text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xFFFD or _is_control(char):
continue
if _is_whitespace(cha... | 9,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
class WordpieceTokenizer:
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""
Toke... | 9,442 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk_token)
continue
is_bad = False
start = 0
sub_tokens = []
... | 9,442 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens | 9,442 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
class TFFunnelEmbeddings(keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.initializer_std = 1.0 if ... | 9,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
if self.built:
return
self.built = True
if getattr(self, "LayerNorm", None) is not None:
with tf.name_scope(self.LayerNorm.name):
self.LayerNorm.build([None, None, self.config.d_model])
def call(self, input_ids=None, inputs_embeds=None, training=False):
... | 9,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelAttentionStructure:
"""
Contains helpers for `TFFunnelRelMultiheadAttention `.
"""
cls_token_type_id: int = 2
def __init__(self, config):
self.d_model = config.d_model
self.attention_type = config.attention_type
self.num_blocks = config.num_blocks
self... | 9,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def init_attention_inputs(self, inputs_embeds, attention_mask=None, token_type_ids=None, training=False):
"""Returns the attention inputs associated to the inputs of the model."""
# inputs_embeds has shape batch_size x seq_len x d_model
# attention_mask and token_type_ids have shape batch_size x... | 9,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def token_type_ids_to_mat(self, token_type_ids):
"""Convert `token_type_ids` to `token_type_mat`."""
token_type_mat = tf.equal(tf.expand_dims(token_type_ids, -1), tf.expand_dims(token_type_ids, -2))
# Treat <cls> as in the same segment as both A & B
cls_ids = tf.equal(token_type_ids, tf.... | 9,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
For the relative shift attention, it returns all possible vectors R used in the paper, appendix A.2.1, final
formula.
Paper link: https://arxiv.org/abs/2006.03236
"""
if self.attention_type == "factorized":
# Notations from the paper, appending A.2.2, final formula.
... | 9,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
sin_embed = tf.sin(sinusoid)
sin_embed_d = self.sin_dropout(sin_embed, training=training)
cos_embed = tf.cos(sinusoid)
cos_embed_d = self.cos_dropout(cos_embed, training=training)
# This is different from the formula on the paper...
phi = tf.concat([sin_embed_... | 9,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
rel_pos_id = tf.range(-seq_len * 2, seq_len * 2, 1.0)
zero_offset = seq_len * tf.constant(2)
sinusoid = tf.einsum("i,d->id", rel_pos_id, inv_freq)
sin_embed = self.sin_dropout(tf.sin(sinusoid), training=training)
cos_embed = self.cos_dropout(tf.cos(sinusoid), training=tra... | 9,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
pos = tf.range(0, seq_len)
pooled_pos = pos
position_embeds_list = []
for block_index in range(0, self.num_blocks):
# For each block with block_index > 0, we need two types position embeddings:
# - Attention(pooled-q, unpooled-kv)
# ... | 9,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
# construct rel_pos_id
stride = 2 ** (block_index - 1)
rel_pos = self.relative_pos(pos, stride, pooled_pos, shift=2)
# rel_pos = tf.expand_dims(rel_pos,1) + zero_offset
# rel_pos = tf.broadcast_to(rel_pos, (rel_pos.shape[0], self.d_model))
... | 9,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
# rel_pos = tf.expand_dims(rel_pos,1) + zero_offset
# rel_pos = tf.broadcast_to(rel_pos, (rel_pos.shape[0], self.d_model))
rel_pos = tf.cast(rel_pos, dtype=zero_offset.dtype)
rel_pos = rel_pos + zero_offset
tf.debugging.assert_less(rel_pos, tf.shape(pos_em... | 9,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def stride_pool_pos(self, pos_id, block_index):
"""
Pool `pos_id` while keeping the cls token separate (if `self.separate_cls=True`).
"""
if self.separate_cls:
# Under separate <cls>, we treat the <cls> as the first token in
# the previous block of the 1st real bl... | 9,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
ref_point = pooled_pos[0] - pos[0]
num_remove = shift * shape_list(pooled_pos)[0]
max_dist = ref_point + num_remove * stride
min_dist = pooled_pos[0] - pos[-1]
return tf.range(max_dist, min_dist - 1, -stride)
def stride_pool(self, tensor, axis):
"""
Perform pooling ... | 9,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
axis_slice = slice(None, -1, 2) if self.separate_cls and self.truncate_seq else slice(None, None, 2)
enc_slice = [slice(None)] * axis + [axis_slice]
if self.separate_cls:
cls_slice = [slice(None)] * axis + [slice(None, 1)]
tensor = tf.concat([tensor[cls_slice], tensor], axis)
... | 9,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
if mode == "mean":
tensor = tf.nn.avg_pool1d(tensor, stride, strides=stride, data_format="NWC", padding="SAME")
elif mode == "max":
tensor = tf.nn.max_pool1d(tensor, stride, strides=stride, data_format="NWC", padding="SAME")
elif mode == "min":
tensor = -tf.nn.max_poo... | 9,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def pre_attention_pooling(self, output, attention_inputs):
"""Pool `output` and the proper parts of `attention_inputs` before the attention layer."""
position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs
if self.pool_q_only:
if self.attention_type == "factorize... | 9,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
output = self.pool_tensor(output, mode=self.pooling_type)
attention_inputs = (position_embeds, token_type_mat, attention_mask, cls_mask)
return output, attention_inputs | 9,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def post_attention_pooling(self, attention_inputs):
"""Pool the proper parts of `attention_inputs` after the attention layer."""
position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs
if self.pool_q_only:
self.pooling_mult *= 2
if self.attention_type... | 9,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelRelMultiheadAttention(keras.layers.Layer):
def __init__(self, config, block_index, **kwargs):
super().__init__(**kwargs)
self.attention_type = config.attention_type
self.n_head = n_head = config.n_head
self.d_head = d_head = config.d_head
self.d_model = d_model ... | 9,445 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
self.post_proj = keras.layers.Dense(d_model, kernel_initializer=initializer, name="post_proj")
self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
self.scale = 1.0 / (d_head**0.5)
def build(self, input_shape=None):
n_head, d_head, d_model = se... | 9,445 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
self.r_w_bias = self.add_weight(
shape=(n_head, d_head), initializer=initializer, trainable=True, name="r_w_bias"
)
self.r_r_bias = self.add_weight(
shape=(n_head, d_head), initializer=initializer, trainable=True, name="r_r_bias"
)
self.r_kernel = self.add_weight(... | 9,445 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
if self.built:
return
self.built = True
if getattr(self, "q_head", None) is not None:
with tf.name_scope(self.q_head.name):
self.q_head.build([None, None, d_model])
if getattr(self, "k_head", None) is not None:
with tf.name_scope(self.k_head.na... | 9,445 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def relative_positional_attention(self, position_embeds, q_head, context_len, cls_mask=None):
"""Relative attention score for the positional encodings"""
# q_head has shape batch_size x sea_len x n_head x d_head
if self.attention_type == "factorized":
# Notations from the paper, appe... | 9,445 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
# Shape batch_size x n_head x seq_len x context_len
positional_attn = tf.einsum("bind,jd->bnij", q_r_attention_1, psi) + tf.einsum(
"bind,jd->bnij", q_r_attention_2, omega
)
else:
# Notations from the paper, appending A.2.1, final formula (https://arxiv.org/ab... | 9,445 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
# Shape max_rel_len x n_head x d_model
r_head = tf.einsum("td,dnh->tnh", r, w_r)
# Shape batch_size x n_head x seq_len x max_rel_len
positional_attn = tf.einsum("binh,tnh->bnit", q_head + v, r_head)
# Shape batch_size x n_head x seq_len x context_len
positiona... | 9,445 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
# Shape batch_size x n_head x seq_len x 2
token_type_bias = tf.einsum("bind,snd->bnis", q_head + r_s_bias, self.seg_embed)
# Shape batch_size x n_head x seq_len x context_len
token_type_mat = tf.tile(token_type_mat[:, None], [1, shape_list(q_head)[2], 1, 1])
# token_type_mat = tf.broadca... | 9,445 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def call(self, query, key, value, attention_inputs, output_attentions=False, training=False):
# query has shape batch_size x seq_len x d_model
# key and value have shapes batch_size x context_len x d_model
position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs
batc... | 9,445 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
q_head = q_head * self.scale
# Shape n_head x d_head
r_w_bias = self.r_w_bias * self.scale
# Shapes batch_size x n_head x seq_len x context_len
content_score = tf.einsum("bind,bjnd->bnij", q_head + r_w_bias, k_head)
positional_attn = self.relative_positional_attention(position_em... | 9,445 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
# attention output, shape batch_size x seq_len x n_head x d_head
attn_vec = tf.einsum("bnij,bjnd->bind", attn_prob, v_head)
# Shape shape batch_size x seq_len x d_model
attn_out = self.post_proj(tf.reshape(attn_vec, [batch_size, seq_len, n_head * d_head]))
attn_out = self.hidden_dropout... | 9,445 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelPositionwiseFFN(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
initializer = get_initializer(config.initializer_range)
self.linear_1 = keras.layers.Dense(config.d_inner, kernel_initializer=initializer, name="linear_1")
self.activat... | 9,446 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def call(self, hidden, training=False):
h = self.linear_1(hidden)
h = self.activation_function(h)
h = self.activation_dropout(h, training=training)
h = self.linear_2(h)
h = self.dropout(h, training=training)
return self.layer_norm(hidden + h)
def build(self, input_sh... | 9,446 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelLayer(keras.layers.Layer):
def __init__(self, config, block_index, **kwargs):
super().__init__(**kwargs)
self.attention = TFFunnelRelMultiheadAttention(config, block_index, name="attention")
self.ffn = TFFunnelPositionwiseFFN(config, name="ffn")
def call(self, query, key, ... | 9,447 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "attention", None) is not None:
with tf.name_scope(self.attention.name):
self.attention.build(None)
if getattr(self, "ffn", None) is not None:
w... | 9,447 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelEncoder(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.separate_cls = config.separate_cls
self.pool_q_only = config.pool_q_only
self.block_repeats = config.block_repeats
self.attention_structure = TFFunnelAttentionStru... | 9,448 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def call(
self,
inputs_embeds,
attention_mask=None,
token_type_ids=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
training=False,
):
# The pooling is not implemented on long tensors, so we convert this mask.
... | 9,448 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
for block_index, block in enumerate(self.blocks):
pooling_flag = shape_list(hidden)[1] > (2 if self.separate_cls else 1)
pooling_flag = pooling_flag and block_index > 0
pooled_hidden = tf.zeros(shape_list(hidden))
if pooling_flag:
pooled_hidden, attention... | 9,448 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
for layer_index, layer in enumerate(block):
for repeat_index in range(self.block_repeats[block_index]):
do_pooling = (repeat_index == 0) and (layer_index == 0) and pooling_flag
if do_pooling:
query = pooled_hidden
ke... | 9,448 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
if output_attentions:
all_attentions = all_attentions + layer_output[1:]
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden,)
if not return_dict:
return tuple(v for v in [hidden, all_hidden_states, all_attenti... | 9,448 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelDecoder(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.separate_cls = config.separate_cls
self.truncate_seq = config.truncate_seq
self.stride = 2 ** (len(config.block_sizes) - 1)
self.attention_structure = TFFunnelAtte... | 9,449 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
hidden = upsampled_hidden + first_block_hidden
all_hidden_states = (hidden,) if output_hidden_states else None
all_attentions = () if output_attentions else None
attention_inputs = self.attention_structure.init_attention_inputs(
hidden,
attention_mask=attention_mask,
... | 9,449 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
if not return_dict:
return tuple(v for v in [hidden, all_hidden_states, all_attentions] if v is not None)
return TFBaseModelOutput(last_hidden_state=hidden, hidden_states=all_hidden_states, attentions=all_attentions)
def build(self, input_shape=None):
if self.built:
return
... | 9,449 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelBaseLayer(keras.layers.Layer):
"""Base model without decoder"""
config_class = FunnelConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.output_attentions = config.output_attentions
self.output_hidden_states = con... | 9,450 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
@unpack_inputs
def call(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
):
if input_ids is not None and inputs... | 9,450 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
encoder_outputs = self.encoder(
inputs_embeds,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
... | 9,450 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelMainLayer(keras.layers.Layer):
"""Base model with decoder"""
config_class = FunnelConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.block_sizes = config.block_sizes
self.output_attentions = config.output_attenti... | 9,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
@unpack_inputs
def call(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
):
if input_ids is not None and inputs... | 9,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
encoder_outputs = self.encoder(
inputs_embeds,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
output_attentions=output_attentions,
output_hidden_states=True,
return_dict=return_dict,
training=training,
)
... | 9,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
if not return_dict:
idx = 0
outputs = (decoder_outputs[0],)
if output_hidden_states:
idx += 1
outputs = outputs + (encoder_outputs[1] + decoder_outputs[idx],)
if output_attentions:
idx += 1
outputs = outputs ... | 9,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embeddings", None) is not None:
with tf.name_scope(self.embeddings.name):
self.embeddings.build(None)
if getattr(self, "encoder", None) is not None:
... | 9,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelDiscriminatorPredictions(keras.layers.Layer):
"""Prediction module for the discriminator, made up of two dense layers."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
initializer = get_initializer(config.initializer_range)
self.dense = keras.layers.Dens... | 9,452 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.d_model])
if getattr(self, "dense_prediction", No... | 9,452 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelMaskedLMHead(keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.input_embeddings = input_embeddings
def build(self, input_shape):
self.bias ... | 9,453 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def call(self, hidden_states, training=False):
seq_length = shape_list(tensor=hidden_states)[1]
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
hidden_states = tf.r... | 9,453 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelClassificationHead(keras.layers.Layer):
def __init__(self, config, n_labels, **kwargs):
super().__init__(**kwargs)
initializer = get_initializer(config.initializer_range)
self.linear_hidden = keras.layers.Dense(config.d_model, kernel_initializer=initializer, name="linear_hidden... | 9,454 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "linear_hidden", None) is not None:
with tf.name_scope(self.linear_hidden.name):
self.linear_hidden.build([None, None, self.config.d_model])
if getattr(self... | 9,454 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = FunnelConfig
base_model_prefix = "funnel"
@property
def dummy_inputs(self):
# Funnel... | 9,455 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelForPreTrainingOutput(ModelOutput):
"""
Output type of [`FunnelForPreTraining`].
Args:
logits (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Prediction scores of the head (scores for each token before SoftMax).
hidden_states (`tuple(tf.Tensor)`, *optional*,... | 9,456 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
logits: tf.Tensor = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = None | 9,456 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelBaseModel(TFFunnelPreTrainedModel):
def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None:
super().__init__(config, *inputs, **kwargs)
self.funnel = TFFunnelBaseLayer(config, name="funnel") | 9,457 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="funnel-transformer/small-base",
output_type=TFBaseModelOutput,
config_class=_CONFIG_FOR_DOC,
)
@unpack_inputs
def call(
self,
... | 9,457 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
) | 9,457 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def serving_output(self, output):
# hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of
# different dimensions
return TFBaseModelOutput(
last_hidden_state=output.last_hidden_state,
hidden_states=output.hidden_states,
... | 9,457 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelModel(TFFunnelPreTrainedModel):
def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None:
super().__init__(config, *inputs, **kwargs)
self.funnel = TFFunnelMainLayer(config, name="funnel") | 9,458 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="funnel-transformer/small",
output_type=TFBaseModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
... | 9,458 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
) | 9,458 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def serving_output(self, output):
# hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of
# different dimensions
return TFBaseModelOutput(
last_hidden_state=output.last_hidden_state,
hidden_states=output.hidden_states,
... | 9,458 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelForPreTraining(TFFunnelPreTrainedModel):
def __init__(self, config: FunnelConfig, **kwargs) -> None:
super().__init__(config, **kwargs)
self.funnel = TFFunnelMainLayer(config, name="funnel")
self.discriminator_predictions = TFFunnelDiscriminatorPredictions(config, name="discri... | 9,459 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFFunnelForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
attention... | 9,459 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
>>> tokenizer = AutoTokenizer.from_pretrained("funnel-transformer/small")
>>> model = TFFunnelForPreTraining.from_pretrained("funnel-transformer/small")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> logits = model(inputs).logits
```"""
discriminator_hi... | 9,459 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
return TFFunnelForPreTrainingOutput(
logits=logits,
hidden_states=discriminator_hidden_states.hidden_states,
attentions=discriminator_hidden_states.attentions,
)
def serving_output(self, output):
# hidden_states and attentions not converted to Tensor with tf.conv... | 9,459 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelForMaskedLM(TFFunnelPreTrainedModel, TFMaskedLanguageModelingLoss):
def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None:
super().__init__(config, *inputs, **kwargs)
self.funnel = TFFunnelMainLayer(config, name="funnel")
self.lm_head = TFFunnelMaskedLMHead(confi... | 9,460 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="funnel-transformer/small",
output_type=TFMaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
i... | 9,460 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
"""
... | 9,460 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
loss = None if labels is None else self.hf_compute_loss(labels, prediction_scores)
if not return_dict:
output = (prediction_scores,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFMaskedLMOutput(
loss=loss,
logits=predic... | 9,460 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "funnel", None) is not None:
with tf.name_scope(self.funnel.name):
self.funnel.build(None)
if getattr(self, "lm_head", None) is not None:
with t... | 9,460 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelForSequenceClassification(TFFunnelPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None:
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.funnel = TFFunnelBaseLayer(config, name="... | 9,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="funnel-transformer/small-base",
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
... | 9,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
... | 9,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
loss = None if labels is None else self.hf_compute_loss(labels, logits)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(
loss=loss,
logits=logits,
... | 9,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "funnel", None) is not None:
with tf.name_scope(self.funnel.name):
self.funnel.build(None)
if getattr(self, "classifier", None) is not None:
wit... | 9,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelForMultipleChoice(TFFunnelPreTrainedModel, TFMultipleChoiceLoss):
def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None:
super().__init__(config, *inputs, **kwargs)
self.funnel = TFFunnelBaseLayer(config, name="funnel")
self.classifier = TFFunnelClassificationHea... | 9,462 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint="funnel-transformer/small-base",
output_type=TFMultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def ... | 9,462 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above)
"""
if input_ids is not None:
num_choices = shape_list(input_ids)[1]
... | 9,462 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else Non... | 9,462 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
last_hidden_state = outputs[0]
pooled_output = last_hidden_state[:, 0]
logits = self.classifier(pooled_output, training=training)
reshaped_logits = tf.reshape(logits, (-1, num_choices))
loss = None if labels is None else self.hf_compute_loss(labels, reshaped_logits)
if not retu... | 9,462 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def serving_output(self, output: TFMultipleChoiceModelOutput) -> TFMultipleChoiceModelOutput:
# hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of
# different dimensions
return TFMultipleChoiceModelOutput(
logits=output.logits, hidden_st... | 9,462 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelForTokenClassification(TFFunnelPreTrainedModel, TFTokenClassificationLoss):
def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None:
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.funnel = TFFunnelMainLayer(config, name="funnel... | 9,463 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="funnel-transformer/small",
output_type=TFTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
... | 9,463 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
outputs = self.funnel(
input_ids,
attention_mask,
token_type_ids,
inputs_embeds,
output_attentions,
output_hidden_states... | 9,463 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
sequence_output = self.dropout(sequence_output, training=training)
logits = self.classifier(sequence_output)
loss = None if labels is None else self.hf_compute_loss(labels, logits)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is... | 9,463 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "funnel", None) is not None:
with tf.name_scope(self.funnel.name):
self.funnel.build(None)
if getattr(self, "classifier", None) is not None:
wit... | 9,463 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class TFFunnelForQuestionAnswering(TFFunnelPreTrainedModel, TFQuestionAnsweringLoss):
def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None:
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.funnel = TFFunnelMainLayer(config, name="funnel")
... | 9,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="funnel-transformer/small",
output_type=TFQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
s... | 9,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`tf.Tenso... | 9,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
outputs = self.funnel(
input_ids,
attention_mask,
token_type_ids,
inputs_embeds,
output_attentions,
output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = outputs[0]
lo... | 9,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
return TFQuestionAnsweringModelOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def serving_output(self, output: TFQuestionAnsweringModelOutput) -> TFQuestio... | 9,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
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