Add files using upload-large-folder tool
Browse files- config.json +35 -0
- configuration_norbert.py +34 -0
- model.safetensors +3 -0
- modeling_norbert.py +635 -0
- optimizer.pt +3 -0
- rng_state.pth +3 -0
- scheduler.pt +3 -0
- special_tokens_map.json +51 -0
- tokenizer.json +0 -0
- tokenizer_config.json +870 -0
- trainer_state.json +2833 -0
- training_args.bin +3 -0
config.json
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{
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"_name_or_path": "/leonardo_work/EUHPC_A02_045/models/ltg_norbert3-base",
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"architectures": [
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"NorbertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"auto_map": {
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"AutoConfig": "configuration_norbert.NorbertConfig",
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"AutoModel": "ltg/norbert3-base--modeling_norbert.NorbertModel",
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"AutoModelForMaskedLM": "ltg/norbert3-base--modeling_norbert.NorbertForMaskedLM",
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"AutoModelForMultipleChoice": "ltg/norbert3-base--modeling_norbert.NorbertForMultipleChoice",
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"AutoModelForQuestionAnswering": "ltg/norbert3-base--modeling_norbert.NorbertForQuestionAnswering",
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"AutoModelForSequenceClassification": "modeling_norbert.NorbertForSequenceClassification",
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"AutoModelForTokenClassification": "ltg/norbert3-base--modeling_norbert.NorbertForTokenClassification"
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},
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"hidden_dropout_prob": 0.0,
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"hidden_size": 768,
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"id2label": {
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"0": "LABEL_0"
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},
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"intermediate_size": 2048,
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"label2id": {
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"LABEL_0": 0
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},
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"layer_norm_eps": 1e-07,
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"max_position_embeddings": 512,
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"output_all_encoded_layers": true,
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"position_bucket_size": 32,
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"problem_type": "regression",
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"torch_dtype": "float32",
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"transformers_version": "4.44.2",
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"vocab_size": 50000
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}
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configuration_norbert.py
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from transformers.configuration_utils import PretrainedConfig
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class NorbertConfig(PretrainedConfig):
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"""Configuration class to store the configuration of a `NorbertModel`.
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"""
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def __init__(
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self,
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vocab_size=50000,
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attention_probs_dropout_prob=0.1,
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hidden_dropout_prob=0.1,
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hidden_size=768,
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intermediate_size=2048,
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max_position_embeddings=512,
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position_bucket_size=32,
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num_attention_heads=12,
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num_hidden_layers=12,
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layer_norm_eps=1.0e-7,
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output_all_encoded_layers=True,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.output_all_encoded_layers = output_all_encoded_layers
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self.position_bucket_size = position_bucket_size
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self.layer_norm_eps = layer_norm_eps
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:8219c3afc3f9d4c8eff51823d770f2ec916a96a845c9c3596e900dce45f56ed0
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size 496139908
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modeling_norbert.py
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import math
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from typing import List, Optional, Tuple, Union
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3 |
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import torch
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5 |
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import torch.nn as nn
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import torch.nn.functional as F
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7 |
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from torch.utils import checkpoint
|
8 |
+
|
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from .configuration_norbert import NorbertConfig
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from transformers.modeling_utils import PreTrainedModel
|
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from transformers.activations import gelu_new
|
12 |
+
from transformers.modeling_outputs import (
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MaskedLMOutput,
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+
MultipleChoiceModelOutput,
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15 |
+
QuestionAnsweringModelOutput,
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+
SequenceClassifierOutput,
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17 |
+
TokenClassifierOutput,
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18 |
+
BaseModelOutput
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)
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from transformers.pytorch_utils import softmax_backward_data
|
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+
|
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+
|
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class Encoder(nn.Module):
|
24 |
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def __init__(self, config, activation_checkpointing=False):
|
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super().__init__()
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self.layers = nn.ModuleList([EncoderLayer(config) for _ in range(config.num_hidden_layers)])
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27 |
+
|
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for i, layer in enumerate(self.layers):
|
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layer.mlp.mlp[1].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i)))
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30 |
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layer.mlp.mlp[-2].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i)))
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31 |
+
|
32 |
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self.activation_checkpointing = activation_checkpointing
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33 |
+
|
34 |
+
def forward(self, hidden_states, attention_mask, relative_embedding):
|
35 |
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hidden_states, attention_probs = [hidden_states], []
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36 |
+
|
37 |
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for layer in self.layers:
|
38 |
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if self.activation_checkpointing:
|
39 |
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hidden_state, attention_p = checkpoint.checkpoint(layer, hidden_states[-1], attention_mask, relative_embedding)
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40 |
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else:
|
41 |
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hidden_state, attention_p = layer(hidden_states[-1], attention_mask, relative_embedding)
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42 |
+
|
43 |
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hidden_states.append(hidden_state)
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44 |
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attention_probs.append(attention_p)
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45 |
+
|
46 |
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return hidden_states, attention_probs
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47 |
+
|
48 |
+
|
49 |
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class MaskClassifier(nn.Module):
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50 |
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def __init__(self, config, subword_embedding):
|
51 |
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super().__init__()
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52 |
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self.nonlinearity = nn.Sequential(
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53 |
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nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False),
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54 |
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nn.Linear(config.hidden_size, config.hidden_size),
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55 |
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nn.GELU(),
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56 |
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nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False),
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57 |
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nn.Dropout(config.hidden_dropout_prob),
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58 |
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nn.Linear(subword_embedding.size(1), subword_embedding.size(0))
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59 |
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)
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60 |
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61 |
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def forward(self, x, masked_lm_labels=None):
|
62 |
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if masked_lm_labels is not None:
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63 |
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x = torch.index_select(x.flatten(0, 1), 0, torch.nonzero(masked_lm_labels.flatten() != -100).squeeze())
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64 |
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x = self.nonlinearity(x)
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65 |
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return x
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66 |
+
|
67 |
+
|
68 |
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class EncoderLayer(nn.Module):
|
69 |
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def __init__(self, config):
|
70 |
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super().__init__()
|
71 |
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self.attention = Attention(config)
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72 |
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self.mlp = FeedForward(config)
|
73 |
+
|
74 |
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def forward(self, x, padding_mask, relative_embedding):
|
75 |
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attention_output, attention_probs = self.attention(x, padding_mask, relative_embedding)
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76 |
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x = x + attention_output
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77 |
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x = x + self.mlp(x)
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78 |
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return x, attention_probs
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79 |
+
|
80 |
+
|
81 |
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class GeGLU(nn.Module):
|
82 |
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def forward(self, x):
|
83 |
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x, gate = x.chunk(2, dim=-1)
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84 |
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x = x * gelu_new(gate)
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85 |
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return x
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86 |
+
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87 |
+
|
88 |
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class FeedForward(nn.Module):
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89 |
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def __init__(self, config):
|
90 |
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super().__init__()
|
91 |
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self.mlp = nn.Sequential(
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92 |
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nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False),
|
93 |
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nn.Linear(config.hidden_size, 2*config.intermediate_size, bias=False),
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94 |
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GeGLU(),
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95 |
+
nn.LayerNorm(config.intermediate_size, eps=config.layer_norm_eps, elementwise_affine=False),
|
96 |
+
nn.Linear(config.intermediate_size, config.hidden_size, bias=False),
|
97 |
+
nn.Dropout(config.hidden_dropout_prob)
|
98 |
+
)
|
99 |
+
|
100 |
+
def forward(self, x):
|
101 |
+
return self.mlp(x)
|
102 |
+
|
103 |
+
|
104 |
+
class MaskedSoftmax(torch.autograd.Function):
|
105 |
+
@staticmethod
|
106 |
+
def forward(self, x, mask, dim):
|
107 |
+
self.dim = dim
|
108 |
+
x.masked_fill_(mask, float('-inf'))
|
109 |
+
x = torch.softmax(x, self.dim)
|
110 |
+
x.masked_fill_(mask, 0.0)
|
111 |
+
self.save_for_backward(x)
|
112 |
+
return x
|
113 |
+
|
114 |
+
@staticmethod
|
115 |
+
def backward(self, grad_output):
|
116 |
+
output, = self.saved_tensors
|
117 |
+
input_grad = softmax_backward_data(self, grad_output, output, self.dim, output)
|
118 |
+
return input_grad, None, None
|
119 |
+
|
120 |
+
|
121 |
+
class Attention(nn.Module):
|
122 |
+
def __init__(self, config):
|
123 |
+
super().__init__()
|
124 |
+
|
125 |
+
self.config = config
|
126 |
+
|
127 |
+
if config.hidden_size % config.num_attention_heads != 0:
|
128 |
+
raise ValueError(f"The hidden size {config.hidden_size} is not a multiple of the number of attention heads {config.num_attention_heads}")
|
129 |
+
|
130 |
+
self.hidden_size = config.hidden_size
|
131 |
+
self.num_heads = config.num_attention_heads
|
132 |
+
self.head_size = config.hidden_size // config.num_attention_heads
|
133 |
+
|
134 |
+
self.in_proj_qk = nn.Linear(config.hidden_size, 2*config.hidden_size, bias=True)
|
135 |
+
self.in_proj_v = nn.Linear(config.hidden_size, config.hidden_size, bias=True)
|
136 |
+
self.out_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=True)
|
137 |
+
|
138 |
+
self.pre_layer_norm = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False)
|
139 |
+
self.post_layer_norm = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True)
|
140 |
+
|
141 |
+
position_indices = torch.arange(config.max_position_embeddings, dtype=torch.long).unsqueeze(1) \
|
142 |
+
- torch.arange(config.max_position_embeddings, dtype=torch.long).unsqueeze(0)
|
143 |
+
position_indices = self.make_log_bucket_position(position_indices, config.position_bucket_size, config.max_position_embeddings)
|
144 |
+
position_indices = config.position_bucket_size - 1 + position_indices
|
145 |
+
self.register_buffer("position_indices", position_indices, persistent=False)
|
146 |
+
|
147 |
+
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
|
148 |
+
self.scale = 1.0 / math.sqrt(3 * self.head_size)
|
149 |
+
|
150 |
+
def make_log_bucket_position(self, relative_pos, bucket_size, max_position):
|
151 |
+
sign = torch.sign(relative_pos)
|
152 |
+
mid = bucket_size // 2
|
153 |
+
abs_pos = torch.where((relative_pos < mid) & (relative_pos > -mid), mid - 1, torch.abs(relative_pos).clamp(max=max_position - 1))
|
154 |
+
log_pos = torch.ceil(torch.log(abs_pos / mid) / math.log((max_position-1) / mid) * (mid - 1)).int() + mid
|
155 |
+
bucket_pos = torch.where(abs_pos <= mid, relative_pos, log_pos * sign).long()
|
156 |
+
return bucket_pos
|
157 |
+
|
158 |
+
def compute_attention_scores(self, hidden_states, relative_embedding):
|
159 |
+
key_len, batch_size, _ = hidden_states.size()
|
160 |
+
query_len = key_len
|
161 |
+
|
162 |
+
if self.position_indices.size(0) < query_len:
|
163 |
+
position_indices = torch.arange(query_len, dtype=torch.long).unsqueeze(1) \
|
164 |
+
- torch.arange(query_len, dtype=torch.long).unsqueeze(0)
|
165 |
+
position_indices = self.make_log_bucket_position(position_indices, self.config.position_bucket_size, 512)
|
166 |
+
position_indices = self.config.position_bucket_size - 1 + position_indices
|
167 |
+
self.position_indices = position_indices.to(hidden_states.device)
|
168 |
+
|
169 |
+
hidden_states = self.pre_layer_norm(hidden_states)
|
170 |
+
|
171 |
+
query, key = self.in_proj_qk(hidden_states).chunk(2, dim=2) # shape: [T, B, D]
|
172 |
+
value = self.in_proj_v(hidden_states) # shape: [T, B, D]
|
173 |
+
|
174 |
+
query = query.reshape(query_len, batch_size * self.num_heads, self.head_size).transpose(0, 1)
|
175 |
+
key = key.reshape(key_len, batch_size * self.num_heads, self.head_size).transpose(0, 1)
|
176 |
+
value = value.view(key_len, batch_size * self.num_heads, self.head_size).transpose(0, 1)
|
177 |
+
|
178 |
+
attention_scores = torch.bmm(query, key.transpose(1, 2) * self.scale)
|
179 |
+
|
180 |
+
pos = self.in_proj_qk(self.dropout(relative_embedding)) # shape: [2T-1, 2D]
|
181 |
+
query_pos, key_pos = pos.view(-1, self.num_heads, 2*self.head_size).chunk(2, dim=2)
|
182 |
+
query = query.view(batch_size, self.num_heads, query_len, self.head_size)
|
183 |
+
key = key.view(batch_size, self.num_heads, query_len, self.head_size)
|
184 |
+
|
185 |
+
attention_c_p = torch.einsum("bhqd,khd->bhqk", query, key_pos.squeeze(1) * self.scale)
|
186 |
+
attention_p_c = torch.einsum("bhkd,qhd->bhqk", key * self.scale, query_pos.squeeze(1))
|
187 |
+
|
188 |
+
position_indices = self.position_indices[:query_len, :key_len].expand(batch_size, self.num_heads, -1, -1)
|
189 |
+
attention_c_p = attention_c_p.gather(3, position_indices)
|
190 |
+
attention_p_c = attention_p_c.gather(2, position_indices)
|
191 |
+
|
192 |
+
attention_scores = attention_scores.view(batch_size, self.num_heads, query_len, key_len)
|
193 |
+
attention_scores.add_(attention_c_p)
|
194 |
+
attention_scores.add_(attention_p_c)
|
195 |
+
|
196 |
+
return attention_scores, value
|
197 |
+
|
198 |
+
def compute_output(self, attention_probs, value):
|
199 |
+
attention_probs = self.dropout(attention_probs)
|
200 |
+
context = torch.bmm(attention_probs.flatten(0, 1), value) # shape: [B*H, Q, D]
|
201 |
+
context = context.transpose(0, 1).reshape(context.size(1), -1, self.hidden_size) # shape: [Q, B, H*D]
|
202 |
+
context = self.out_proj(context)
|
203 |
+
context = self.post_layer_norm(context)
|
204 |
+
context = self.dropout(context)
|
205 |
+
return context
|
206 |
+
|
207 |
+
def forward(self, hidden_states, attention_mask, relative_embedding):
|
208 |
+
attention_scores, value = self.compute_attention_scores(hidden_states, relative_embedding)
|
209 |
+
attention_probs = MaskedSoftmax.apply(attention_scores, attention_mask, -1)
|
210 |
+
return self.compute_output(attention_probs, value), attention_probs.detach()
|
211 |
+
|
212 |
+
|
213 |
+
class Embedding(nn.Module):
|
214 |
+
def __init__(self, config):
|
215 |
+
super().__init__()
|
216 |
+
self.hidden_size = config.hidden_size
|
217 |
+
|
218 |
+
self.word_embedding = nn.Embedding(config.vocab_size, config.hidden_size)
|
219 |
+
self.word_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False)
|
220 |
+
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
221 |
+
|
222 |
+
self.relative_embedding = nn.Parameter(torch.empty(2 * config.position_bucket_size - 1, config.hidden_size))
|
223 |
+
self.relative_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
224 |
+
|
225 |
+
def forward(self, input_ids):
|
226 |
+
word_embedding = self.dropout(self.word_layer_norm(self.word_embedding(input_ids)))
|
227 |
+
relative_embeddings = self.relative_layer_norm(self.relative_embedding)
|
228 |
+
return word_embedding, relative_embeddings
|
229 |
+
|
230 |
+
|
231 |
+
#
|
232 |
+
# HuggingFace wrappers
|
233 |
+
#
|
234 |
+
|
235 |
+
class NorbertPreTrainedModel(PreTrainedModel):
|
236 |
+
config_class = NorbertConfig
|
237 |
+
base_model_prefix = "norbert3"
|
238 |
+
supports_gradient_checkpointing = True
|
239 |
+
|
240 |
+
def _set_gradient_checkpointing(self, module, value=False):
|
241 |
+
if isinstance(module, Encoder):
|
242 |
+
module.activation_checkpointing = value
|
243 |
+
|
244 |
+
def _init_weights(self, module):
|
245 |
+
std = math.sqrt(2.0 / (5.0 * self.hidden_size))
|
246 |
+
|
247 |
+
if isinstance(module, nn.Linear):
|
248 |
+
nn.init.trunc_normal_(module.weight.data, mean=0.0, std=std, a=-2*std, b=2*std)
|
249 |
+
if module.bias is not None:
|
250 |
+
module.bias.data.zero_()
|
251 |
+
elif isinstance(module, nn.Embedding):
|
252 |
+
nn.init.trunc_normal_(module.weight.data, mean=0.0, std=std, a=-2*std, b=2*std)
|
253 |
+
elif isinstance(module, nn.LayerNorm):
|
254 |
+
module.bias.data.zero_()
|
255 |
+
module.weight.data.fill_(1.0)
|
256 |
+
|
257 |
+
|
258 |
+
class NorbertModel(NorbertPreTrainedModel):
|
259 |
+
def __init__(self, config, add_mlm_layer=False, gradient_checkpointing=False, **kwargs):
|
260 |
+
super().__init__(config, **kwargs)
|
261 |
+
self.config = config
|
262 |
+
self.hidden_size = config.hidden_size
|
263 |
+
|
264 |
+
self.embedding = Embedding(config)
|
265 |
+
self.transformer = Encoder(config, activation_checkpointing=gradient_checkpointing)
|
266 |
+
self.classifier = MaskClassifier(config, self.embedding.word_embedding.weight) if add_mlm_layer else None
|
267 |
+
|
268 |
+
def get_input_embeddings(self):
|
269 |
+
return self.embedding.word_embedding
|
270 |
+
|
271 |
+
def set_input_embeddings(self, value):
|
272 |
+
self.embedding.word_embedding = value
|
273 |
+
|
274 |
+
def get_contextualized_embeddings(
|
275 |
+
self,
|
276 |
+
input_ids: Optional[torch.Tensor] = None,
|
277 |
+
attention_mask: Optional[torch.Tensor] = None
|
278 |
+
) -> List[torch.Tensor]:
|
279 |
+
if input_ids is not None:
|
280 |
+
input_shape = input_ids.size()
|
281 |
+
else:
|
282 |
+
raise ValueError("You have to specify input_ids")
|
283 |
+
|
284 |
+
batch_size, seq_length = input_shape
|
285 |
+
device = input_ids.device
|
286 |
+
|
287 |
+
if attention_mask is None:
|
288 |
+
attention_mask = torch.zeros(batch_size, seq_length, dtype=torch.bool, device=device)
|
289 |
+
else:
|
290 |
+
attention_mask = ~attention_mask.bool()
|
291 |
+
attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
|
292 |
+
|
293 |
+
static_embeddings, relative_embedding = self.embedding(input_ids.t())
|
294 |
+
contextualized_embeddings, attention_probs = self.transformer(static_embeddings, attention_mask, relative_embedding)
|
295 |
+
contextualized_embeddings = [e.transpose(0, 1) for e in contextualized_embeddings]
|
296 |
+
last_layer = contextualized_embeddings[-1]
|
297 |
+
contextualized_embeddings = [contextualized_embeddings[0]] + [
|
298 |
+
contextualized_embeddings[i] - contextualized_embeddings[i - 1]
|
299 |
+
for i in range(1, len(contextualized_embeddings))
|
300 |
+
]
|
301 |
+
return last_layer, contextualized_embeddings, attention_probs
|
302 |
+
|
303 |
+
def forward(
|
304 |
+
self,
|
305 |
+
input_ids: Optional[torch.Tensor] = None,
|
306 |
+
attention_mask: Optional[torch.Tensor] = None,
|
307 |
+
token_type_ids: Optional[torch.Tensor] = None,
|
308 |
+
position_ids: Optional[torch.Tensor] = None,
|
309 |
+
output_hidden_states: Optional[bool] = None,
|
310 |
+
output_attentions: Optional[bool] = None,
|
311 |
+
return_dict: Optional[bool] = None,
|
312 |
+
**kwargs
|
313 |
+
) -> Union[Tuple[torch.Tensor], BaseModelOutput]:
|
314 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
315 |
+
|
316 |
+
sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
|
317 |
+
|
318 |
+
if not return_dict:
|
319 |
+
return (
|
320 |
+
sequence_output,
|
321 |
+
*([contextualized_embeddings] if output_hidden_states else []),
|
322 |
+
*([attention_probs] if output_attentions else [])
|
323 |
+
)
|
324 |
+
|
325 |
+
return BaseModelOutput(
|
326 |
+
last_hidden_state=sequence_output,
|
327 |
+
hidden_states=contextualized_embeddings if output_hidden_states else None,
|
328 |
+
attentions=attention_probs if output_attentions else None
|
329 |
+
)
|
330 |
+
|
331 |
+
|
332 |
+
class NorbertForMaskedLM(NorbertModel):
|
333 |
+
_keys_to_ignore_on_load_unexpected = ["head"]
|
334 |
+
|
335 |
+
def __init__(self, config, **kwargs):
|
336 |
+
super().__init__(config, add_mlm_layer=True, **kwargs)
|
337 |
+
|
338 |
+
def get_output_embeddings(self):
|
339 |
+
return self.classifier.nonlinearity[-1].weight
|
340 |
+
|
341 |
+
def set_output_embeddings(self, new_embeddings):
|
342 |
+
self.classifier.nonlinearity[-1].weight = new_embeddings
|
343 |
+
|
344 |
+
def forward(
|
345 |
+
self,
|
346 |
+
input_ids: Optional[torch.Tensor] = None,
|
347 |
+
attention_mask: Optional[torch.Tensor] = None,
|
348 |
+
token_type_ids: Optional[torch.Tensor] = None,
|
349 |
+
position_ids: Optional[torch.Tensor] = None,
|
350 |
+
output_hidden_states: Optional[bool] = None,
|
351 |
+
output_attentions: Optional[bool] = None,
|
352 |
+
return_dict: Optional[bool] = None,
|
353 |
+
labels: Optional[torch.LongTensor] = None,
|
354 |
+
**kwargs
|
355 |
+
) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
|
356 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
357 |
+
|
358 |
+
sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
|
359 |
+
subword_prediction = self.classifier(sequence_output)
|
360 |
+
subword_prediction[:, :, :106+1] = float("-inf")
|
361 |
+
|
362 |
+
masked_lm_loss = None
|
363 |
+
if labels is not None:
|
364 |
+
masked_lm_loss = F.cross_entropy(subword_prediction.flatten(0, 1), labels.flatten())
|
365 |
+
|
366 |
+
if not return_dict:
|
367 |
+
output = (
|
368 |
+
subword_prediction,
|
369 |
+
*([contextualized_embeddings] if output_hidden_states else []),
|
370 |
+
*([attention_probs] if output_attentions else [])
|
371 |
+
)
|
372 |
+
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
|
373 |
+
|
374 |
+
return MaskedLMOutput(
|
375 |
+
loss=masked_lm_loss,
|
376 |
+
logits=subword_prediction,
|
377 |
+
hidden_states=contextualized_embeddings if output_hidden_states else None,
|
378 |
+
attentions=attention_probs if output_attentions else None
|
379 |
+
)
|
380 |
+
|
381 |
+
|
382 |
+
class Classifier(nn.Module):
|
383 |
+
def __init__(self, config, num_labels: int):
|
384 |
+
super().__init__()
|
385 |
+
|
386 |
+
drop_out = getattr(config, "cls_dropout", None)
|
387 |
+
drop_out = config.hidden_dropout_prob if drop_out is None else drop_out
|
388 |
+
|
389 |
+
self.nonlinearity = nn.Sequential(
|
390 |
+
nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False),
|
391 |
+
nn.Linear(config.hidden_size, config.hidden_size),
|
392 |
+
nn.GELU(),
|
393 |
+
nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False),
|
394 |
+
nn.Dropout(drop_out),
|
395 |
+
nn.Linear(config.hidden_size, num_labels)
|
396 |
+
)
|
397 |
+
|
398 |
+
def forward(self, x):
|
399 |
+
x = self.nonlinearity(x)
|
400 |
+
return x
|
401 |
+
|
402 |
+
|
403 |
+
class NorbertForSequenceClassification(NorbertModel):
|
404 |
+
_keys_to_ignore_on_load_unexpected = ["classifier"]
|
405 |
+
_keys_to_ignore_on_load_missing = ["head"]
|
406 |
+
|
407 |
+
def __init__(self, config, **kwargs):
|
408 |
+
super().__init__(config, add_mlm_layer=False, **kwargs)
|
409 |
+
|
410 |
+
self.num_labels = config.num_labels
|
411 |
+
self.head = Classifier(config, self.num_labels)
|
412 |
+
|
413 |
+
def forward(
|
414 |
+
self,
|
415 |
+
input_ids: Optional[torch.Tensor] = None,
|
416 |
+
attention_mask: Optional[torch.Tensor] = None,
|
417 |
+
token_type_ids: Optional[torch.Tensor] = None,
|
418 |
+
position_ids: Optional[torch.Tensor] = None,
|
419 |
+
output_attentions: Optional[bool] = None,
|
420 |
+
output_hidden_states: Optional[bool] = None,
|
421 |
+
return_dict: Optional[bool] = None,
|
422 |
+
labels: Optional[torch.LongTensor] = None,
|
423 |
+
**kwargs
|
424 |
+
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
|
425 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
426 |
+
|
427 |
+
sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
|
428 |
+
logits = self.head(sequence_output[:, 0, :])
|
429 |
+
|
430 |
+
loss = None
|
431 |
+
if labels is not None:
|
432 |
+
if self.config.problem_type is None:
|
433 |
+
if self.num_labels == 1:
|
434 |
+
self.config.problem_type = "regression"
|
435 |
+
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
436 |
+
self.config.problem_type = "single_label_classification"
|
437 |
+
else:
|
438 |
+
self.config.problem_type = "multi_label_classification"
|
439 |
+
|
440 |
+
if self.config.problem_type == "regression":
|
441 |
+
loss_fct = nn.MSELoss()
|
442 |
+
if self.num_labels == 1:
|
443 |
+
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
444 |
+
else:
|
445 |
+
loss = loss_fct(logits, labels)
|
446 |
+
elif self.config.problem_type == "single_label_classification":
|
447 |
+
loss_fct = nn.CrossEntropyLoss()
|
448 |
+
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
449 |
+
elif self.config.problem_type == "multi_label_classification":
|
450 |
+
loss_fct = nn.BCEWithLogitsLoss()
|
451 |
+
loss = loss_fct(logits, labels)
|
452 |
+
|
453 |
+
if not return_dict:
|
454 |
+
output = (
|
455 |
+
logits,
|
456 |
+
*([contextualized_embeddings] if output_hidden_states else []),
|
457 |
+
*([attention_probs] if output_attentions else [])
|
458 |
+
)
|
459 |
+
return ((loss,) + output) if loss is not None else output
|
460 |
+
|
461 |
+
return SequenceClassifierOutput(
|
462 |
+
loss=loss,
|
463 |
+
logits=logits,
|
464 |
+
hidden_states=contextualized_embeddings if output_hidden_states else None,
|
465 |
+
attentions=attention_probs if output_attentions else None
|
466 |
+
)
|
467 |
+
|
468 |
+
|
469 |
+
class NorbertForTokenClassification(NorbertModel):
|
470 |
+
_keys_to_ignore_on_load_unexpected = ["classifier"]
|
471 |
+
_keys_to_ignore_on_load_missing = ["head"]
|
472 |
+
|
473 |
+
def __init__(self, config, **kwargs):
|
474 |
+
super().__init__(config, add_mlm_layer=False, **kwargs)
|
475 |
+
|
476 |
+
self.num_labels = config.num_labels
|
477 |
+
self.head = Classifier(config, self.num_labels)
|
478 |
+
|
479 |
+
def forward(
|
480 |
+
self,
|
481 |
+
input_ids: Optional[torch.Tensor] = None,
|
482 |
+
attention_mask: Optional[torch.Tensor] = None,
|
483 |
+
token_type_ids: Optional[torch.Tensor] = None,
|
484 |
+
position_ids: Optional[torch.Tensor] = None,
|
485 |
+
output_attentions: Optional[bool] = None,
|
486 |
+
output_hidden_states: Optional[bool] = None,
|
487 |
+
return_dict: Optional[bool] = None,
|
488 |
+
labels: Optional[torch.LongTensor] = None,
|
489 |
+
**kwargs
|
490 |
+
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
|
491 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
492 |
+
|
493 |
+
sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
|
494 |
+
logits = self.head(sequence_output)
|
495 |
+
|
496 |
+
loss = None
|
497 |
+
if labels is not None:
|
498 |
+
loss_fct = nn.CrossEntropyLoss()
|
499 |
+
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
500 |
+
|
501 |
+
if not return_dict:
|
502 |
+
output = (
|
503 |
+
logits,
|
504 |
+
*([contextualized_embeddings] if output_hidden_states else []),
|
505 |
+
*([attention_probs] if output_attentions else [])
|
506 |
+
)
|
507 |
+
return ((loss,) + output) if loss is not None else output
|
508 |
+
|
509 |
+
return TokenClassifierOutput(
|
510 |
+
loss=loss,
|
511 |
+
logits=logits,
|
512 |
+
hidden_states=contextualized_embeddings if output_hidden_states else None,
|
513 |
+
attentions=attention_probs if output_attentions else None
|
514 |
+
)
|
515 |
+
|
516 |
+
|
517 |
+
class NorbertForQuestionAnswering(NorbertModel):
|
518 |
+
_keys_to_ignore_on_load_unexpected = ["classifier"]
|
519 |
+
_keys_to_ignore_on_load_missing = ["head"]
|
520 |
+
|
521 |
+
def __init__(self, config, **kwargs):
|
522 |
+
super().__init__(config, add_mlm_layer=False, **kwargs)
|
523 |
+
|
524 |
+
self.num_labels = config.num_labels
|
525 |
+
self.head = Classifier(config, self.num_labels)
|
526 |
+
|
527 |
+
def forward(
|
528 |
+
self,
|
529 |
+
input_ids: Optional[torch.Tensor] = None,
|
530 |
+
attention_mask: Optional[torch.Tensor] = None,
|
531 |
+
token_type_ids: Optional[torch.Tensor] = None,
|
532 |
+
position_ids: Optional[torch.Tensor] = None,
|
533 |
+
output_attentions: Optional[bool] = None,
|
534 |
+
output_hidden_states: Optional[bool] = None,
|
535 |
+
return_dict: Optional[bool] = None,
|
536 |
+
start_positions: Optional[torch.Tensor] = None,
|
537 |
+
end_positions: Optional[torch.Tensor] = None,
|
538 |
+
**kwargs
|
539 |
+
) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
|
540 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
541 |
+
|
542 |
+
sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
|
543 |
+
logits = self.head(sequence_output)
|
544 |
+
|
545 |
+
start_logits, end_logits = logits.split(1, dim=-1)
|
546 |
+
start_logits = start_logits.squeeze(-1).contiguous()
|
547 |
+
end_logits = end_logits.squeeze(-1).contiguous()
|
548 |
+
|
549 |
+
total_loss = None
|
550 |
+
if start_positions is not None and end_positions is not None:
|
551 |
+
# If we are on multi-GPU, split add a dimension
|
552 |
+
if len(start_positions.size()) > 1:
|
553 |
+
start_positions = start_positions.squeeze(-1)
|
554 |
+
if len(end_positions.size()) > 1:
|
555 |
+
end_positions = end_positions.squeeze(-1)
|
556 |
+
|
557 |
+
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
558 |
+
ignored_index = start_logits.size(1)
|
559 |
+
start_positions = start_positions.clamp(0, ignored_index)
|
560 |
+
end_positions = end_positions.clamp(0, ignored_index)
|
561 |
+
|
562 |
+
loss_fct = nn.CrossEntropyLoss(ignore_index=ignored_index)
|
563 |
+
start_loss = loss_fct(start_logits, start_positions)
|
564 |
+
end_loss = loss_fct(end_logits, end_positions)
|
565 |
+
total_loss = (start_loss + end_loss) / 2
|
566 |
+
|
567 |
+
if not return_dict:
|
568 |
+
output = (
|
569 |
+
start_logits,
|
570 |
+
end_logits,
|
571 |
+
*([contextualized_embeddings] if output_hidden_states else []),
|
572 |
+
*([attention_probs] if output_attentions else [])
|
573 |
+
)
|
574 |
+
return ((total_loss,) + output) if total_loss is not None else output
|
575 |
+
|
576 |
+
return QuestionAnsweringModelOutput(
|
577 |
+
loss=total_loss,
|
578 |
+
start_logits=start_logits,
|
579 |
+
end_logits=end_logits,
|
580 |
+
hidden_states=contextualized_embeddings if output_hidden_states else None,
|
581 |
+
attentions=attention_probs if output_attentions else None
|
582 |
+
)
|
583 |
+
|
584 |
+
|
585 |
+
class NorbertForMultipleChoice(NorbertModel):
|
586 |
+
_keys_to_ignore_on_load_unexpected = ["classifier"]
|
587 |
+
_keys_to_ignore_on_load_missing = ["head"]
|
588 |
+
|
589 |
+
def __init__(self, config, **kwargs):
|
590 |
+
super().__init__(config, add_mlm_layer=False, **kwargs)
|
591 |
+
|
592 |
+
self.num_labels = getattr(config, "num_labels", 2)
|
593 |
+
self.head = Classifier(config, self.num_labels)
|
594 |
+
|
595 |
+
def forward(
|
596 |
+
self,
|
597 |
+
input_ids: Optional[torch.Tensor] = None,
|
598 |
+
attention_mask: Optional[torch.Tensor] = None,
|
599 |
+
token_type_ids: Optional[torch.Tensor] = None,
|
600 |
+
position_ids: Optional[torch.Tensor] = None,
|
601 |
+
labels: Optional[torch.Tensor] = None,
|
602 |
+
output_attentions: Optional[bool] = None,
|
603 |
+
output_hidden_states: Optional[bool] = None,
|
604 |
+
return_dict: Optional[bool] = None,
|
605 |
+
**kwargs
|
606 |
+
) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
|
607 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
608 |
+
num_choices = input_ids.shape[1]
|
609 |
+
|
610 |
+
flat_input_ids = input_ids.view(-1, input_ids.size(-1))
|
611 |
+
flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
|
612 |
+
|
613 |
+
sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(flat_input_ids, flat_attention_mask)
|
614 |
+
logits = self.head(sequence_output)
|
615 |
+
reshaped_logits = logits.view(-1, num_choices)
|
616 |
+
|
617 |
+
loss = None
|
618 |
+
if labels is not None:
|
619 |
+
loss_fct = nn.CrossEntropyLoss()
|
620 |
+
loss = loss_fct(reshaped_logits, labels)
|
621 |
+
|
622 |
+
if not return_dict:
|
623 |
+
output = (
|
624 |
+
reshaped_logits,
|
625 |
+
*([contextualized_embeddings] if output_hidden_states else []),
|
626 |
+
*([attention_probs] if output_attentions else [])
|
627 |
+
)
|
628 |
+
return ((loss,) + output) if loss is not None else output
|
629 |
+
|
630 |
+
return MultipleChoiceModelOutput(
|
631 |
+
loss=loss,
|
632 |
+
logits=reshaped_logits,
|
633 |
+
hidden_states=contextualized_embeddings if output_hidden_states else None,
|
634 |
+
attentions=attention_probs if output_attentions else None
|
635 |
+
)
|
optimizer.pt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:144912b41c99ce11c9ff41ade8ce11e6432b0eedfae30a55a60152bdfb7a5c5d
|
3 |
+
size 4735250
|
rng_state.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:e78c2b2dfd67dd86ab81cb66717a46e57aba66c9b9e57f20766946e1e48703d2
|
3 |
+
size 14180
|
scheduler.pt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:a20310f80bd8c93b2778c2b9d92d7e31c911403d1bad2087e2913900421f86d1
|
3 |
+
size 1064
|
special_tokens_map.json
ADDED
@@ -0,0 +1,51 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"bos_token": {
|
3 |
+
"content": "[BOS]",
|
4 |
+
"lstrip": false,
|
5 |
+
"normalized": false,
|
6 |
+
"rstrip": false,
|
7 |
+
"single_word": false
|
8 |
+
},
|
9 |
+
"cls_token": {
|
10 |
+
"content": "[CLS]",
|
11 |
+
"lstrip": false,
|
12 |
+
"normalized": false,
|
13 |
+
"rstrip": false,
|
14 |
+
"single_word": false
|
15 |
+
},
|
16 |
+
"eos_token": {
|
17 |
+
"content": "[EOS]",
|
18 |
+
"lstrip": false,
|
19 |
+
"normalized": false,
|
20 |
+
"rstrip": false,
|
21 |
+
"single_word": false
|
22 |
+
},
|
23 |
+
"mask_token": {
|
24 |
+
"content": "[MASK]",
|
25 |
+
"lstrip": false,
|
26 |
+
"normalized": false,
|
27 |
+
"rstrip": false,
|
28 |
+
"single_word": false
|
29 |
+
},
|
30 |
+
"pad_token": {
|
31 |
+
"content": "[PAD]",
|
32 |
+
"lstrip": false,
|
33 |
+
"normalized": false,
|
34 |
+
"rstrip": false,
|
35 |
+
"single_word": false
|
36 |
+
},
|
37 |
+
"sep_token": {
|
38 |
+
"content": "[SEP]",
|
39 |
+
"lstrip": false,
|
40 |
+
"normalized": false,
|
41 |
+
"rstrip": false,
|
42 |
+
"single_word": false
|
43 |
+
},
|
44 |
+
"unk_token": {
|
45 |
+
"content": "[UNK]",
|
46 |
+
"lstrip": false,
|
47 |
+
"normalized": false,
|
48 |
+
"rstrip": false,
|
49 |
+
"single_word": false
|
50 |
+
}
|
51 |
+
}
|
tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,870 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
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|
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|
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|
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|
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|
|
|
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|
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|
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|
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|
1 |
+
{
|
2 |
+
"added_tokens_decoder": {
|
3 |
+
"0": {
|
4 |
+
"content": "[UNK]",
|
5 |
+
"lstrip": false,
|
6 |
+
"normalized": false,
|
7 |
+
"rstrip": false,
|
8 |
+
"single_word": false,
|
9 |
+
"special": true
|
10 |
+
},
|
11 |
+
"1": {
|
12 |
+
"content": "[CLS]",
|
13 |
+
"lstrip": false,
|
14 |
+
"normalized": false,
|
15 |
+
"rstrip": false,
|
16 |
+
"single_word": false,
|
17 |
+
"special": true
|
18 |
+
},
|
19 |
+
"2": {
|
20 |
+
"content": "[SEP]",
|
21 |
+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": true
|
26 |
+
},
|
27 |
+
"3": {
|
28 |
+
"content": "[PAD]",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false,
|
33 |
+
"special": true
|
34 |
+
},
|
35 |
+
"4": {
|
36 |
+
"content": "[MASK]",
|
37 |
+
"lstrip": false,
|
38 |
+
"normalized": false,
|
39 |
+
"rstrip": false,
|
40 |
+
"single_word": false,
|
41 |
+
"special": true
|
42 |
+
},
|
43 |
+
"5": {
|
44 |
+
"content": "[BOS]",
|
45 |
+
"lstrip": false,
|
46 |
+
"normalized": false,
|
47 |
+
"rstrip": false,
|
48 |
+
"single_word": false,
|
49 |
+
"special": true
|
50 |
+
},
|
51 |
+
"6": {
|
52 |
+
"content": "[EOS]",
|
53 |
+
"lstrip": false,
|
54 |
+
"normalized": false,
|
55 |
+
"rstrip": false,
|
56 |
+
"single_word": false,
|
57 |
+
"special": true
|
58 |
+
},
|
59 |
+
"7": {
|
60 |
+
"content": "[MASK_0]",
|
61 |
+
"lstrip": false,
|
62 |
+
"normalized": false,
|
63 |
+
"rstrip": false,
|
64 |
+
"single_word": false,
|
65 |
+
"special": true
|
66 |
+
},
|
67 |
+
"8": {
|
68 |
+
"content": "[MASK_1]",
|
69 |
+
"lstrip": false,
|
70 |
+
"normalized": false,
|
71 |
+
"rstrip": false,
|
72 |
+
"single_word": false,
|
73 |
+
"special": true
|
74 |
+
},
|
75 |
+
"9": {
|
76 |
+
"content": "[MASK_2]",
|
77 |
+
"lstrip": false,
|
78 |
+
"normalized": false,
|
79 |
+
"rstrip": false,
|
80 |
+
"single_word": false,
|
81 |
+
"special": true
|
82 |
+
},
|
83 |
+
"10": {
|
84 |
+
"content": "[MASK_3]",
|
85 |
+
"lstrip": false,
|
86 |
+
"normalized": false,
|
87 |
+
"rstrip": false,
|
88 |
+
"single_word": false,
|
89 |
+
"special": true
|
90 |
+
},
|
91 |
+
"11": {
|
92 |
+
"content": "[MASK_4]",
|
93 |
+
"lstrip": false,
|
94 |
+
"normalized": false,
|
95 |
+
"rstrip": false,
|
96 |
+
"single_word": false,
|
97 |
+
"special": true
|
98 |
+
},
|
99 |
+
"12": {
|
100 |
+
"content": "[MASK_5]",
|
101 |
+
"lstrip": false,
|
102 |
+
"normalized": false,
|
103 |
+
"rstrip": false,
|
104 |
+
"single_word": false,
|
105 |
+
"special": true
|
106 |
+
},
|
107 |
+
"13": {
|
108 |
+
"content": "[MASK_6]",
|
109 |
+
"lstrip": false,
|
110 |
+
"normalized": false,
|
111 |
+
"rstrip": false,
|
112 |
+
"single_word": false,
|
113 |
+
"special": true
|
114 |
+
},
|
115 |
+
"14": {
|
116 |
+
"content": "[MASK_7]",
|
117 |
+
"lstrip": false,
|
118 |
+
"normalized": false,
|
119 |
+
"rstrip": false,
|
120 |
+
"single_word": false,
|
121 |
+
"special": true
|
122 |
+
},
|
123 |
+
"15": {
|
124 |
+
"content": "[MASK_8]",
|
125 |
+
"lstrip": false,
|
126 |
+
"normalized": false,
|
127 |
+
"rstrip": false,
|
128 |
+
"single_word": false,
|
129 |
+
"special": true
|
130 |
+
},
|
131 |
+
"16": {
|
132 |
+
"content": "[MASK_9]",
|
133 |
+
"lstrip": false,
|
134 |
+
"normalized": false,
|
135 |
+
"rstrip": false,
|
136 |
+
"single_word": false,
|
137 |
+
"special": true
|
138 |
+
},
|
139 |
+
"17": {
|
140 |
+
"content": "[MASK_10]",
|
141 |
+
"lstrip": false,
|
142 |
+
"normalized": false,
|
143 |
+
"rstrip": false,
|
144 |
+
"single_word": false,
|
145 |
+
"special": true
|
146 |
+
},
|
147 |
+
"18": {
|
148 |
+
"content": "[MASK_11]",
|
149 |
+
"lstrip": false,
|
150 |
+
"normalized": false,
|
151 |
+
"rstrip": false,
|
152 |
+
"single_word": false,
|
153 |
+
"special": true
|
154 |
+
},
|
155 |
+
"19": {
|
156 |
+
"content": "[MASK_12]",
|
157 |
+
"lstrip": false,
|
158 |
+
"normalized": false,
|
159 |
+
"rstrip": false,
|
160 |
+
"single_word": false,
|
161 |
+
"special": true
|
162 |
+
},
|
163 |
+
"20": {
|
164 |
+
"content": "[MASK_13]",
|
165 |
+
"lstrip": false,
|
166 |
+
"normalized": false,
|
167 |
+
"rstrip": false,
|
168 |
+
"single_word": false,
|
169 |
+
"special": true
|
170 |
+
},
|
171 |
+
"21": {
|
172 |
+
"content": "[MASK_14]",
|
173 |
+
"lstrip": false,
|
174 |
+
"normalized": false,
|
175 |
+
"rstrip": false,
|
176 |
+
"single_word": false,
|
177 |
+
"special": true
|
178 |
+
},
|
179 |
+
"22": {
|
180 |
+
"content": "[MASK_15]",
|
181 |
+
"lstrip": false,
|
182 |
+
"normalized": false,
|
183 |
+
"rstrip": false,
|
184 |
+
"single_word": false,
|
185 |
+
"special": true
|
186 |
+
},
|
187 |
+
"23": {
|
188 |
+
"content": "[MASK_16]",
|
189 |
+
"lstrip": false,
|
190 |
+
"normalized": false,
|
191 |
+
"rstrip": false,
|
192 |
+
"single_word": false,
|
193 |
+
"special": true
|
194 |
+
},
|
195 |
+
"24": {
|
196 |
+
"content": "[MASK_17]",
|
197 |
+
"lstrip": false,
|
198 |
+
"normalized": false,
|
199 |
+
"rstrip": false,
|
200 |
+
"single_word": false,
|
201 |
+
"special": true
|
202 |
+
},
|
203 |
+
"25": {
|
204 |
+
"content": "[MASK_18]",
|
205 |
+
"lstrip": false,
|
206 |
+
"normalized": false,
|
207 |
+
"rstrip": false,
|
208 |
+
"single_word": false,
|
209 |
+
"special": true
|
210 |
+
},
|
211 |
+
"26": {
|
212 |
+
"content": "[MASK_19]",
|
213 |
+
"lstrip": false,
|
214 |
+
"normalized": false,
|
215 |
+
"rstrip": false,
|
216 |
+
"single_word": false,
|
217 |
+
"special": true
|
218 |
+
},
|
219 |
+
"27": {
|
220 |
+
"content": "[MASK_20]",
|
221 |
+
"lstrip": false,
|
222 |
+
"normalized": false,
|
223 |
+
"rstrip": false,
|
224 |
+
"single_word": false,
|
225 |
+
"special": true
|
226 |
+
},
|
227 |
+
"28": {
|
228 |
+
"content": "[MASK_21]",
|
229 |
+
"lstrip": false,
|
230 |
+
"normalized": false,
|
231 |
+
"rstrip": false,
|
232 |
+
"single_word": false,
|
233 |
+
"special": true
|
234 |
+
},
|
235 |
+
"29": {
|
236 |
+
"content": "[MASK_22]",
|
237 |
+
"lstrip": false,
|
238 |
+
"normalized": false,
|
239 |
+
"rstrip": false,
|
240 |
+
"single_word": false,
|
241 |
+
"special": true
|
242 |
+
},
|
243 |
+
"30": {
|
244 |
+
"content": "[MASK_23]",
|
245 |
+
"lstrip": false,
|
246 |
+
"normalized": false,
|
247 |
+
"rstrip": false,
|
248 |
+
"single_word": false,
|
249 |
+
"special": true
|
250 |
+
},
|
251 |
+
"31": {
|
252 |
+
"content": "[MASK_24]",
|
253 |
+
"lstrip": false,
|
254 |
+
"normalized": false,
|
255 |
+
"rstrip": false,
|
256 |
+
"single_word": false,
|
257 |
+
"special": true
|
258 |
+
},
|
259 |
+
"32": {
|
260 |
+
"content": "[MASK_25]",
|
261 |
+
"lstrip": false,
|
262 |
+
"normalized": false,
|
263 |
+
"rstrip": false,
|
264 |
+
"single_word": false,
|
265 |
+
"special": true
|
266 |
+
},
|
267 |
+
"33": {
|
268 |
+
"content": "[MASK_26]",
|
269 |
+
"lstrip": false,
|
270 |
+
"normalized": false,
|
271 |
+
"rstrip": false,
|
272 |
+
"single_word": false,
|
273 |
+
"special": true
|
274 |
+
},
|
275 |
+
"34": {
|
276 |
+
"content": "[MASK_27]",
|
277 |
+
"lstrip": false,
|
278 |
+
"normalized": false,
|
279 |
+
"rstrip": false,
|
280 |
+
"single_word": false,
|
281 |
+
"special": true
|
282 |
+
},
|
283 |
+
"35": {
|
284 |
+
"content": "[MASK_28]",
|
285 |
+
"lstrip": false,
|
286 |
+
"normalized": false,
|
287 |
+
"rstrip": false,
|
288 |
+
"single_word": false,
|
289 |
+
"special": true
|
290 |
+
},
|
291 |
+
"36": {
|
292 |
+
"content": "[MASK_29]",
|
293 |
+
"lstrip": false,
|
294 |
+
"normalized": false,
|
295 |
+
"rstrip": false,
|
296 |
+
"single_word": false,
|
297 |
+
"special": true
|
298 |
+
},
|
299 |
+
"37": {
|
300 |
+
"content": "[MASK_30]",
|
301 |
+
"lstrip": false,
|
302 |
+
"normalized": false,
|
303 |
+
"rstrip": false,
|
304 |
+
"single_word": false,
|
305 |
+
"special": true
|
306 |
+
},
|
307 |
+
"38": {
|
308 |
+
"content": "[MASK_31]",
|
309 |
+
"lstrip": false,
|
310 |
+
"normalized": false,
|
311 |
+
"rstrip": false,
|
312 |
+
"single_word": false,
|
313 |
+
"special": true
|
314 |
+
},
|
315 |
+
"39": {
|
316 |
+
"content": "[MASK_32]",
|
317 |
+
"lstrip": false,
|
318 |
+
"normalized": false,
|
319 |
+
"rstrip": false,
|
320 |
+
"single_word": false,
|
321 |
+
"special": true
|
322 |
+
},
|
323 |
+
"40": {
|
324 |
+
"content": "[MASK_33]",
|
325 |
+
"lstrip": false,
|
326 |
+
"normalized": false,
|
327 |
+
"rstrip": false,
|
328 |
+
"single_word": false,
|
329 |
+
"special": true
|
330 |
+
},
|
331 |
+
"41": {
|
332 |
+
"content": "[MASK_34]",
|
333 |
+
"lstrip": false,
|
334 |
+
"normalized": false,
|
335 |
+
"rstrip": false,
|
336 |
+
"single_word": false,
|
337 |
+
"special": true
|
338 |
+
},
|
339 |
+
"42": {
|
340 |
+
"content": "[MASK_35]",
|
341 |
+
"lstrip": false,
|
342 |
+
"normalized": false,
|
343 |
+
"rstrip": false,
|
344 |
+
"single_word": false,
|
345 |
+
"special": true
|
346 |
+
},
|
347 |
+
"43": {
|
348 |
+
"content": "[MASK_36]",
|
349 |
+
"lstrip": false,
|
350 |
+
"normalized": false,
|
351 |
+
"rstrip": false,
|
352 |
+
"single_word": false,
|
353 |
+
"special": true
|
354 |
+
},
|
355 |
+
"44": {
|
356 |
+
"content": "[MASK_37]",
|
357 |
+
"lstrip": false,
|
358 |
+
"normalized": false,
|
359 |
+
"rstrip": false,
|
360 |
+
"single_word": false,
|
361 |
+
"special": true
|
362 |
+
},
|
363 |
+
"45": {
|
364 |
+
"content": "[MASK_38]",
|
365 |
+
"lstrip": false,
|
366 |
+
"normalized": false,
|
367 |
+
"rstrip": false,
|
368 |
+
"single_word": false,
|
369 |
+
"special": true
|
370 |
+
},
|
371 |
+
"46": {
|
372 |
+
"content": "[MASK_39]",
|
373 |
+
"lstrip": false,
|
374 |
+
"normalized": false,
|
375 |
+
"rstrip": false,
|
376 |
+
"single_word": false,
|
377 |
+
"special": true
|
378 |
+
},
|
379 |
+
"47": {
|
380 |
+
"content": "[MASK_40]",
|
381 |
+
"lstrip": false,
|
382 |
+
"normalized": false,
|
383 |
+
"rstrip": false,
|
384 |
+
"single_word": false,
|
385 |
+
"special": true
|
386 |
+
},
|
387 |
+
"48": {
|
388 |
+
"content": "[MASK_41]",
|
389 |
+
"lstrip": false,
|
390 |
+
"normalized": false,
|
391 |
+
"rstrip": false,
|
392 |
+
"single_word": false,
|
393 |
+
"special": true
|
394 |
+
},
|
395 |
+
"49": {
|
396 |
+
"content": "[MASK_42]",
|
397 |
+
"lstrip": false,
|
398 |
+
"normalized": false,
|
399 |
+
"rstrip": false,
|
400 |
+
"single_word": false,
|
401 |
+
"special": true
|
402 |
+
},
|
403 |
+
"50": {
|
404 |
+
"content": "[MASK_43]",
|
405 |
+
"lstrip": false,
|
406 |
+
"normalized": false,
|
407 |
+
"rstrip": false,
|
408 |
+
"single_word": false,
|
409 |
+
"special": true
|
410 |
+
},
|
411 |
+
"51": {
|
412 |
+
"content": "[MASK_44]",
|
413 |
+
"lstrip": false,
|
414 |
+
"normalized": false,
|
415 |
+
"rstrip": false,
|
416 |
+
"single_word": false,
|
417 |
+
"special": true
|
418 |
+
},
|
419 |
+
"52": {
|
420 |
+
"content": "[MASK_45]",
|
421 |
+
"lstrip": false,
|
422 |
+
"normalized": false,
|
423 |
+
"rstrip": false,
|
424 |
+
"single_word": false,
|
425 |
+
"special": true
|
426 |
+
},
|
427 |
+
"53": {
|
428 |
+
"content": "[MASK_46]",
|
429 |
+
"lstrip": false,
|
430 |
+
"normalized": false,
|
431 |
+
"rstrip": false,
|
432 |
+
"single_word": false,
|
433 |
+
"special": true
|
434 |
+
},
|
435 |
+
"54": {
|
436 |
+
"content": "[MASK_47]",
|
437 |
+
"lstrip": false,
|
438 |
+
"normalized": false,
|
439 |
+
"rstrip": false,
|
440 |
+
"single_word": false,
|
441 |
+
"special": true
|
442 |
+
},
|
443 |
+
"55": {
|
444 |
+
"content": "[MASK_48]",
|
445 |
+
"lstrip": false,
|
446 |
+
"normalized": false,
|
447 |
+
"rstrip": false,
|
448 |
+
"single_word": false,
|
449 |
+
"special": true
|
450 |
+
},
|
451 |
+
"56": {
|
452 |
+
"content": "[MASK_49]",
|
453 |
+
"lstrip": false,
|
454 |
+
"normalized": false,
|
455 |
+
"rstrip": false,
|
456 |
+
"single_word": false,
|
457 |
+
"special": true
|
458 |
+
},
|
459 |
+
"57": {
|
460 |
+
"content": "[MASK_50]",
|
461 |
+
"lstrip": false,
|
462 |
+
"normalized": false,
|
463 |
+
"rstrip": false,
|
464 |
+
"single_word": false,
|
465 |
+
"special": true
|
466 |
+
},
|
467 |
+
"58": {
|
468 |
+
"content": "[MASK_51]",
|
469 |
+
"lstrip": false,
|
470 |
+
"normalized": false,
|
471 |
+
"rstrip": false,
|
472 |
+
"single_word": false,
|
473 |
+
"special": true
|
474 |
+
},
|
475 |
+
"59": {
|
476 |
+
"content": "[MASK_52]",
|
477 |
+
"lstrip": false,
|
478 |
+
"normalized": false,
|
479 |
+
"rstrip": false,
|
480 |
+
"single_word": false,
|
481 |
+
"special": true
|
482 |
+
},
|
483 |
+
"60": {
|
484 |
+
"content": "[MASK_53]",
|
485 |
+
"lstrip": false,
|
486 |
+
"normalized": false,
|
487 |
+
"rstrip": false,
|
488 |
+
"single_word": false,
|
489 |
+
"special": true
|
490 |
+
},
|
491 |
+
"61": {
|
492 |
+
"content": "[MASK_54]",
|
493 |
+
"lstrip": false,
|
494 |
+
"normalized": false,
|
495 |
+
"rstrip": false,
|
496 |
+
"single_word": false,
|
497 |
+
"special": true
|
498 |
+
},
|
499 |
+
"62": {
|
500 |
+
"content": "[MASK_55]",
|
501 |
+
"lstrip": false,
|
502 |
+
"normalized": false,
|
503 |
+
"rstrip": false,
|
504 |
+
"single_word": false,
|
505 |
+
"special": true
|
506 |
+
},
|
507 |
+
"63": {
|
508 |
+
"content": "[MASK_56]",
|
509 |
+
"lstrip": false,
|
510 |
+
"normalized": false,
|
511 |
+
"rstrip": false,
|
512 |
+
"single_word": false,
|
513 |
+
"special": true
|
514 |
+
},
|
515 |
+
"64": {
|
516 |
+
"content": "[MASK_57]",
|
517 |
+
"lstrip": false,
|
518 |
+
"normalized": false,
|
519 |
+
"rstrip": false,
|
520 |
+
"single_word": false,
|
521 |
+
"special": true
|
522 |
+
},
|
523 |
+
"65": {
|
524 |
+
"content": "[MASK_58]",
|
525 |
+
"lstrip": false,
|
526 |
+
"normalized": false,
|
527 |
+
"rstrip": false,
|
528 |
+
"single_word": false,
|
529 |
+
"special": true
|
530 |
+
},
|
531 |
+
"66": {
|
532 |
+
"content": "[MASK_59]",
|
533 |
+
"lstrip": false,
|
534 |
+
"normalized": false,
|
535 |
+
"rstrip": false,
|
536 |
+
"single_word": false,
|
537 |
+
"special": true
|
538 |
+
},
|
539 |
+
"67": {
|
540 |
+
"content": "[MASK_60]",
|
541 |
+
"lstrip": false,
|
542 |
+
"normalized": false,
|
543 |
+
"rstrip": false,
|
544 |
+
"single_word": false,
|
545 |
+
"special": true
|
546 |
+
},
|
547 |
+
"68": {
|
548 |
+
"content": "[MASK_61]",
|
549 |
+
"lstrip": false,
|
550 |
+
"normalized": false,
|
551 |
+
"rstrip": false,
|
552 |
+
"single_word": false,
|
553 |
+
"special": true
|
554 |
+
},
|
555 |
+
"69": {
|
556 |
+
"content": "[MASK_62]",
|
557 |
+
"lstrip": false,
|
558 |
+
"normalized": false,
|
559 |
+
"rstrip": false,
|
560 |
+
"single_word": false,
|
561 |
+
"special": true
|
562 |
+
},
|
563 |
+
"70": {
|
564 |
+
"content": "[MASK_63]",
|
565 |
+
"lstrip": false,
|
566 |
+
"normalized": false,
|
567 |
+
"rstrip": false,
|
568 |
+
"single_word": false,
|
569 |
+
"special": true
|
570 |
+
},
|
571 |
+
"71": {
|
572 |
+
"content": "[MASK_64]",
|
573 |
+
"lstrip": false,
|
574 |
+
"normalized": false,
|
575 |
+
"rstrip": false,
|
576 |
+
"single_word": false,
|
577 |
+
"special": true
|
578 |
+
},
|
579 |
+
"72": {
|
580 |
+
"content": "[MASK_65]",
|
581 |
+
"lstrip": false,
|
582 |
+
"normalized": false,
|
583 |
+
"rstrip": false,
|
584 |
+
"single_word": false,
|
585 |
+
"special": true
|
586 |
+
},
|
587 |
+
"73": {
|
588 |
+
"content": "[MASK_66]",
|
589 |
+
"lstrip": false,
|
590 |
+
"normalized": false,
|
591 |
+
"rstrip": false,
|
592 |
+
"single_word": false,
|
593 |
+
"special": true
|
594 |
+
},
|
595 |
+
"74": {
|
596 |
+
"content": "[MASK_67]",
|
597 |
+
"lstrip": false,
|
598 |
+
"normalized": false,
|
599 |
+
"rstrip": false,
|
600 |
+
"single_word": false,
|
601 |
+
"special": true
|
602 |
+
},
|
603 |
+
"75": {
|
604 |
+
"content": "[MASK_68]",
|
605 |
+
"lstrip": false,
|
606 |
+
"normalized": false,
|
607 |
+
"rstrip": false,
|
608 |
+
"single_word": false,
|
609 |
+
"special": true
|
610 |
+
},
|
611 |
+
"76": {
|
612 |
+
"content": "[MASK_69]",
|
613 |
+
"lstrip": false,
|
614 |
+
"normalized": false,
|
615 |
+
"rstrip": false,
|
616 |
+
"single_word": false,
|
617 |
+
"special": true
|
618 |
+
},
|
619 |
+
"77": {
|
620 |
+
"content": "[MASK_70]",
|
621 |
+
"lstrip": false,
|
622 |
+
"normalized": false,
|
623 |
+
"rstrip": false,
|
624 |
+
"single_word": false,
|
625 |
+
"special": true
|
626 |
+
},
|
627 |
+
"78": {
|
628 |
+
"content": "[MASK_71]",
|
629 |
+
"lstrip": false,
|
630 |
+
"normalized": false,
|
631 |
+
"rstrip": false,
|
632 |
+
"single_word": false,
|
633 |
+
"special": true
|
634 |
+
},
|
635 |
+
"79": {
|
636 |
+
"content": "[MASK_72]",
|
637 |
+
"lstrip": false,
|
638 |
+
"normalized": false,
|
639 |
+
"rstrip": false,
|
640 |
+
"single_word": false,
|
641 |
+
"special": true
|
642 |
+
},
|
643 |
+
"80": {
|
644 |
+
"content": "[MASK_73]",
|
645 |
+
"lstrip": false,
|
646 |
+
"normalized": false,
|
647 |
+
"rstrip": false,
|
648 |
+
"single_word": false,
|
649 |
+
"special": true
|
650 |
+
},
|
651 |
+
"81": {
|
652 |
+
"content": "[MASK_74]",
|
653 |
+
"lstrip": false,
|
654 |
+
"normalized": false,
|
655 |
+
"rstrip": false,
|
656 |
+
"single_word": false,
|
657 |
+
"special": true
|
658 |
+
},
|
659 |
+
"82": {
|
660 |
+
"content": "[MASK_75]",
|
661 |
+
"lstrip": false,
|
662 |
+
"normalized": false,
|
663 |
+
"rstrip": false,
|
664 |
+
"single_word": false,
|
665 |
+
"special": true
|
666 |
+
},
|
667 |
+
"83": {
|
668 |
+
"content": "[MASK_76]",
|
669 |
+
"lstrip": false,
|
670 |
+
"normalized": false,
|
671 |
+
"rstrip": false,
|
672 |
+
"single_word": false,
|
673 |
+
"special": true
|
674 |
+
},
|
675 |
+
"84": {
|
676 |
+
"content": "[MASK_77]",
|
677 |
+
"lstrip": false,
|
678 |
+
"normalized": false,
|
679 |
+
"rstrip": false,
|
680 |
+
"single_word": false,
|
681 |
+
"special": true
|
682 |
+
},
|
683 |
+
"85": {
|
684 |
+
"content": "[MASK_78]",
|
685 |
+
"lstrip": false,
|
686 |
+
"normalized": false,
|
687 |
+
"rstrip": false,
|
688 |
+
"single_word": false,
|
689 |
+
"special": true
|
690 |
+
},
|
691 |
+
"86": {
|
692 |
+
"content": "[MASK_79]",
|
693 |
+
"lstrip": false,
|
694 |
+
"normalized": false,
|
695 |
+
"rstrip": false,
|
696 |
+
"single_word": false,
|
697 |
+
"special": true
|
698 |
+
},
|
699 |
+
"87": {
|
700 |
+
"content": "[MASK_80]",
|
701 |
+
"lstrip": false,
|
702 |
+
"normalized": false,
|
703 |
+
"rstrip": false,
|
704 |
+
"single_word": false,
|
705 |
+
"special": true
|
706 |
+
},
|
707 |
+
"88": {
|
708 |
+
"content": "[MASK_81]",
|
709 |
+
"lstrip": false,
|
710 |
+
"normalized": false,
|
711 |
+
"rstrip": false,
|
712 |
+
"single_word": false,
|
713 |
+
"special": true
|
714 |
+
},
|
715 |
+
"89": {
|
716 |
+
"content": "[MASK_82]",
|
717 |
+
"lstrip": false,
|
718 |
+
"normalized": false,
|
719 |
+
"rstrip": false,
|
720 |
+
"single_word": false,
|
721 |
+
"special": true
|
722 |
+
},
|
723 |
+
"90": {
|
724 |
+
"content": "[MASK_83]",
|
725 |
+
"lstrip": false,
|
726 |
+
"normalized": false,
|
727 |
+
"rstrip": false,
|
728 |
+
"single_word": false,
|
729 |
+
"special": true
|
730 |
+
},
|
731 |
+
"91": {
|
732 |
+
"content": "[MASK_84]",
|
733 |
+
"lstrip": false,
|
734 |
+
"normalized": false,
|
735 |
+
"rstrip": false,
|
736 |
+
"single_word": false,
|
737 |
+
"special": true
|
738 |
+
},
|
739 |
+
"92": {
|
740 |
+
"content": "[MASK_85]",
|
741 |
+
"lstrip": false,
|
742 |
+
"normalized": false,
|
743 |
+
"rstrip": false,
|
744 |
+
"single_word": false,
|
745 |
+
"special": true
|
746 |
+
},
|
747 |
+
"93": {
|
748 |
+
"content": "[MASK_86]",
|
749 |
+
"lstrip": false,
|
750 |
+
"normalized": false,
|
751 |
+
"rstrip": false,
|
752 |
+
"single_word": false,
|
753 |
+
"special": true
|
754 |
+
},
|
755 |
+
"94": {
|
756 |
+
"content": "[MASK_87]",
|
757 |
+
"lstrip": false,
|
758 |
+
"normalized": false,
|
759 |
+
"rstrip": false,
|
760 |
+
"single_word": false,
|
761 |
+
"special": true
|
762 |
+
},
|
763 |
+
"95": {
|
764 |
+
"content": "[MASK_88]",
|
765 |
+
"lstrip": false,
|
766 |
+
"normalized": false,
|
767 |
+
"rstrip": false,
|
768 |
+
"single_word": false,
|
769 |
+
"special": true
|
770 |
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771 |
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"96": {
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772 |
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773 |
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774 |
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775 |
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776 |
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777 |
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778 |
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780 |
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781 |
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782 |
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783 |
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784 |
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785 |
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786 |
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787 |
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788 |
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789 |
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790 |
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791 |
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792 |
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794 |
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795 |
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796 |
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797 |
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798 |
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799 |
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800 |
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802 |
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803 |
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804 |
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805 |
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806 |
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807 |
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808 |
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810 |
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811 |
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812 |
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813 |
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814 |
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815 |
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816 |
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817 |
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818 |
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819 |
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820 |
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821 |
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|
822 |
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|
823 |
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|
824 |
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|
825 |
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|
826 |
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|
827 |
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"103": {
|
828 |
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|
829 |
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830 |
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|
831 |
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832 |
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|
833 |
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|
834 |
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|
835 |
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|
836 |
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|
837 |
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838 |
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|
839 |
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|
840 |
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|
841 |
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|
842 |
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|
843 |
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"105": {
|
844 |
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|
845 |
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|
846 |
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|
847 |
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|
848 |
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|
849 |
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|
850 |
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851 |
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"106": {
|
852 |
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|
853 |
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|
854 |
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|
855 |
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|
856 |
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|
857 |
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|
858 |
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}
|
859 |
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},
|
860 |
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"bos_token": "[BOS]",
|
861 |
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"clean_up_tokenization_spaces": true,
|
862 |
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"cls_token": "[CLS]",
|
863 |
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"eos_token": "[EOS]",
|
864 |
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"mask_token": "[MASK]",
|
865 |
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"model_max_length": 512,
|
866 |
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"pad_token": "[PAD]",
|
867 |
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"sep_token": "[SEP]",
|
868 |
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"tokenizer_class": "PreTrainedTokenizerFast",
|
869 |
+
"unk_token": "[UNK]"
|
870 |
+
}
|
trainer_state.json
ADDED
@@ -0,0 +1,2833 @@
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