Transformers
PyTorch
t5
generative-retrieval
information-retrieval
msmarco
robustness
reproducibility
text-generation-inference
Instructions to use kiyam/lost-in-decoding-pag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kiyam/lost-in-decoding-pag with Transformers:
# Load model directly from transformers import AutoTokenizer, T5ForLexicalSemanticGeneration tokenizer = AutoTokenizer.from_pretrained("kiyam/lost-in-decoding-pag") model = T5ForLexicalSemanticGeneration.from_pretrained("kiyam/lost-in-decoding-pag", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 3,043 Bytes
247baec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | {
"model_name_or_path": "t5-base",
"num_decoder_layers": null,
"teacher_score_path": "\/home\/ec2-user\/quic-efs\/user\/hansizeng\/work\/term_generative_retriever\/experiments-full-lexical-ripor\/t5-full-dense-1-5e-4-12l\/out\/MSMARCO_TRAIN\/qrel_added_merged_teacher_scores.json",
"collection_path": "\/home\/ec2-user\/quic-efs\/user\/hansizeng\/work\/data\/msmarco-full\/full_collection\/",
"queries_path": "\/home\/ec2-user\/quic-efs\/user\/hansizeng\/work\/data\/msmarco-full\/all_train_queries\/train_queries",
"qrels_path": "\/home\/ec2-user\/quic-efs\/user\/hansizeng\/work\/data\/msmarco\/train_queries\/qrels.json",
"output_dir": "\/home\/ec2-user\/quic-efs\/user\/hansizeng\/work\/term_generative_retriever\/experiments-full-lexical-ripor\/lexical_ripor_direct_lng_knp_seq2seq_1\/checkpoint",
"example_path": null,
"pseudo_queries_to_docid_path": null,
"pseudo_queries_to_mul_docid_path": null,
"docid_to_smtid_path": "",
"docid_to_tokenids_path": null,
"qid_to_smtid_path": null,
"first_centroid_path": null,
"second_centroid_path": null,
"third_centroid_path": null,
"qid_to_rrpids_path": null,
"docid_decode_eval_path": null,
"centroid_path": null,
"centroid_idx": null,
"triple_margin_mse_path": null,
"query_to_docid_path": null,
"teacher_rerank_nway_path": null,
"bce_example_path": null,
"smt_docid_to_smtid_path": "\/home\/ec2-user\/quic-efs\/user\/hansizeng\/work\/term_generative_retriever\/experiments-full-lexical-ripor\/t5-full-dense-1-5e-4-12l\/aq_smtid\/docid_to_tokenids.json",
"lex_docid_to_smtid_path": "\/home\/ec2-user\/quic-efs\/user\/hansizeng\/work\/term_generative_retriever\/experiments-splade\/t5-splade-0-12l\/\/top_bow\/docid_to_tokenids.json",
"run_name": "lexical_ripor_direct_lng_knp_seq2seq_1",
"pretrained_path": "\/home\/ec2-user\/quic-efs\/user\/hansizeng\/work\/term_generative_retriever\/experiments-full-lexical-ripor\/ripor_direct_lng_knp_seq2seq_1\/checkpoint\/",
"loss_type": "direct_lng_knp_margin_mse",
"model_type": "lexical_ripor",
"do_eval": false,
"max_length": 64,
"learning_rate": 0.0005,
"warmup_ratio": 0.04,
"per_device_train_batch_size": 64,
"logging_steps": 50,
"max_steps": -1,
"epochs": 120,
"local_rank": 0,
"task_names": [
"rank_4",
"rank",
"lexical_rank"
],
"ln_to_weight": {
"rank_4": 1.0,
"rank": 1.0,
"lexical_rank": 1.0
},
"multi_weights": null,
"nway_label_type": null,
"nway_rrpids": 24,
"nway": 12,
"eval_steps": 50,
"use_fp16": true,
"multi_vocab_sizes": false,
"save_steps": 15000,
"wandb_project_name": "full_lexical_ripor",
"pad_token_id": null,
"smtid_as_docid": false,
"apply_lex_loss": false,
"eval_collection_path": null,
"eval_queries_path": null,
"full_rank_eval_qrel_path": null,
"full_rank_eval_topk": 200,
"full_rank_index_dir": null,
"full_rank_out_dir": null
} |