gerardovaldez0113
commited on
Commit
·
3a2109f
1
Parent(s):
8481f56
Initial model
Browse files- README.md +12 -0
- config.json +1 -0
- customSigLIP.py +237 -0
- model.safetensors +3 -0
- open_clip_config.json +45 -0
- open_clip_model.safetensors +3 -0
- open_clip_pytorch_model.bin +3 -0
- preprocessor_config.json +1 -0
- special_tokens_map.json +125 -0
- spiece.model +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +939 -0
README.md
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---
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license: apache-2.0
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tags:
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- clip
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- ecommerce
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- multimodal retrieval
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- transformers
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- openCLIP
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---
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# Product Embedding Model
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This model is a multimodal embedding model trained for ecommerce products.
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config.json
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{"auto_map": {"AutoConfig": "customSigLIP.SigLIPConfig", "AutoModel": "customSigLIP.SigLIP", "AutoProcessor": "customSigLIP.SigLIPProcessor"}, "open_clip_model_name": "hf-hub:gerardovaldez0113/product-embeddings", "model_type": "siglip", "type": "siglip"}
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customSigLIP.py
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import torch
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from open_clip import create_model
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from transformers import PretrainedConfig, PreTrainedModel
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from transformers.models.siglip.modeling_siglip import SiglipOutput
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from typing import Optional, Tuple, Union, List
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from transformers.feature_extraction_utils import BatchFeature
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from transformers.image_utils import ImageInput
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from transformers.processing_utils import ProcessorMixin
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from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
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from transformers.utils import TensorType
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import string
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import ftfy
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import html
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def basic_clean(text):
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text = ftfy.fix_text(text)
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text = html.unescape(html.unescape(text))
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return text.strip()
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def canonicalize_text(
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text,
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*,
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keep_punctuation_exact_string=None,
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trans_punctuation: dict = str.maketrans("", "", string.punctuation),
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):
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"""Returns canonicalized `text` (lowercase and punctuation removed).
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From: https://github.com/google-research/big_vision/blob/53f18caf27a9419231bbf08d3388b07671616d3d/big_vision/evaluators/proj/image_text/prompt_engineering.py#L94
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Args:
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text: string to be canonicalized.
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keep_punctuation_exact_string: If provided, then this exact string kept.
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For example providing '{}' will keep any occurrences of '{}' (but will
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still remove '{' and '}' that appear separately).
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"""
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text = text.replace("_", " ")
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if keep_punctuation_exact_string:
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text = keep_punctuation_exact_string.join(
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part.translate(trans_punctuation)
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for part in text.split(keep_punctuation_exact_string)
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)
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else:
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text = text.translate(trans_punctuation)
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text = text.lower()
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text = " ".join(text.split())
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return text.strip()
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def _clean_canonicalize(x):
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# basic, remove whitespace, remove punctuation, lower case
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return canonicalize_text(basic_clean(x))
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class SigLIPConfig(PretrainedConfig):
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def __init__(
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self,
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open_clip_model_name: str = "",
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**kwargs,
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):
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super().__init__(**kwargs)
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self.open_clip_model_name = open_clip_model_name
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class SigLIPProcessor(ProcessorMixin):
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r"""
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Constructs a Siglip processor which wraps a Siglip image processor and a Siglip tokenizer into a single processor.
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[`SiglipProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`SiglipTokenizer`]. See the
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[`~SiglipProcessor.__call__`] and [`~SiglipProcessor.decode`] for more information.
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Args:
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image_processor ([`SiglipImageProcessor`]):
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The image processor is a required input.
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tokenizer ([`T5TokenizerFast`]):
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The tokenizer is a required input.
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"""
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attributes = ["image_processor", "tokenizer"]
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image_processor_class = "SiglipImageProcessor"
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tokenizer_class = "T5TokenizerFast"
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def __init__(self, image_processor, tokenizer):
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super().__init__(image_processor, tokenizer)
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def __call__(
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self,
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text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
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images: ImageInput = None,
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padding: Union[bool, str, PaddingStrategy] = False,
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truncation: Union[bool, str, TruncationStrategy] = None,
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max_length: int = None,
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return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
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) -> BatchFeature:
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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 SiglipTokenizer's [`~SiglipTokenizer.__call__`] if `text` is not `None` to encode
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the text. To prepare the image(s), this method forwards the `images` argument to
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SiglipImageProcessor's [`~SiglipImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
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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. Both channels-first and channels-last formats are supported.
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padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
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Select a strategy to pad the returned sequences (according to the model's padding side and padding
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index) among:
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- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
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sequence if provided).
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- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
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acceptable input length for the model if that argument is not provided.
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- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
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lengths).
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max_length (`int`, *optional*):
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Maximum length of the returned list and optionally padding length (see above).
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truncation (`bool`, *optional*):
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Activates truncation to cut input sequences longer than `max_length` to `max_length`.
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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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[`BatchFeature`]: A [`BatchFeature`] 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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if isinstance(text, str):
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text = [text]
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text = [_clean_canonicalize(raw_text) for raw_text in text]
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encoding = self.tokenizer(
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text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
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)
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if images is not None:
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try:
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images = [image.convert('RGB') for image in images] if isinstance(images, list) else images.convert('RGB')
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except:
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images = images
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image_features = self.image_processor(images, return_tensors=return_tensors)
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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 BatchFeature(data=dict(**image_features), tensor_type=return_tensors)
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def decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to SiglipTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
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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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def batch_decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to SiglipTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
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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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@property
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# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names with CLIP->Siglip, T5->Siglip
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def model_input_names(self):
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tokenizer_input_names = self.tokenizer.model_input_names
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image_processor_input_names = self.image_processor.model_input_names
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return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
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class SigLIP(PreTrainedModel):
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config_class = SigLIPConfig
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def __init__(self, config: SigLIPConfig):
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super().__init__(config)
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self.config = config
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self.model = create_model(config.open_clip_model_name, output_dict=True)
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self.model.eval()
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self.model.to(self.device)
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def get_image_features(
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self,
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pixel_values: torch.FloatTensor,
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normalize: bool = False,
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**kwargs
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) -> torch.FloatTensor:
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with torch.inference_mode():
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image_features = self.model.encode_image(pixel_values, normalize=normalize)
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return image_features
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def get_text_features(
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self,
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input_ids: torch.Tensor,
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normalize: bool = False,
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**kwargs
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) -> torch.FloatTensor:
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with torch.inference_mode():
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text_features = self.model.encode_text(input_ids, normalize=normalize)
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return text_features
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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pixel_values: Optional[torch.FloatTensor] = None,
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return_dict: Optional[bool] = None,
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) -> Union[Tuple, SiglipOutput]:
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vision_outputs = self.get_image_features(pixel_values=pixel_values, normalize=True)
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text_outputs = self.get_text_features(input_ids=input_ids, normalize=True)
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logits_per_text = text_outputs @ vision_outputs.T
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logits_per_image = logits_per_text.T
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if not return_dict:
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return logits_per_image, logits_per_text, text_outputs, vision_outputs
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return SiglipOutput(
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logits_per_image=logits_per_image,
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logits_per_text=logits_per_text,
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text_embeds=text_outputs,
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image_embeds=vision_outputs
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)
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model.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:5f54e3323fc98caddba9626aa9771efd873c3cb9d63cc65b4619c2ccb6213e4e
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size 2608674872
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open_clip_config.json
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{
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"model_cfg": {
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"embed_dim": 1024,
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"init_logit_bias": -10,
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"custom_text": true,
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"vision_cfg": {
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"image_size": 256,
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"timm_model_name": "vit_large_patch16_siglip_256",
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"timm_model_pretrained": false,
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"timm_pool": "map",
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"timm_proj": "none"
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},
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"text_cfg": {
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"context_length": 64,
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"vocab_size": 32000,
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"hf_tokenizer_name": "timm/ViT-B-16-SigLIP",
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"tokenizer_kwargs": {
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"clean": "canonicalize"
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},
|
20 |
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"width": 1024,
|
21 |
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|
22 |
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|
23 |
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|
24 |
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|
25 |
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"pool_type": "last",
|
26 |
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"norm_kwargs": {
|
27 |
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"eps": 1e-06
|
28 |
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}
|
29 |
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}
|
30 |
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|
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"preprocess_cfg": {
|
32 |
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"mean": [
|
33 |
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|
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|
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|
36 |
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|
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|
38 |
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|
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|
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|
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|
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|
43 |
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|
44 |
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|
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}
|
open_clip_model.safetensors
ADDED
@@ -0,0 +1,3 @@
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|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:a586579224d119f10efb37d893970036654fb83653889953bb612dcbc8adf740
|
3 |
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size 2608671232
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open_clip_pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
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|
1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:8efc7166e45945cb23eb935d2a947f8c14338e0534d01d0440e097580a465b0f
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size 2608847582
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preprocessor_config.json
ADDED
@@ -0,0 +1 @@
|
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|
1 |
+
{"auto_map": {"AutoProcessor": "customSigLIP.SigLIPProcessor"}, "do_normalize": true, "do_rescale": true, "do_resize": true, "do_convert_rgb": true, "image_processor_type": "SiglipImageProcessor", "image_mean": [0.5, 0.5, 0.5], "processor_class": "customSigLIP.SigLIPProcessor", "resample": 3, "rescale_factor": 0.00392156862745098, "size": {"height": 256, "width": 256}, "image_std": [0.5, 0.5, 0.5]}
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special_tokens_map.json
ADDED
@@ -0,0 +1,125 @@
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
124 |
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|
125 |
+
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|
spiece.model
ADDED
@@ -0,0 +1,3 @@
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|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:d60acb128cf7b7f2536e8f38a5b18a05535c9e14c7a355904270e15b0945ea86
|
3 |
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size 791656
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tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,939 @@
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1 |
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"single_word": false,
|
825 |
+
"special": true
|
826 |
+
}
|
827 |
+
},
|
828 |
+
"additional_special_tokens": [
|
829 |
+
"<extra_id_0>",
|
830 |
+
"<extra_id_1>",
|
831 |
+
"<extra_id_2>",
|
832 |
+
"<extra_id_3>",
|
833 |
+
"<extra_id_4>",
|
834 |
+
"<extra_id_5>",
|
835 |
+
"<extra_id_6>",
|
836 |
+
"<extra_id_7>",
|
837 |
+
"<extra_id_8>",
|
838 |
+
"<extra_id_9>",
|
839 |
+
"<extra_id_10>",
|
840 |
+
"<extra_id_11>",
|
841 |
+
"<extra_id_12>",
|
842 |
+
"<extra_id_13>",
|
843 |
+
"<extra_id_14>",
|
844 |
+
"<extra_id_15>",
|
845 |
+
"<extra_id_16>",
|
846 |
+
"<extra_id_17>",
|
847 |
+
"<extra_id_18>",
|
848 |
+
"<extra_id_19>",
|
849 |
+
"<extra_id_20>",
|
850 |
+
"<extra_id_21>",
|
851 |
+
"<extra_id_22>",
|
852 |
+
"<extra_id_23>",
|
853 |
+
"<extra_id_24>",
|
854 |
+
"<extra_id_25>",
|
855 |
+
"<extra_id_26>",
|
856 |
+
"<extra_id_27>",
|
857 |
+
"<extra_id_28>",
|
858 |
+
"<extra_id_29>",
|
859 |
+
"<extra_id_30>",
|
860 |
+
"<extra_id_31>",
|
861 |
+
"<extra_id_32>",
|
862 |
+
"<extra_id_33>",
|
863 |
+
"<extra_id_34>",
|
864 |
+
"<extra_id_35>",
|
865 |
+
"<extra_id_36>",
|
866 |
+
"<extra_id_37>",
|
867 |
+
"<extra_id_38>",
|
868 |
+
"<extra_id_39>",
|
869 |
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"<extra_id_40>",
|
870 |
+
"<extra_id_41>",
|
871 |
+
"<extra_id_42>",
|
872 |
+
"<extra_id_43>",
|
873 |
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"<extra_id_44>",
|
874 |
+
"<extra_id_45>",
|
875 |
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"<extra_id_46>",
|
876 |
+
"<extra_id_47>",
|
877 |
+
"<extra_id_48>",
|
878 |
+
"<extra_id_49>",
|
879 |
+
"<extra_id_50>",
|
880 |
+
"<extra_id_51>",
|
881 |
+
"<extra_id_52>",
|
882 |
+
"<extra_id_53>",
|
883 |
+
"<extra_id_54>",
|
884 |
+
"<extra_id_55>",
|
885 |
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"<extra_id_56>",
|
886 |
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"<extra_id_57>",
|
887 |
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"<extra_id_58>",
|
888 |
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"<extra_id_59>",
|
889 |
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"<extra_id_60>",
|
890 |
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"<extra_id_61>",
|
891 |
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"<extra_id_62>",
|
892 |
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"<extra_id_63>",
|
893 |
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"<extra_id_64>",
|
894 |
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"<extra_id_65>",
|
895 |
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"<extra_id_66>",
|
896 |
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"<extra_id_67>",
|
897 |
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"<extra_id_68>",
|
898 |
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"<extra_id_69>",
|
899 |
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"<extra_id_70>",
|
900 |
+
"<extra_id_71>",
|
901 |
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"<extra_id_72>",
|
902 |
+
"<extra_id_73>",
|
903 |
+
"<extra_id_74>",
|
904 |
+
"<extra_id_75>",
|
905 |
+
"<extra_id_76>",
|
906 |
+
"<extra_id_77>",
|
907 |
+
"<extra_id_78>",
|
908 |
+
"<extra_id_79>",
|
909 |
+
"<extra_id_80>",
|
910 |
+
"<extra_id_81>",
|
911 |
+
"<extra_id_82>",
|
912 |
+
"<extra_id_83>",
|
913 |
+
"<extra_id_84>",
|
914 |
+
"<extra_id_85>",
|
915 |
+
"<extra_id_86>",
|
916 |
+
"<extra_id_87>",
|
917 |
+
"<extra_id_88>",
|
918 |
+
"<extra_id_89>",
|
919 |
+
"<extra_id_90>",
|
920 |
+
"<extra_id_91>",
|
921 |
+
"<extra_id_92>",
|
922 |
+
"<extra_id_93>",
|
923 |
+
"<extra_id_94>",
|
924 |
+
"<extra_id_95>",
|
925 |
+
"<extra_id_96>",
|
926 |
+
"<extra_id_97>",
|
927 |
+
"<extra_id_98>",
|
928 |
+
"<extra_id_99>"
|
929 |
+
],
|
930 |
+
"clean_up_tokenization_spaces": true,
|
931 |
+
"eos_token": "</s>",
|
932 |
+
"extra_ids": 100,
|
933 |
+
"legacy": false,
|
934 |
+
"model_max_length": 64,
|
935 |
+
"pad_token": "</s>",
|
936 |
+
"sp_model_kwargs": {},
|
937 |
+
"tokenizer_class": "T5Tokenizer",
|
938 |
+
"unk_token": "<unk>"
|
939 |
+
}
|