||
|
return len(self.vocab)
|
||||||
|
|
||||||
|
def get_vocab(self):
|
||||||
|
return dict(self.vocab, **self.added_tokens_encoder)
|
||||||
|
|
||||||
|
def _tokenize(self, text):
|
||||||
|
split_tokens = []
|
||||||
|
if self.do_basic_tokenize:
|
||||||
|
for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
|
||||||
|
|
||||||
|
# If the token is part of the never_split set
|
||||||
|
if token in self.basic_tokenizer.never_split:
|
||||||
|
split_tokens.append(token)
|
||||||
|
else:
|
||||||
|
split_tokens += self.wordpiece_tokenizer.tokenize(token)
|
||||||
|
else:
|
||||||
|
split_tokens = self.wordpiece_tokenizer.tokenize(text)
|
||||||
|
return split_tokens
|
||||||
|
|
||||||
|
def _convert_token_to_id(self, token):
|
||||||
|
"""Converts a token (str) in an id using the vocab."""
|
||||||
|
return self.vocab.get(token, self.vocab.get(self.unk_token))
|
||||||
|
|
||||||
|
def _convert_id_to_token(self, index):
|
||||||
|
"""Converts an index (integer) in a token (str) using the vocab."""
|
||||||
|
return self.ids_to_tokens.get(index, self.unk_token)
|
||||||
|
|
||||||
|
def convert_tokens_to_string(self, tokens):
|
||||||
|
"""Converts a sequence of tokens (string) in a single string."""
|
||||||
|
out_string = " ".join(tokens).replace(" ##", "").strip()
|
||||||
|
return out_string
|
||||||
|
|
||||||
def batch_encode_candidates(self, text, **kwargs):
|
def batch_encode_candidates(self, text, **kwargs):
|
||||||
r"""
|
r"""
|
||||||
@@ -147,3 +291,311 @@ class RealmTokenizer(BertTokenizer):
|
|||||||
output_data = dict((key, item) for key, item in output_data.items() if len(item) != 0)
|
output_data = dict((key, item) for key, item in output_data.items() if len(item) != 0)
|
||||||
|
|
||||||
return BatchEncoding(output_data, tensor_type=return_tensors)
|
return BatchEncoding(output_data, tensor_type=return_tensors)
|
||||||
|
|
||||||
|
def build_inputs_with_special_tokens(
|
||||||
|
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
||||||
|
) -> List[int]:
|
||||||
|
"""
|
||||||
|
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
|
||||||
|
adding special tokens. A REALM sequence has the following format:
|
||||||
|
|
||||||
|
- single sequence: `[CLS] X [SEP]`
|
||||||
|
- pair of sequences: `[CLS] A [SEP] B [SEP]`
|
||||||
|
|
||||||
|
Args:
|
||||||
|
token_ids_0 (`List[int]`):
|
||||||
|
List of IDs to which the special tokens will be added.
|
||||||
|
token_ids_1 (`List[int]`, *optional*):
|
||||||
|
Optional second list of IDs for sequence pairs.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
|
||||||
|
"""
|
||||||
|
if token_ids_1 is None:
|
||||||
|
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
|
||||||
|
cls = [self.cls_token_id]
|
||||||
|
sep = [self.sep_token_id]
|
||||||
|
return cls + token_ids_0 + sep + token_ids_1 + sep
|
||||||
|
|
||||||
|
def get_special_tokens_mask(
|
||||||
|
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
|
||||||
|
) -> List[int]:
|
||||||
|
"""
|
||||||
|
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
||||||
|
special tokens using the tokenizer `prepare_for_model` method.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
token_ids_0 (`List[int]`):
|
||||||
|
List of IDs.
|
||||||
|
token_ids_1 (`List[int]`, *optional*):
|
||||||
|
Optional second list of IDs for sequence pairs.
|
||||||
|
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
||||||
|
Whether or not the token list is already formatted with special tokens for the model.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
||||||
|
"""
|
||||||
|
|
||||||
|
if already_has_special_tokens:
|
||||||
|
return super().get_special_tokens_mask(
|
||||||
|
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
|
||||||
|
)
|
||||||
|
|
||||||
|
if token_ids_1 is not None:
|
||||||
|
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
|
||||||
|
return [1] + ([0] * len(token_ids_0)) + [1]
|
||||||
|
|
||||||
|
def create_token_type_ids_from_sequences(
|
||||||
|
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
||||||
|
) -> List[int]:
|
||||||
|
"""
|
||||||
|
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A REALM sequence
|
||||||
|
pair mask has the following format:
|
||||||
|
|
||||||
|
```
|
||||||
|
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
|
||||||
|
| first sequence | second sequence |
|
||||||
|
```
|
||||||
|
|
||||||
|
If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
|
||||||
|
|
||||||
|
Args:
|
||||||
|
token_ids_0 (`List[int]`):
|
||||||
|
List of IDs.
|
||||||
|
token_ids_1 (`List[int]`, *optional*):
|
||||||
|
Optional second list of IDs for sequence pairs.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
|
||||||
|
"""
|
||||||
|
sep = [self.sep_token_id]
|
||||||
|
cls = [self.cls_token_id]
|
||||||
|
if token_ids_1 is None:
|
||||||
|
return len(cls + token_ids_0 + sep) * [0]
|
||||||
|
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
|
||||||
|
|
||||||
|
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
||||||
|
index = 0
|
||||||
|
if os.path.isdir(save_directory):
|
||||||
|
vocab_file = os.path.join(
|
||||||
|
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
|
||||||
|
with open(vocab_file, "w", encoding="utf-8") as writer:
|
||||||
|
for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
|
||||||
|
if index != token_index:
|
||||||
|
logger.warning(
|
||||||
|
f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
|
||||||
|
" Please check that the vocabulary is not corrupted!"
|
||||||
|
)
|
||||||
|
index = token_index
|
||||||
|
writer.write(token + "\n")
|
||||||
|
index += 1
|
||||||
|
return (vocab_file,)
|
||||||
|
|
||||||
|
|
||||||
|
class BasicTokenizer(object):
|
||||||
|
"""
|
||||||
|
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
|
||||||
|
|
||||||
|
Args:
|
||||||
|
do_lower_case (`bool`, *optional*, defaults to `True`):
|
||||||
|
Whether or not to lowercase the input when tokenizing.
|
||||||
|
never_split (`Iterable`, *optional*):
|
||||||
|
Collection of tokens which will never be split during tokenization. Only has an effect when
|
||||||
|
`do_basic_tokenize=True`
|
||||||
|
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
|
||||||
|
Whether or not to tokenize Chinese characters.
|
||||||
|
|
||||||
|
This should likely be deactivated for Japanese (see this
|
||||||
|
[issue](https://github.com/huggingface/transformers/issues/328)).
|
||||||
|
strip_accents (`bool`, *optional*):
|
||||||
|
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
|
||||||
|
value for `lowercase` (as in the original BERT).
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True, strip_accents=None):
|
||||||
|
if never_split is None:
|
||||||
|
never_split = []
|
||||||
|
self.do_lower_case = do_lower_case
|
||||||
|
self.never_split = set(never_split)
|
||||||
|
self.tokenize_chinese_chars = tokenize_chinese_chars
|
||||||
|
self.strip_accents = strip_accents
|
||||||
|
|
||||||
|
def tokenize(self, text, never_split=None):
|
||||||
|
"""
|
||||||
|
Basic Tokenization of a piece of text. Split on "white spaces" only, for sub-word tokenization, see
|
||||||
|
WordPieceTokenizer.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
never_split (`List[str]`, *optional*)
|
||||||
|
Kept for backward compatibility purposes. Now implemented directly at the base class level (see
|
||||||
|
[`PreTrainedTokenizer.tokenize`]) List of token not to split.
|
||||||
|
"""
|
||||||
|
# union() returns a new set by concatenating the two sets.
|
||||||
|
never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
|
||||||
|
text = self._clean_text(text)
|
||||||
|
|
||||||
|
# This was added on November 1st, 2018 for the multilingual and Chinese
|
||||||
|
# models. This is also applied to the English models now, but it doesn't
|
||||||
|
# matter since the English models were not trained on any Chinese data
|
||||||
|
# and generally don't have any Chinese data in them (there are Chinese
|
||||||
|
# characters in the vocabulary because Wikipedia does have some Chinese
|
||||||
|
# words in the English Wikipedia.).
|
||||||
|
if self.tokenize_chinese_chars:
|
||||||
|
text = self._tokenize_chinese_chars(text)
|
||||||
|
orig_tokens = whitespace_tokenize(text)
|
||||||
|
split_tokens = []
|
||||||
|
for token in orig_tokens:
|
||||||
|
if token not in never_split:
|
||||||
|
if self.do_lower_case:
|
||||||
|
token = token.lower()
|
||||||
|
if self.strip_accents is not False:
|
||||||
|
token = self._run_strip_accents(token)
|
||||||
|
elif self.strip_accents:
|
||||||
|
token = self._run_strip_accents(token)
|
||||||
|
split_tokens.extend(self._run_split_on_punc(token, never_split))
|
||||||
|
|
||||||
|
output_tokens = whitespace_tokenize(" ".join(split_tokens))
|
||||||
|
return output_tokens
|
||||||
|
|
||||||
|
def _run_strip_accents(self, text):
|
||||||
|
"""Strips accents from a piece of text."""
|
||||||
|
text = unicodedata.normalize("NFD", text)
|
||||||
|
output = []
|
||||||
|
for char in text:
|
||||||
|
cat = unicodedata.category(char)
|
||||||
|
if cat == "Mn":
|
||||||
|
continue
|
||||||
|
output.append(char)
|
||||||
|
return "".join(output)
|
||||||
|
|
||||||
|
def _run_split_on_punc(self, text, never_split=None):
|
||||||
|
"""Splits punctuation on a piece of text."""
|
||||||
|
if never_split is not None and text in never_split:
|
||||||
|
return [text]
|
||||||
|
chars = list(text)
|
||||||
|
i = 0
|
||||||
|
start_new_word = True
|
||||||
|
output = []
|
||||||
|
while i < len(chars):
|
||||||
|
char = chars[i]
|
||||||
|
if _is_punctuation(char):
|
||||||
|
output.append([char])
|
||||||
|
start_new_word = True
|
||||||
|
else:
|
||||||
|
if start_new_word:
|
||||||
|
output.append([])
|
||||||
|
start_new_word = False
|
||||||
|
output[-1].append(char)
|
||||||
|
i += 1
|
||||||
|
|
||||||
|
return ["".join(x) for x in output]
|
||||||
|
|
||||||
|
def _tokenize_chinese_chars(self, text):
|
||||||
|
"""Adds whitespace around any CJK character."""
|
||||||
|
output = []
|
||||||
|
for char in text:
|
||||||
|
cp = ord(char)
|
||||||
|
if self._is_chinese_char(cp):
|
||||||
|
output.append(" ")
|
||||||
|
output.append(char)
|
||||||
|
output.append(" ")
|
||||||
|
else:
|
||||||
|
output.append(char)
|
||||||
|
return "".join(output)
|
||||||
|
|
||||||
|
def _is_chinese_char(self, cp):
|
||||||
|
"""Checks whether CP is the codepoint of a CJK character."""
|
||||||
|
# This defines a "chinese character" as anything in the CJK Unicode block:
|
||||||
|
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
|
||||||
|
#
|
||||||
|
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
|
||||||
|
# despite its name. The modern Korean Hangul alphabet is a different block,
|
||||||
|
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
|
||||||
|
# space-separated words, so they are not treated specially and handled
|
||||||
|
# like the all of the other languages.
|
||||||
|
if (
|
||||||
|
(cp >= 0x4E00 and cp <= 0x9FFF)
|
||||||
|
or (cp >= 0x3400 and cp <= 0x4DBF) #
|
||||||
|
or (cp >= 0x20000 and cp <= 0x2A6DF) #
|
||||||
|
or (cp >= 0x2A700 and cp <= 0x2B73F) #
|
||||||
|
or (cp >= 0x2B740 and cp <= 0x2B81F) #
|
||||||
|
or (cp >= 0x2B820 and cp <= 0x2CEAF) #
|
||||||
|
or (cp >= 0xF900 and cp <= 0xFAFF)
|
||||||
|
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
|
||||||
|
): #
|
||||||
|
return True
|
||||||
|
|
||||||
|
return False
|
||||||
|
|
||||||
|
def _clean_text(self, text):
|
||||||
|
"""Performs invalid character removal and whitespace cleanup on text."""
|
||||||
|
output = []
|
||||||
|
for char in text:
|
||||||
|
cp = ord(char)
|
||||||
|
if cp == 0 or cp == 0xFFFD or _is_control(char):
|
||||||
|
continue
|
||||||
|
if _is_whitespace(char):
|
||||||
|
output.append(" ")
|
||||||
|
else:
|
||||||
|
output.append(char)
|
||||||
|
return "".join(output)
|
||||||
|
|
||||||
|
|
||||||
|
class WordpieceTokenizer(object):
|
||||||
|
"""Runs WordPiece tokenization."""
|
||||||
|
|
||||||
|
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
|
||||||
|
self.vocab = vocab
|
||||||
|
self.unk_token = unk_token
|
||||||
|
self.max_input_chars_per_word = max_input_chars_per_word
|
||||||
|
|
||||||
|
def tokenize(self, text):
|
||||||
|
"""
|
||||||
|
Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
|
||||||
|
tokenization using the given vocabulary.
|
||||||
|
|
||||||
|
For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
text: A single token or whitespace separated tokens. This should have
|
||||||
|
already been passed through *BasicTokenizer*.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
A list of wordpiece tokens.
|
||||||
|
"""
|
||||||
|
|
||||||
|
output_tokens = []
|
||||||
|
for token in whitespace_tokenize(text):
|
||||||
|
chars = list(token)
|
||||||
|
if len(chars) > self.max_input_chars_per_word:
|
||||||
|
output_tokens.append(self.unk_token)
|
||||||
|
continue
|
||||||
|
|
||||||
|
is_bad = False
|
||||||
|
start = 0
|
||||||
|
sub_tokens = []
|
||||||
|
while start < len(chars):
|
||||||
|
end = len(chars)
|
||||||
|
cur_substr = None
|
||||||
|
while start < end:
|
||||||
|
substr = "".join(chars[start:end])
|
||||||
|
if start > 0:
|
||||||
|
substr = "##" + substr
|
||||||
|
if substr in self.vocab:
|
||||||
|
cur_substr = substr
|
||||||
|
break
|
||||||
|
end -= 1
|
||||||
|
if cur_substr is None:
|
||||||
|
is_bad = True
|
||||||
|
break
|
||||||
|
sub_tokens.append(cur_substr)
|
||||||
|
start = end
|
||||||
|
|
||||||
|
if is_bad:
|
||||||
|
output_tokens.append(self.unk_token)
|
||||||
|
else:
|
||||||
|
output_tokens.extend(sub_tokens)
|
||||||
|
return output_tokens
|
||||||
|
|||||||
298
src/transformers/models/realm/tokenization_realm_fast.py
Normal file
298
src/transformers/models/realm/tokenization_realm_fast.py
Normal file
@@ -0,0 +1,298 @@
|
|||||||
|
# coding=utf-8
|
||||||
|
# Copyright 2022 The REALM authors and The HuggingFace Inc. team.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
"""Fast Tokenization classes for REALM."""
|
||||||
|
|
||||||
|
import json
|
||||||
|
from typing import List, Optional, Tuple
|
||||||
|
|
||||||
|
from tokenizers import normalizers
|
||||||
|
|
||||||
|
from ...file_utils import PaddingStrategy
|
||||||
|
from ...tokenization_utils_base import BatchEncoding
|
||||||
|
from ...tokenization_utils_fast import PreTrainedTokenizerFast
|
||||||
|
from ...utils import logging
|
||||||
|
from .tokenization_realm import RealmTokenizer
|
||||||
|
|
||||||
|
|
||||||
|
logger = logging.get_logger(__name__)
|
||||||
|
|
||||||
|
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
|
||||||
|
|
||||||
|
PRETRAINED_VOCAB_FILES_MAP = {
|
||||||
|
"vocab_file": {
|
||||||
|
"qqaatw/realm-cc-news-pretrained-embedder": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt",
|
||||||
|
"qqaatw/realm-cc-news-pretrained-encoder": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt",
|
||||||
|
"qqaatw/realm-cc-news-pretrained-scorer": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt",
|
||||||
|
"qqaatw/realm-cc-news-pretrained-openqa": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt",
|
||||||
|
"qqaatw/realm-orqa-nq-openqa": "https://huggingface.co/qqaatw/realm-orqa-nq-openqa/resolve/main/vocab.txt",
|
||||||
|
"qqaatw/realm-orqa-nq-reader": "https://huggingface.co/qqaatw/realm-orqa-nq-reader/resolve/main/vocab.txt",
|
||||||
|
"qqaatw/realm-orqa-wq-openqa": "https://huggingface.co/qqaatw/realm-orqa-wq-openqa/resolve/main/vocab.txt",
|
||||||
|
"qqaatw/realm-orqa-wq-reader": "https://huggingface.co/qqaatw/realm-orqa-wq-reader/resolve/main/vocab.txt",
|
||||||
|
},
|
||||||
|
"tokenizer_file": {
|
||||||
|
"qqaatw/realm-cc-news-pretrained-embedder": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont",
|
||||||
|
"qqaatw/realm-cc-news-pretrained-encoder": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json",
|
||||||
|
"qqaatw/realm-cc-news-pretrained-scorer": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json",
|
||||||
|
"qqaatw/realm-cc-news-pretrained-openqa": "https://huggingface.co/qqaatw/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json",
|
||||||
|
"qqaatw/realm-orqa-nq-openqa": "https://huggingface.co/qqaatw/realm-orqa-nq-openqa/resolve/main/tokenizer.json",
|
||||||
|
"qqaatw/realm-orqa-nq-reader": "https://huggingface.co/qqaatw/realm-orqa-nq-reader/resolve/main/tokenizer.json",
|
||||||
|
"qqaatw/realm-orqa-wq-openqa": "https://huggingface.co/qqaatw/realm-orqa-wq-openqa/resolve/main/tokenizer.json",
|
||||||
|
"qqaatw/realm-orqa-wq-reader": "https://huggingface.co/qqaatw/realm-orqa-wq-reader/resolve/main/tokenizer.json",
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
|
||||||
|
"qqaatw/realm-cc-news-pretrained-embedder": 512,
|
||||||
|
"qqaatw/realm-cc-news-pretrained-encoder": 512,
|
||||||
|
"qqaatw/realm-cc-news-pretrained-scorer": 512,
|
||||||
|
"qqaatw/realm-cc-news-pretrained-openqa": 512,
|
||||||
|
"qqaatw/realm-orqa-nq-openqa": 512,
|
||||||
|
"qqaatw/realm-orqa-nq-reader": 512,
|
||||||
|
"qqaatw/realm-orqa-wq-openqa": 512,
|
||||||
|
"qqaatw/realm-orqa-wq-reader": 512,
|
||||||
|
}
|
||||||
|
|
||||||
|
PRETRAINED_INIT_CONFIGURATION = {
|
||||||
|
"qqaatw/realm-cc-news-pretrained-embedder": {"do_lower_case": True},
|
||||||
|
"qqaatw/realm-cc-news-pretrained-encoder": {"do_lower_case": True},
|
||||||
|
"qqaatw/realm-cc-news-pretrained-scorer": {"do_lower_case": True},
|
||||||
|
"qqaatw/realm-cc-news-pretrained-openqa": {"do_lower_case": True},
|
||||||
|
"qqaatw/realm-orqa-nq-openqa": {"do_lower_case": True},
|
||||||
|
"qqaatw/realm-orqa-nq-reader": {"do_lower_case": True},
|
||||||
|
"qqaatw/realm-orqa-wq-openqa": {"do_lower_case": True},
|
||||||
|
"qqaatw/realm-orqa-wq-reader": {"do_lower_case": True},
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
class RealmTokenizerFast(PreTrainedTokenizerFast):
|
||||||
|
r"""
|
||||||
|
Construct a "fast" REALM tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
|
||||||
|
|
||||||
|
[`RealmTokenizerFast`] is identical to [`BertTokenizerFast`] and runs end-to-end tokenization: punctuation
|
||||||
|
splitting and wordpiece.
|
||||||
|
|
||||||
|
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
|
||||||
|
refer to this superclass for more information regarding those methods.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
vocab_file (`str`):
|
||||||
|
File containing the vocabulary.
|
||||||
|
do_lower_case (`bool`, *optional*, defaults to `True`):
|
||||||
|
Whether or not to lowercase the input when tokenizing.
|
||||||
|
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
|
||||||
|
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
|
||||||
|
token instead.
|
||||||
|
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
|
||||||
|
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
|
||||||
|
sequence classification or for a text and a question for question answering. It is also used as the last
|
||||||
|
token of a sequence built with special tokens.
|
||||||
|
pad_token (`str`, *optional*, defaults to `"[PAD]"`):
|
||||||
|
The token used for padding, for example when batching sequences of different lengths.
|
||||||
|
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
|
||||||
|
The classifier token which is used when doing sequence classification (classification of the whole sequence
|
||||||
|
instead of per-token classification). It is the first token of the sequence when built with special tokens.
|
||||||
|
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
|
||||||
|
The token used for masking values. This is the token used when training this model with masked language
|
||||||
|
modeling. This is the token which the model will try to predict.
|
||||||
|
clean_text (`bool`, *optional*, defaults to `True`):
|
||||||
|
Whether or not to clean the text before tokenization by removing any control characters and replacing all
|
||||||
|
whitespaces by the classic one.
|
||||||
|
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
|
||||||
|
Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this
|
||||||
|
issue](https://github.com/huggingface/transformers/issues/328)).
|
||||||
|
strip_accents (`bool`, *optional*):
|
||||||
|
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
|
||||||
|
value for `lowercase` (as in the original BERT).
|
||||||
|
wordpieces_prefix (`str`, *optional*, defaults to `"##"`):
|
||||||
|
The prefix for subwords.
|
||||||
|
"""
|
||||||
|
|
||||||
|
vocab_files_names = VOCAB_FILES_NAMES
|
||||||
|
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
||||||
|
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
|
||||||
|
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
|
||||||
|
slow_tokenizer_class = RealmTokenizer
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
vocab_file=None,
|
||||||
|
tokenizer_file=None,
|
||||||
|
do_lower_case=True,
|
||||||
|
unk_token="[UNK]",
|
||||||
|
sep_token="[SEP]",
|
||||||
|
pad_token="[PAD]",
|
||||||
|
cls_token="[CLS]",
|
||||||
|
mask_token="[MASK]",
|
||||||
|
tokenize_chinese_chars=True,
|
||||||
|
strip_accents=None,
|
||||||
|
**kwargs
|
||||||
|
):
|
||||||
|
super().__init__(
|
||||||
|
vocab_file,
|
||||||
|
tokenizer_file=tokenizer_file,
|
||||||
|
do_lower_case=do_lower_case,
|
||||||
|
unk_token=unk_token,
|
||||||
|
sep_token=sep_token,
|
||||||
|
pad_token=pad_token,
|
||||||
|
cls_token=cls_token,
|
||||||
|
mask_token=mask_token,
|
||||||
|
tokenize_chinese_chars=tokenize_chinese_chars,
|
||||||
|
strip_accents=strip_accents,
|
||||||
|
**kwargs,
|
||||||
|
)
|
||||||
|
|
||||||
|
normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
|
||||||
|
if (
|
||||||
|
normalizer_state.get("lowercase", do_lower_case) != do_lower_case
|
||||||
|
or normalizer_state.get("strip_accents", strip_accents) != strip_accents
|
||||||
|
or normalizer_state.get("handle_chinese_chars", tokenize_chinese_chars) != tokenize_chinese_chars
|
||||||
|
):
|
||||||
|
normalizer_class = getattr(normalizers, normalizer_state.pop("type"))
|
||||||
|
normalizer_state["lowercase"] = do_lower_case
|
||||||
|
normalizer_state["strip_accents"] = strip_accents
|
||||||
|
normalizer_state["handle_chinese_chars"] = tokenize_chinese_chars
|
||||||
|
self.backend_tokenizer.normalizer = normalizer_class(**normalizer_state)
|
||||||
|
|
||||||
|
self.do_lower_case = do_lower_case
|
||||||
|
|
||||||
|
def batch_encode_candidates(self, text, **kwargs):
|
||||||
|
r"""
|
||||||
|
Encode a batch of text or text pair. This method is similar to regular __call__ method but has the following
|
||||||
|
differences:
|
||||||
|
|
||||||
|
1. Handle additional num_candidate axis. (batch_size, num_candidates, text)
|
||||||
|
2. Always pad the sequences to *max_length*.
|
||||||
|
3. Must specify *max_length* in order to stack packs of candidates into a batch.
|
||||||
|
|
||||||
|
- single sequence: `[CLS] X [SEP]`
|
||||||
|
- pair of sequences: `[CLS] A [SEP] B [SEP]`
|
||||||
|
|
||||||
|
Args:
|
||||||
|
text (`List[List[str]]`):
|
||||||
|
The batch of sequences to be encoded. Each sequence must be in this format: (batch_size,
|
||||||
|
num_candidates, text).
|
||||||
|
text_pair (`List[List[str]]`, *optional*):
|
||||||
|
The batch of sequences to be encoded. Each sequence must be in this format: (batch_size,
|
||||||
|
num_candidates, text).
|
||||||
|
**kwargs:
|
||||||
|
Keyword arguments of the __call__ method.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
[`BatchEncoding`]: Encoded text or text pair.
|
||||||
|
|
||||||
|
Example:
|
||||||
|
|
||||||
|
```python
|
||||||
|
>>> from transformers import RealmTokenizerFast
|
||||||
|
|
||||||
|
>>> # batch_size = 2, num_candidates = 2
|
||||||
|
>>> text = [["Hello world!", "Nice to meet you!"], ["The cute cat.", "The adorable dog."]]
|
||||||
|
|
||||||
|
>>> tokenizer = RealmTokenizerFast.from_pretrained("qqaatw/realm-cc-news-pretrained-encoder")
|
||||||
|
>>> tokenized_text = tokenizer.batch_encode_candidates(text, max_length=10, return_tensors="pt")
|
||||||
|
```"""
|
||||||
|
|
||||||
|
# Always using a fixed sequence length to encode in order to stack candidates into a batch.
|
||||||
|
kwargs["padding"] = PaddingStrategy.MAX_LENGTH
|
||||||
|
|
||||||
|
batch_text = text
|
||||||
|
batch_text_pair = kwargs.pop("text_pair", None)
|
||||||
|
return_tensors = kwargs.pop("return_tensors", None)
|
||||||
|
|
||||||
|
output_data = {
|
||||||
|
"input_ids": [],
|
||||||
|
"attention_mask": [],
|
||||||
|
"token_type_ids": [],
|
||||||
|
}
|
||||||
|
|
||||||
|
for idx, candidate_text in enumerate(batch_text):
|
||||||
|
if batch_text_pair is not None:
|
||||||
|
candidate_text_pair = batch_text_pair[idx]
|
||||||
|
else:
|
||||||
|
candidate_text_pair = None
|
||||||
|
|
||||||
|
encoded_candidates = super().__call__(candidate_text, candidate_text_pair, return_tensors=None, **kwargs)
|
||||||
|
|
||||||
|
encoded_input_ids = encoded_candidates.get("input_ids")
|
||||||
|
encoded_attention_mask = encoded_candidates.get("attention_mask")
|
||||||
|
encoded_token_type_ids = encoded_candidates.get("token_type_ids")
|
||||||
|
|
||||||
|
if encoded_input_ids is not None:
|
||||||
|
output_data["input_ids"].append(encoded_input_ids)
|
||||||
|
if encoded_attention_mask is not None:
|
||||||
|
output_data["attention_mask"].append(encoded_attention_mask)
|
||||||
|
if encoded_token_type_ids is not None:
|
||||||
|
output_data["token_type_ids"].append(encoded_token_type_ids)
|
||||||
|
|
||||||
|
output_data = dict((key, item) for key, item in output_data.items() if len(item) != 0)
|
||||||
|
|
||||||
|
return BatchEncoding(output_data, tensor_type=return_tensors)
|
||||||
|
|
||||||
|
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
||||||
|
"""
|
||||||
|
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
|
||||||
|
adding special tokens. A REALM sequence has the following format:
|
||||||
|
|
||||||
|
- single sequence: `[CLS] X [SEP]`
|
||||||
|
- pair of sequences: `[CLS] A [SEP] B [SEP]`
|
||||||
|
|
||||||
|
Args:
|
||||||
|
token_ids_0 (`List[int]`):
|
||||||
|
List of IDs to which the special tokens will be added.
|
||||||
|
token_ids_1 (`List[int]`, *optional*):
|
||||||
|
Optional second list of IDs for sequence pairs.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
|
||||||
|
"""
|
||||||
|
output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
|
||||||
|
|
||||||
|
if token_ids_1:
|
||||||
|
output += token_ids_1 + [self.sep_token_id]
|
||||||
|
|
||||||
|
return output
|
||||||
|
|
||||||
|
def create_token_type_ids_from_sequences(
|
||||||
|
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
||||||
|
) -> List[int]:
|
||||||
|
"""
|
||||||
|
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A REALM sequence
|
||||||
|
pair mask has the following format:
|
||||||
|
|
||||||
|
```
|
||||||
|
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
|
||||||
|
| first sequence | second sequence |
|
||||||
|
```
|
||||||
|
|
||||||
|
If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
|
||||||
|
|
||||||
|
Args:
|
||||||
|
token_ids_0 (`List[int]`):
|
||||||
|
List of IDs.
|
||||||
|
token_ids_1 (`List[int]`, *optional*):
|
||||||
|
Optional second list of IDs for sequence pairs.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
|
||||||
|
"""
|
||||||
|
sep = [self.sep_token_id]
|
||||||
|
cls = [self.cls_token_id]
|
||||||
|
if token_ids_1 is None:
|
||||||
|
return len(cls + token_ids_0 + sep) * [0]
|
||||||
|
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
|
||||||
|
|
||||||
|
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
||||||
|
files = self._tokenizer.model.save(save_directory, name=filename_prefix)
|
||||||
|
return tuple(files)
|
||||||
@@ -234,6 +234,13 @@ class PegasusTokenizerFast(metaclass=DummyObject):
|
|||||||
requires_backends(self, ["tokenizers"])
|
requires_backends(self, ["tokenizers"])
|
||||||
|
|
||||||
|
|
||||||
|
class RealmTokenizerFast(metaclass=DummyObject):
|
||||||
|
_backends = ["tokenizers"]
|
||||||
|
|
||||||
|
def __init__(self, *args, **kwargs):
|
||||||
|
requires_backends(self, ["tokenizers"])
|
||||||
|
|
||||||
|
|
||||||
class ReformerTokenizerFast(metaclass=DummyObject):
|
class ReformerTokenizerFast(metaclass=DummyObject):
|
||||||
_backends = ["tokenizers"]
|
_backends = ["tokenizers"]
|
||||||
|
|
||||||
|
|||||||
@@ -16,6 +16,7 @@
|
|||||||
import os
|
import os
|
||||||
import unittest
|
import unittest
|
||||||
|
|
||||||
|
from transformers import RealmTokenizerFast
|
||||||
from transformers.models.bert.tokenization_bert import (
|
from transformers.models.bert.tokenization_bert import (
|
||||||
VOCAB_FILES_NAMES,
|
VOCAB_FILES_NAMES,
|
||||||
BasicTokenizer,
|
BasicTokenizer,
|
||||||
@@ -34,8 +35,8 @@ from .test_tokenization_common import TokenizerTesterMixin, filter_non_english
|
|||||||
class RealmTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
|
class RealmTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
|
||||||
|
|
||||||
tokenizer_class = RealmTokenizer
|
tokenizer_class = RealmTokenizer
|
||||||
rust_tokenizer_class = None
|
rust_tokenizer_class = RealmTokenizerFast
|
||||||
test_rust_tokenizer = False
|
test_rust_tokenizer = True
|
||||||
space_between_special_tokens = True
|
space_between_special_tokens = True
|
||||||
from_pretrained_filter = filter_non_english
|
from_pretrained_filter = filter_non_english
|
||||||
|
|
||||||
@@ -301,14 +302,21 @@ class RealmTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
|
|||||||
|
|
||||||
@slow
|
@slow
|
||||||
def test_batch_encode_candidates(self):
|
def test_batch_encode_candidates(self):
|
||||||
tokenizer = self.tokenizer_class.from_pretrained("bert-base-uncased")
|
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
|
||||||
|
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
|
||||||
|
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
|
||||||
|
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
|
||||||
|
text = [["Hello world!", "Nice to meet you!"], ["The cute cat.", "The adorable dog."]]
|
||||||
|
|
||||||
text = [["Hello world!", "Nice to meet you!"], ["The cute cat.", "The adorable dog."]]
|
encoded_sentence_r = tokenizer_r.batch_encode_candidates(text, max_length=10, return_tensors="np")
|
||||||
|
encoded_sentence_p = tokenizer_p.batch_encode_candidates(text, max_length=10, return_tensors="np")
|
||||||
|
|
||||||
encoded_sentence = tokenizer.batch_encode_candidates(text, max_length=10, return_tensors="pt")
|
expected_shape = (2, 2, 10)
|
||||||
|
|
||||||
expected_shape = (2, 2, 10)
|
self.assertEqual(encoded_sentence_r["input_ids"].shape, expected_shape)
|
||||||
|
self.assertEqual(encoded_sentence_r["attention_mask"].shape, expected_shape)
|
||||||
|
self.assertEqual(encoded_sentence_r["token_type_ids"].shape, expected_shape)
|
||||||
|
|
||||||
assert encoded_sentence["input_ids"].shape == expected_shape
|
self.assertEqual(encoded_sentence_p["input_ids"].shape, expected_shape)
|
||||||
assert encoded_sentence["attention_mask"].shape == expected_shape
|
self.assertEqual(encoded_sentence_p["attention_mask"].shape, expected_shape)
|
||||||
assert encoded_sentence["token_type_ids"].shape == expected_shape
|
self.assertEqual(encoded_sentence_p["token_type_ids"].shape, expected_shape)
|
||||||
|
|||||||
Reference in New Issue
Block a user