| import torch |
| import torch.nn as nn |
|
|
| import numpy as np |
| import numpy as np |
| import pandas as pd |
|
|
| import torch.nn.functional as F |
|
|
| from transformers import PretrainedConfig |
| import torch.optim as optim |
|
|
| class BertCustomConfig(PretrainedConfig): |
| model_type = "bert" |
|
|
| def __init__( |
| self, |
| vocab_size=30873, |
| hidden_size=768, |
| num_hidden_layers=12, |
| num_attention_heads=12, |
| intermediate_size=3072, |
| hidden_act="gelu", |
| hidden_dropout_prob=0.1, |
| attention_probs_dropout_prob=0.1, |
| max_position_embeddings=512, |
| type_vocab_size=2, |
| initializer_range=0.02, |
| layer_norm_eps=1e-12, |
| pad_token_id=0, |
| position_embedding_type="absolute", |
| use_cache=True, |
| classifier_dropout=None, |
| max_length=512, |
| id2label={"0": "Neutral", "1": "Hawkish", "2": "Dovish"}, |
| label2id={"positive": 1, "negative": 2, "neutral": 0}, |
| hyperparams=None, |
| **kwargs |
| ): |
| super().__init__(pad_token_id=pad_token_id, **kwargs) |
| self.vocab_size = vocab_size |
| self.hidden_size = hidden_size |
| self.num_hidden_layers = num_hidden_layers |
| self.num_attention_heads = num_attention_heads |
| self.hidden_act = hidden_act |
| self.intermediate_size = intermediate_size |
| self.hidden_dropout_prob = hidden_dropout_prob |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob |
| self.max_position_embeddings = max_position_embeddings |
| self.type_vocab_size = type_vocab_size |
| self.initializer_range = initializer_range |
| self.layer_norm_eps = layer_norm_eps |
| self.position_embedding_type = position_embedding_type |
| self.use_cache = use_cache |
| self.classifier_dropout = classifier_dropout |
| self.max_length = max_length |
| self.id2label = id2label |
| self.label2id = label2id |
| self.hyperparams = hyperparams |
|
|
