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.ipynb_checkpoints/README-checkpoint.md

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+---
+language:
+- en
+- fa
+tags:
+- translation
+license: cc-by-4.0
+datasets:
+- quickmt/quickmt-train.fa-en
+model-index:
+- name: quickmt-fa-en
+  results:
+  - task:
+      name: Translation pes-eng
+      type: translation
+      args: pes-eng
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: pes_Arab eng_Latn devtest
+    metrics:
+    - name: BLEU
+      type: bleu
+      value: 37.57
+    - name: CHRF
+      type: chrf
+      value: 63.37
+    - name: COMET
+      type: comet
+      value: 87.76
+---
+
+
+# `quickmt-fa-en` Neural Machine Translation Model 
+
+`quickmt-fa-en` is a reasonably fast and reasonably accurate neural machine translation model for translation from `fa` 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
+* 50k joint Sentencepiece vocabulary
+* Expested for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format
+* Training data: https://huggingface.co/datasets/quickmt/quickmt-train.fa-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-fa-en ./quickmt-fa-en
+```
+
+Finally use the model in python:
+
+```python
+from quickmt impest Translator
+
+# Auto-detects GPU, set to "cpu" to force CPU inference
+t = Translator("./quickmt-fa-en/", device="auto")
+
+# Translate - set beam size to 1 for faster speed (but lower quality)
+sample_text = 'دکتر ایهود اور، استاد پزشکی دانشگاه دالهاوزی در هلیفکس، نوااسکوشیا و رئیس بخش کلینیکی و علمی انجمن دیابت کانادا هشدار داد که این تحقیق هنوز در روزهای آغازین خود می\u200cباشد.'
+
+t(sample_text, beam_size=5)
+```
+
+> 'Dr. Ehudover, a professor of medicine at Dalhousie University in Halifax, Nova Scotia and head of the clinical and scientific division of the Canadian Diabetes Association, warned that the study was still in its early days.'
+
+```python
+# 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. Ehudver, professor of medicine at Dalhousie University in Halifax, Nova Scotia and head of the Clinical and Scientific Section of the Canadian Diabetes Society, cautioned the new study was still very early.'
+
+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) ("pes_Arab"->"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 the flores-devtest dataset (1012 sentences) on an RTX 4070s GPU with batch size 32 (faster speed is possible using a larger batch size).
+
+|                                  |   bleu |   chrf2 |   comet22 |   Time (s) |
+|:---------------------------------|-------:|--------:|----------:|-----------:|
+| quickmt-fa-en                    |  37.57 |   63.37 |     87.76 |       1.16 |
+| facebook/nllb-200-distilled-600M |  34.79 |   60.86 |     86.49 |      21.17 |
+| facebook/nllb-200-distilled-1.3B |  37.91 |   63.39 |     87.82 |      36.9  |
+| facebook/m2m100_418M             |  27.2  |   55.82 |     82.9  |      18.24 |
+| facebook/m2m100_1.2B             |  29.12 |   56.39 |     83.5  |      35.14 |

README.md

@@ -1,3 +1,98 @@
----
-license: cc-by-4.0
----
+---
+language:
+- en
+- fa
+tags:
+- translation
+license: cc-by-4.0
+datasets:
+- quickmt/quickmt-train.fa-en
+model-index:
+- name: quickmt-fa-en
+  results:
+  - task:
+      name: Translation pes-eng
+      type: translation
+      args: pes-eng
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: pes_Arab eng_Latn devtest
+    metrics:
+    - name: BLEU
+      type: bleu
+      value: 37.57
+    - name: CHRF
+      type: chrf
+      value: 63.37
+    - name: COMET
+      type: comet
+      value: 87.76
+---
+
+
+# `quickmt-fa-en` Neural Machine Translation Model 
+
+`quickmt-fa-en` is a reasonably fast and reasonably accurate neural machine translation model for translation from `fa` 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
+* 50k joint Sentencepiece vocabulary
+* Expested for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format
+* Training data: https://huggingface.co/datasets/quickmt/quickmt-train.fa-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-fa-en ./quickmt-fa-en
+```
+
+Finally use the model in python:
+
+```python
+from quickmt impest Translator
+
+# Auto-detects GPU, set to "cpu" to force CPU inference
+t = Translator("./quickmt-fa-en/", device="auto")
+
+# Translate - set beam size to 1 for faster speed (but lower quality)
+sample_text = 'دکتر ایهود اور، استاد پزشکی دانشگاه دالهاوزی در هلیفکس، نوااسکوشیا و رئیس بخش کلینیکی و علمی انجمن دیابت کانادا هشدار داد که این تحقیق هنوز در روزهای آغازین خود می\u200cباشد.'
+
+t(sample_text, beam_size=5)
+```
+
+> 'Dr. Ehudover, a professor of medicine at Dalhousie University in Halifax, Nova Scotia and head of the clinical and scientific division of the Canadian Diabetes Association, warned that the study was still in its early days.'
+
+```python
+# 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. Ehudver, professor of medicine at Dalhousie University in Halifax, Nova Scotia and head of the Clinical and Scientific Section of the Canadian Diabetes Society, cautioned the new study was still very early.'
+
+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) ("pes_Arab"->"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 the flores-devtest dataset (1012 sentences) on an RTX 4070s GPU with batch size 32 (faster speed is possible using a larger batch size).
+
+|                                  |   bleu |   chrf2 |   comet22 |   Time (s) |
+|:---------------------------------|-------:|--------:|----------:|-----------:|
+| quickmt-fa-en                    |  37.57 |   63.37 |     87.76 |       1.16 |
+| facebook/nllb-200-distilled-600M |  34.79 |   60.86 |     86.49 |      21.17 |
+| facebook/nllb-200-distilled-1.3B |  37.91 |   63.39 |     87.82 |      36.9  |
+| facebook/m2m100_418M             |  27.2  |   55.82 |     82.9  |      18.24 |
+| facebook/m2m100_1.2B             |  29.12 |   56.39 |     83.5  |      35.14 |

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: fa.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.id-en/id
+        # path_tgt: hf://quickmt/quickmt-train.id-en/en
+        # path_sco: hf://quickmt/quickmt-train.id-en/sco
+        path_src: train.fa
+        path_tgt: train.en
+    valid:
+        path_src: dev.fa
+        path_tgt: dev.en
+
+transforms: [sentencepiece, filtertoolong]
+transforms_configs:
+  sentencepiece:
+    src_subword_model: "fa.spm.model"
+    tgt_subword_model: "en.spm.model"
+  filtertoolong:
+    src_seq_length: 256
+    tgt_seq_length: 256
+
+training:
+    # Run configuration
+    model_path: quickmt-fa-en-eole-model
+    #train_from: model
+    keep_checkpoint: 4
+    train_steps: 100000
+    save_checkpoint_steps: 5000
+    valid_steps: 5000
+    
+    # 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: [10]
+    accum_steps: [0]
+
+    # Optimizer & Compute
+    compute_dtype: "fp16"
+    optim: "adamw"
+    #use_amp: False
+    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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+{
+  "share_vocab": false,
+  "vocab_size_multiple": 8,
+  "src_vocab_size": 20000,
+  "seed": 1234,
+  "valid_metrics": [
+    "BLEU"
+  ],
+  "save_data": "data",
+  "tensorboard": true,
+  "tgt_vocab": "en.eole.vocab",
+  "tensorboard_log_dir": "tensorboard",
+  "tensorboard_log_dir_dated": "tensorboard/Jun-03_06-03-40",
+  "overwrite": true,
+  "tgt_vocab_size": 20000,
+  "src_vocab": "fa.eole.vocab",
+  "n_sample": 0,
+  "report_every": 100,
+  "transforms": [
+    "sentencepiece",
+    "filtertoolong"
+  ],
+  "training": {
+    "train_steps": 100000,
+    "dropout_steps": [
+      0
+    ],
+    "batch_size": 8000,
+    "attention_dropout": [
+      0.1
+    ],
+    "accum_count": [
+      10
+    ],
+    "prefetch_factor": 32,
+    "valid_steps": 5000,
+    "adam_beta2": 0.998,
+    "world_size": 1,
+    "accum_steps": [
+      0
+    ],
+    "model_path": "quickmt-fa-en-eole-model",
+    "label_smoothing": 0.1,
+    "keep_checkpoint": 4,
+    "gpu_ranks": [
+      0
+    ],
+    "batch_size_multiple": 8,
+    "warmup_steps": 4000,
+    "decay_method": "noam",
+    "max_grad_norm": 0.0,
+    "batch_type": "tokens",
+    "save_checkpoint_steps": 5000,
+    "param_init_method": "xavier_uniform",
+    "normalization": "tokens",
+    "learning_rate": 2.0,
+    "optim": "adamw",
+    "compute_dtype": "torch.float16",
+    "bucket_size": 128000,
+    "dropout": [
+      0.1
+    ],
+    "average_decay": 0.0001,
+    "num_workers": 0,
+    "valid_batch_size": 4096
+  },
+  "data": {
+    "corpus_1": {
+      "transforms": [
+        "sentencepiece",
+        "filtertoolong"
+      ],
+      "path_src": "train.fa",
+      "path_align": null,
+      "path_tgt": "train.en"
+    },
+    "valid": {
+      "transforms": [
+        "sentencepiece",
+        "filtertoolong"
+      ],
+      "path_src": "dev.fa",
+      "path_align": null,
+      "path_tgt": "dev.en"
+    }
+  },
+  "transforms_configs": {
+    "sentencepiece": {
+      "src_subword_model": "${MODEL_PATH}/fa.spm.model",
+      "tgt_subword_model": "${MODEL_PATH}/en.spm.model"
+    },
+    "filtertoolong": {
+      "src_seq_length": 256,
+      "tgt_seq_length": 256
+    }
+  },
+  "model": {
+    "share_embeddings": false,
+    "hidden_size": 1024,
+    "architecture": "transformer",
+    "share_decoder_embeddings": false,
+    "heads": 8,
+    "transformer_ff": 4096,
+    "position_encoding_type": "SinusoidalInterleaved",
+    "decoder": {
+      "decoder_type": "transformer",
+      "hidden_size": 1024,
+      "n_positions": null,
+      "transformer_ff": 4096,
+      "heads": 8,
+      "position_encoding_type": "SinusoidalInterleaved",
+      "tgt_word_vec_size": 1024,
+      "layers": 2
+    },
+    "encoder": {
+      "hidden_size": 1024,
+      "encoder_type": "transformer",
+      "n_positions": null,
+      "src_word_vec_size": 1024,
+      "heads": 8,
+      "transformer_ff": 4096,
+      "position_encoding_type": "SinusoidalInterleaved",
+      "layers": 8
+    },
+    "embeddings": {
+      "word_vec_size": 1024,
+      "src_word_vec_size": 1024,
+      "tgt_word_vec_size": 1024,
+      "position_encoding_type": "SinusoidalInterleaved"
+    }
+  }
+}

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