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.ipynb_checkpoints/README-checkpoint.md

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+---
+language:
+- en
+- hi
+tags:
+- translation
+license: cc-by-4.0
+datasets:
+- quickmt/quickmt-train.hi-en
+model-index:
+- name: quickmt-hi-en
+  results:
+  - task:
+      name: Translation hin-eng
+      type: translation
+      args: hin-eng
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: hin_Deva eng_Latn devtest
+    metrics:
+    - name: CHRF
+      type: chrf
+      value: 65.04
+    - name: BLEU
+      type: bleu
+      value: 39.9
+    - name: COMET
+      type: comet
+      value: 88.77
+---
+
+
+# `quickmt-hi-en` Neural Machine Translation Model 
+
+`quickmt-hi-en` is a reasonably fast and reasonably accurate neural machine translation model for translation from `hi` 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
+* Exported for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format
+* Training data: https://huggingface.co/datasets/quickmt/quickmt-train.it-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-hi-en ./quickmt-hi-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-hi-en/", device="auto")
+
+# Translate - set beam size to 1 for faster speed (but lower quality)
+sample_text = 'डॉ. एहुड उर, नोवा स्कोटिया के हैलिफ़ैक्स में डलहौज़ी विश्वविद्यालय में चिकित्सा के प्रोफ़ेसर और कनाडाई डायबिटीज़ एसोसिएशन के नैदानिक \u200b\u200bऔर वैज्ञानिक विभाग के अध्यक्ष ने आगाह किया कि शोध अभी भी अपने शुरुआती दिनों में है.'
+t(sample_text, beam_size=5)
+
+> 'On the other hand, Dr Ehud ur, professor of medicine at Dalhousie University in Halifax, Nova Scotia and chairman of the clinical and scientific department of the Canadian Diabetes Association, cautioned that the research is still in its early days.'
+
+# 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)
+
+> 'A group of young men Ehud ur, professor of medicine at Dalhousie University in Halifax, Nova Scotia, and president of the Diagnostics and Scientific Department of the Canadian Diabetes Association warned that the research is still in its early days.'
+```
+
+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) ("hin_Deva"->"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).
+
+## it -> en flores-devtest metrics
+
+| model                            |   bleu |   chrf2 |   comet22 |   Time (s) |
+|:---------------------------------|-------:|--------:|----------:|-----------:|
+| quickmt/quickmt-hi-en            |  39.9  |   65.04 |     88.77 |       1.14 |
+| Helsink-NLP/opus-mt-hi-en        |  18.83 |   45.9  |     75.81 |       4.38 |
+| facebook/nllb-200-distilled-600M |  38.8  |   64.29 |     88.87 |      22.49 |
+| facebook/nllb-200-distilled-1.3B |  41.71 |   66.67 |     89.69 |      38.91 |
+| facebook/m2m100_418M             |  29.81 |   57.66 |     85    |      19.65 |
+| facebook/m2m100_1.2B             |  33.79 |   60.21 |     86.3  |      38.33 |
+
+
+`quickmt-hi-en` is the fastest and second highest quality next to `nllb-200-distilled-1.3B` (which has a non-commercial license). 

README.md

@@ -1,3 +1,102 @@
----
-license: cc-by-4.0
----
+---
+language:
+- en
+- hi
+tags:
+- translation
+license: cc-by-4.0
+datasets:
+- quickmt/quickmt-train.hi-en
+model-index:
+- name: quickmt-hi-en
+  results:
+  - task:
+      name: Translation hin-eng
+      type: translation
+      args: hin-eng
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: hin_Deva eng_Latn devtest
+    metrics:
+    - name: CHRF
+      type: chrf
+      value: 65.04
+    - name: BLEU
+      type: bleu
+      value: 39.9
+    - name: COMET
+      type: comet
+      value: 88.77
+---
+
+
+# `quickmt-hi-en` Neural Machine Translation Model 
+
+`quickmt-hi-en` is a reasonably fast and reasonably accurate neural machine translation model for translation from `hi` 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
+* Exported for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format
+* Training data: https://huggingface.co/datasets/quickmt/quickmt-train.it-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-hi-en ./quickmt-hi-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-hi-en/", device="auto")
+
+# Translate - set beam size to 1 for faster speed (but lower quality)
+sample_text = 'डॉ. एहुड उर, नोवा स्कोटिया के हैलिफ़ैक्स में डलहौज़ी विश्वविद्यालय में चिकित्सा के प्रोफ़ेसर और कनाडाई डायबिटीज़ एसोसिएशन के नैदानिक \u200b\u200bऔर वैज्ञानिक विभाग के अध्यक्ष ने आगाह किया कि शोध अभी भी अपने शुरुआती दिनों में है.'
+t(sample_text, beam_size=5)
+
+> 'On the other hand, Dr Ehud ur, professor of medicine at Dalhousie University in Halifax, Nova Scotia and chairman of the clinical and scientific department of the Canadian Diabetes Association, cautioned that the research is still in its early days.'
+
+# 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)
+
+> 'A group of young men Ehud ur, professor of medicine at Dalhousie University in Halifax, Nova Scotia, and president of the Diagnostics and Scientific Department of the Canadian Diabetes Association warned that the research is still in its early days.'
+```
+
+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) ("hin_Deva"->"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).
+
+## it -> en flores-devtest metrics
+
+| model                            |   bleu |   chrf2 |   comet22 |   Time (s) |
+|:---------------------------------|-------:|--------:|----------:|-----------:|
+| quickmt/quickmt-hi-en            |  39.9  |   65.04 |     88.77 |       1.14 |
+| Helsink-NLP/opus-mt-hi-en        |  18.83 |   45.9  |     75.81 |       4.38 |
+| facebook/nllb-200-distilled-600M |  38.8  |   64.29 |     88.87 |      22.49 |
+| facebook/nllb-200-distilled-1.3B |  41.71 |   66.67 |     89.69 |      38.91 |
+| facebook/m2m100_418M             |  29.81 |   57.66 |     85    |      19.65 |
+| facebook/m2m100_1.2B             |  33.79 |   60.21 |     86.3  |      38.33 |
+
+
+`quickmt-hi-en` is the fastest and second highest quality next to `nllb-200-distilled-1.3B` (which has a non-commercial license). 

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: hi.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.hi-en/hi
+        # path_tgt: hf://quickmt/quickmt-train.hi-en/en
+        # path_sco: hf://quickmt/quickmt-train.hi-en/sco
+        path_src: train.hi
+        path_tgt: train.en
+    valid:
+        path_src: dev.hi
+        path_tgt: dev.en
+
+transforms: [sentencepiece, filtertoolong]
+transforms_configs:
+  sentencepiece:
+    src_subword_model: "hi.spm.model"
+    tgt_subword_model: "en.spm.model"
+  filtertoolong:
+    src_seq_length: 256
+    tgt_seq_length: 256
+
+training:
+    # Run configuration
+    model_path: quickmt-hi-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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+{
+  "transforms": [
+    "sentencepiece",
+    "filtertoolong"
+  ],
+  "src_vocab": "hi.eole.vocab",
+  "tgt_vocab": "en.eole.vocab",
+  "share_vocab": false,
+  "tensorboard_log_dir": "tensorboard",
+  "src_vocab_size": 20000,
+  "report_every": 100,
+  "vocab_size_multiple": 8,
+  "tgt_vocab_size": 20000,
+  "seed": 1234,
+  "tensorboard": true,
+  "n_sample": 0,
+  "overwrite": true,
+  "valid_metrics": [
+    "BLEU"
+  ],
+  "tensorboard_log_dir_dated": "tensorboard/Apr-24_17-46-43",
+  "save_data": "data",
+  "training": {
+    "average_decay": 0.0001,
+    "normalization": "tokens",
+    "save_checkpoint_steps": 5000,
+    "accum_steps": [
+      0
+    ],
+    "warmup_steps": 4000,
+    "compute_dtype": "torch.float16",
+    "batch_size": 8000,
+    "adam_beta2": 0.998,
+    "batch_type": "tokens",
+    "dropout": [
+      0.1
+    ],
+    "max_grad_norm": 0.0,
+    "valid_batch_size": 4096,
+    "attention_dropout": [
+      0.1
+    ],
+    "param_init_method": "xavier_uniform",
+    "accum_count": [
+      10
+    ],
+    "bucket_size": 128000,
+    "learning_rate": 2.0,
+    "num_workers": 0,
+    "gpu_ranks": [
+      0
+    ],
+    "valid_steps": 5000,
+    "decay_method": "noam",
+    "keep_checkpoint": 4,
+    "train_steps": 100000,
+    "batch_size_multiple": 8,
+    "dropout_steps": [
+      0
+    ],
+    "label_smoothing": 0.1,
+    "world_size": 1,
+    "optim": "adamw",
+    "prefetch_factor": 32,
+    "model_path": "quickmt-hi-en-eole-model"
+  },
+  "model": {
+    "position_encoding_type": "SinusoidalInterleaved",
+    "heads": 8,
+    "share_decoder_embeddings": false,
+    "transformer_ff": 4096,
+    "share_embeddings": false,
+    "architecture": "transformer",
+    "hidden_size": 1024,
+    "encoder": {
+      "src_word_vec_size": 1024,
+      "position_encoding_type": "SinusoidalInterleaved",
+      "heads": 8,
+      "transformer_ff": 4096,
+      "n_positions": null,
+      "encoder_type": "transformer",
+      "hidden_size": 1024,
+      "layers": 8
+    },
+    "embeddings": {
+      "tgt_word_vec_size": 1024,
+      "src_word_vec_size": 1024,
+      "word_vec_size": 1024,
+      "position_encoding_type": "SinusoidalInterleaved"
+    },
+    "decoder": {
+      "position_encoding_type": "SinusoidalInterleaved",
+      "tgt_word_vec_size": 1024,
+      "heads": 8,
+      "transformer_ff": 4096,
+      "n_positions": null,
+      "decoder_type": "transformer",
+      "hidden_size": 1024,
+      "layers": 2
+    }
+  },
+  "transforms_configs": {
+    "filtertoolong": {
+      "src_seq_length": 256,
+      "tgt_seq_length": 256
+    },
+    "sentencepiece": {
+      "tgt_subword_model": "${MODEL_PATH}/en.spm.model",
+      "src_subword_model": "${MODEL_PATH}/hi.spm.model"
+    }
+  },
+  "data": {
+    "corpus_1": {
+      "transforms": [
+        "sentencepiece",
+        "filtertoolong"
+      ],
+      "path_align": null,
+      "path_src": "train.hi",
+      "path_tgt": "train.en"
+    },
+    "valid": {
+      "transforms": [
+        "sentencepiece",
+        "filtertoolong"
+      ],
+      "path_align": null,
+      "path_src": "dev.hi",
+      "path_tgt": "dev.en"
+    }
+  }
+}

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