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model.py
--------
Production-grade embedding model with fine-tuning auto-detection.
Priority order:
1. models/smarthire-finetuned/ (your fine-tuned model -- best accuracy)
2. sentence-transformers/all-MiniLM-L6-v2 (pretrained -- strong baseline)
3. distilbert-base-uncased (legacy fallback if sentence-transformers missing)
Run train/finetune.py once to create the fine-tuned model.
The app auto-detects and loads it on next restart.
Author: SmartHire AI
"""
import logging
import math
from pathlib import Path
from typing import List, Optional, Union
import torch
import torch.nn.functional as F
logger = logging.getLogger(__name__)
# Fine-tuned model path (auto-used if it exists after running train/finetune.py)
FINETUNED_MODEL = "models/smarthire-finetuned"
# Primary pretrained model -- fast, accurate, 384-dim
PRIMARY_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
# Fallback model -- larger, slightly more accurate
FALLBACK_MODEL = "sentence-transformers/all-mpnet-base-v2"
# Legacy fallback if sentence-transformers not installed
LEGACY_MODEL = "distilbert-base-uncased"
# Chunking config for long documents
CHUNK_SIZE = 400 # words per chunk
CHUNK_OVERLAP = 50 # overlap between chunks
class EmbeddingModel:
"""
Production embedding model with smart chunking and multi-backend support.
Tries sentence-transformers first for best accuracy.
Falls back to raw HuggingFace DistilBERT if not available.
"""
def __init__(
self,
model_name: str = PRIMARY_MODEL,
device: Optional[str] = None,
use_chunking: bool = True,
) -> None:
self.model_name = model_name
self.use_chunking = use_chunking
self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
self._load_model()
def _load_model(self) -> None:
"""Try sentence-transformers, fall back to raw transformers."""
try:
from sentence_transformers import SentenceTransformer
logger.info(f"Loading sentence-transformers model: {self.model_name}")
self._st_model = SentenceTransformer(self.model_name, device=self.device)
self.backend = "sentence-transformers"
self._hidden_size = self._st_model.get_sentence_embedding_dimension()
self._max_tokens = 512
logger.info(f"sentence-transformers loaded. dim={self._hidden_size}")
except ImportError:
logger.warning("sentence-transformers not found -- falling back to DistilBERT")
self._load_distilbert()
except Exception as e:
logger.warning(f"sentence-transformers load failed ({e}) -- falling back to DistilBERT")
self._load_distilbert()
def _load_distilbert(self) -> None:
from transformers import AutoModel, AutoTokenizer
logger.info(f"Loading HuggingFace model: {LEGACY_MODEL}")
self._tokenizer = AutoTokenizer.from_pretrained(LEGACY_MODEL)
self._hf_model = AutoModel.from_pretrained(LEGACY_MODEL)
self._hf_model.to(self.device)
self._hf_model.eval()
self.model_name = LEGACY_MODEL
self.backend = "transformers"
self._hidden_size = self._hf_model.config.hidden_size
self._max_tokens = self._hf_model.config.max_position_embeddings
logger.info(f"DistilBERT loaded. dim={self._hidden_size}")
def encode(
self,
texts: Union[str, List[str]],
batch_size: int = 32,
show_progress: bool = False,
) -> torch.Tensor:
"""
Encode texts into L2-normalized embedding vectors.
Automatically applies smart chunking for long documents.
Returns: Tensor [N, hidden_size], L2-normalized.
"""
if isinstance(texts, str):
texts = [texts]
if not texts:
raise ValueError("Cannot encode empty text list.")
if self.use_chunking:
embeddings = []
for text in texts:
chunks = self._chunk_text(text)
if len(chunks) == 1:
emb = self._encode_batch(chunks, batch_size)
else:
chunk_embs = self._encode_batch(chunks, batch_size)
emb = chunk_embs.mean(dim=0, keepdim=True)
emb = F.normalize(emb, p=2, dim=1)
embeddings.append(emb)
return torch.cat(embeddings, dim=0)
else:
return self._encode_batch(texts, batch_size)
def _encode_batch(self, texts: List[str], batch_size: int) -> torch.Tensor:
"""Encode a flat list of texts -- no chunking."""
if self.backend == "sentence-transformers":
vecs = self._st_model.encode(
texts,
batch_size=batch_size,
convert_to_tensor=True,
normalize_embeddings=True,
show_progress_bar=False,
)
return vecs.cpu()
else:
return self._hf_encode(texts, batch_size)
def _hf_encode(self, texts: List[str], batch_size: int) -> torch.Tensor:
"""DistilBERT encoding with attention-weighted mean pooling."""
all_embeddings = []
num_batches = math.ceil(len(texts) / batch_size)
with torch.no_grad():
for i in range(num_batches):
batch = texts[i * batch_size:(i + 1) * batch_size]
encoded = self._tokenizer(batch, padding=True, truncation=True, max_length=512, return_tensors="pt")
input_ids = encoded["input_ids"].to(self.device)
attention_mask = encoded["attention_mask"].to(self.device)
output = self._hf_model(input_ids=input_ids, attention_mask=attention_mask)
token_embs = output.last_hidden_state
mask_expanded = attention_mask.unsqueeze(-1).expand(token_embs.size()).float()
pooled = torch.sum(token_embs * mask_expanded, dim=1)
pooled = pooled / torch.clamp(mask_expanded.sum(dim=1), min=1e-9)
normalized = F.normalize(pooled, p=2, dim=1)
all_embeddings.append(normalized.cpu())
return torch.cat(all_embeddings, dim=0)
def _chunk_text(self, text: str) -> List[str]:
"""Split long text into overlapping chunks."""
words = text.split()
if len(words) <= CHUNK_SIZE:
return [text]
chunks = []
start = 0
while start < len(words):
end = min(start + CHUNK_SIZE, len(words))
chunks.append(" ".join(words[start:end]))
if end == len(words):
break
start += CHUNK_SIZE - CHUNK_OVERLAP
logger.debug(f"Document split into {len(chunks)} chunks")
return chunks
def encode_single(self, text: str) -> torch.Tensor:
"""Encode a single text -- returns 1D tensor [hidden_size]."""
return self.encode([text])[0]
def get_model_info(self) -> dict:
"""Return live metadata about the loaded model."""
finetuned = Path(FINETUNED_MODEL).exists() and any(Path(FINETUNED_MODEL).iterdir())
return {
"model_name" : self.model_name,
"backend" : self.backend,
"embedding_dim" : self._hidden_size,
"max_tokens" : self._max_tokens,
"device" : self.device,
"chunking" : self.use_chunking,
"chunk_size" : CHUNK_SIZE,
"chunk_overlap" : CHUNK_OVERLAP,
"is_finetuned" : finetuned,
"pooling" : "sentence-transformers mean" if self.backend == "sentence-transformers" else "attention-weighted mean",
"similarity" : "Cosine",
"framework" : "HuggingFace Transformers + PyTorch",
}
# -- Singleton ---------------------------------------------------------
_model_instance: Optional[EmbeddingModel] = None
def get_model(model_name: str = None) -> EmbeddingModel:
"""
Return module-level singleton EmbeddingModel (lazy init).
Auto-detects fine-tuned model:
- If models/smarthire-finetuned/ exists and is non-empty -> fine-tuned
- Otherwise -> pretrained all-MiniLM-L6-v2
"""
global _model_instance
if _model_instance is None:
if model_name is None:
finetuned_path = Path(FINETUNED_MODEL)
if finetuned_path.exists() and any(finetuned_path.iterdir()):
model_name = FINETUNED_MODEL
logger.info(f"Fine-tuned model detected -- loading: {FINETUNED_MODEL}")
else:
model_name = PRIMARY_MODEL
logger.info(f"No fine-tuned model found -- loading pretrained: {PRIMARY_MODEL}")
_model_instance = EmbeddingModel(model_name=model_name)
return _model_instance
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