Buckets:
configs
Helper module for using model configs. For more information, see the corresponding Python documentation.
Example: Load an AutoConfig.
import { AutoConfig } from '@huggingface/transformers';
const config = await AutoConfig.from_pretrained('bert-base-uncased');
console.log(config);
// PretrainedConfig {
// "model_type": "bert",
// "is_encoder_decoder": false,
// "architectures": [
// "BertForMaskedLM"
// ],
// "vocab_size": 30522
// "num_attention_heads": 12,
// "num_hidden_layers": 12,
// "hidden_size": 768,
// "max_position_embeddings": 512,
// ...
// }
- configs
- static
- .PretrainedConfig
new PretrainedConfig(configJSON)- instance
.model_type: string | null.is_encoder_decoder: boolean.max_position_embeddings: number
- static
.from_pretrained(pretrained_model_name_or_path, options)⇒ Promise.<PretrainedConfig>
- .AutoConfig
.getCacheShapes(config)⇒ Record.<string, Array<number>>~cache_values: Record.<string, Array<number>>
- .PretrainedConfig
- inner
~loadConfig(pretrained_model_name_or_path, options)⇒ Promise.<Object>~getNormalizedConfig(config)⇒ Object~getKeyValueShapes(): *~decoderFeeds: Record.<string, Array<number>>
~PretrainedOptions: *~ProgressCallback: *~ProgressInfo: *
- static
configs.PretrainedConfig
Base class for all configuration classes. For more information, see the corresponding Python documentation.
Kind: static class of configs
- .PretrainedConfig
new PretrainedConfig(configJSON)- instance
.model_type: string | null.is_encoder_decoder: boolean.max_position_embeddings: number
- static
.from_pretrained(pretrained_model_name_or_path, options)⇒ Promise.<PretrainedConfig>
new PretrainedConfig(configJSON)
Create a new PreTrainedTokenizer instance.
ParamTypeDescription
configJSONObjectThe JSON of the config.
pretrainedConfig.model_type : string | null
Kind: instance property of PretrainedConfig
pretrainedConfig.is_encoder_decoder : boolean
Kind: instance property of PretrainedConfig
pretrainedConfig.max_position_embeddings : number
Kind: instance property of PretrainedConfig
PretrainedConfig.from_pretrained(pretrained_model_name_or_path, options) ⇒ Promise.<PretrainedConfig>
Loads a pre-trained config from the given pretrained_model_name_or_path.
Kind: static method of PretrainedConfig
Returns: Promise.<PretrainedConfig> - A new instance of the PretrainedConfig class.
Throws:
Error Throws an error if the config.json is not found in the
pretrained_model_name_or_path.ParamTypeDescriptionpretrained_model_name_or_pathstringThe path to the pre-trained config.
optionsPretrainedOptionsAdditional options for loading the config.
configs.AutoConfig
Helper class which is used to instantiate pretrained configs with the from_pretrained function.
Kind: static class of configs
new AutoConfig()
Example
const config = await AutoConfig.from_pretrained('Xenova/bert-base-uncased');
AutoConfig.from_pretrained() : *
Kind: static method of AutoConfig
configs.getCacheShapes(config) ⇒ Record.<string, Array<number>>
Kind: static method of configs
ParamType
configPretrainedConfig
getCacheShapes~cache_values : Record.<string, Array<number>>
Kind: inner constant of getCacheShapes
configs~loadConfig(pretrained_model_name_or_path, options) ⇒ Promise.<Object>
Loads a config from the specified path.
Kind: inner method of configs
Returns: Promise.<Object> - A promise that resolves with information about the loaded config.
ParamTypeDescription
pretrained_model_name_or_pathstringThe path to the config directory.
optionsPretrainedOptionsAdditional options for loading the config.
configs~getNormalizedConfig(config) ⇒ Object
Kind: inner method of configs
Returns: Object - The normalized configuration.
ParamType
configPretrainedConfig
configs~getKeyValueShapes() : *
Kind: inner method of configs
getKeyValueShapes~decoderFeeds : Record.<string, Array<number>>
Kind: inner constant of getKeyValueShapes
configs~PretrainedOptions : *
Kind: inner typedef of configs
configs~ProgressCallback : *
Kind: inner typedef of configs
configs~ProgressInfo : *
Kind: inner typedef of configs
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