File size: 2,315 Bytes
f5f428c 9afdd09 f5f428c 79a2289 f5f428c f410520 f5f428c d6fa360 f5f428c 984513d f5f428c 54656f1 79a2289 f5f428c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
---
language: en
license: llama3.1
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- beeformer/recsys-goodbooks-10k
pipeline_tag: sentence-similarity
base_model:
- sentence-transformers/all-mpnet-base-v2
---
# Llama-goodbooks-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-goodbooks-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 Goodbooks-10k 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-goodbooks-10k`](https://huggingface.co/datasets/beeformer/recsys-goodbooks-10k).
## Evaluation Results
For ids of items used for coldstart evaluation please see (links TBA).
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 |