Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10M - 100M
DOI:
File size: 5,513 Bytes
6d1a3ae |
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 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
# TREC RAG baselines using arctic-l and arctic-m-v1.5
First, download the data including documents, queries, qrels.
## Generate The doc and query embeddings
```sh
bash get_data.sh
```
Next, go ahead and convert the qrels format into json using the script below.
```sh
python convert_qrels_to_json.py
```
After that, go ahead and generate the query embeddings using the command below.
```sh
python generate_query_embeddings.py
```
After that, go ahead and generate embeddings for each shard. This will take ~ 20m per shard on a single H100. Feel free to parallelize. Make sure you have at least 600 gbs free.
```sh
python generate_doc_embeddings.py
```
## Retrieval Runs
Once you have query and doc embeddings go ahead and retrieve. Given the size of the vectors we do this in shards. First we retrieve the top_n from each shard for each queryset on each shard. Feel free to parrelize.
```sh
python retrieve_from_shard.py <path to embeddings> <query_embedding_prefix> <shard> <num_retrieved> <use_faiss>
```
Alternatively, you can just run retrieve.sh in the background.
```sh
python merge_retrieved_shard.py <shard_retrieved_results> <output_filename> <top_n_docs> <qrel json> <metric to get per_query breakdown>
```
## Retrieval Scores
### NDCG@10
| NDCG @10 | | | | | | | |
|--------------------|--------|----------------|----------|---------------|---------------|---------------|---------------------------|
| Dataset | BM25 | GTE-Large-v1.5 | Arctic-L | Arctic-M-V1.5 | Arctic-M-V1.5 | Arctic-M-V1.5 | Cohere Embed3 - Trunc 128 |
| Dim | N/A | 1024 | 1024 | 768 | 256 | 128 | 128 |
| Deep Learning 2021 | 0.5778 | 0.71928 | 0.70682 | 0.6936 | 0.69392 | 0.60578 | 0.6962 |
| Deep Learning 2022 | 0.3576 | 0.53576 | 0.5444 | 0.55199 | 0.55608 | 0.47348 | 0.5396 |
| Deep Learning 2023 | 0.3356 | 0.46423 | 0.47372 | 0.46963 | 0.45196 | 0.32789 | 0.4473 |
| msmarcov2-dev | N/A | 0.3538 | 0.35844 | 0.346 | 0.34074 | 0.28499 | N/A |
| msmarcov2-dev2 | N/A | 0.34698 | 0.35821 | 0.34518 | 0.34339 | 0.29606 | N/A |
| Raggy Queries | 0.4227 | 0.56782 | 0.57759 | 0.57439 | 0.56686 | 0.47555 | N/A |
### Recall @100
| Recall@100 | | | | | | | |
|--------------------|--------|----------------|----------|---------------|---------------|---------------|---------------------------|
| Dataset | BM25 | GTE-Large-v1.5 | Arctic-L | Arctic-M-V1.5 | Arctic-M-V1.5 | Arctic-M-V1.5 | Cohere Embed3 - Trunc 128 |
| Dim | N/A | 1024 | 1024 | 768 | 256 | 128 | 128 |
| Deep Learning 2021 | 0.3811 | 0.4156 | 0.41361 | 0.43 | 0.42245 | 0.3488 | 0.3914 |
| Deep Learning 2022 | 0.233 | 0.31173 | 0.31351 | 0.32125 | 0.3165 | 0.26714 | 0.3019 |
| Deep Learning 2023 | 0.3049 | 0.35236 | 0.34793 | 0.37622 | 0.36089 | 0.28314 | 0.3438 |
| msmarcov2-dev | 0.6683 | 0.85135 | 0.85131 | 0.85435 | 0.84985 | 0.76201 | N/A |
| msmarcov2-dev2 | 0.6771 | 0.84333 | 0.84767 | 0.8576 | 0.8526 | 0.78987 | N/A |
| Raggy Queries | 0.2807 | 0.35125 | 0.36228 | 0.36915 | 0.36149 | 0.30272 | N/A |
### Recall @1000
| Recall@1000 | | | | | | | |
|--------------------|--------|----------------|----------|---------------|---------------|---------------|---------------------------|
| Dataset | BM25 | GTE-Large-v1.5 | Arctic-L | Arctic-M-V1.5 | Arctic-M-V1.5 | Arctic-M-V1.5 | Cohere Embed3 - Trunc 128 |
| Dim | N/A | 1024 | 1024 | 768 | 256 | 128 | 128 |
| Deep Learning 2021 | 0.7115 | 0.73185 | 0.7193 | 0.74895 | 0.73511 | 0.63253 | 0.7188 |
| Deep Learning 2022 | 0.479 | 0.55174 | 0.54566 | 0.55413 | 0.54499 | 0.47823 | 0.5558 |
| Deep Learning 2023 | 0.5852 | 0.6167 | 0.59577 | 0.62262 | 0.61199 | 0.49188 | 0.6025 |
| msmarcov2-dev | 0.8528 | 0.93549 | 0.93966 | 0.94156 | 0.94014 | 0.87705 | N/A |
| msmarcov2-dev2 | 0.8577 | 0.93997 | 0.93947 | 0.94277 | 0.94047 | 0.91683 | N/A |
| Raggy Queries | 0.5745 | 0.63515 | 0.63092 | 0.64527 | 0.63826 | 0.55002 | N/A | |