Update README.md

README.md

@@ -5,197 +5,149 @@ language:
 - ja
 ---
 
-# Model Card for Model ID
+# NAIST-NICT WMT’23 General MT Task Submission
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
-
 ## Model Details
 
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
 
+Translation models for submission to WMT'23 English ↔ Japanese general machine translation task.
+This repository provides:
+- seven models per language direction using various combinations of hyperparameters ( `ckpt/` )
+- a datastore per language direction for kNN-MT ( `index/` )
 
+For more details, please see [NAIST-NICT WMT’23 General MT Task Submission](https://aclanthology.org/2023.wmt-1.7/).
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- **Developed by:** Hiroyuki Deguchi, Kenji Imamura, Yuto Nishida, Yusuke Sakai, Justin Vasselli, Taro Watanabe.
+- **Model type:** Translation model
+- **Language pairs:** Japanese-to-English and English-to-Japanese
+- **License:** MIT Licence
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
+You can use our models with [fairseq](https://github.com/facebookresearch/fairseq).
+```
+git clone https://github.com/pytorch/fairseq
+cd fairseq
+pip install --editable ./
+```
+
+### Preprocess
+First preprocess the data:
+```
+DATA_BIN=<path to save preprocessed data>
+fairseq-preprocess --source-lang <source language> --target-lang <target language> \
+    --testpref <prefix of test text> \
+    --destdir ${DATA_BIN} \
+    --workers 20
+```
+
+
+### Beam Search
+Inference with beam search:
+```
+fairseq-generate \
+    --gen-subset test \
+    --task translation \
+    --source-lang <source language> \
+    --target-lang <target language> \
+    --path <path to model> \
+    --nbest 50 \
+    --beam 50 \
+    --max-tokens 1024 \
+    --required-batch-size-multiple 1 \
+    ${DATA_BIN}/
+```
+
+### Ensemble
+Inference with model ensembling:
+```
+MODEL1=<path to model1>
+MODEL2=<path to model2>
+...
+MODEL7=<path to model7>
+
+fairseq-generate \
+    --gen-subset test \
+    --task translation \
+    --source-lang <source language> \
+    --target-lang <target language> \
+    --path ${MODEL1}:${MODEL2}:${MODEL3}:${MODEL4}:${MODEL5}:${MODEL6}:${MODEL7} \
+    --seed 0 \
+    --nbest 50 \
+    --beam 50 \
+    --max-tokens 1024 \
+    --required-batch-size-multiple 1 \
+    ${DATA_BIN}/
+```
+
+### Diversified Decoding (Nucleus Sampling)
+Inference with nucleus (top-p) sampling:
+```
+
+fairseq-generate \
+    --gen-subset test \
+    --task translation \
+    --source-lang <source language> \
+    --target-lang <target language> \
+    --seed 0 \
+    --path <path to model> \
+    --nbest 50 \
+    --beam 50 \
+    --max-tokens 1024 \
+    --sampling \
+    --sampling-topp <hyperparameter> \
+    --required-batch-size-multiple 1 \
+    ${DATA_BIN}/
+```
+
+### kNN-MT
+#### Concat index files
+We uploaded splitted index files.
+You can concat files and check md5sum as follows:
+```
+echo '68b29d7d1483c88b33804828854b28d7' > original.md5 # for English
+echo '77ecbd3aaad7f48814f1c4ae95821256' > original.md5 # for Japanese
+
+cat index.ffn_in.l2.bin.part* > index.ffn_in.l2.bin.reconstructed
+md5sum index.ffn_in.l2.bin.reconstructed > reconstructed.md5
+diff original.md5 reconstructed.md5
+```
+
+#### Inference
+You can use [knn-seq](https://github.com/naist-nlp/knn-seq).
+
+## Citation
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
 **BibTeX:**
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+```
+@inproceedings{deguchi-etal-2023-naist,
+    title = "{NAIST}-{NICT} {WMT}{'}23 General {MT} Task Submission",
+    author = "Deguchi, Hiroyuki  and
+      Imamura, Kenji  and
+      Nishida, Yuto  and
+      Sakai, Yusuke  and
+      Vasselli, Justin  and
+      Watanabe, Taro",
+    editor = "Koehn, Philipp  and
+      Haddow, Barry  and
+      Kocmi, Tom  and
+      Monz, Christof",
+    booktitle = "Proceedings of the Eighth Conference on Machine Translation",
+    month = dec,
+    year = "2023",
+    address = "Singapore",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.wmt-1.7",
+    doi = "10.18653/v1/2023.wmt-1.7",
+    pages = "110--118",
+    abstract = "In this paper, we describe our NAIST-NICT submission to the WMT{'}23 English ↔ Japanese general machine translation task. Our system generates diverse translation candidates and reranks them using a two-stage reranking system to find the best translation. First, we generated 50 candidates each from 18 translation methods using a variety of techniques to increase the diversity of the translation candidates. We trained seven models per language direction using various combinations of hyperparameters. From these models we used various decoding algorithms, ensembling the models, and using kNN-MT (Khandelwal et al., 2021). We processed the 900 translation candidates through a two-stage reranking system to find the most promising candidate. In the first step, we compared 50 candidates from each translation method using DrNMT (Lee et al., 2021) and returned the candidate with the best score. We ranked the final 18 candidates using COMET-MBR (Fernandes et al., 2022) and returned the best score as the system output. We found that generating diverse translation candidates improved translation quality using the well-designed reranker model.",
+}
+```