readme: ---
  language:
  - en
  tags:
  - conversational-search  # Example: audio
  metrics:
  - f1
  datasets:
  - uva-irlab/canard_quretec
  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))