jiaxin-wen
commited on
Commit
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Parent(s):
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Browse files- README.md +32 -0
- added_tokens.json +1 -0
- config.json +28 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
- vocab.txt +0 -0
README.md
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This model has been trained on massive Chinese plain-text open-domain dialogues following the approach described in [Re$^3$Dial: Retrieve, Reorganize and Rescale Conversations for Long-Turn Open-Domain Dialogue Pre-training](https://arxiv.org/abs/2305.02606). The associated Github repository is available here https://github.com/thu-coai/Re3Dial.
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### Usage
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```python
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from transformers import BertTokenizer, BertModel
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import torch
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def get_embedding(encoder, inputs):
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outputs = encoder(**inputs)
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pooled_output = outputs[0][:, 0, :]
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return pooled_output
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tokenizer = BertTokenizer.from_pretrained('xwwwww/bert-chinese-dialogue-retriever-query')
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tokenizer.add_tokens(['<uttsep>'])
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query_encoder = BertModel.from_pretrained('xwwwww/bert-chinese-dialogue-retriever-query')
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context_encoder = BertModel.from_pretrained('xwwwww/bert-chinese-dialogue-retriever-context')
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query = '你好<uttsep>好久不见,最近在干嘛'
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context = '正在准备考试<uttsep>是什么考试呀,很辛苦吧'
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query_inputs = tokenizer([query], return_tensors='pt')
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context_inputs = tokenizer([context], return_tensors='pt')
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query_embedding = get_embedding(query_encoder, query_inputs)
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context_embedding = get_embedding(context_encoder, context_inputs)
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score = torch.cosine_similarity(query_embedding, context_embedding, dim=1)
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print('similarity score = ', score)
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```
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added_tokens.json
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{}
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config.json
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{
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"architectures": [
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"BertForMaskedLM"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"directionality": "bidi",
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"eos_token_id": 2,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"output_past": true,
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"pad_token_id": 1,
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"type_vocab_size": 2,
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"vocab_size": 21129
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:3f29bd3a31022813466042d30c9391714dcb57a72d362855e21bccf7682f8dec
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size 409141297
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special_tokens_map.json
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{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
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tokenizer.json
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tokenizer_config.json
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{"init_inputs": []}
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vocab.txt
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