Internal-LinkLM
A fine-tuned Cross-Encoder Reranker based on BAAI/bge-reranker-large for semantic document ranking and internal link recommendation.
This model was fine-tuned to score the relevance between a query and a candidate document. It is designed for retrieval and reranking tasks such as:
- Internal Link Recommendation
- Semantic Search
- Information Retrieval
- Document Ranking
- Search Result Reranking
Base Model
- BAAI/bge-reranker-large
- Architecture: XLM-RoBERTa Cross Encoder
Training
The model was fine-tuned using the Hugging Face Transformers Trainer.
Training configuration:
- Epochs: 3
- Framework: PyTorch
- Library: Transformers
- Loss: Cross-Entropy Loss
- Optimizer: AdamW
Evaluation
| Metric | Score |
|---|---|
| Accuracy | 0.8476 |
| Precision | 0.6279 |
| Recall | 0.5841 |
| F1 Score | 0.6052 |
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained(
"NayonAhmed09/internal-linklm"
)
model = AutoModelForSequenceClassification.from_pretrained(
"NayonAhmed09/internal-linklm"
)
Example:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained(
"NayonAhmed09/internal-linklm"
)
model = AutoModelForSequenceClassification.from_pretrained(
"NayonAhmed09/internal-linklm"
)
query = "Best moisturizer for oily skin"
document = "A complete guide to choosing skincare products for oily skin."
inputs = tokenizer(
query,
document,
return_tensors="pt",
truncation=True,
max_length=512
)
with torch.no_grad():
score = model(**inputs).logits.squeeze().item()
print(score)
Higher scores indicate higher semantic relevance.
Files
- model.safetensors
- config.json
- tokenizer.json
- tokenizer_config.json
- training_args.bin
License
MIT License
Author
Nayon Ahmed
GitHub: https://github.com/nayonahmedjoy
Hugging Face: https://huggingface.co/NayonAhmed09