dialogue-rewriter

README.md

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+---
+inference:
+  parameters:
+    max_length: 512
+    temperature: 0.7
+    top_p: 1
+widget:
+- text: <extra_id_0> loghub是什么<extra_id_1> AWS 上的loghub解决方案可帮助组织在单个控制面板上收集、分析和显示 Amazon CloudWatch Logs。该解决方案可整合、管理和分析来自各种来源的日志文件,例如访问、配置更改和计费事件的审计日志。您也可以从多个账户和 AWS 区域收集 Amazon CloudWatch Logs。<extra_id_0> 它的优点是什么?
+- text: <extra_id_0> 基督山伯爵讲的什么故事<extra_id_1> 电影版的基督山伯爵里面的台词太经典了<extra_id_0> 是呢剧情是啥
+- text: <extra_id_0> 你知道明朝那些事儿吗<extra_id_1> 有趣有趣寓教于乐的典型相当不错啊<extra_id_0> 它都讲了什么故事呀
+language:
+- en
+- zh
+---
+
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+model_name = 'csdc-atl/doc2query'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+def create_queries(history, next_question):
+    inputs_ids = []
+    for line in history:
+        inputs_ids.extend([32127]+tokenizer.encode(line[0], add_special_tokens=False)+[32126]+tokenizer.encode(line[1], add_special_tokens=False))
+    inputs_ids.extend([32127]+tokenizer.encode(next_question, add_special_tokens=False))
+    inputs_ids = inputs_ids + [1]
+    inputs_ids = torch.Tensor([inputs_ids]).long()
+    with torch.no_grad():
+        sampling_outputs = model.generate(
+            input_ids=inputs_ids,
+            max_length=512,
+            do_sample=True,
+            top_p=0.95,
+            top_k=10
+            )
+    print("\nSampling Outputs:")
+    for i in range(len(sampling_outputs)):
+        rewrite_question = tokenizer.decode(sampling_outputs[i], skip_special_tokens=True)
+        print(f'{i + 1}: {rewrite_question}')
+history = [['loghub是什么', 'AWS 上的loghub解决方案可帮助组织在单个控制面板上收集、分析和显示 Amazon CloudWatch Logs。该解决方案可整合、管理和分析来自各种来源的日志文件，例如访问、配置更改和计费事件的审计日志。您也可以从多个账户和 AWS 区域收集 Amazon CloudWatch Logs。']]
+next_question = '它的优点是什么？'
+create_queries(history, next_question)
+# 1: loghub解决方案的优点是什么?
+```

config.json

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+{
+  "_name_or_path": "output/Langboat-mengzi-t5-base",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 2048,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "gradient_checkpointing": false,
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.31.0",
+  "use_cache": true,
+  "vocab_size": 32128
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "decoder_start_token_id": 0,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.31.0"
+}

pytorch_model.bin

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special_tokens_map.json

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spiece.model

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+size 725135

tokenizer_config.json

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+}