---
license: mit
datasets:
- AdamLucek/apple-environmental-report-QA-retrieval
base_model: sentence-transformers/all-MiniLM-L6-v2
pipeline_tag: feature-extraction
library_name: peft
---
# all-MiniLM-L6-v2-query-only-linear-adapter-AppleQA

Query-only linear adapter for [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) with the [AdamLucek/apple-environmental-report-QA-retrieval](https://huggingface.co/datasets/AdamLucek/apple-environmental-report-QA-retrieval) dataset. 

6 adapters trained at 10, 20, 30, and 40 epochs with:
- Triplet Margin Loss, Margin=1.0, Euclidean Distance=2
- AdamW Optimizer
- Random negative sampling from irrelevant document
- LR: 0.003
- Batch size: 32
- Grad Norm: 1.0
- Warmup Steps: 100

Training script and model creation available on [Github Repo](https://github.com/ALucek/linear-adapter-embedding)

# Assessment

**Baseline Hit Rate @10**: 61.860%  
**Baseline Reciprocal Rank @10**: 0.31108 (Average Rank 3.2)  

*Best performing checkpoint at 30epochs*  
**Average Hit Rate @10**: 66.628%  
**Mean Reciprocal Rank @10**: 0.33119 (Average Rank 3.0)  

A **7.7% Improvement** in hit rate and a **6.5% improvement** in mean reciprocal rank against base embedding model.

<img src="https://cdn-uploads.huggingface.co/production/uploads/65ba68a15d2ef0a4b2c892b4/ZsbVzv81cn2XW24eqbicU.png" width=800>

# Usage

```python
import torch
from torch import nn
from sentence_transformers import SentenceTransformer

class LinearAdapter(nn.Module):
    def __init__(self, input_dim):
        super().__init__()
        self.linear = nn.Linear(input_dim, input_dim)
    
    def forward(self, x):
        return self.linear(x)

# Load the base model
base_model = SentenceTransformer('all-MiniLM-L6-v2')

# Load Adapter
adapter = LinearAdapter(base_model.get_sentence_embedding_dimension())
adapter.load_state_dict(torch.load('adapters/linear_adapter_30epochs.pth'))

# Example function for encoding
def encode_query(query, base_model, adapter):
    device = next(adapter.parameters()).device
    query_emb = base_model.encode(query, convert_to_tensor=True).to(device)
    adapted_query_emb = adapter(query_emb)
    return adapted_query_emb.cpu().detach().numpy()

emb = encode_query("Hello", base_model, adapter)

print(emb[:5])
```
**output**

```
[-0.13122843  0.02912715  0.07466945  0.09387457  0.13010463]
```