The associated GitHub repository for fine-tuning is available
here
and the reproduce from pyserini is [here]. The following variants are also available:
Using the model directly available in HuggingFace transformers. We use the implemented Aggretriever from pyserini
here
.
from pyserini.encode._aggretriever import AggretrieverQueryEncoder
from pyserini.encode._aggretriever import AggretrieverDocumentEncoder
model_name = '/store/scratch/s269lin/experiments/aggretriever/hf_model/aggretriever-cocondenser'
query_encoder = AggretrieverQueryEncoder(model_name, device='cpu')
context_encoder = AggretrieverDocumentEncoder(model_name, device='cpu')
query = ["Where was Marie Curie born?"]
contexts = [
"Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.",
"Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace."
]
# Compute embeddings: take the last-layer hidden state of the [CLS] token
query_emb = query_encoder.encode(query)
ctx_emb = context_encoder.encode(contexts)
# Compute similarity scores using dot product
score1 = query_emb @ ctx_emb[0] # 45.56658
score2 = query_emb @ ctx_emb[1] # 45.81762
Runs of castorini aggretriever-cocondenser on huggingface.co
20
Total runs
0
24-hour runs
-3
3-day runs
-15
7-day runs
-66
30-day runs
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