kimsan0622
commited on
Commit
•
12fa22c
1
Parent(s):
a22f89c
Upload processor
Browse files- preprocessor_config.json +21 -0
- processing_veld.py +103 -0
- special_tokens_map.json +107 -0
- spiece.model +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +117 -0
preprocessor_config.json
ADDED
@@ -0,0 +1,21 @@
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{
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"auto_map": {
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"AutoProcessor": "processing_veld.VELDProcessor"
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},
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"do_normalize": true,
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"do_resize": true,
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"feature_extractor_type": "ViTFeatureExtractor",
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"image_mean": [
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0.5,
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0.5,
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0.5
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],
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"image_std": [
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0.5,
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0.5,
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0.5
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],
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"processor_class": "VELDProcessor",
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"resample": 2,
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"size": 384
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}
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processing_veld.py
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# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team and san kim.
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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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"""
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Processor class for VELD
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"""
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from transformers.processing_utils import ProcessorMixin
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from transformers.tokenization_utils_base import BatchEncoding
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class VELDProcessor(ProcessorMixin):
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r"""
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Constructs a VELD processor which wraps a vision feature extractor and a tokenizer into a single
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processor.
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[`VELDProcessor`] offers all the functionalities of [`AutoFeatureExtractor`] and
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[`AutoTokenizer`]. See the [`~VELDProcessor.__call__`] and
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[`~VELDProcessor.decode`] for more information.
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Args:
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feature_extractor ([`AutoFeatureExtractor`]):
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The feature extractor is a required input.
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tokenizer ([`PreTrainedTokenizer`]):
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The tokenizer is a required input.
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"""
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feature_extractor_class = "AutoFeatureExtractor"
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tokenizer_class = "AutoTokenizer"
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def __init__(self, feature_extractor, tokenizer):
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super().__init__(feature_extractor, tokenizer)
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self.current_processor = self.feature_extractor
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def __call__(self, text=None, images=None, return_tensors=None, **kwargs):
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"""
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Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
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and `kwargs` arguments to VisionTextDualEncoderTokenizer's [`~PreTrainedTokenizer.__call__`] if `text` is not
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`None` to encode the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
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AutoFeatureExtractor's [`~AutoFeatureExtractor.__call__`] if `images` is not `None`. Please refer to the
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doctsring of the above two methods for more information.
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Args:
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text (`str`, `List[str]`, `List[List[str]]`):
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The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
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(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
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`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
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images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
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The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
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tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
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number of channels, H and W are image height and width.
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return_tensors (`str` or [`~utils.TensorType`], *optional*):
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If set, will return tensors of a particular framework. Acceptable values are:
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- `'tf'`: Return TensorFlow `tf.constant` objects.
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- `'pt'`: Return PyTorch `torch.Tensor` objects.
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- `'np'`: Return NumPy `np.ndarray` objects.
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- `'jax'`: Return JAX `jnp.ndarray` objects.
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Returns:
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[`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
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- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
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- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
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`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
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`None`).
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- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
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"""
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if text is None and images is None:
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raise ValueError("You have to specify either text or images. Both cannot be none.")
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if text is not None:
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encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs)
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if images is not None:
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image_features = self.feature_extractor(images, return_tensors=return_tensors, **kwargs)
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if text is not None and images is not None:
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encoding["pixel_values"] = image_features.pixel_values
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return encoding
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elif text is not None:
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return encoding
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else:
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return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
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def batch_decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to VELDProcessor's
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[`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information.
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"""
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return self.tokenizer.batch_decode(*args, **kwargs)
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def decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to VELDProcessor's [`~PreTrainedTokenizer.decode`].
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Please refer to the docstring of this method for more information.
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"""
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return self.tokenizer.decode(*args, **kwargs)
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special_tokens_map.json
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@@ -0,0 +1,107 @@
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{
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"additional_special_tokens": [
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"<extra_id_0>",
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"<extra_id_1>",
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"<extra_id_2>",
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"<extra_id_3>",
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"<extra_id_4>",
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"<extra_id_5>",
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"<extra_id_6>",
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"<extra_id_7>",
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"<extra_id_8>",
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"<extra_id_9>",
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"<extra_id_10>",
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"<extra_id_11>",
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"<extra_id_12>",
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"<extra_id_13>",
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"<extra_id_14>",
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"<extra_id_15>",
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"<extra_id_16>",
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"<extra_id_17>",
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"<extra_id_18>",
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"<extra_id_19>",
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"<extra_id_20>",
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"<extra_id_21>",
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"<extra_id_22>",
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"<extra_id_23>",
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"<extra_id_24>",
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"<extra_id_25>",
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"<extra_id_26>",
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"<extra_id_27>",
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"<extra_id_28>",
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"<extra_id_29>",
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"<extra_id_30>",
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"<extra_id_31>",
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"<extra_id_32>",
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"<extra_id_33>",
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"<extra_id_34>",
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"<extra_id_35>",
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"<extra_id_36>",
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"<extra_id_37>",
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"<extra_id_38>",
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"<extra_id_39>",
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"<extra_id_40>",
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"<extra_id_41>",
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"<extra_id_42>",
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"<extra_id_43>",
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"<extra_id_44>",
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"<extra_id_45>",
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"<extra_id_46>",
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"<extra_id_47>",
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"<extra_id_48>",
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"<extra_id_49>",
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"<extra_id_50>",
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"<extra_id_51>",
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"<extra_id_52>",
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"<extra_id_53>",
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"<extra_id_54>",
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"<extra_id_55>",
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"<extra_id_56>",
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"<extra_id_57>",
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"<extra_id_58>",
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"<extra_id_59>",
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"<extra_id_60>",
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"<extra_id_61>",
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"<extra_id_62>",
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"<extra_id_63>",
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"<extra_id_64>",
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"<extra_id_65>",
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"<extra_id_66>",
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"<extra_id_67>",
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"<extra_id_68>",
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"<extra_id_69>",
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"<extra_id_70>",
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"<extra_id_71>",
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"<extra_id_72>",
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"<extra_id_73>",
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"<extra_id_74>",
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"<extra_id_75>",
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"<extra_id_76>",
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"<extra_id_77>",
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"<extra_id_78>",
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"<extra_id_79>",
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"<extra_id_80>",
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"<extra_id_81>",
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"<extra_id_82>",
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"<extra_id_83>",
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"<extra_id_84>",
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"<extra_id_85>",
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"<extra_id_86>",
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"<extra_id_87>",
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"<extra_id_88>",
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"<extra_id_89>",
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"<extra_id_90>",
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"<extra_id_91>",
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"<extra_id_92>",
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"<extra_id_93>",
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"<extra_id_94>",
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"<extra_id_95>",
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"<extra_id_96>",
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"<extra_id_97>",
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"<extra_id_98>",
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"<extra_id_99>"
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],
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"eos_token": "</s>",
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"pad_token": "<pad>",
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"unk_token": "<unk>"
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}
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spiece.model
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:edc595fcd800672d6ef0e2aea5ba0f1dc8826471f6f40a87b70619dcc60ddd4a
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size 1466734
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tokenizer.json
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See raw diff
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tokenizer_config.json
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{
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"additional_special_tokens": [
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"<extra_id_0>",
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"<extra_id_1>",
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"<extra_id_2>",
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"<extra_id_3>",
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"<extra_id_4>",
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"<extra_id_5>",
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"<extra_id_6>",
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"<extra_id_7>",
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"<extra_id_8>",
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"<extra_id_9>",
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"<extra_id_10>",
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"<extra_id_11>",
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"<extra_id_12>",
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"<extra_id_13>",
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"<extra_id_14>",
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"<extra_id_15>",
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"<extra_id_16>",
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"<extra_id_17>",
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"<extra_id_18>",
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"<extra_id_19>",
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"<extra_id_20>",
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"<extra_id_21>",
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"<extra_id_22>",
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"<extra_id_23>",
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"<extra_id_24>",
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"<extra_id_25>",
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"<extra_id_26>",
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"<extra_id_27>",
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"<extra_id_28>",
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"<extra_id_29>",
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"<extra_id_30>",
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"<extra_id_31>",
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"<extra_id_32>",
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"<extra_id_33>",
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