We recommend using the
RAGatouille
for using our ColBERT model.
pip install ragatouille
from ragatouille import RAGPretrainedModel
# Let's create a ragatouille instance
RAG = RAGPretrainedModel.from_pretrained("mixedbread-ai/mxbai-colbert-v1")
documents = [
"'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.",
"The novel 'Moby-Dick' was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.",
"Harper Lee, an American novelist widely known for her novel 'To Kill a Mockingbird', was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.",
"Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.",
"The 'Harry Potter' series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.",
"'The Great Gatsby', a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan."
]
# index documents
RAG.index(documents, index_name="mockingbird")
# search
query = "Who wrote 'To Kill a Mockingbird'?"
results = RAG.search(query)
The result looks like this:
[
{
'content': "'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.",
'score': 28.453125,
'rank': 1,
'document_id': '9d564e82-f14f-433a-ab40-b10bda9dc370',
'passage_id': 0
},
{
'content': "Harper Lee, an American novelist widely known for her novel 'To Kill a Mockingbird', was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.",
'score': 27.03125,
'rank': 2,
'document_id': 'a35a89c3-b610-4e2e-863e-fa1e7e0710a6',
'passage_id': 2
},
...
]
Evaluation
1. Reranking Performance
Setup:
we use BM25 as the first-stage retrieval model, and then use ColBERT for reranking. We evaluate the out-of-domain performance on 13 public BEIR datasets. Following common practice, we report NDCG@10 as the metrics.
Here, we compare our model with two widely used ColBERT models, as follows:
We also test our model's performance on retrieval tasks on a subset of the BEIR benchmarks. We'll be providing the full results for the benchmark soon (actively working on it).
Due to resource limitations, we only test our model on three beir tasks. NDCG@10 servers as the main metric.
Model
ColBERTv2
Jina-ColBERT-V1
mxbai-colbert-large-v1
NFCorpus
33.7
33.8
36.5
SciFact
68.9
70.1
71.3
TREC-COVID
72.6
75.0
80.5
Although our ColBERT also performs well on retrieval tasks, we still recommend using our flagship embedding model
mixedbread-ai/mxbai-embed-large-v1
for that.
Community
Please join our
Discord Community
and share your feedback and thoughts! We are here to help and also always happy to chat.
License
Apache 2.0
Runs of mixedbread-ai mxbai-colbert-large-v1 on huggingface.co
2.2K
Total runs
0
24-hour runs
8
3-day runs
-136
7-day runs
190
30-day runs
More Information About mxbai-colbert-large-v1 huggingface.co Model
mxbai-colbert-large-v1 huggingface.co is an AI model on huggingface.co that provides mxbai-colbert-large-v1's model effect (), which can be used instantly with this mixedbread-ai mxbai-colbert-large-v1 model. huggingface.co supports a free trial of the mxbai-colbert-large-v1 model, and also provides paid use of the mxbai-colbert-large-v1. Support call mxbai-colbert-large-v1 model through api, including Node.js, Python, http.
mxbai-colbert-large-v1 huggingface.co is an online trial and call api platform, which integrates mxbai-colbert-large-v1's modeling effects, including api services, and provides a free online trial of mxbai-colbert-large-v1, you can try mxbai-colbert-large-v1 online for free by clicking the link below.
mixedbread-ai mxbai-colbert-large-v1 online free url in huggingface.co:
mxbai-colbert-large-v1 is an open source model from GitHub that offers a free installation service, and any user can find mxbai-colbert-large-v1 on GitHub to install. At the same time, huggingface.co provides the effect of mxbai-colbert-large-v1 install, users can directly use mxbai-colbert-large-v1 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
mxbai-colbert-large-v1 install url in huggingface.co: