Spaces:
Sleeping
Sleeping
File size: 5,850 Bytes
54c4d86 6a5d824 54c4d86 6a5d824 54c4d86 6a5d824 54c4d86 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 |
import torch
import gradio as gr
import tiktoken
import pandas as pd
import torch.nn as nn
GPT_CONFIG_124M = {
"vocab_size": 50257,
"context_length": 1024,
"emb_dim": 768,
"n_heads": 12,
"n_layers": 12,
"drop_rate": 0.1,
"qkv_bias": True
}
class multiheadv2(nn.Module):
def __init__(self, d_in, d_out, context_length, dropout, attention_head, boolbias):
super().__init__()
self.head_dim = d_out // attention_head
self.d_out = d_out
self.attention_head = attention_head
self.W_query = nn.Linear(d_in, d_out, bias=boolbias)
self.W_key = nn.Linear(d_in, d_out, bias=boolbias)
self.W_value = nn.Linear(d_in, d_out, bias=boolbias)
self.out_proj = nn.Linear(d_out, d_out)
self.dropout = nn.Dropout(dropout)
self.register_buffer('mask', torch.triu(torch.ones(context_length, context_length), diagonal=1))
def forward(self, x):
b, num_token, d_out = x.shape
keys = self.W_key(x)
queries = self.W_query(x)
values = self.W_value(x)
keys = keys.view(b, num_token, self.attention_head, self.head_dim).transpose(1, 2)
queries = queries.view(b, num_token, self.attention_head, self.head_dim).transpose(1, 2)
values = values.view(b, num_token, self.attention_head, self.head_dim).transpose(1, 2)
attn_score = queries @ keys.transpose(2, 3)
mask_bool = self.mask.bool()[:num_token, :num_token]
attn_score.masked_fill_(mask_bool, -torch.inf)
attn_weights = torch.softmax(attn_score / keys.shape[-1]**0.5, dim=-1)
attn_weights = self.dropout(attn_weights)
context_vec = (attn_weights @ values).transpose(1, 2).contiguous().view(b, num_token, self.d_out)
context_vec = self.out_proj(context_vec)
return context_vec
class LayerNorm(nn.Module):
def __init__(self, emb_dim):
super().__init__()
self.eps = 1e-5
self.scale_params = nn.Parameter(torch.ones(emb_dim))
self.shift_params = nn.Parameter(torch.zeros(emb_dim))
def forward(self, x):
mean = x.mean(dim=-1, keepdim=True)
var = x.var(dim=-1, keepdim=True, unbiased=False)
norm = (x - mean) / torch.sqrt(var + self.eps)
return norm * self.scale_params + self.shift_params
class GELU(nn.Module):
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(torch.sqrt(torch.tensor(2.0 / torch.pi)) * (x + 0.044715 * torch.pow(x, 3))))
class feedforward(nn.Module):
def __init__(self, config):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(config['emb_dim'], config['emb_dim'] * 4),
GELU(),
nn.Linear(config['emb_dim'] * 4, config['emb_dim']),
)
def forward(self, x):
return self.layers(x)
class TransformerBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.attn = multiheadv2(d_in=config['emb_dim'], d_out=config['emb_dim'], context_length=config['context_length'], dropout=config['drop_rate'], attention_head=config['n_heads'], boolbias=config['qkv_bias'])
self.Layernorm1 = LayerNorm(config['emb_dim'])
self.Layernorm2 = LayerNorm(config['emb_dim'])
self.feedforw = feedforward(config)
self.dropout = nn.Dropout(config['drop_rate'])
def forward(self, x):
skip = x
x = self.Layernorm1(x)
x = self.attn(x)
x = self.dropout(x)
x = x + skip
skip = x
x = self.Layernorm2(x)
x = self.feedforw(x)
x = self.dropout(x)
x = x + skip
return x
class GPT_2(nn.Module):
def __init__(self, cfg, num_classes):
super().__init__()
self.token_emb = nn.Embedding(cfg['vocab_size'], cfg["emb_dim"])
self.pos_emb = nn.Embedding(cfg['context_length'], cfg["emb_dim"])
self.drop_emb = nn.Dropout(cfg["drop_rate"])
self.trf_blocks = nn.Sequential(*[TransformerBlock(cfg) for _ in range(cfg["n_layers"])])
self.final_norm = LayerNorm(cfg["emb_dim"])
self.out_head = nn.Linear(cfg["emb_dim"], num_classes)
def forward(self, inputidx):
batch_size, seq = inputidx.shape
tokens = self.token_emb(inputidx)
pos_embeds = self.pos_emb(torch.arange(seq, device=inputidx.device))
x = tokens + pos_embeds
x = self.drop_emb(x)
x = self.trf_blocks(x)
x = self.final_norm(x)
logits = self.out_head(x[:, -1])
return logits
tokenizer = tiktoken.get_encoding("gpt2")
pad_token_id = tokenizer.eot_token
df_temp = pd.read_csv("train.csv")
label_mapping = dict(enumerate(df_temp["target"].astype("category").cat.categories))
num_classes = len(label_mapping)
inv_label_mapping = {v: k for k, v in label_mapping.items()}
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = GPT_2(GPT_CONFIG_124M, num_classes)
model.load_state_dict(torch.load("biofinetuned_partialEpoch1.pth", map_location=device))
model.to(device)
model.eval()
def classify_review(text, max_length=128):
input_ids = tokenizer.encode(text)[:max_length]
input_ids += [pad_token_id] * (max_length - len(input_ids))
input_tensor = torch.tensor(input_ids, device=device).unsqueeze(0)
with torch.no_grad():
logits = model(input_tensor)
predicted_label = torch.argmax(logits, dim=-1).item()
return label_mapping[predicted_label]
iface = gr.Interface(
fn=classify_review,
inputs=gr.Textbox(label="Enter a biomedical abstract section (e.g., Background, Objective, Methodology, etc.)"),
outputs=gr.Textbox(label="Predicted Section Category"),
title="MedGPT",
description="A domain-specific classifier for biomedical abstract sections: Background, Objective, Methodology, Results, Conclusion."
)
iface.launch()
|