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.ipynb_checkpoints/README-checkpoint.md

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+---
+language:
+- en
+- ja
+tags:
+- translation
+license: cc-by-4.0
+datasets:
+- quickmt/quickmt-train.ja-en
+model-index:
+- name: quickmt-ja-en
+  results:
+  - task:
+      name: Translation jpn-eng
+      type: translation
+      args: jpn-eng
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: jpn_Japn eng_Latn devtest
+    metrics:
+    - name: CHRF
+      type: chrf
+      value: 57.06
+    - name: BLEU
+      type: bleu
+      value: 27.91 
+    - name: COMET
+      type: comet
+      value: 87.29
+---
+
+
+# `quickmt-ja-en` Neural Machine Translation Model 
+
+`quickmt-ja-en` is a reasonably fast and reasonably accurate neural machine translation model for translation from `ja` into `en`.
+
+
+## Model Information
+
+* Trained using [`eole`](https://github.com/eole-nlp/eole)
+* 185M parameter transformer 'big' with 8 encoder layers and 2 decoder layers
+* 20k sentencepiece vocabularies
+* Exported for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format
+* Training data: https://huggingface.co/datasets/quickmt/quickmt-train.ja-en/tree/main
+
+See the `eole` model configuration in this repository for further details and the `eole-model` for the raw `eole` (pytorch) model.
+
+
+## Usage with `quickmt`
+
+You must install the Nvidia cuda toolkit first, if you want to do GPU inference.
+
+Next, install the `quickmt` python library and download the model:
+
+```bash
+git clone https://github.com/quickmt/quickmt.git
+pip install ./quickmt/
+
+quickmt-model-download quickmt/quickmt-ja-en ./quickmt-ja-en
+```
+
+Finally use the model in python:
+
+```python
+from quickmt import Translator
+
+# Auto-detects GPU, set to "cpu" to force CPU inference
+t = Translator("./quickmt-ja-en/", device="auto")
+
+# Translate - set beam size to 5 for higher quality (but slower speed)
+sample_text = 'ノバスコシア州ハリファックスにあるダルハウジー大学医学部教授でカナダ糖尿病協会の臨床・科学部門の責任者を務めるエフード・ウル博士は、この研究はまだ初期段階にあるとして注意を促しました。'
+t(sample_text, beam_size=5)
+
+> 'Dr. Ehud Ulu, a professor of medicine at Dalhousie University in Halifax, Nova Scotia and head of the clinical and scientific division of the Canadian Diabetes Association, cautioned that the study is still in its early stages.'
+
+# Get alternative translations by sampling
+# You can pass any cTranslate2 `translate_batch` arguments
+t([sample_text], sampling_temperature=1.2, beam_size=1, sampling_topk=50, sampling_topp=0.9)
+
+> 'Dr Ehud Ul, professor of medicine at the University of Dalhousie’s Halifax, Nova Scotia and head of the clinical and scientific division of the Canadian Diabetes Society, noted the study is in its early stages.'
+```
+
+The model is in `ctranslate2` format, and the tokenizers are `sentencepiece`, so you can use `ctranslate2` directly instead of through `quickmt`. It is also possible  to get this model to work with e.g. [LibreTranslate](https://libretranslate.com/) which also uses `ctranslate2` and `sentencepiece`.
+
+
+## Metrics
+
+`bleu` and `chrf2` are calculated with [sacrebleu](https://github.com/mjpost/sacrebleu) on the [Flores200 `devtest` test set](https://huggingface.co/datasets/facebook/flores) ("jpn_Jpan"->"eng_Latn"). `comet22` with the [`comet`](https://github.com/Unbabel/COMET) library and the [default model](https://huggingface.co/Unbabel/wmt22-comet-da). "Time (s)" is the time in seconds to translate (using `ctranslate2`) the flores-devtest dataset (1012 sentences) on an RTX 4070s GPU with batch size 32 (faster speed is possible using a large batch size).
+
+|                                  |   bleu |   chrf2 |   comet22 |   Time (s) |
+|:---------------------------------|-------:|--------:|----------:|-----------:|
+| quickmt/quickmt-ja-en            |  27.91 |   57.06 |     87.29 |       1.00 |
+| Helsink-NLP/opus-mt-ja-en        |  19.22 |   49.15 |     82.64 |       3.54 |
+| facebook/nllb-200-distilled-600M |  24.05 |   53.39 |     85.84 |      22.5  |
+| facebook/nllb-200-distilled-1.3B |  28.4  |   56.96 |     87.47 |      37.15 |
+| facebook/m2m100_418M             |  18.82 |   49.55 |     82.59 |      18.27 |
+| facebook/m2m100_1.2B             |  23.32 |   53.46 |     85.43 |      35.44 |
+
+`quickmt-ja-en` is the fastest and nearly as high-quality as facebook/nllb-200-distilled-1.3B.

README.md

@@ -1,3 +1,100 @@
----
-license: cc-by-4.0
----
+---
+language:
+- en
+- ja
+tags:
+- translation
+license: cc-by-4.0
+datasets:
+- quickmt/quickmt-train.ja-en
+model-index:
+- name: quickmt-ja-en
+  results:
+  - task:
+      name: Translation jpn-eng
+      type: translation
+      args: jpn-eng
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: jpn_Japn eng_Latn devtest
+    metrics:
+    - name: CHRF
+      type: chrf
+      value: 57.06
+    - name: BLEU
+      type: bleu
+      value: 27.91 
+    - name: COMET
+      type: comet
+      value: 87.29
+---
+
+
+# `quickmt-ja-en` Neural Machine Translation Model 
+
+`quickmt-ja-en` is a reasonably fast and reasonably accurate neural machine translation model for translation from `ja` into `en`.
+
+
+## Model Information
+
+* Trained using [`eole`](https://github.com/eole-nlp/eole)
+* 185M parameter transformer 'big' with 8 encoder layers and 2 decoder layers
+* 20k sentencepiece vocabularies
+* Exported for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format
+* Training data: https://huggingface.co/datasets/quickmt/quickmt-train.ja-en/tree/main
+
+See the `eole` model configuration in this repository for further details and the `eole-model` for the raw `eole` (pytorch) model.
+
+
+## Usage with `quickmt`
+
+You must install the Nvidia cuda toolkit first, if you want to do GPU inference.
+
+Next, install the `quickmt` python library and download the model:
+
+```bash
+git clone https://github.com/quickmt/quickmt.git
+pip install ./quickmt/
+
+quickmt-model-download quickmt/quickmt-ja-en ./quickmt-ja-en
+```
+
+Finally use the model in python:
+
+```python
+from quickmt import Translator
+
+# Auto-detects GPU, set to "cpu" to force CPU inference
+t = Translator("./quickmt-ja-en/", device="auto")
+
+# Translate - set beam size to 5 for higher quality (but slower speed)
+sample_text = 'ノバスコシア州ハリファックスにあるダルハウジー大学医学部教授でカナダ糖尿病協会の臨床・科学部門の責任者を務めるエフード・ウル博士は、この研究はまだ初期段階にあるとして注意を促しました。'
+t(sample_text, beam_size=5)
+
+> 'Dr. Ehud Ulu, a professor of medicine at Dalhousie University in Halifax, Nova Scotia and head of the clinical and scientific division of the Canadian Diabetes Association, cautioned that the study is still in its early stages.'
+
+# Get alternative translations by sampling
+# You can pass any cTranslate2 `translate_batch` arguments
+t([sample_text], sampling_temperature=1.2, beam_size=1, sampling_topk=50, sampling_topp=0.9)
+
+> 'Dr Ehud Ul, professor of medicine at the University of Dalhousie’s Halifax, Nova Scotia and head of the clinical and scientific division of the Canadian Diabetes Society, noted the study is in its early stages.'
+```
+
+The model is in `ctranslate2` format, and the tokenizers are `sentencepiece`, so you can use `ctranslate2` directly instead of through `quickmt`. It is also possible  to get this model to work with e.g. [LibreTranslate](https://libretranslate.com/) which also uses `ctranslate2` and `sentencepiece`.
+
+
+## Metrics
+
+`bleu` and `chrf2` are calculated with [sacrebleu](https://github.com/mjpost/sacrebleu) on the [Flores200 `devtest` test set](https://huggingface.co/datasets/facebook/flores) ("jpn_Jpan"->"eng_Latn"). `comet22` with the [`comet`](https://github.com/Unbabel/COMET) library and the [default model](https://huggingface.co/Unbabel/wmt22-comet-da). "Time (s)" is the time in seconds to translate (using `ctranslate2`) the flores-devtest dataset (1012 sentences) on an RTX 4070s GPU with batch size 32 (faster speed is possible using a large batch size).
+
+|                                  |   bleu |   chrf2 |   comet22 |   Time (s) |
+|:---------------------------------|-------:|--------:|----------:|-----------:|
+| quickmt/quickmt-ja-en            |  27.91 |   57.06 |     87.29 |       1.00 |
+| Helsink-NLP/opus-mt-ja-en        |  19.22 |   49.15 |     82.64 |       3.54 |
+| facebook/nllb-200-distilled-600M |  24.05 |   53.39 |     85.84 |      22.5  |
+| facebook/nllb-200-distilled-1.3B |  28.4  |   56.96 |     87.47 |      37.15 |
+| facebook/m2m100_418M             |  18.82 |   49.55 |     82.59 |      18.27 |
+| facebook/m2m100_1.2B             |  23.32 |   53.46 |     85.43 |      35.44 |
+
+`quickmt-ja-en` is the fastest and nearly as high-quality as facebook/nllb-200-distilled-1.3B.

config.json

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+{
+  "add_source_bos": false,
+  "add_source_eos": false,
+  "bos_token": "<s>",
+  "decoder_start_token": "<s>",
+  "eos_token": "</s>",
+  "layer_norm_epsilon": 1e-06,
+  "multi_query_attention": false,
+  "unk_token": "<unk>"
+}

eole-config.yaml

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+## IO
+save_data: data
+overwrite: True
+seed: 1234
+report_every: 100
+valid_metrics: ["BLEU"]
+tensorboard: true
+tensorboard_log_dir: tensorboard
+
+### Vocab
+src_vocab: ja.eole.vocab
+tgt_vocab: en.eole.vocab
+src_vocab_size: 20000
+tgt_vocab_size: 20000
+vocab_size_multiple: 8
+share_vocab: false
+n_sample: 0
+
+data:
+    corpus_1:
+        path_src: hf://quickmt/quickmt-train.ja-en/ja
+        path_tgt: hf://quickmt/quickmt-train.ja-en/en
+        path_sco: hf://quickmt/quickmt-train.ja-en/sco
+    valid:
+        path_src: valid.ja
+        path_tgt: valid.en
+
+transforms: [sentencepiece, filtertoolong]
+transforms_configs:
+  sentencepiece:
+    src_subword_model: "ja.spm.model"
+    tgt_subword_model: "en.spm.model"
+  filtertoolong:
+    src_seq_length: 256
+    tgt_seq_length: 256
+
+training:
+    # Run configuration
+    model_path: eole-model
+    keep_checkpoint: 4
+    train_steps: 108000
+    save_checkpoint_steps: 2000
+    valid_steps: 2000
+    
+    # Train on a single GPU
+    world_size: 1
+    gpu_ranks: [0]
+
+    # Batching 10240
+    batch_type: "tokens"
+    batch_size: 8000
+    valid_batch_size: 4096
+    batch_size_multiple: 8
+    accum_count: [8]
+    accum_steps: [0]
+
+    # Optimizer & Compute
+    compute_dtype: "fp16"
+    optim: "adamw"
+    learning_rate: 2.0
+    warmup_steps: 4000
+    decay_method: "noam"
+    adam_beta2: 0.998
+
+    # Data loading
+    bucket_size: 128000
+    num_workers: 4
+    prefetch_factor: 32
+    
+    # Hyperparams
+    dropout_steps: [0]
+    dropout: [0.1]
+    attention_dropout: [0.1]
+    max_grad_norm: 0
+    label_smoothing: 0.1
+    average_decay: 0.0001
+    param_init_method: xavier_uniform
+    normalization: "tokens"
+
+model:
+    architecture: "transformer"
+    share_embeddings: false
+    share_decoder_embeddings: false
+    hidden_size: 1024
+    encoder:
+        layers: 8
+    decoder:
+        layers: 2
+    heads: 8
+    transformer_ff: 4096
+    embeddings:
+        word_vec_size: 1024
+        position_encoding_type: "SinusoidalInterleaved"
+

eole-model/config.json

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+{
+  "n_sample": 0,
+  "overwrite": true,
+  "save_data": "data",
+  "tgt_vocab_size": 20000,
+  "seed": 1234,
+  "share_vocab": false,
+  "tensorboard_log_dir": "tensorboard",
+  "report_every": 100,
+  "tensorboard": true,
+  "tgt_vocab": "en.eole.vocab",
+  "vocab_size_multiple": 8,
+  "src_vocab": "ja.eole.vocab",
+  "src_vocab_size": 20000,
+  "tensorboard_log_dir_dated": "tensorboard/Apr-15_05-55-59",
+  "transforms": [
+    "sentencepiece",
+    "filtertoolong"
+  ],
+  "valid_metrics": [
+    "BLEU"
+  ],
+  "training": {
+    "save_checkpoint_steps": 2000,
+    "max_grad_norm": 0.0,
+    "dropout": [
+      0.1
+    ],
+    "dropout_steps": [
+      0
+    ],
+    "label_smoothing": 0.1,
+    "keep_checkpoint": 4,
+    "prefetch_factor": 32,
+    "valid_steps": 2000,
+    "optim": "adamw",
+    "gpu_ranks": [
+      0
+    ],
+    "bucket_size": 128000,
+    "batch_size": 8000,
+    "average_decay": 0.0001,
+    "compute_dtype": "torch.float16",
+    "param_init_method": "xavier_uniform",
+    "decay_method": "noam",
+    "num_workers": 0,
+    "warmup_steps": 4000,
+    "attention_dropout": [
+      0.1
+    ],
+    "model_path": "quickmt-ja-en-eole-model",
+    "accum_count": [
+      8
+    ],
+    "batch_size_multiple": 8,
+    "train_steps": 200000,
+    "accum_steps": [
+      0
+    ],
+    "adam_beta2": 0.998,
+    "learning_rate": 2.0,
+    "normalization": "tokens",
+    "valid_batch_size": 4096,
+    "world_size": 1,
+    "batch_type": "tokens"
+  },
+  "model": {
+    "transformer_ff": 4096,
+    "share_decoder_embeddings": false,
+    "architecture": "transformer",
+    "position_encoding_type": "SinusoidalInterleaved",
+    "share_embeddings": false,
+    "heads": 8,
+    "hidden_size": 1024,
+    "encoder": {
+      "layers": 8,
+      "transformer_ff": 4096,
+      "position_encoding_type": "SinusoidalInterleaved",
+      "src_word_vec_size": 1024,
+      "n_positions": null,
+      "encoder_type": "transformer",
+      "heads": 8,
+      "hidden_size": 1024
+    },
+    "embeddings": {
+      "tgt_word_vec_size": 1024,
+      "position_encoding_type": "SinusoidalInterleaved",
+      "word_vec_size": 1024,
+      "src_word_vec_size": 1024
+    },
+    "decoder": {
+      "layers": 2,
+      "transformer_ff": 4096,
+      "position_encoding_type": "SinusoidalInterleaved",
+      "n_positions": null,
+      "decoder_type": "transformer",
+      "tgt_word_vec_size": 1024,
+      "heads": 8,
+      "hidden_size": 1024
+    }
+  },
+  "transforms_configs": {
+    "sentencepiece": {
+      "src_subword_model": "${MODEL_PATH}/ja.spm.model",
+      "tgt_subword_model": "${MODEL_PATH}/en.spm.model"
+    },
+    "filtertoolong": {
+      "tgt_seq_length": 256,
+      "src_seq_length": 256
+    }
+  },
+  "data": {
+    "corpus_1": {
+      "path_tgt": "en.txt",
+      "path_align": null,
+      "transforms": [
+        "sentencepiece",
+        "filtertoolong"
+      ],
+      "path_src": "ja.txt"
+    },
+    "valid": {
+      "path_tgt": "valid.en",
+      "path_align": null,
+      "transforms": [
+        "sentencepiece",
+        "filtertoolong"
+      ],
+      "path_src": "valid.ja"
+    }
+  }
+}

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