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---
language:
- en
tags:
- conversational-search # Example: audio
- question-answering # Example: automatic-speech-recognition
metrics:
- f1
model-index:
- name: QuReTec
results:
- task:
name: Conversational search # Example: Speech Recognition
type: conversational # Example: automatic-speech-recognition
dataset:
name: CANARD # Example: Common Voice zh-CN
type: canard # Example: common_voice
metrics:
- name: Micro F1 # Example: Test WER
type: f1 # Example: wer
value: 68.7 # Example: 20.90
- name: Micro Recall
type: recall
value: 66.1
- name: Micro Precision
type: precision
value: 71.5
---
# QuReTec: query resolution model
QuReTeC is a query resolution model. It finds the relevant terms in a question history.
It is based on **bert-large-uncased** with a max sequence length of 300.
# Config details
Training and evaluation was done using the following BertConfig:
```json
BertConfig {
"_name_or_path": "uva-irlab/quretec",
"architectures": ["BertForMaskedLM"],
"attention_probs_dropout_prob": 0.1,
"finetuning_task": "ner",
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.4,
"hidden_size": 1024,
"id2label": {
"0": "[PAD]",
"1": "O",
"2": "REL",
"3": "[CLS]",
"4": "[SEP]"
},
"initializer_range": 0.02,
"intermediate_size": 4096,
"label2id": {
"O": 1,
"REL": 2,
"[CLS]": 3,
"[PAD]": 0,
"[SEP]": 4
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.6.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
```
# Original authors
QuReTeC model from the published SIGIR 2020 paper: Query Resolution for Conversational Search with Limited Supervision by N. Voskarides, D. Li, P. Ren, E. Kanoulas and M. de Rijke. [[pdf]](https://arxiv.org/abs/2005.11723).
# Contributions
Uploaded by G. Scheuer ([website](https://giguruscheuer.com)) |