Initial commit

README.md

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+monot5-3b-inpars-v2-arguana-promptagator is a monoT5-3B model finetuned on ArguAna synthetic data generated by [InPars](https://github.com/zetaalphavector/inPars).
+This model 
+
+Currently, if you use this tool you can cite the original [InPars paper published at SIGIR](https://dl.acm.org/doi/10.1145/3477495.3531863) or [InPars-v2](https://arxiv.org/abs/2301.01820).
+
+```
+@inproceedings{inpars,
+  author = {Bonifacio, Luiz and Abonizio, Hugo and Fadaee, Marzieh and Nogueira, Rodrigo},
+  title = {{InPars}: Unsupervised Dataset Generation for Information Retrieval},
+  year = {2022},
+  isbn = {9781450387323},
+  publisher = {Association for Computing Machinery},
+  address = {New York, NY, USA},
+  url = {https://doi.org/10.1145/3477495.3531863},
+  doi = {10.1145/3477495.3531863},
+  booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
+  pages = {2387–2392},
+  numpages = {6},
+  keywords = {generative models, large language models, question generation, synthetic datasets, few-shot models, multi-stage ranking},
+  location = {Madrid, Spain},
+  series = {SIGIR '22}
+}
+```
+
+```
+@misc{inparsv2,
+  doi = {10.48550/ARXIV.2301.01820},
+  url = {https://arxiv.org/abs/2301.01820},
+  author = {Jeronymo, Vitor and Bonifacio, Luiz and Abonizio, Hugo and Fadaee, Marzieh and Lotufo, Roberto and Zavrel, Jakub and Nogueira, Rodrigo},
+  title = {{InPars-v2}: Large Language Models as Efficient Dataset Generators for Information Retrieval},
+  publisher = {arXiv},
+  year = {2023},
+  copyright = {Creative Commons Attribution 4.0 International}
+}
+```

config.json

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+{
+  "_name_or_path": "castorini/monot5-3b-msmarco-10k",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 16384,
+  "d_kv": 128,
+  "d_model": 1024,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "gradient_checkpointing": false,
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 24,
+  "num_heads": 32,
+  "num_layers": 24,
+  "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: "
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.6.1",
+  "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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trainer_state.json

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+{
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+}