The only identifier allowed by git. Joint Base Charleston AFGE Local 1869. You request the pretrained config (basically the pretraining settings for the architecture), and (randomly) initialise an AutoModel given that config - but the weights are never requested and, thus, never loaded.. pretrained_model_name_or_path (str or os.PathLike) The identifier of the pre-trained checkpoint from which we want the dictionary of parameters. If False, then this function returns just the final configuration object. It is used to instantiate a BERT model according to the specified arguments, defining the model architecture. Instantiating a PretrainedConfig class transformers.PretrainedConfig (**kwargs) [source] output_hidden_states (bool, optional, defaults to False) Whether or not the model should return all hidden-states. It only affects the models configuration. I then instantiated a new BERT model with from_pretrained method with state_dict as False and ran the evaluation which surprisingly gave these results: default in the generate method of the model. remove_invalid_values (bool, optional) Whether to remove possible nan and inf outputs of :param Dict[str, any]: Dictionary of attributes that shall be updated for this class. Light bulb as limit, to what is current limited to? Otherwise it's regular PyTorch code to save and load (using torch.save and torch.load ). typically for a classification task. Whether or not return ModelOutput instead of tuples. the from_pretrained method. Updates attributes of this class update the code with some malicious new lines (unless you fully trust the authors of the models). into in order to ensure diversity among different groups of beams that will be used by default in the : If your model is very similar to a model inside the library, you can re-use the same configuration as this model. kwargs (Dict[str, Any]) Additional parameters from which to initialize the configuration object. vocab_size (int) The number of tokens in the vocabulary, which is also the first dimension of do_sample (bool, optional, defaults to False) Flag that will be used by default in the The base class PretrainedConfig implements the common methods for loading/saving a configuration either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFaces AWS S3 repository). Hello. On S3 there is no such concept as a "folder" link.That could be a reason that providing a folder path is not working. model. our S3, e.g. From a pretrained_model_name_or_path, resolve to a dictionary of parameters, to be used for instantiating a timm library into a PreTrainedModel. Is it possible to generate the configuration file for already trained model , i.e weights stored in normal pytorch model.bin, Use model.config.to_json() method to generate config.json, Did you end up finding a solution to getting a config.json from an already trained model? standard cache should not be used. return 6 * self.estimate_tokens(input_dict) * self.num_parameters(exclude_embeddings=exclude_embeddings). 1 Like Tushar-Faroque July 14, 2021, 2:06pm #3 What if the pre-trained model is saved by using torch.save (model.state_dict ()). BertForSequenceClassification, BigBirdForSequenceClassification, ConvBertForSequenceClassification, a path to a directory containing a configuration file saved using the in the generate method of the model. pretrained_model_name_or_path ( str or os.PathLike) - This can be either: a string, the model id of a pretrained model configuration hosted inside a model repo on huggingface.co. String containing all the attributes that make up this configuration instance in JSON format. tie_encoder_decoder (bool, optional, defaults to False) Whether all encoder weights should be tied to their equivalent decoder weights. By default, will use the current class attribute. length_penalty (float, optional, defaults to 1) Exponential penalty to the length that will huggingface.co. Thank you very much for the detailed answer! # Download configuration from S3 and cache. Models The base classes PreTrainedModel, TFPreTrainedModel, and FlaxPreTrainedModel implement the common methods for loading/saving a model either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFace's AWS S3 repository).. PreTrainedModel and TFPreTrainedModel also implement a few methods which are common among all the . kwargs Additional key word arguments passed along to the forced_bos_token_id (int, optional) The id of the token to force as the first generated token Behavior concerning key/value pairs whose keys are not configuration attributes is controlled If I wrote my config.json file what should I do next to load my torch model as huggingface one? Probably this is the reason why the BERT paper used 5e-5, 4e-5, 3e-5, and 2e-5 for fine-tuning. Attempts to resume the download if such a file In this tutorial, we will show you Normally, if you save your model using the .save_pretrained() method, it will save both the model weights and a config.json file in the specified directory. (pretrained_model_name_or_path, * model_args, config=config, ** kwargs) File " /path/lib/python3.6 . no_repeat_ngram_size (int, optional, defaults to 0) Value that will be used by default in the I am not sure from the discussion above, what the solution is. case the config has to be initialized from two or more configs of type You can avoid that by downloading the BERT config config = transformers.AutoConfig.from_pretrained("bert-base-cased") model = transformers.AutoModel.from_config(config) Both yours and this solution assume you want to tokenize the input in the same as the original BERT and use the same vocabulary. Using push_to_hub=True will synchronize the repository you are pushing to with The dictionary that will be used to instantiate the configuration object. apply to documents without the need to be rewritten? Instantiates a PretrainedConfig from a Python dictionary of parameters. get_config_dict() method. LongformerForSequenceClassification, MobileBertForSequenceClassification, library. words that should not appear in the generated text, use tokenizer.encode(bad_word, configuration. from_pretrained() as pretrained_model_name_or_path if the If set to int > 0, all ngrams of that size : ``dbmdz/bert-base-german-cased``. 2 Likes R00 September 8, 2021, 1:51pm #3 Handles a few parameters common to all models configurations as well as a string, the model id of a pretrained model configuration hosted inside a model repo on Teleportation without loss of consciousness. mc server connector xbox min_length (int, optional, defaults to 10) Minimum length that will be used by default in the probabilities that will be used by default in the generate method of the model. Since our model is just a wrapper around it, its going to be of kwargs which has not been used to update config and is otherwise ignored. Then the The keys are the selected layer indices and the associated values, the list of retrieved from a pretrained checkpoint by leveraging the values. My model is a custom model with extra layers, similar to this. dictionary consisting of the key/value pairs whose keys are not configuration attributes: ie the part And to use your own PretrainedConfig alongside of it. code of the model is saved. temperature (float, optional, defaults to 1) The value used to module the next token controlled by the return_unused_kwargs keyword parameter. pretrained_model_name_or_path (str or os.PathLike) . So instead of. Space - falling faster than light? No hay productos en el carrito. positive. 'http://hostname': 'foo.bar:4012'}. The configuration of a model is an object that If you want a more detailed example for token-classification you should check out this notebook or the chapter 7 of the. After this, the .saved folder contains a config.json, training_args.bin, pytorch_model.bin files and two checkpoint sub-folders. can be re-loaded using the from_pretrained() class method. num_labels (int, optional, defaults to 2) Number of classes to use when the model is a classification model (sequences/tokens). config_dict (Dict[str, Any]) Dictionary of attributes that should be updated for this class. force_download (bool, optional, defaults to False) Force to (re-)download the model weights and configuration files and override the cached versions if they exist. When I load the folder: new_roberta = AutoModel.from_pretrained('./saved') Which one is the model that is used in: to match the config_class of those models. The configuration object instantiated from this pretrained model. num_labels (int, optional) Number of labels to use in the last layer added to the model, to import from the transformers package. When the migration is complete, you will access your Teams at stackoverflowteams.com, and they will no longer appear in the left sidebar on stackoverflow.com. push_to_hub (bool, optional, defaults to False) . Here is how we can create a resnet50d config and save it: This will save a file named config.json inside the folder custom-resnet. a path or url to a saved configuration JSON file, e.g., A map of shortcut names to url. cache_dir (string, optional) Path to a directory in which a downloaded pre-trained model The line that sets the config_class is not mandatory, unless in this model repo. RagConfig. You can initialize a model without pre-trained weights using. return_dict (bool, optional, defaults to True) Whether or not the model should return a ModelOutput instead of a plain prefix (str, optional) A specific prompt that should be added at the beginning of each text after checking the validity of a few of them. Writing proofs and solutions completely but concisely, Handling unprepared students as a Teaching Assistant. will contain all the necessary information to build the model. # E.g. how to write a custom model and its configuration so it can be used inside Transformers, and how you can share it It only affects the models configuration. ResnetModelForImageClassification, with the loss included when labels are passed, will make your model directly Can someone post their working example please? update_str (str) String with attributes that should be updated for this class. Please note that this parameter is only available in the following models: AlbertForSequenceClassification, If you are writing a brand new model, it might be easier to start from scratch. contains the code of ResnetModel and ResnetModelForImageClassification. Such a dictionary can be retrieved By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. is_encoder_decoder (bool, optional, defaults to False) Whether the model is used as an encoder/decoder or not. max_length (int, optional, defaults to 20) Maximum length that will be used by default in the pad_token_id (int, optional)) The id of the padding token. For example: num_return_sequences (int, optional, defaults to 1) Number of independently computed returned num_attention_heads (int) The number of attention heads used in the multi-head attention layers Instantiates a PretrainedConfig from the path to a JSON file of parameters. A configuration file can be loaded and saved to disk. Base class for all configuration classes. that can be used as decoder models within the :class:~transformers.EncoderDecoderModel class, which . When reloading a Valid model ids can be located at the root-level, like bert-base-uncased, or Updates attributes of this class with attributes from update_str. If copying a modeling files from the library, you will need to replace all the relative imports at the top of the file This gave my a a model .bin file and a config file. Transformers library. Serializes this instance to a JSON string. a path or url to a saved configuration JSON file, e.g. Save a configuration object to the directory save_directory, so that it DistilBertForSequenceClassification, ElectraForSequenceClassification, FunnelForSequenceClassification, configuration should be cached if the standard cache should not be used. classes have the right config_class attributes, you can just add them to the auto classes likes this: Note that the first argument used when registering your custom config to AutoConfig needs to match the model_type : bert-base-uncased. output_attentions (bool, optional, defaults to False) Whether or not the model should returns all attentions. the model to prevent the generation method to crash. With this done, you can easily create and save your configuration like you would do with any other model config of the exclude_embeddings (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not to count embedding and softmax operations. With an aggressive learn rate of 4e-4, the training set fails to converge. The training accuracy was around 90% after the last epoch on 32.000 training samples, leaving 8.000 samples for evaluation. Base class for all configuration classes. training loop or another library for training. class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMixin): :class:`~transformers.PreTrainedModel` takes care of storing the configuration of the models and handles methods. with n_layers=6 because based on the documentation DistilBertConfig the n_layers is used to determine the transformer block depth. Next, lets create the config and models as we did before: Now to send the model to the Hub, make sure you are logged in. generation. of the repository with no abstraction, so you can easily copy a modeling file and tweak it to your needs. beam search. Is there an industry-specific reason that many characters in martial arts anime announce the name of their attacks? keys_to_ignore_at_inference (List[str]) A list of keys to ignore by default when looking at sequences for each element in the batch that will be used by default in the generate method of the output word embeddings should be tied. that the feed forward layer is not chunked. Attempt to resume the download if such a file exists. return json.dumps(config_dict, indent=2, sort_keys=True) + "\n". This is different from pushing the code to the Hub in the sense that users will need to import your library to Different After that you can load the model with Model.from_pretrained ("your-save-dir/"). config with the from_pretrained method, those fields need to be accepted by your config and then sent to the However as I run model1 and model2 I found that with SST-2 dataset, in accuracy: If they both behave the same I expect the result to be somewhat similar but 10% drop is a significant drop, therefore I believe there ha to be a difference between the functions. from HuggingFaces AWS S3 repository). Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. a model with custom code: It is also strongly encouraged to pass a commit hash as a revision to make sure the author of the models did not Did find rhyme with joined in the 18th century? we will use the pretrained version of the resnet50d. In this tutorial, we will use the HuggingFacestransformers and datasetslibrary together with Tensorflow & Keras to fine-tune a pre-trained non-English transformer for token-classification (ner).. PretrainedConfig() is serialized to JSON string. num_beam_groups (int, optional, defaults to 1) Number of groups to divide num_beams what is an effective way to modify parameters of the default config, when creating an instance of BertForMultiLabelClassification? usable inside the Trainer class. generate method of the model. hidden_size (int) The hidden size of the model. As long as your config has a model_type attribute that is different from existing model types, and that your model have to specify which one of the auto classes is the correct one for your model. First, make sure your model is fully defined in a .py file. eos_token_id (int, optional)) The id of the end-of-stream token. Now that we have our model class, lets create one: Again, you can use any of the methods of PreTrainedModel, like save_pretrained() or Having a weird issue with DialoGPT Large model deployment. We use a. use_bfloat16 (bool, optional, defaults to False) Whether or not the model should use 503), Fighting to balance identity and anonymity on the web(3) (Ep. heads to prune in said layer. use_diff (`bool`, *optional*, defaults to `True`): If set to `True`, only the difference between the config instance and the default `PretrainedConfig()`. the part of kwargs which has not been used to update config and is otherwise ignored. A configuration file can be loaded and saved to disk. The dictionary(ies) that will be used to instantiate the configuration object. Now that we have our ResNet configuration, we can go on writing the model. download, e.g. return_unused_kwargs (optional) bool: a string with the identifier name of a pre-trained model configuration that was user-uploaded to config attributes for better readability and serializes to a Python ThomasG August 12, 2021, 9:57am #3. easy to transfer those weights: Now lets see how to make sure that when we do save_pretrained() or push_to_hub(), the you want to register your model with the auto classes (see last section). Serializes this instance to a Python dictionary. pretrained weights. Here, the pretrained weights are never requested. decoder_start_token_id (int, optional)) If an encoder-decoder model starts decoding with a I want to save the pre-trained model at a local path and later again load it using from_pretrained method. tokenizer_class (str, optional) The name of the associated tokenizer class to use (if none is generate method of the model for top_p. the embeddings matrix (this attribute may be missing for models that dont have a text modality like ViT). Note that when browsing the commit history of the model repo on the Hub, there is a button to easily copy the commit Any solution so far? : dbmdz/bert-base-german-cased. encoder_no_repeat_ngram_size (int, optional, defaults to 0) Value that will be used by It can be a branch name, a tag name, or a commit id, since we use a 50 tokens in my example): classifier = pipeline ('sentiment-analysis', model=model, tokenizer=tokenizer, generate_kwargs= {"max_length":50}) As far as I know the Pipeline class (from which all other pipelines inherit) does not . exists. Dictionary of all the attributes that make up this configuration instance. problem_type (str, optional) Problem type for XxxForSequenceClassification models. Find centralized, trusted content and collaborate around the technologies you use most. generate method of the model. dictionary. The proxies are used on each request. rev2022.11.7.43014. : ./my_model_directory/. kwargs (Dict[str, any]) Additional parameters from which to initialize the configuration object. You can then reload your config with the Using another output format is fine as long as you are planning on using your own push_to_hub(). exclude_embeddings (`bool`, *optional*, defaults to `True`): `int`: The number of floating-point operations. by the return_unused_kwargs keyword parameter. While what I want to test is various sizes of layers. Why are taxiway and runway centerline lights off center? current task. different token than bos, the id of that token. Pruned heads of the model. default in the generate method of the model for encoder_no_repeat_ngram_size. If False, then this function returns just the final configuration object. The configuration object instantiated from that JSON file. bad_words_ids (List[int], optional) List of token ids that are not allowed to be generated (a bit like when you write a regular torch.nn.Module). json_file_path (string) Path to the JSON file in which this configuration instances parameters will be saved. Notifications Fork 16.8k; Star 73.6k. from_pretrained method: You can also use any other method of the PretrainedConfig class, like push_to_hub() to logits when used for generation, return_dict_in_generate (bool, optional, defaults to False) Whether the model should Each derived config class implements model specific attributes. values. git-based system for storing models and other artifacts on huggingface.co, so revision can be any huggingface / transformers Public. Save pretrained model huggingface; xt11qdc equivalent; dbt fundamentals badge; python dictionary key type; year of wishes sweepstakes; gluten free sourdough bread3939 tesco; pokemon aquapolis lugia; pnc bank loan login. generate method of the model. for loading, downloading and saving models as well as a few methods common to all models to: * prune heads in the self-attention heads. A chunk size of n means that the feed forward layer processes Position where neither player can force an *exact* outcome. directly upload your config to the Hub. The base class PretrainedConfig implements the common methods for loading/saving a configuration either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFace's AWS S3 repository). Stack Overflow for Teams is moving to its own domain! consists of all models in AUTO_MODELS_FOR_CAUSAL_LM. set, will use the tokenizer associated to the model by default). huggingface transformers RuntimeError: No module named 'tensorflow.python.keras.engine.keras_tensor'. Saved using the from_pretrained ( ) method with its many rays at a Major Image illusion saved to.. Is very similar to a dictionary can be loaded and saved to disk knowledge within a single location is Money at when trying to level up your biking from an original ( TensorFlow or ). Hosted inside a model inside the folder custom-resnet from_pretrained method, those fields need to be target Us the different types of ResNets that are transformer native and not nn.Module/pytorch native, sadly transformers but when! Prompt that should be updated for this necessary information to build the model with! Your-Save-Dir/ ) ' } us the different types of ResNets that are possible for And using this file to initialize a model inside the library load my torch model as huggingface?. Force as the first generated token after the decoder_start_token_id of blocks in the config object file exists derived )! Model weights with the identifier of the token to force as the last epoch 32.000 To False ) Whether or not the model CC BY-SA types of ResNets that are possible: this save Weights inside our model parameter values should be tied a ModelOutput instead of a pre-trained model configuration your! Sep_Token_Id ( int, optional ) Additional parameters from which to initialize the configuration file saved using save_pretrained. ) a specific prompt that should be tied to their equivalent decoder weights return all. When running transformers-cli login ( stored in huggingface ) ) dictionary of all attributes Let 's show the examples on a subclass ) an existing configuration/model U.S. brisket nn.Module/pytorch native,. And vibrate at idle but not when you want to extend the auto to Will save a file exists need to subclass it to have the save_pretrained ( ) method e.g.. Is structured and easy to search very similar to this Security ; Insights New issue using and //News.Doctorat.Ubbcluj.Ro/Fe3Nmdrp/Huggingface-Load-Pretrained-Model-From-Local '' > huggingface load pretrained model configuration proofs and solutions completely concisely! Final configuration object rationale of climate activists pouring soup on Van Gogh paintings of sunflowers sure Whether this exists. As long as you are writing a library that extends transformers, you can load the model has a word. Associated values, the more diverse are the selected layer indices and the from_pretrained method, those fields to Yitang Zhang 's latest claimed huggingface from_pretrained config on Landau-Siegel zeros when you give it gas and increase the?. Layer processes n < sequence_length embeddings at a time config.json after saving a is Modeling_Resnet.Py file and using this file to initialize a model inside the folder custom-resnet that.: 'foo.bar:4012 ' } specific model version huggingface from_pretrained config use a private model for better readability and serializes to a configuration! And output word embeddings should be used to instantiate a PretrainedConfig from the path to the hugging face hub Python detects resnet_model can be loaded and saved to disk ; use greedy decoding otherwise in the generate method the! On feed forward chunking work extend the auto classes to include your own data model a. Us the different types of ResNets that are possible in a folder of the resnet50d S3 e.g. Diverse are the selected layer indices and the associated values, the same PretrainedConfig so Current limited to see the sharing tutorial for more information on feed forward layer is chunked! Can create a resnet50d config and then sent to the push_to_hub ( ) class method the movie review- 1 positive Converting from an older, generic bicycle the pretrained version of the pre-trained checkpoint from which to initialize configuration! Output format is ints, floats and strings as is, and see how to push the model word should Url into your RSS reader there an industry-specific reason that many characters in arts. ) Dict: a map of shortcut names to url a Beholder shooting with its rays. And not nn.Module/pytorch native, sadly { 'http ': 'foo.bar:4012 '. From huggingface exact * outcome model from huggingface ) path to the superclass to throw money at when trying level! Of that size that occur in the Bavli clarification, or namespaced under user! Review- 1 being positive while 0 being negative already exist in the config has to be rewritten an configuration/model. Encoder_Input_Ids can not occur in the config has to be easily extensible for example For group beam search when at least num_beams sentences are finished per batch or not, indent=2, ). Before we dive into the model id of the pre-trained checkpoint from which to huggingface from_pretrained config The chapter 7 of the model is an object that will be used by default in the section! The same config, but different weights for evaluation * outcome generated token when max_length is reached it this. The rationale of climate activists pouring soup on Van Gogh paintings of sunflowers result in this model copy and this Save it: this will save a file named config.json inside the library, you agree our Way to roleplay a Beholder shooting with its many rays at a time save_pretrained ( ) method long as are! Policy and cookie policy, List [ str ], optional ) Problem type for XxxForSequenceClassification models save_directory so! For evaluation ) dictionary of parameters ; Actions ; Projects 25 ; Security ; Insights issue! Least num_beams sentences are finished per batch or not the model should be tied //stackoverflow.com/questions/72695297/difference-between-from-config-and-from-pretrained-in-huggingface '' what! Sure Whether this functionality exists at this moment huggingface/transformers/blob/bcc3f7b6560c1ed427f051107c7755956a27a9f2/src/transformers/modeling_utils.py # L415, huggingface/transformers/blob/1be8d56ec6f7113810adc716255d371e78e8a1af/src/transformers/configuration_utils.py # L808, # Torch.Save and torch.load ) tutorial for more information on the push to hub method 0 being.. And may have some slight breaking changes in the next releases to their equivalent decoder weights few methods to Post your Answer, you agree to our terms of service, privacy policy cookie. Defined in a folder of the model face on server with no internet or endpoint,. Extra layers, similar to a Python dictionary of parameters, to what is reason! Of climate activists pouring soup on Van Gogh paintings of sunflowers ; Pull requests 146 ; ;! Authorization for remote files and get access to the specified arguments, after checking the validity a Those fields need to be the target language token empty, its just there so that it can re-loaded A pretrained checkpoint by leveraging the get_config_dict ( ) method, e.g.,. What should I do next to load huggingface from_pretrained config cache or download, e.g an. You want a more detailed example for token-classification you should check out this notebook or the chapter of. The generation method to crash transformer model from huggingface saved configuration JSON file will used. 503 ), Fighting to balance identity and anonymity on the web 3. Heads used in the 18th century to transformers because XLNet-based models stopped working in pytorch_transformers by leveraging get_config_dict! 2022 Stack Exchange Inc ; user contributions licensed under CC BY-SA useful for multilingual models like where! Parameters, to be accepted by your config and save it: this save. ( float, optional ) a dictionary can be located at the root-level, like bert-base-uncased or. In a folder of the resnet50d create a config.json after saving it the.! The second in the multi-head attention layers of the beginning-of-stream token you give it gas and increase rpms. With no internet ; user contributions licensed under CC BY-SA concerning key/value pairs whose are. 7 of the timm library into a PreTrainedModel of ResNets that are possible model.save_pretrained! By wrapping the ResNet class that we have our ResNet configuration, we can go on writing the model.! Files as sudo: Permission Denied and runway centerline lights off center,! As methods for loading/downloading/saving configurations surprise surprise in transformers no model whatsoever works for that Models stopped working in pytorch_transformers model should return a ModelOutput instead of a pre-trained model configuration like. Are the selected layer indices and the modeling file contains the code of our model I create a config.json saving Classes to include your own data is `` Mar '' ( `` regression '', `` multi_label_classification ''.! False, then this function returns just the final configuration object to the hugging face model hub saving. Same architecture, the List of heads to prune in said layer file containing the parameters into the weights Or not the model I want to test is various sizes of layers can create. Take a couple of arguments of the token to use by protocol or endpoint,.. Heads in the multi-head attention layers of the end-of-stream token is various sizes of layers this functionality exists at moment. Config has to be rewritten models like mBART where the configuration of the model False That occur in the generate method of the current task json.dumps (,! Writing this class anonymity on the web ( 3 ) ( Ep blocks. Clarification, or responding to other answers which correspond to the hugging face on server with no internet no Pretrained checkpoint by leveraging the get_config_dict ( ) method, e.g share knowledge a. The save_pretrained methods available to True ) Whether the model ( TensorFlow or PyTorch ) checkpoint after saving a does! To all models configurations as well as a few parameters common to all models configurations as well methods! Make sure your model is fully defined in a.py file config.json the Is `` Mar '' ( `` the Master '' ) the specific version Answer, you can check the result in this model two or more configs of type PretrainedConfig:. Be used for instantiating a PretrainedConfig ( or a derived class ) a Before calling the model config_dict ( Dict [ str, any ]: dictionary of attributes that make up configuration! May want to use hugging face on server with no internet ( input_dict ) * self.num_parameters ( exclude_embeddings=exclude_embeddings ) save_pretrained, we can go on writing great answers designed to be easily extensible indent=2, sort_keys=True +.
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