initial

README.md

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+Aggretriever is an encoder to aggregate both lexical and semantic text information into a single-vector dense vector for dense retrieval, which is finetued on MS MARCO corpus with BM25 negative sampling, following the approach described in [Aggretriever: A Simple Approach to Aggregate Textual Representation for Robust Dense Passage Retrieval](https://arxiv.org/abs/2208.00511).
+
+<p align="center">
+<img src="https://raw.githubusercontent.com/castorini/dhr/main/fig/aggretriever_teaser.png" width="600">
+</p>
+
+The associated GitHub repository for fine-tuning is available [here](https://github.com/castorini/dhr) and the reproduce from pyserini is [here]. The following variants are also available:
+
+Model | Initialization | MARCO Dev | Encoder Path
+|---|---|---|---
+aggretriever-distilbert | distilbert-base-uncased | 34.1 | [castorini/aggretriever-distilbert](https://huggingface.co/castorini/aggretriever-distilbert)
+aggretriever-cocondenser | Luyu/co-condenser-marco | 36.2 | [castorini/aggretriever-cocondenser](https://huggingface.co/castorini/aggretriever-cocondenser)
+
+## Usage (HuggingFace Transformers)
+Using the model directly available in HuggingFace transformers. We use the implemented Aggretriever from pyserini [here](https://github.com/castorini/pyserini/blob/master/pyserini/encode/_aggretriever.py).
+
+```python
+from pyserini.encode._aggretriever import AggretrieverQueryEncoder
+from pyserini.encode._aggretriever import AggretrieverDocumentEncoder
+
+model_name = '/store/scratch/s269lin/experiments/aggretriever/hf_model/aggretriever-distilbert'
+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
+query_emb = query_encoder.encode(query)
+ctx_emb = context_encoder.encode(contexts)
+# Compute similarity scores using dot product
+score1 = query_emb @ ctx_emb[0]  # 47.667152
+score2 = query_emb @ ctx_emb[1]  # 39.054127
+```

config.json

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+{
+  "_name_or_path": "/store2/scratch/s269lin/Aggretriever/results/experiments/msmarco/DistilBERT-Aggretriever",
+  "activation": "gelu",
+  "architectures": [
+    "AggretrieverEncoder"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_hidden_states": true,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.15.0",
+  "vocab_size": 30522
+}

pytorch_model.bin

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+oid sha256:038ff235cdeb2a975b0b351ce0dfe9e435ff870c511ad31a925db888dbb71199
+size 268380775

special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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+{"do_lower_case": true, "do_basic_tokenize": true, "never_split": null, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "name_or_path": "Luyu/co-condenser-marco", "special_tokens_map_file": "/bos/tmp0/luyug/outputs/condenser/models/l2-s6-km-L128-e8-lr1e-4-b256/special_tokens_map.json", "tokenizer_file": null, "tokenizer_class": "BertTokenizer"}