@huggingface/transformers
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State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server!
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TypeScript
/**
*
* @param {PretrainedConfig} config
* @returns {Record<string, number[]>}
*/
export function getKeyValueShapes(config: PretrainedConfig, { prefix, batch_size, }?: {
prefix?: string;
batch_size?: number;
}): Record<string, number[]>;
/**
* Base class for all configuration classes. For more information, see the corresponding
* [Python documentation](https://huggingface.co/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig).
*/
export class PretrainedConfig {
/**
* Loads a pre-trained config from the given `pretrained_model_name_or_path`.
*
* @param {string} pretrained_model_name_or_path The path to the pre-trained config.
* @param {PretrainedOptions} options Additional options for loading the config.
* @throws {Error} Throws an error if the config.json is not found in the `pretrained_model_name_or_path`.
*
* @returns {Promise<PretrainedConfig>} A new instance of the `PretrainedConfig` class.
*/
static from_pretrained(pretrained_model_name_or_path: string, { progress_callback, config, cache_dir, local_files_only, revision, }?: PretrainedOptions): Promise<PretrainedConfig>;
/**
* Create a new PreTrainedTokenizer instance.
* @param {Object} configJSON The JSON of the config.
*/
constructor(configJSON: any);
/** @type {string|null} */
model_type: string | null;
/** @type {boolean} */
is_encoder_decoder: boolean;
/** @type {number} */
max_position_embeddings: number;
/** @type {TransformersJSConfig} */
'transformers.js_config': TransformersJSConfig;
normalized_config: any;
}
/**
* Helper class which is used to instantiate pretrained configs with the `from_pretrained` function.
*
* @example
* const config = await AutoConfig.from_pretrained('Xenova/bert-base-uncased');
*/
export class AutoConfig {
/**
* Loads a pre-trained config from the given `pretrained_model_name_or_path`.
*
* @param {string} pretrained_model_name_or_path The path to the pre-trained config.
* @param {PretrainedOptions} options Additional options for loading the config.
* @throws {Error} Throws an error if the config.json is not found in the `pretrained_model_name_or_path`.
*
* @returns {Promise<PretrainedConfig>} A new instance of the `PretrainedConfig` class.
*/
static from_pretrained(pretrained_model_name_or_path: string, { progress_callback, config, cache_dir, local_files_only, revision, }?: PretrainedOptions): Promise<PretrainedConfig>;
}
export type PretrainedOptions = import("./utils/hub.js").PretrainedOptions;
export type ProgressCallback = import("./utils/core.js").ProgressCallback;
export type ProgressInfo = import("./utils/core.js").ProgressInfo;
/**
* Transformers.js-specific configuration, possibly present in config.json under the key `transformers.js_config`.
*/
export type TransformersJSConfig = {
/**
* The data type of the key-value cache.
*/
kv_cache_dtype?: import("./utils/tensor.js").DataType | Record<import("./utils/dtypes.js").DataType, import("./utils/tensor.js").DataType>;
/**
* Override the free dimensions of the model.
* See https://onnxruntime.ai/docs/tutorials/web/env-flags-and-session-options.html#freedimensionoverrides
* for more information.
*/
free_dimension_overrides?: Record<string, number>;
/**
* The default device to use for the model.
*/
device?: import("./utils/devices.js").DeviceType;
/**
* The default data type to use for the model.
*/
dtype?: import("./utils/dtypes.js").DataType | Record<string, import("./utils/dtypes.js").DataType>;
/**
* Whether to load the model using the external data format (used for models >= 2GB in size).
*/
use_external_data_format?: import("./utils/hub.js").ExternalData | Record<string, import("./utils/hub.js").ExternalData>;
};
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