README.md

---
language: en
license: llama3.1
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- beeformer/recsys-movielens-20m
pipeline_tag: sentence-similarity
---
# Llama-movielens-mpnet

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and it is designed to use in recommender systems for content-base filtering and as a side information for cold-start recommendation.

## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example product description", "Each product description is converted"]
model = SentenceTransformer('beeformer/Llama-movielens-mpnet')
embeddings = model.encode(sentences)
print(embeddings)
```

## Training procedure

### Pre-training 

We use the pretrained [`sentence-transformers/all-mpnet-base-v2`](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) model. Please refer to the model card for more detailed information about the pre-training procedure.

### Fine-tuning

 We use the initial model without modifying its architecture or pre-trained model parameters. 
 However, we reduce the processed sequence length to 384 to reduce the training time of the model. 

### Dataset
  
 We finetuned our model on the MovieLens-20M dataset with item descriptions generated with [`meta-llama/Meta-Llama-3.1-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) model. For details please see the dataset page [`beeformer/recsys-movielens-20m`](https://huggingface.co/datasets/beeformer/recsys-movielens-20m).

## Evaluation Results

Table with results TBA.

## Intended uses

This model was trained as a demonstration of capabilities of the beeFormer training framework (link and details TBA) and is intended for research purposes only.

## Citation

Preprint available [here](https://arxiv.org/pdf/2409.10309)

TBA