text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
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# Load config if we don't provide a configuration
if not isinstance(config, PretrainedConfig):
config_path = config if config is not None else pretrained_model_name_or_path
config, model_kwargs = cls.config_class.from_pretrained(
config_path,
cache_dir=cac... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# This variable will flag if we're loading a sharded checkpoint. In this case the archive file is just the
# index of the files.
is_sharded = False
# Load model
if pretrained_model_name_or_path is not None:
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
elif use_safetensors is not False and os.path.isfile(
os.path.join(pretrained_model_name_or_path, SAFE_WEIGHTS_NAME)
):
# Load from a safetensors checkpoint
archive_file = os.path.join(pretrained_model_name_or_path, SAFE_WEIGHTS_NAME)
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
elif os.path.isfile(os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_INDEX_NAME)):
# Load from a sharded TF 2.0 checkpoint
archive_file = os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_INDEX_NAME)
is_sharded = True | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# At this stage we don't have a weight file so we will raise an error.
elif use_safetensors:
raise EnvironmentError(
f"Error no file named {SAFE_WEIGHTS_NAME} or {SAFE_WEIGHTS_INDEX_NAME} found in directory {pretrained_model_name_or_path}. "
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
"but there is a file for PyTorch weights. Use `from_pt=True` to load this model from those "
"weights."
)
else:
raise EnvironmentError(
f"Error no file named {TF2_WEIGHTS_NAME}, {SAFE_WEIGHTS_NAME} or {WEIGHTS_NAME} ... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# set correct filename
if from_pt:
filename = WEIGHTS_NAME
elif use_safetensors is not False:
filename = SAFE_WEIGHTS_NAME
else:
filename = TF2_WEIGHTS_NAME | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
try:
# Load from URL or cache if already cached
cached_file_kwargs = {
"cache_dir": cache_dir,
"force_download": force_download,
"proxies": proxies,
"resume_download": resume_download,... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# Since we set _raise_exceptions_for_missing_entries=False, we don't get an exception but a None
# result when internet is up, the repo and revision exist, but the file does not.
if resolved_archive_file is None and filename == SAFE_WEIGHTS_NAME:
# Did not... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
pretrained_model_name_or_path, TF2_WEIGHTS_INDEX_NAME, **cached_file_kwargs
)
if resolved_archive_file is not None:
is_sharded = True
if resolved_archive_file is None and filename == WEIGHTS_NAME:
# M... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
"proxies": proxies,
"token": token,
"cache_dir": cache_dir,
"local_files_only": local_files_only,
}
if has_file(pretrained_model_name_or_path, SAFE_WEIGHTS_INDEX_NAME, **has_file_kwargs):
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
f"{pretrained_model_name_or_path} does not appear to have a file named {WEIGHTS_NAME},"
f" {TF2_WEIGHTS_NAME} or {TF_WEIGHTS_NAME}"
) | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
except EnvironmentError:
# Raise any environment error raise by `cached_file`. It will have a helpful error message adapted
# to the original exception.
raise
except Exception:
# For any other exception, we throw a generic e... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
raise EnvironmentError(
f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it"
" from 'https://huggingface.co/models', make sure you don't have a local directory with the"
f" same name. Otherwise, make sure '{p... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# We'll need to download and cache each checkpoint shard if the checkpoint is sharded.
if is_sharded:
# resolved_archive_file becomes a list of files that point to the different checkpoint shards in this case.
resolved_archive_file, sharded_metadata = get_checkpoint_shard_files(
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
safetensors_from_pt = False
if filename == SAFE_WEIGHTS_NAME:
with safe_open(resolved_archive_file, framework="tf") as f:
safetensors_metadata = f.metadata()
if safetensors_metadata is None or safetensors_metadata.get("format") not in ["pt", "tf", "flax", "mlx"]:
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
f"The safetensors archive passed at {resolved_archive_file} does not contain the valid metadata."
" Make sure you save your model with the `save_pretrained` method."
)
safetensors_from_pt = safetensors_metadata.get("format") == "pt" | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
config.name_or_path = pretrained_model_name_or_path
# composed models, *e.g.* TFRag, require special treatment when it comes to loading
# pre-trained weights.
if cls._requires_load_weight_prefix and model_kwargs.get("name") is not None:
model_kwargs["load_weight_prefix"] = load_weig... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if from_pt:
from .modeling_tf_pytorch_utils import load_pytorch_checkpoint_in_tf2_model
# Load from a PyTorch checkpoint
return load_pytorch_checkpoint_in_tf2_model(
model,
resolved_archive_file,
allow_missing_keys=True,
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
with safe_open(resolved_archive_file, framework="tf") as safetensors_archive:
# Load from a PyTorch safetensors checkpoint
# We load in TF format here because PT weights often need to be transposed, and this is much
# faster on GPU. Loading as numpy and transposing on CPU... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
return load_sharded_pytorch_safetensors_in_tf2_model(
model,
resolved_archive_file,
tf_inputs=False,
allow_missing_keys=True,
output_loading_info=output_loading_info,
_prefix=load_weight_prefix,
ignore_mismat... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# 'by_name' allow us to do transfer learning by skipping/adding layers
# see https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1339-L1357
try:
if is_sharded:
for file in resolved_archive_file:
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
ignore_mismatched_sizes=ignore_mismatched_sizes,
_prefix=load_weight_prefix,
)
else:
# Handles both H5 and safetensors
missing_keys, unexpected_keys, mismatched_keys = load_tf_weights(
model,
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
raise ValueError from e
except (UnicodeDecodeError, ValueError):
raise OSError(
"Unable to load weights from h5 file. "
"If you tried to load a TF 2.0 model from a PyTorch checkpoint, please set from_pt=True. "
) | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if cls._keys_to_ignore_on_load_missing is not None:
for pat in cls._keys_to_ignore_on_load_missing:
missing_keys = [k for k in missing_keys if re.search(pat, k) is None]
if cls._keys_to_ignore_on_load_unexpected is not None:
for pat in cls._keys_to_ignore_on_load_unexpec... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if len(unexpected_keys) > 0:
logger.warning(
f"Some layers from the model checkpoint at {pretrained_model_name_or_path} were not used when"
f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are"
f" initializing {model.__c... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if len(missing_keys) > 0:
logger.warning(
f"Some layers of {model.__class__.__name__} were not initialized from the model checkpoint at"
f" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\nYou should probably"
" TRAIN this model on a ... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated"
for key, shape1, shape2 in mismatched_keys
]
)
logger.warning(
f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# If it is a model with generation capabilities, attempt to load the generation config
if model.can_generate():
try:
model.generation_config = GenerationConfig.from_pretrained(
pretrained_model_name_or_path,
cache_dir=cache_dir,
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if output_loading_info:
loading_info = {
"missing_keys": missing_keys,
"unexpected_keys": unexpected_keys,
"mismatched_keys": mismatched_keys,
}
return model, loading_info
return model
def push_to_hub(
self,
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
Parameters:
repo_id (`str`):
The name of the repository you want to push your model to. It should contain your organization name
when pushing to a given organization.
use_temp_dir (`bool`, *optional*):
Whether or not to use a temporary directory to... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `huggingface-cli login` (stored in `~/.huggingface`). Will default to `True` if `repo_url`
is not specified.
max_shard_size (`int` or `str`, *optional*, defaul... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
Examples:
```python
from transformers import TFAutoModel
model = TFAutoModel.from_pretrained("google-bert/bert-base-cased")
# Push the model to your namespace with the name "my-finetuned-bert".
model.push_to_hub("my-finetuned-bert")
# Push the model to an organization... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if "repo_path_or_name" in base_model_card_args:
warnings.warn(
"The `repo_path_or_name` argument is deprecated and will be removed in v5 of Transformers. Use "
"`repo_id` instead."
)
repo_id = base_model_card_args.pop("repo_path_or_name")
# Dep... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
with working_or_temp_dir(working_dir=working_dir, use_temp_dir=use_temp_dir) as work_dir:
files_timestamps = self._get_files_timestamps(work_dir)
# Save all files.
self.save_pretrained(work_dir, max_shard_size=max_shard_size)
if hasattr(self, "history") and hasattr(self,... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
@classmethod
def register_for_auto_class(cls, auto_class="TFAutoModel"):
"""
Register this class with a given auto class. This should only be used for custom models as the ones in the
library are already mapped with an auto class.
<Tip warning={true}>
This API is experiment... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
class TFConv1D(keras.layers.Layer):
"""
1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2).
Basically works like a linear layer but the weights are transposed.
Args:
nf (`int`):
The number of output features.
nx (`int`):
... | 282 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
def build(self, input_shape):
if self.built:
return
self.built = True
self.weight = self.add_weight(
"weight", shape=[self.nx, self.nf], initializer=get_initializer(self.initializer_range)
)
self.bias = self.add_weight("bias", shape=[1, self.nf], initializ... | 282 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
class TFSharedEmbeddings(keras.layers.Layer):
r"""
Construct shared token embeddings.
The weights of the embedding layer is usually shared with the weights of the linear decoder when doing language
modeling.
Args:
vocab_size (`int`):
The size of the vocabulary, e.g., the number... | 283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
def __init__(self, vocab_size: int, hidden_size: int, initializer_range: Optional[float] = None, **kwargs):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.initializer_range = hidden_size**-0.5 if initializer_range is None else initializer_rang... | 283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
def get_config(self):
config = {
"vocab_size": self.vocab_size,
"hidden_size": self.hidden_size,
"initializer_range": self.initializer_range,
}
base_config = super().get_config()
return dict(list(base_config.items()) + list(config.items()))
def c... | 283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
Returns:
`tf.Tensor`: In embedding mode, the output is a float32 embedding tensor, with shape `[batch_size, length,
embedding_size]`.
In linear mode, the output is a float32 with shape `[batch_size, length, vocab_size]`.
Raises:
ValueError: if `mode` is not vali... | 283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
Args:
inputs: A float32 tensor with shape [..., hidden_size]
Returns:
float32 tensor with shape [..., vocab_size].
"""
first_dims = shape_list(inputs)[:-1]
x = tf.reshape(inputs, [-1, self.hidden_size])
logits = tf.matmul(x, self.weight, transpose_b=True)... | 283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
class TFSequenceSummary(keras.layers.Layer):
"""
Compute a single vector summary of a sequence hidden states.
Args:
config ([`PretrainedConfig`]):
The config used by the model. Relevant arguments in the config class of the model are (refer to the actual
config class of your ... | 284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
- **summary_use_proj** (`bool`) -- Add a projection after the vector extraction.
- **summary_proj_to_labels** (`bool`) -- If `True`, the projection outputs to `config.num_labels` classes
(otherwise to `config.hidden_size`).
- **summary_activation** (`Optional[str]`) -- Set to `"tan... | 284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
def __init__(self, config: PretrainedConfig, initializer_range: float = 0.02, **kwargs):
super().__init__(**kwargs)
self.summary_type = config.summary_type if hasattr(config, "summary_use_proj") else "last"
if self.summary_type == "attn":
# We should use a standard multi-head attent... | 284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
self.has_summary = hasattr(config, "summary_use_proj") and config.summary_use_proj
if self.has_summary:
if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0:
num_classes = config.num_labels
else:
num_clas... | 284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
self.has_last_dropout = hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0
if self.has_last_dropout:
self.last_dropout = keras.layers.Dropout(config.summary_last_dropout)
self.hidden_size = config.hidden_size
def call(self, inputs, cls_index=None, training=False... | 284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if self.summary_type == "last":
output = hidden_states[:, -1]
elif self.summary_type == "first":
output = hidden_states[:, 0]
elif self.summary_type == "mean":
output = tf.reduce_mean(hidden_states, axis=1)
elif self.summary_type == "cls_index":
hi... | 284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states
output = tf.gather(hidden_states, cls_index, batch_dims=len(hidden_shape) - 2)
output = tf.squeeze(
output, axis=len(hidden_shape) - 2
) # shape of output: (batch, num ... | 284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if self.has_first_dropout:
output = self.first_dropout(output, training=training)
if self.has_summary:
output = self.summary(output)
if self.has_activation:
output = self.activation(output)
if self.has_last_dropout:
output = self.last_dropout(ou... | 284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
class DebugUnderflowOverflow:
"""
This debug class helps detect and understand where the model starts getting very large or very small, and more
importantly `nan` or `inf` weight and activation elements.
There are 2 working modes:
1. Underflow/overflow detection (default)
2. Specific batch abs... | 285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/debug_utils.py |
For example, here is the header and the last few frames in detection report for `google/mt5-small` run in fp16
mixed precision :
```
Detected inf/nan during batch_number=0
Last 21 forward frames:
abs min abs max metadata
[...]
encoder.block.2.layer.1.DenseReluDense.wi_0 ... | 285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/debug_utils.py |
You can see here, that `T5DenseGatedGeluDense.forward` resulted in output activations, whose absolute max value was
around 62.7K, which is very close to fp16's top limit of 64K. In the next frame we have `Dropout` which
renormalizes the weights, after it zeroed some of the elements, which pushes the absolute ma... | 285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/debug_utils.py |
To validate that you have set up this debugging feature correctly, and you intend to use it in a training that
may take hours to complete, first run it with normal tracing enabled for one of a few batches as explained in
the next section.
Mode 2. Specific batch absolute min/max tracing without... | 285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/debug_utils.py |
Early stopping:
You can also specify the batch number after which to stop the training, with :
```python
debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1, 3], abort_after_batch_num=3)
```
This feature is mainly useful in the tracing mode, but you can use it for any mode.
*... | 285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/debug_utils.py |
def __init__(self, model, max_frames_to_save=21, trace_batch_nums=[], abort_after_batch_num=None):
self.model = model
self.trace_batch_nums = trace_batch_nums
self.abort_after_batch_num = abort_after_batch_num
# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to g... | 285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/debug_utils.py |
def reset_saved_frames(self):
self.frames = []
def dump_saved_frames(self):
print(f"\nDetected inf/nan during batch_number={self.batch_number}")
print(f"Last {len(self.frames)} forward frames:")
print(f"{'abs min':8} {'abs max':8} metadata")
print("\n".join(self.frames))
... | 285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/debug_utils.py |
def analyse_variable(self, var, ctx):
if torch.is_tensor(var):
self.expand_frame(get_abs_min_max(var, ctx))
if detect_overflow(var, ctx):
self.detected_overflow = True
elif var is None:
self.expand_frame(f"{'None':>17} {ctx}")
else:
... | 285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/debug_utils.py |
# inputs
if isinstance(input, tuple):
for i, x in enumerate(input):
self.analyse_variable(x, f"input[{i}]")
else:
self.analyse_variable(input, "input")
# outputs
if isinstance(output, tuple):
for i, x in enumerate(output):
... | 285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/debug_utils.py |
def forward_hook(self, module, input, output):
# - input is a tuple of packed inputs (could be non-Tensors)
# - output could be a Tensor or a tuple of Tensors and non-Tensors
last_frame_of_batch = False
trace_mode = True if self.batch_number in self.trace_batch_nums else False
... | 285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/debug_utils.py |
if last_frame_of_batch:
self.batch_start_frame()
if self.detected_overflow and not trace_mode:
self.dump_saved_frames()
# now we can abort, as it's pointless to continue running
raise ValueError(
"DebugUnderflowOverflow: inf/nan detected, abortin... | 285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/debug_utils.py |
class DebugOption(ExplicitEnum):
UNDERFLOW_OVERFLOW = "underflow_overflow"
TPU_METRICS_DEBUG = "tpu_metrics_debug" | 286 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/debug_utils.py |
class TFBaseModelOutput(ModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
... | 287 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads... | 287 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFBaseModelOutputWithNoAttention(ModelOutput):
"""
Base class for model's outputs, with potential hidden states.
Args:
last_hidden_state (`tf.Tensor` shape `(batch_size, num_channels, height, width)`):
Sequence of hidden-states at the output of the last layer of the model.
... | 288 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFBaseModelOutputWithPooling(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last la... | 289 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
This output is usually *not* a good summary of the semantic content of the input, you're often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `conf... | 289 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
last_hidden_state: tf.Tensor = None
pooler_output: tf.Tensor = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = None | 289 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFBaseModelOutputWithPoolingAndNoAttention(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
Sequence of hidden-states at the output o... | 290 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
last_hidden_state: tf.Tensor = None
pooler_output: tf.Tensor = None
hidden_states: Optional[Tuple[tf.Tensor, ...]] = None | 290 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFBaseModelOutputWithPoolingAndCrossAttentions(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the out... | 291 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
This output is usually *not* a good summary of the semantic content of the input, you're often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.
past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cac... | 291 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads... | 291 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
last_hidden_state: tf.Tensor = None
pooler_output: tf.Tensor = None
past_key_values: List[tf.Tensor] | None = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = None
cross_attentions: Tuple[tf.Tensor] | None = None | 291 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFBaseModelOutputWithPast(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the outpu... | 292 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):... | 292 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
last_hidden_state: tf.Tensor = None
past_key_values: List[tf.Tensor] | None = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = No... | 292 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFBaseModelOutputWithCrossAttentions(ModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last laye... | 293 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads... | 293 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
last_hidden_state: tf.Tensor = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = None
cross_attentions: Tuple[tf.Tensor] | None = None | 293 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFBaseModelOutputWithPastAndCrossAttentions(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-s... | 294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(tf.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=Tr... | 294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for eac... | 294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFSeq2SeqModelOutput(ModelOutput):
"""
Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-... | 295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.outpu... | 295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tens... | 295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states ... | 295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size,... | 295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFCausalLMOutput(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided):
Language modeling loss (for next-token predict... | 296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads... | 296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFCausalLMOutputWithPast(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided):
Language modeling loss (for next-token... | 297 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):... | 297 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: tf.Tensor | None = None
logits: tf.Tensor = None
past_key_values: List[tf.Tensor] | None = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple... | 297 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFCausalLMOutputWithCrossAttentions(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided):
Language modeling loss (for... | 298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads... | 298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
List of `t... | 298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFMaskedLMOutput(ModelOutput):
"""
Base class for masked language models outputs.
Args:
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided):
Masked language modeling (MLM) loss.
logits (`tf.Tensor` ... | 299 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads... | 299 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
class TFSeq2SeqLMOutput(ModelOutput):
"""
Base class for sequence-to-sequence language models outputs.
Args:
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided):
Language modeling loss.
logits (`tf.Tensor... | 300 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py |
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