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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
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