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.ipynb_checkpoints/README-checkpoint.md

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+---
+language:
+- en
+- cs
+tags:
+- translation
+license: cc-by-4.0
+datasets:
+- quickmt/quickmt-train.cs-en
+model-index:
+- name: quickmt-en-cs
+  results:
+  - task:
+      name: Translation eng-ces
+      type: translation
+      args: eng-ces
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: eng_Latn ces_Latn devtest
+    metrics:
+    - name: BLEU
+      type: bleu
+      value: 33.73
+    - name: CHRF
+      type: chrf
+      value: 60.29
+    - name: COMET
+      type: comet
+      value: 88.77
+---
+
+
+# `quickmt-en-cs` Neural Machine Translation Model 
+
+`quickmt-en-cs` is a reasonably fast and reasonably accurate neural machine translation model for translation from `en` into `cs`.
+
+
+## Try it on our Huggingface Space
+
+Give it a try before downloading here: https://huggingface.co/spaces/quickmt/QuickMT-Demo
+
+
+## Model Information
+
+* Trained using [`eole`](https://github.com/eole-nlp/eole) 
+* 200M parameter transformer 'big' with 8 encoder layers and 2 decoder layers
+* 32k separate Sentencepiece vocabs
+* Expested for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format
+* Training data: https://huggingface.co/datasets/quickmt/quickmt-train.lv-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-en-cs ./quickmt-en-cs
+```
+
+Finally use the model in python:
+
+```python
+from quickmt import Translator
+
+# Auto-detects GPU, set to "cpu" to force CPU inference
+t = Translator("./quickmt-en-cs/", device="auto")
+
+# Translate - set beam size to 1 for faster speed (but lower quality)
+sample_text = 'Dr. Ehud Ur, professor of medicine at Dalhousie University in Halifax, Nova Scotia and chair of the clinical and scientific division of the Canadian Diabetes Association cautioned that the research is still in its early days.'
+
+t(sample_text, beam_size=5)
+```
+
+> 'Dr. Ehud Ur, profesor medicíny na Dalhousie University v Halifaxu v Novém Skotsku a předseda klinické a vědecké divize Canadian Diabetes Association varoval, že výzkum je stále v počátcích.'
+
+```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)
+```
+
+> 'Profesor medicíny z Dalhousie University v Halifaxu v Novém Skotsku doktor Ehud Ur a předseda klinického a vědeckého oddělení Kanadské Diabetesové asociace upozornil, že jejich výzkum je stále ve svých počátcích.'
+
+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`. A model in safetensors format to be used with `eole` is also provided.
+
+
+## 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) ("eng_Latn"->"ces_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 Nvidia RTX 4070s GPU with batch size 32.
+
+
+|                                  |   bleu |   chrf2 |   comet22 |   Time (s) |
+|:---------------------------------|-------:|--------:|----------:|-----------:|
+| quickmt/quickmt-en-cs            |  33.73 |   60.29 |     88.77 |       1.35 |
+| Helsinki-NLP/opus-mt-en-cs       |  29.64 |   57.06 |     86.29 |       4.05 |
+| facebook/nllb-200-distilled-600M |  28.39 |   56.53 |     88.74 |      25.7  |
+| facebook/nllb-200-distilled-1.3B |  32.25 |   59.24 |     90.97 |      44.96 |
+| facebook/m2m100_418M             |  25.31 |   53.58 |     83.78 |      21.06 |
+| facebook/m2m100_1.2B             |  30.94 |   58.28 |     88.67 |      41.31 |
+

README.md

@@ -1,3 +1,108 @@
----
-license: cc-by-4.0
----
+---
+language:
+- en
+- cs
+tags:
+- translation
+license: cc-by-4.0
+datasets:
+- quickmt/quickmt-train.cs-en
+model-index:
+- name: quickmt-en-cs
+  results:
+  - task:
+      name: Translation eng-ces
+      type: translation
+      args: eng-ces
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: eng_Latn ces_Latn devtest
+    metrics:
+    - name: BLEU
+      type: bleu
+      value: 33.73
+    - name: CHRF
+      type: chrf
+      value: 60.29
+    - name: COMET
+      type: comet
+      value: 88.77
+---
+
+
+# `quickmt-en-cs` Neural Machine Translation Model 
+
+`quickmt-en-cs` is a reasonably fast and reasonably accurate neural machine translation model for translation from `en` into `cs`.
+
+
+## Try it on our Huggingface Space
+
+Give it a try before downloading here: https://huggingface.co/spaces/quickmt/QuickMT-Demo
+
+
+## Model Information
+
+* Trained using [`eole`](https://github.com/eole-nlp/eole) 
+* 200M parameter transformer 'big' with 8 encoder layers and 2 decoder layers
+* 32k separate Sentencepiece vocabs
+* Expested for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format
+* Training data: https://huggingface.co/datasets/quickmt/quickmt-train.lv-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-en-cs ./quickmt-en-cs
+```
+
+Finally use the model in python:
+
+```python
+from quickmt import Translator
+
+# Auto-detects GPU, set to "cpu" to force CPU inference
+t = Translator("./quickmt-en-cs/", device="auto")
+
+# Translate - set beam size to 1 for faster speed (but lower quality)
+sample_text = 'Dr. Ehud Ur, professor of medicine at Dalhousie University in Halifax, Nova Scotia and chair of the clinical and scientific division of the Canadian Diabetes Association cautioned that the research is still in its early days.'
+
+t(sample_text, beam_size=5)
+```
+
+> 'Dr. Ehud Ur, profesor medicíny na Dalhousie University v Halifaxu v Novém Skotsku a předseda klinické a vědecké divize Canadian Diabetes Association varoval, že výzkum je stále v počátcích.'
+
+```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)
+```
+
+> 'Profesor medicíny z Dalhousie University v Halifaxu v Novém Skotsku doktor Ehud Ur a předseda klinického a vědeckého oddělení Kanadské Diabetesové asociace upozornil, že jejich výzkum je stále ve svých počátcích.'
+
+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`. A model in safetensors format to be used with `eole` is also provided.
+
+
+## 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) ("eng_Latn"->"ces_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 Nvidia RTX 4070s GPU with batch size 32.
+
+
+|                                  |   bleu |   chrf2 |   comet22 |   Time (s) |
+|:---------------------------------|-------:|--------:|----------:|-----------:|
+| quickmt/quickmt-en-cs            |  33.73 |   60.29 |     88.77 |       1.35 |
+| Helsinki-NLP/opus-mt-en-cs       |  29.64 |   57.06 |     86.29 |       4.05 |
+| facebook/nllb-200-distilled-600M |  28.39 |   56.53 |     88.74 |      25.7  |
+| facebook/nllb-200-distilled-1.3B |  32.25 |   59.24 |     90.97 |      44.96 |
+| facebook/m2m100_418M             |  25.31 |   53.58 |     83.78 |      21.06 |
+| facebook/m2m100_1.2B             |  30.94 |   58.28 |     88.67 |      41.31 |
+

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: en.eole.vocab
+tgt_vocab: cs.eole.vocab
+src_vocab_size: 32000
+tgt_vocab_size: 32000
+vocab_size_multiple: 8
+share_vocab: false
+n_sample: 0
+
+data:
+    corpus_1:
+        # path_src: hf://quickmt/quickmt-train.cs-en/en
+        # path_tgt: hf://quickmt/quickmt-train.cs-en/cs
+        # path_sco: hf://quickmt/quickmt-train.cs-en/sco
+        path_src: train.en
+        path_tgt: train.cs
+    valid:
+        path_src: valid.en
+        path_tgt: valid.cs
+
+transforms: [sentencepiece, filtertoolong]
+transforms_configs:
+  sentencepiece:
+    src_subword_model: "en.spm.model"
+    tgt_subword_model: "cs.spm.model"
+  filtertoolong:
+    src_seq_length: 256
+    tgt_seq_length: 256
+
+training:
+    # Run configuration
+    model_path: quickmt-en-cs-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: 6000
+    valid_batch_size: 2048
+    batch_size_multiple: 8
+    accum_count: [15]
+    accum_steps: [0]
+
+    # Optimizer & Compute
+    compute_dtype: "fp16"
+    optim: "adamw"
+    #use_amp: False
+    learning_rate: 2.0
+    warmup_steps: 2000
+    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: true
+    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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+{
+  "tensorboard_log_dir_dated": "tensorboard/Oct-07_17-10-35",
+  "n_sample": 0,
+  "share_vocab": false,
+  "tensorboard": true,
+  "src_vocab_size": 32000,
+  "seed": 1234,
+  "vocab_size_multiple": 8,
+  "src_vocab": "en.eole.vocab",
+  "transforms": [
+    "sentencepiece",
+    "filtertoolong"
+  ],
+  "tensorboard_log_dir": "tensorboard",
+  "tgt_vocab_size": 32000,
+  "tgt_vocab": "cs.eole.vocab",
+  "overwrite": true,
+  "report_every": 100,
+  "save_data": "data",
+  "valid_metrics": [
+    "BLEU"
+  ],
+  "training": {
+    "gpu_ranks": [
+      0
+    ],
+    "valid_batch_size": 2048,
+    "dropout_steps": [
+      0
+    ],
+    "adam_beta2": 0.998,
+    "warmup_steps": 2000,
+    "prefetch_factor": 32,
+    "model_path": "quickmt-en-cs-eole-model",
+    "bucket_size": 128000,
+    "label_smoothing": 0.1,
+    "max_grad_norm": 0.0,
+    "accum_steps": [
+      0
+    ],
+    "optim": "adamw",
+    "attention_dropout": [
+      0.1
+    ],
+    "batch_type": "tokens",
+    "average_decay": 0.0001,
+    "valid_steps": 5000,
+    "compute_dtype": "torch.float16",
+    "world_size": 1,
+    "num_workers": 0,
+    "decay_method": "noam",
+    "accum_count": [
+      15
+    ],
+    "keep_checkpoint": 4,
+    "param_init_method": "xavier_uniform",
+    "batch_size_multiple": 8,
+    "train_steps": 100000,
+    "normalization": "tokens",
+    "save_checkpoint_steps": 5000,
+    "dropout": [
+      0.1
+    ],
+    "batch_size": 6000,
+    "learning_rate": 2.0
+  },
+  "data": {
+    "corpus_1": {
+      "path_tgt": "train.cs",
+      "transforms": [
+        "sentencepiece",
+        "filtertoolong"
+      ],
+      "path_src": "train.en",
+      "path_align": null
+    },
+    "valid": {
+      "path_tgt": "valid.cs",
+      "transforms": [
+        "sentencepiece",
+        "filtertoolong"
+      ],
+      "path_src": "valid.en",
+      "path_align": null
+    }
+  },
+  "model": {
+    "transformer_ff": 4096,
+    "hidden_size": 1024,
+    "heads": 8,
+    "architecture": "transformer",
+    "share_decoder_embeddings": true,
+    "share_embeddings": false,
+    "position_encoding_type": "SinusoidalInterleaved",
+    "embeddings": {
+      "tgt_word_vec_size": 1024,
+      "word_vec_size": 1024,
+      "src_word_vec_size": 1024,
+      "position_encoding_type": "SinusoidalInterleaved"
+    },
+    "decoder": {
+      "transformer_ff": 4096,
+      "hidden_size": 1024,
+      "n_positions": null,
+      "heads": 8,
+      "tgt_word_vec_size": 1024,
+      "decoder_type": "transformer",
+      "layers": 2,
+      "position_encoding_type": "SinusoidalInterleaved"
+    },
+    "encoder": {
+      "transformer_ff": 4096,
+      "encoder_type": "transformer",
+      "hidden_size": 1024,
+      "n_positions": null,
+      "src_word_vec_size": 1024,
+      "heads": 8,
+      "layers": 8,
+      "position_encoding_type": "SinusoidalInterleaved"
+    }
+  },
+  "transforms_configs": {
+    "filtertoolong": {
+      "tgt_seq_length": 256,
+      "src_seq_length": 256
+    },
+    "sentencepiece": {
+      "tgt_subword_model": "${MODEL_PATH}/cs.spm.model",
+      "src_subword_model": "${MODEL_PATH}/en.spm.model"
+    }
+  }
+}

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