File size: 10,044 Bytes
e78f911 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 |
"""
TinyByteCNN Model for Fiction vs Non-Fiction Classification
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import unicodedata
import re
from typing import Union, List
class SE(nn.Module):
"""Squeeze-Excitation module"""
def __init__(self, c, r=8):
super().__init__()
m = max(c // r, 4)
self.fc1 = nn.Linear(c, m)
self.fc2 = nn.Linear(m, c)
def forward(self, x):
# x: [B, C, T]
s = x.mean(dim=-1) # [B, C]
s = F.silu(self.fc1(s))
s = torch.sigmoid(self.fc2(s)) # [B, C]
return x * s.unsqueeze(-1)
class SepResBlock(nn.Module):
"""Separable Residual Block with SE attention"""
def __init__(self, c_in, c_out, k=7, stride=1, dilation=1, use_gn=False, se_ratio=8, drop=0.0):
super().__init__()
Norm = (lambda c: nn.GroupNorm(32, c)) if use_gn else nn.BatchNorm1d
self.dw = nn.Conv1d(c_in, c_in, k, stride=stride, dilation=dilation,
padding=((k-1)//2)*dilation, groups=c_in, bias=False)
self.bn1 = Norm(c_in)
self.pw = nn.Conv1d(c_in, c_out, 1, bias=False)
self.bn2 = Norm(c_out)
self.se = SE(c_out, se_ratio)
self.drop = nn.Dropout(p=drop)
self.proj = None
if stride != 1 or c_in != c_out:
self.proj = nn.Conv1d(c_in, c_out, 1, stride=stride, bias=False)
def forward(self, x):
y = self.dw(x)
y = F.silu(self.bn1(y))
y = self.pw(y)
y = self.bn2(y)
y = self.se(y)
if self.proj is not None:
x = self.proj(x)
y = self.drop(y)
return F.silu(x + y)
class TinyByteCNN(nn.Module):
"""TinyByteCNN for Fiction vs Non-Fiction Classification"""
def __init__(self, config=None):
super().__init__()
# Default configuration
if config is None:
config = type('Config', (), {
'vocab_size': 256,
'embed_dim': 32,
'widths': [128, 192, 256, 320],
'use_gn': False,
'head_drop': 0.1,
'stochastic_depth': 0.05
})()
self.config = config
# Embedding layer for bytes
self.embed = nn.Embedding(config.vocab_size, config.embed_dim)
# Stem convolution
self.stem = nn.Conv1d(config.embed_dim, config.widths[0], 5, stride=2, padding=2, bias=False)
self.bn0 = nn.BatchNorm1d(config.widths[0]) if not config.use_gn else nn.GroupNorm(32, config.widths[0])
# Build stages
cfg = [
(2, config.widths[0], [1, 2]),
(2, config.widths[1], [1, 2]),
(3, config.widths[2], [1, 2, 4]),
(3, config.widths[3], [1, 2, 8])
]
stages = []
c_prev = config.widths[0]
for blocks, c, ds in cfg:
for i in range(blocks):
stride = 2 if i == 0 else 1
d = ds[i]
stages.append(SepResBlock(c_prev, c, k=7, stride=stride, dilation=d,
use_gn=config.use_gn, drop=config.stochastic_depth))
c_prev = c
self.stages = nn.Sequential(*stages)
# Classification head
self.head = nn.Sequential(
nn.Dropout(p=config.head_drop),
nn.Linear(2 * config.widths[-1], 1)
)
def forward(self, x_bytes):
"""
Args:
x_bytes: [B, T] uint8 tensor of byte values
Returns:
logits: [B] tensor of binary classification logits
"""
x = self.embed(x_bytes.long()) # [B, T, E]
x = x.transpose(1, 2).contiguous() # [B, E, T]
x = F.silu(self.bn0(self.stem(x))) # [B, C0, T/2]
x = self.stages(x) # [B, C, T/32]
# Global pooling
avg = x.mean(dim=-1)
mx = x.amax(dim=-1)
feats = torch.cat([avg, mx], dim=1)
logits = self.head(feats).squeeze(1)
return logits
@classmethod
def from_pretrained(cls, path_or_repo, use_safetensors=True):
"""Load pretrained model (supports both .bin and .safetensors)"""
import os
from pathlib import Path
# Determine if it's a file or directory/repo
if os.path.isdir(path_or_repo):
# Directory path - look for model files
base_path = Path(path_or_repo)
safetensors_path = base_path / "model.safetensors"
pytorch_path = base_path / "pytorch_model.bin"
if use_safetensors and safetensors_path.exists():
# Load from safetensors
from safetensors.torch import load_file
state_dict = load_file(str(safetensors_path))
# Load config if available
config_path = base_path / "config.json"
if config_path.exists():
import json
with open(config_path) as f:
config_dict = json.load(f)
config = type('Config', (), config_dict)()
else:
config = None
model = cls(config)
model.load_state_dict(state_dict)
return model
elif pytorch_path.exists():
checkpoint = torch.load(pytorch_path, weights_only=False, map_location='cpu')
elif os.path.isfile(path_or_repo):
if path_or_repo.endswith('.safetensors'):
from safetensors.torch import load_file
state_dict = load_file(path_or_repo)
model = cls()
model.load_state_dict(state_dict)
return model
else:
checkpoint = torch.load(path_or_repo, weights_only=False, map_location='cpu')
else:
# HuggingFace hub loading
from huggingface_hub import hf_hub_download
if use_safetensors:
try:
model_file = hf_hub_download(repo_id=path_or_repo, filename="model.safetensors")
from safetensors.torch import load_file
state_dict = load_file(model_file)
model = cls()
model.load_state_dict(state_dict)
return model
except:
pass # Fall back to pytorch format
model_file = hf_hub_download(repo_id=path_or_repo, filename="pytorch_model.bin")
checkpoint = torch.load(model_file, weights_only=False, map_location='cpu')
# Load from checkpoint (pytorch format)
if 'checkpoint' in locals():
config = checkpoint.get('config', None)
model = cls(config)
state_dict = checkpoint.get('model_state_dict', checkpoint)
model.load_state_dict(state_dict)
return model
def save_pretrained(self, save_path):
"""Save model to directory"""
import os
os.makedirs(save_path, exist_ok=True)
torch.save({
'model_state_dict': self.state_dict(),
'config': self.config
}, os.path.join(save_path, 'pytorch_model.bin'))
def preprocess_text(text: str, max_len: int = 4096) -> torch.Tensor:
"""
Preprocess text to bytes for model input
Args:
text: Input text string
max_len: Maximum sequence length (default 4096)
Returns:
Tensor of shape [1, max_len] containing byte values
"""
# Unicode NFC normalize
text = unicodedata.normalize('NFC', text)
# Replace \r\n → \n
text = text.replace('\r\n', '\n')
# Collapse runs of whitespace to at most 2
text = re.sub(r'\s{3,}', ' ', text)
# Convert to bytes
text_bytes = text.encode('utf-8', errors='ignore')
# Pad or truncate to max_len
input_ids = np.zeros(max_len, dtype=np.uint8)
input_ids[:min(len(text_bytes), max_len)] = list(text_bytes[:max_len])
return torch.from_numpy(input_ids).unsqueeze(0) # Add batch dimension
def classify_text(text: Union[str, List[str]], model=None, device='cpu'):
"""
Classify text as fiction or non-fiction
Args:
text: Single string or list of strings to classify
model: Pre-loaded model (optional)
device: Device to run on ('cpu', 'cuda', 'mps')
Returns:
Dictionary with predictions and confidence scores
"""
if model is None:
model = TinyByteCNN.from_pretrained("fiction_classifier_hf")
model = model.to(device)
model.eval()
# Handle single text or batch
if isinstance(text, str):
texts = [text]
else:
texts = text
results = []
for t in texts:
input_ids = preprocess_text(t).to(device)
with torch.no_grad():
logits = model(input_ids)
prob = torch.sigmoid(logits).item()
pred_class = "Non-Fiction" if prob > 0.5 else "Fiction"
confidence = prob if prob > 0.5 else (1 - prob)
results.append({
'text': t[:100] + '...' if len(t) > 100 else t,
'prediction': pred_class,
'confidence': confidence,
'probability_nonfiction': prob
})
return results[0] if isinstance(text, str) else results
if __name__ == "__main__":
# Example usage
sample_text = "The detective's coffee had gone cold hours ago, but she hardly noticed."
# Load and use model
model = TinyByteCNN.from_pretrained("fiction_model_output_cnn/best_model.pt")
result = classify_text(sample_text, model)
print(f"Text: {result['text']}")
print(f"Prediction: {result['prediction']}")
print(f"Confidence: {result['confidence']:.1%}") |