push model

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,91 @@
 ---
+language: 
+- en
+- zh
+tags:
+- clir
+- colbertx
+- plaidx
+- xlm-roberta-large
+datasets:
+- ms_marco
+- hltcoe/tdist-msmarco-scores
+task_categories:
+- text-retrieval
+- information-retrieval
+task_ids:
+- passage-retrieval
+- cross-language-retrieval
 license: mit
 ---
+
+# ColBERT-X for English-Chinese CLIR using Translate-Distill
+
+## CLIR Model Setting
+
+- Query language: English
+- Query length: 32 token max
+- Document language: Chinese
+- Document length: 180 token max (please use MaxP to aggregate the passage score if needed)
+
+## Model Description
+
+Translate-Distill is a training technique that produces state-of-the-art CLIR dense retrieval model through translation and distillation.
+`plaidx-large-zho-tdist-mt5xxl-engeng` is trained with KL-Divergence from the mt5xxl MonoT5 reranker inferenced on 
+English MS MARCO training queries and English passages. 
+
+### Teacher Models:
+
+- `t53b`: [`castorini/monot5-3b-msmarco-10k`](https://huggingface.co/castorini/monot5-3b-msmarco-10k)
+- `mt5xxl`: [`unicamp-dl/mt5-13b-mmarco-100k`](https://huggingface.co/unicamp-dl/mt5-13b-mmarco-100k)
+
+### Training Parameters
+
+- learning rate: 5e-6
+- update steps: 200,000
+- nway (number of passages per query): 6 (randomly selected from 50)
+- per device batch size (number of query-passage set): 8
+- training GPU: 8 NVIDIA V100 with 32 GB memory
+
+## Usage
+
+To properly load ColBERT-X models from Huggingface Hub, please use the following version of PLAID-X. 
+```bash
+pip install git+https://github.com/hltcoe/ColBERT-X.git@plaid-x
+```
+
+Following code snippet loads the model through Huggingface API. 
+```python
+from colbert.modeling.checkpoint import Checkpoint
+from colbert.infra import ColBERTConfig
+
+Checkpoint('hltcoe/plaidx-large-zho-tdist-mt5xxl-engeng', colbert_config=ColBERTConfig())
+```
+
+For full tutorial, please refer to the [PLAID-X Jupyter Notebook](https://colab.research.google.com/github/hltcoe/clir-tutorial/blob/main/notebooks/clir_tutorial_plaidx.ipynb), 
+which is part of the [SIGIR 2023 CLIR Tutorial](https://github.com/hltcoe/clir-tutorial). 
+
+## BibTeX entry and Citation Info
+
+Please cite the following two papers if you use the model. 
+
+
+```bibtex
+@inproceedings{colbert-x,
+    author = {Suraj Nair and Eugene Yang and Dawn Lawrie and Kevin Duh and Paul McNamee and Kenton Murray and James Mayfield and Douglas W. Oard},
+    title = {Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models},
+    booktitle = {Proceedings of the 44th European Conference on Information Retrieval (ECIR)},
+    year = {2022},
+    url = {https://arxiv.org/abs/2201.08471}
+}
+```
+
+```bibtex
+@inproceedings{translate-distill,
+    author = {Eugene Yang and Dawn Lawrie and James Mayfield and Douglas W. Oard and Scott Miller},
+    title = {Translate-Distill: Learning Cross-Language Dense Retrieval by Translation and Distillation},
+    booktitle = {Proceedings of the 46th European Conference on Information Retrieval (ECIR)},
+    year = {2024},
+    url = {https://arxiv.org/abs/2401.04810}
+}
+```

added_tokens.json

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+  "[unused1]": 250003
+}

artifact.metadata

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+    "doc_token_id": "[unused1]",
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+    "doc_token": "[D]",
+    "ncells": null,
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+    "ndocs": null,
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+    "nbits": 1,
+    "kmeans_niters": 4,
+    "resume": false,
+    "max_sampled_pid": -1,
+    "max_num_partitions": -1,
+    "use_lagacy_build_ivf": false,
+    "reuse_centroids_from": null,
+    "similarity": "cosine",
+    "bsize": 8,
+    "accumsteps": 1,
+    "lr": 5e-6,
+    "maxsteps": 200000,
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+    "resume_optimizer": false,
+    "fix_broken_optimizer_state": false,
+    "warmup": null,
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+    "relu": false,
+    "nway": 6,
+    "n_query_alternative": 1,
+    "use_ib_negatives": false,
+    "kd_loss": "KLD",
+    "reranker": false,
+    "distillation_alpha": 1.0,
+    "ignore_scores": false,
+    "model_name": "xlm-roberta-large",
+    "force_resize_embeddings": true,
+    "shuffle_passages": true,
+    "sampling_max_beta": 1.0,
+    "over_one_epoch": true,
+    "query_maxlen": 32,
+    "attend_to_mask_tokens": false,
+    "interaction": "colbert",
+    "dim": 128,
+    "doc_maxlen": 220,
+    "mask_punctuation": true,
+    "checkpoint": "xlm-roberta-large",
+    "triples": "\/expscratch\/eyang\/workspace\/plaid-aux\/training_triples\/msmarco-passages\/triples_mt5xxl-monot5-mmarco-engeng.jsonl",
+    "collection": null,
+    "queries": null,
+    "index_name": null,
+    "overwrite": false,
+    "root": "\/expscratch\/eyang\/workspace\/plaid-aux\/experiments",
+    "experiment": "plaid_xlm-roberta-large_fixeddp",
+    "index_root": null,
+    "name": "zho-KLD-shuf-5e-6\/mt5xxl-monot5-mmarco-engeng\/64bat.6way",
+    "rank": 0,
+    "nranks": 8,
+    "amp": true,
+    "ivf_num_processes": 20,
+    "gpus": 8,
+    "meta": {
+        "hostname": "r10n01",
+        "git_branch": "eugene-training",
+        "git_hash": "220bdf8155f0615e1bb2d997d8efe924ffe806b5",
+        "git_commit_datetime": "2023-09-29 16:36:59-04:00",
+        "current_datetime": "Oct 03, 2023 ;  9:41PM EDT (-0400)",
+        "cmd": "train.py --model_name xlm-roberta-large --training_triples \/expscratch\/eyang\/workspace\/plaid-aux\/training_triples\/msmarco-passages\/triples_mt5xxl-monot5-mmarco-engeng.jsonl --training_irds_id neumarco\/zh\/train --maxsteps 200000 --learning_rate 5e-6 --kd_loss KLD --per_device_batch_size 8 --nway 6 --run_tag zho-KLD-shuf-5e-6\/mt5xxl-monot5-mmarco-engeng --experiment plaid_xlm-roberta-large_fixeddp",
+        "version": "colbert-v0.4"
+    }
+}

config.json

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+{
+  "_name_or_path": "xlm-roberta-large",
+  "architectures": [
+    "HF_ColBERT"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.28.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250004
+}

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special_tokens_map.json

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tokenizer_config.json

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