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README.md

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+# Query Generation
+This model is the t5-base model from [docTTTTTquery](https://github.com/castorini/docTTTTTquery).
+
+The T5-base model was trained on the [MS MARCO Passage Dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking), which consists of about 500k real search queries from Bing together with the relevant passage.
+
+The model can be used for query generation to learn semantic search models without requiring annotated training data: [Synthetic Query Generation](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/query_generation).
+
+
+## Usage
+
+```python
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+tokenizer = T5Tokenizer.from_pretrained('model-name')
+model = T5ForConditionalGeneration.from_pretrained('model-name')
+
+para = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."
+
+input_ids = tokenizer.encode(para, return_tensors='pt')
+outputs = model.generate(
+	input_ids=input_ids,
+	max_length=64,
+	do_sample=True,
+	top_p=0.95,
+	num_return_sequences=3)
+
+print("Paragraph:")
+print(para)
+
+print("\nGenerated Queries:")
+for i in range(len(outputs)):
+	query = tokenizer.decode(outputs[i], skip_special_tokens=True)
+	print(f'{i + 1}: {query}')
+```

config.json

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+{
+  "_name_or_path": "model_raw/t5-base/model.ckpt-1004000",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "transformers_version": "4.2.2",
+  "use_cache": true,
+  "vocab_size": 32128
+}

pytorch_model.bin

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special_tokens_map.json

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spiece.model

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tokenizer_config.json

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