To use a custom transformer model (not provided as a pre-trained Sentence Transformer) for generating sentence embeddings, you follow a process similar to standard transformer models but adapt it to your specific architecture. Here's a step-by-step explanation:
1. Load the Model and Tokenizer
First, load your custom transformer model and its corresponding tokenizer using a library like Hugging Face’s transformers. For example, if your model is saved in PyTorch, you might use AutoModel and AutoTokenizer classes. Ensure the tokenizer matches the model’s architecture (e.g., BERT, RoBERTa) to align vocabulary and tokenization rules. If the model is entirely custom (not a variant of existing architectures), you’ll need to implement a tokenizer or adapt an existing one. For example:
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("your-custom-model-path")
model = AutoModel.from_pretrained("your-custom-model-path")
2. Tokenize Input and Generate Hidden States Tokenize the input sentences using the tokenizer, ensuring padding and truncation for batch processing. Pass the tokenized inputs through the model to get hidden states. Transformer models typically return all token-level embeddings in the final layer. For example:
inputs = tokenizer(
["Your input sentence here"],
padding=True,
truncation=True,
return_tensors="pt"
)
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state # Shape: [batch_size, sequence_length, hidden_size]
3. Pool Token Embeddings into Sentence Embeddings Since transformers output token-level embeddings, you need to aggregate them into a fixed-length sentence embedding. Common methods include:
- Mean Pooling: Average all token embeddings (excluding padding tokens).
- [CLS] Token: Use the embedding of the first token (common in models like BERT).
- Max Pooling: Take the maximum value across tokens for each dimension.
For mean pooling, compute the average while masking padding tokens:
import torch
# Mask padding tokens using attention_mask
attention_mask = inputs["attention_mask"]
# Expand mask to match hidden_size dimensions
mask = attention_mask.unsqueeze(-1).expand(last_hidden_states.size()).float()
# Sum embeddings and divide by number of active tokens
sum_embeddings = torch.sum(last_hidden_states * mask, 1)
sum_mask = torch.clamp(mask.sum(1), min=1e-9)
sentence_embeddings = sum_embeddings / sum_mask
Key Considerations
- If your model wasn’t fine-tuned for semantic tasks (e.g., using contrastive loss), the embeddings may not perform well for similarity tasks without further training.
- Normalize embeddings (e.g., using L2 normalization) if required for downstream tasks like cosine similarity comparisons.
- For custom architectures, ensure the model outputs are compatible with standard pooling techniques. If the model uses a unique pooling layer (e.g., a learned weighted average), use that instead.
This approach gives you flexibility but requires careful alignment between the model’s architecture, tokenization, and pooling strategy.