wav2vec2-ctc-tedlium / models /configuration_bart.py

Add training scripts and weights

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	# coding=utf-8
	# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	""" BART model configuration"""
	import warnings

	from transformers.configuration_utils import PretrainedConfig
	from transformers.utils import logging


	logger = logging.get_logger(__name__)

	BART_PRETRAINED_CONFIG_ARCHIVE_MAP = {
	"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json",
	# See all BART models at https://huggingface.co/models?filter=bart
	}


	class BartConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`BartModel`]. It is used to instantiate a BART
	model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
	defaults will yield a similar configuration to that of the BART
	[facebook/bart-large](https://huggingface.co/facebook/bart-large) architecture.

	Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
	documentation from [`PretrainedConfig`] for more information.


	Args:
	vocab_size (`int`, optional, defaults to 50265):
	Vocabulary size of the BART model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`BartModel`] or [`TFBartModel`].
	d_model (`int`, optional, defaults to 1024):
	Dimensionality of the layers and the pooler layer.
	encoder_layers (`int`, optional, defaults to 12):
	Number of encoder layers.
	decoder_layers (`int`, optional, defaults to 12):
	Number of decoder layers.
	encoder_attention_heads (`int`, optional, defaults to 16):
	Number of attention heads for each attention layer in the Transformer encoder.
	decoder_attention_heads (`int`, optional, defaults to 16):
	Number of attention heads for each attention layer in the Transformer decoder.
	decoder_ffn_dim (`int`, optional, defaults to 4096):
	Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
	encoder_ffn_dim (`int`, optional, defaults to 4096):
	Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
	activation_function (`str` or `function`, optional, defaults to `"gelu"`):
	The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
	`"relu"`, `"silu"` and `"gelu_new"` are supported.
	dropout (`float`, optional, defaults to 0.1):
	The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
	attention_dropout (`float`, optional, defaults to 0.0):
	The dropout ratio for the attention probabilities.
	activation_dropout (`float`, optional, defaults to 0.0):
	The dropout ratio for activations inside the fully connected layer.
	classifier_dropout (`float`, optional, defaults to 0.0):
	The dropout ratio for classifier.
	max_position_embeddings (`int`, optional, defaults to 1024):
	The maximum sequence length that this model might ever be used with. Typically set this to something large
	just in case (e.g., 512 or 1024 or 2048).
	init_std (`float`, optional, defaults to 0.02):
	The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
	encoder_layerdrop: (`float`, optional, defaults to 0.0):
	The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
	for more details.
	decoder_layerdrop: (`float`, optional, defaults to 0.0):
	The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
	for more details.
	scale_embedding (`bool`, optional, defaults to `False`):
	Scale embeddings by diving by sqrt(d_model).
	use_cache (`bool`, optional, defaults to `True`):
	Whether or not the model should return the last key/values attentions (not used by all models).
	num_labels: (`int`, optional, defaults to 3):
	The number of labels to use in [`BartForSequenceClassification`].
	forced_eos_token_id (`int`, optional, defaults to 2):
	The id of the token to force as the last generated token when `max_length` is reached. Usually set to
	`eos_token_id`.
	use_scan (`bool`, optional, defaults to `False`):
	Whether or not to use nn.scan in the Flax Bart attention layers.

	Example:

	```python
	>>> from transformers import BartModel, BartConfig

	>>> # Initializing a BART facebook/bart-large style configuration
	>>> configuration = BartConfig()

	>>> # Initializing a model from the facebook/bart-large style configuration
	>>> model = BartModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```"""
	model_type = "bart"
	keys_to_ignore_at_inference = ["past_key_values"]
	attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

	def __init__(
	self,
	vocab_size=50265,
	max_position_embeddings=1024,
	encoder_layers=12,
	encoder_ffn_dim=4096,
	encoder_attention_heads=16,
	decoder_layers=12,
	decoder_ffn_dim=4096,
	decoder_attention_heads=16,
	encoder_layerdrop=0.0,
	decoder_layerdrop=0.0,
	activation_function="gelu",
	d_model=1024,
	dropout=0.1,
	attention_dropout=0.0,
	activation_dropout=0.0,
	init_std=0.02,
	classifier_dropout=0.0,
	scale_embedding=False,
	use_cache=True,
	use_scan=False,
	fuse_matmuls=False,
	num_labels=3,
	pad_token_id=1,
	bos_token_id=0,
	eos_token_id=2,
	is_encoder_decoder=True,
	decoder_start_token_id=2,
	forced_eos_token_id=2,
	**kwargs
	):
	self.vocab_size = vocab_size
	self.max_position_embeddings = max_position_embeddings
	self.d_model = d_model
	self.encoder_ffn_dim = encoder_ffn_dim
	self.encoder_layers = encoder_layers
	self.encoder_attention_heads = encoder_attention_heads
	self.decoder_ffn_dim = decoder_ffn_dim
	self.decoder_layers = decoder_layers
	self.decoder_attention_heads = decoder_attention_heads
	self.dropout = dropout
	self.attention_dropout = attention_dropout
	self.activation_dropout = activation_dropout
	self.activation_function = activation_function
	self.init_std = init_std
	self.encoder_layerdrop = encoder_layerdrop
	self.decoder_layerdrop = decoder_layerdrop
	self.classifier_dropout = classifier_dropout
	self.use_cache = use_cache
	self.use_scan = use_scan
	self.fuse_matmuls = fuse_matmuls
	self.num_hidden_layers = encoder_layers
	self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True

	super().__init__(
	num_labels=num_labels,
	pad_token_id=pad_token_id,
	bos_token_id=bos_token_id,
	eos_token_id=eos_token_id,
	is_encoder_decoder=is_encoder_decoder,
	decoder_start_token_id=decoder_start_token_id,
	forced_eos_token_id=forced_eos_token_id,
	**kwargs,
	)

	# ensure backward compatibility for BART CNN models
	if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False):
	self.forced_bos_token_id = self.bos_token_id
	warnings.warn(
	f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
	"The config can simply be saved and uploaded again to be fixed."
	)