text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
Labels are currently not supported. | 3,786 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
Returns:
Examples:
```python
>>> from transformers import ViltProcessor, ViltForImageAndTextRetrieval
>>> import requests
>>> from PIL import Image
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, strea... | 3,786 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
>>> # forward pass
>>> scores = dict()
>>> for text in texts:
... # prepare inputs
... encoding = processor(image, text, return_tensors="pt")
... outputs = model(**encoding)
... scores[text] = outputs.logits[0, :].item()
```"""
return_dict ... | 3,786 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
pooler_output = outputs.pooler_output if return_dict else outputs[1]
logits = self.rank_output(pooler_output)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
los... | 3,786 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class ViltForImagesAndTextClassification(ViltPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.vilt = ViltModel(config)
# Classifier head
num_images = config.num_images
self.classifier = nn.Sequential(
... | 3,787 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
@add_start_docstrings_to_model_forward(VILT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=ViltForImagesAndTextClassificationOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None... | 3,787 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Binary classification labels. | 3,787 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
Returns:
Examples:
```python
>>> from transformers import ViltProcessor, ViltForImagesAndTextClassification
>>> import requests
>>> from PIL import Image
>>> image1 = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg", stream=True).raw)
>>... | 3,787 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
>>> # forward pass
>>> outputs = model(input_ids=encoding.input_ids, pixel_values=encoding.pixel_values.unsqueeze(0))
>>> logits = outputs.logits
>>> idx = logits.argmax(-1).item()
>>> print("Predicted answer:", model.config.id2label[idx])
Predicted answer: True
```"""
... | 3,787 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
num_images = pixel_values.shape[1] if pixel_values is not None else None
if num_images is None:
num_images = image_embeds.shape[1] if image_embeds is not None else None
if num_images != self.config.num_images:
raise ValueError(
"Make sure to match the number of im... | 3,787 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
inputs_embeds=inputs_embeds,
image_embeds=image_embeds[:, i, :, :] if image_embeds is not None else None,
image_token_type_idx=i + 1,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
... | 3,787 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
pooled_output = torch.cat(pooler_outputs, dim=-1)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
# move labels to correct device to enable PP
labels = labels.to(logits.device)
loss = loss_f... | 3,787 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class ViltForTokenClassification(ViltPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.vilt = ViltModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.... | 3,788 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
@add_start_docstrings_to_model_forward(VILT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_typ... | 3,788 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. | 3,788 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
Returns:
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.vilt(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
pixel_values=pixel_values,
pixel_mask=pix... | 3,788 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
# move labels to correct device to enable PP
labels = labels.to(logits.device)
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (lo... | 3,788 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py |
class FlaxElectraForPreTrainingOutput(ModelOutput):
"""
Output type of [`ElectraForPreTraining`].
Args:
logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
... | 3,789 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_h... | 3,789 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
config: ElectraConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation | 3,790 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
def setup(self):
self.word_embeddings = nn.Embed(
self.config.vocab_size,
self.config.embedding_size,
embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
)
self.position_embeddings = nn.Embed(
self.config.max_position_e... | 3,790 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEmbeddings.__call__
def __call__(self, input_ids, token_type_ids, position_ids, attention_mask, deterministic: bool = True):
# Embed
inputs_embeds = self.word_embeddings(input_ids.astype("i4"))
position_embeds = self.position_... | 3,790 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraSelfAttention(nn.Module):
config: ElectraConfig
causal: bool = False
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.head_dim = self.config.hidden_size // self.config.num_attention_heads
if self.config.hidden_size % self.config.num_a... | 3,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
self.query = nn.Dense(
self.config.hidden_size,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
)
self.key = nn.Dense(
self.config.hidden_size,
dtype=self.dtype,
kernel_init=jax.nn.initialize... | 3,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
def _merge_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.config.hidden_size,)) | 3,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
@nn.compact
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention._concatenate_to_cache
def _concatenate_to_cache(self, key, value, query, attention_mask):
"""
This function takes projected key, value states from a single input token and concatenates the states to cached
... | 3,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
if is_initialized:
*batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
# update key, value caches with our new 1d spatial slices
cur_index = cache_index.value
indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
key = lax.dynamic_update... | 3,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
)
attention_mask = combine_masks(pad_mask, attention_mask)
return key, value, attention_mask | 3,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
def __call__(
self,
hidden_states,
attention_mask,
layer_head_mask,
key_value_states: Optional[jnp.ndarray] = None,
init_cache: bool = False,
deterministic=True,
output_attentions: bool = False,
):
# if key_value_states are provided this layer ... | 3,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
query_states = self._split_heads(query_states)
key_states = self._split_heads(key_states)
value_states = self._split_heads(value_states)
# handle cache prepare causal attention mask
if self.causal:
query_length, key_length = query_states.shape[1], key_states.shape[1]
... | 3,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
# combine masks if needed
if attention_mask is not None and self.causal:
attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
attention_mask = combine_masks(attention_mask, causal_mask)
elif self.causal:
attention_mask =... | 3,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
# Convert the boolean attention mask to an attention bias.
if attention_mask is not None:
# attention mask in the form of attention bias
attention_bias = lax.select(
attention_mask > 0,
jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
... | 3,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
# Mask heads if we want to
if layer_head_mask is not None:
attn_weights = jnp.einsum("...hqk,h->...hqk", attn_weights, layer_head_mask)
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,))
... | 3,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraSelfOutput(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dt... | 3,792 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraAttention(nn.Module):
config: ElectraConfig
causal: bool = False
dtype: jnp.dtype = jnp.float32
def setup(self):
self.self = FlaxElectraSelfAttention(self.config, causal=self.causal, dtype=self.dtype)
self.output = FlaxElectraSelfOutput(self.config, dtype=self.dtype) | 3,793 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
def __call__(
self,
hidden_states,
attention_mask,
layer_head_mask,
key_value_states=None,
init_cache=False,
deterministic=True,
output_attentions: bool = False,
):
# Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length)
... | 3,793 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
if output_attentions:
outputs += (attn_outputs[1],)
return outputs | 3,793 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraIntermediate(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.intermediate_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
... | 3,794 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraOutput(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=... | 3,795 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraLayer(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.attention = FlaxElectraAttention(self.config, causal=self.config.is_decoder, dtype=self.dtype)
self.intermediate = FlaxElectraIntermediate(self.confi... | 3,796 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
def __call__(
self,
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
deterministic: bool = True,
output_attentions: ... | 3,796 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
# Cross-Attention Block
if encoder_hidden_states is not None:
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask=encoder_attention_mask,
layer_head_mask=layer_head_mask,
key_value_states=encoder_hidden_state... | 3,796 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraLayerCollection(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
if self.gradient_checkpointing:
FlaxElectraCheckpointLayer = remat(FlaxElectraLayer, static_argnums... | 3,797 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
def __call__(
self,
hidden_states,
attention_mask,
head_mask,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
deterministic: bool = True,
output_attentions: bool =... | 3,797 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
# Check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
if head_mask.shape[0] != (len(self.layers)):
raise ValueError(
f"The head_mask should be specified for {len(self.layers)} layers, but it is for "
... | 3,797 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = (hidden_states, all_hidden_states, all_attentions, all_cross_attentions)
if not return_dict:
r... | 3,797 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraEncoder(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
self.layer = FlaxElectraLayerCollection(
self.config,
dtype=self.dtype,
gradient_ch... | 3,798 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
def __call__(
self,
hidden_states,
attention_mask,
head_mask,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
deterministic: bool = True,
output_attentions: bool =... | 3,798 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraGeneratorPredictions(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
self.dense = nn.Dense(self.config.embedding_size, dtype=self.dtype)
def __call__(s... | 3,799 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraDiscriminatorPredictions(nn.Module):
"""Prediction module for the discriminator, made up of two dense layers."""
config: ElectraConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype)
self.dense_prediction... | 3,800 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ElectraConfig
base_model_prefix = "electra"
module_class: nn.Module = None
def __init__... | 3,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.enable_gradient_checkpointing
def enable_gradient_checkpointing(self):
self._module = self.module_class(
config=self.config,
dtype=self.dtype,
gradient_checkpointing=True,
)
# C... | 3,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
if self.config.add_cross_attention:
encoder_hidden_states = jnp.zeros(input_shape + (self.config.hidden_size,))
encoder_attention_mask = attention_mask
module_init_o... | 3,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freez... | 3,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderPreTrainedModel.init_cache
def init_cache(self, batch_size, max_length):
r"""
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized ca... | 3,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def __call__(
self,
input_ids,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
encoder_hidden_states=None,
encoder_atten... | 3,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
# init input tensors if not passed
if token_type_ids is None:
token_type_ids = jnp.ones_like(input_ids)
if position_ids is None:
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
if attention_mask is None:
atte... | 3,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
if self.config.add_cross_attention:
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed
# down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be
# changed by FlaxElectraAttention ... | 3,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
outputs = self.module.apply(
inputs,
jnp.array(input_ids, dtype="i4"),
jnp.array(attention_mask, dtype="i4"),
token_type_ids=jnp.array(token_type_ids, dtype="i4"),
position_ids=jnp.array(position_ids, dtype="i4"),
head_mask=... | 3,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs, past_key_values = outputs
outputs["past_key_values"] = unfreeze(past_key_values["cache"])
return outputs
elif past_key_values is not None and not return_di... | 3,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
else:
outputs = self.module.apply(
inputs,
jnp.array(input_ids, dtype="i4"),
jnp.array(attention_mask, dtype="i4"),
token_type_ids=jnp.array(token_type_ids, dtype="i4"),
position_ids=jnp.array(position_ids, dtype="i4"),
... | 3,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraModule(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
self.embeddings = FlaxElectraEmbeddings(self.config, dtype=self.dtype)
if self.config.embedding_size != self.con... | 3,802 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
def __call__(
self,
input_ids,
attention_mask,
token_type_ids,
position_ids,
head_mask: Optional[np.ndarray] = None,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False... | 3,802 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
return self.encoder(
embeddings,
attention_mask,
head_mask=head_mask,
deterministic=deterministic,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
init_cache=init_cache,
output_att... | 3,802 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraModel(FlaxElectraPreTrainedModel):
module_class = FlaxElectraModule | 3,803 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraTiedDense(nn.Module):
embedding_size: int
dtype: jnp.dtype = jnp.float32
precision = None
bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.bias = self.param("bias", self.bias_init, (self.embedding_size,))
def __call__(self, x, kern... | 3,804 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraForMaskedLMModule(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.electra = FlaxElectraModule(
config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
... | 3,805 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
def __call__(
self,
input_ids,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
... | 3,805 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
if self.config.tie_word_embeddings:
shared_embedding = self.electra.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
prediction_scores = self.generator_lm_head(prediction_scores, shared_embedding.T)
else:
prediction_scores = self.generator_lm_head(prediction_... | 3,805 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraForMaskedLM(FlaxElectraPreTrainedModel):
module_class = FlaxElectraForMaskedLMModule | 3,806 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraForPreTrainingModule(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.electra = FlaxElectraModule(
config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
... | 3,807 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
def __call__(
self,
input_ids,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
... | 3,807 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
return FlaxElectraForPreTrainingOutput(
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 3,807 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraForPreTraining(FlaxElectraPreTrainedModel):
module_class = FlaxElectraForPreTrainingModule | 3,808 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraForTokenClassificationModule(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.electra = FlaxElectraModule(
config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpoin... | 3,809 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
def __call__(
self,
input_ids,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
... | 3,809 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
return FlaxTokenClassifierOutput(
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 3,809 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraForTokenClassification(FlaxElectraPreTrainedModel):
module_class = FlaxElectraForTokenClassificationModule | 3,810 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraSequenceSummary(nn.Module):
r"""
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... | 3,811 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.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... | 3,811 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
def setup(self):
self.summary = identity
if hasattr(self.config, "summary_use_proj") and self.config.summary_use_proj:
if (
hasattr(self.config, "summary_proj_to_labels")
and self.config.summary_proj_to_labels
and self.config.num_labels > 0
... | 3,811 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
self.last_dropout = identity
if hasattr(self.config, "summary_last_dropout") and self.config.summary_last_dropout > 0:
self.last_dropout = nn.Dropout(self.config.summary_last_dropout)
def __call__(self, hidden_states, cls_index=None, deterministic: bool = True):
"""
Compute a si... | 3,811 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
Returns:
`jnp.ndarray`: The summary of the sequence hidden states.
"""
# NOTE: this doest "first" type summary always
output = hidden_states[:, 0]
output = self.first_dropout(output, deterministic=deterministic)
output = self.summary(output)
output = self.acti... | 3,811 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraForMultipleChoiceModule(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.electra = FlaxElectraModule(
config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
... | 3,812 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
def __call__(
self,
input_ids,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
... | 3,812 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
# Model
outputs = self.electra(
input_ids,
attention_mask,
token_type_ids,
position_ids,
head_mask,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
... | 3,812 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraForMultipleChoice(FlaxElectraPreTrainedModel):
module_class = FlaxElectraForMultipleChoiceModule | 3,813 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraForQuestionAnsweringModule(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.electra = FlaxElectraModule(
config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointi... | 3,814 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
def __call__(
self,
input_ids,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
... | 3,814 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
if not return_dict:
return (start_logits, end_logits) + outputs[1:]
return FlaxQuestionAnsweringModelOutput(
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 3,814 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraForQuestionAnswering(FlaxElectraPreTrainedModel):
module_class = FlaxElectraForQuestionAnsweringModule | 3,815 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
config: ElectraConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype)
classifier_dropout = (
self.config.clas... | 3,816 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraForSequenceClassificationModule(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.electra = FlaxElectraModule(
config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkp... | 3,817 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
def __call__(
self,
input_ids,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
... | 3,817 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
return FlaxSequenceClassifierOutput(
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 3,817 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraForSequenceClassification(FlaxElectraPreTrainedModel):
module_class = FlaxElectraForSequenceClassificationModule | 3,818 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
class FlaxElectraForCausalLMModule(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.electra = FlaxElectraModule(
config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
... | 3,819 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
def __call__(
self,
input_ids,
attention_mask: Optional[jnp.ndarray] = None,
token_type_ids: Optional[jnp.ndarray] = None,
position_ids: Optional[jnp.ndarray] = None,
head_mask: Optional[jnp.ndarray] = None,
encoder_hidden_states: Optional[jnp.ndarray] = None,
... | 3,819 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
prediction_scores = self.generator_predictions(hidden_states) | 3,819 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
if self.config.tie_word_embeddings:
shared_embedding = self.electra.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
prediction_scores = self.generator_lm_head(prediction_scores, shared_embedding.T)
else:
prediction_scores = self.generator_lm_head(prediction_... | 3,819 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.