silver-retriever

Evaluation

Legend:

• Acc is the Accuracy at 10
• NDCG is the Normalized Discounted Cumulative Gain at 10

Usage

Preparing inputs

The model was trained on question-passage pairs and works best when the input is the same format as that used during training:

• We added the phrase Pytanie: to the beginning of the question.
• The training passages consisted of title and text concatenated with the special token . Even if your passages don't have a `title`, it is still beneficial to prefix a passage with the token.
• Although we used the dot product during training, the model usually works better with the cosine distance.

Inference with Sentence-Transformers

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = [
    "Pytanie: W jakim mieście urodził się Zbigniew Herbert?",
    "Zbigniew HerbertZbigniew Bolesław Ryszard Herbert (ur. 29 października 1924 we Lwowie, zm. 28 lipca 1998 w Warszawie) – polski poeta, eseista i dramaturg.",
]

model = SentenceTransformer('ipipan/silver-retriever-base-v1.1')
embeddings = model.encode(sentences)
print(embeddings)

Inference with HuggingFace Transformers

Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

from transformers import AutoTokenizer, AutoModel
import torch

def cls_pooling(model_output, attention_mask):
    return model_output[0][:,0]

# Sentences we want sentence embeddings for
sentences = [
    "Pytanie: W jakim mieście urodził się Zbigniew Herbert?",
    "Zbigniew HerbertZbigniew Bolesław Ryszard Herbert (ur. 29 października 1924 we Lwowie, zm. 28 lipca 1998 w Warszawie) – polski poeta, eseista i dramaturg.",
]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('ipipan/silver-retriever-base-v1.1')
model = AutoModel.from_pretrained('ipipan/silver-retriever-base-v1.1')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embe