Delete .ipynb_checkpoints

.ipynb_checkpoints/README-checkpoint.md

@@ -1,120 +0,0 @@
----
-language:
-- en
-- zh
-tags:
-- translation
-license: cc-by-4.0
-datasets:
-- quickmt/quickmt-train.zh-en
-- quickmt/madlad400-en-backtranslated-zh
-- quickmt/newscrawl2024-en-backtranslated-zh
-model-index:
-- name: quickmt-zh-en
-  results:
-  - task:
-      name: Translation zho-eng
-      type: translation
-      args: zho-eng
-    dataset:
-      name: flores101-devtest
-      type: flores_101
-      args: zho_Hans eng_Latn devtest
-    metrics:
-    - name: BLEU
-      type: bleu
-      value: 30.0
-    - name: CHRF
-      type: chrf
-      value: 58.42
-    - name: COMET
-      type: comet
-      value: 86.72
----
-
-
-# `quickmt-zh-en` Neural Machine Translation Model 
-
-`quickmt-zh-en` is a reasonably fast and reasonably accurate neural machine translation model for translation from `zh` into `en`.
-
-`quickmt` models are roughly 3 times faster for GPU inference than OpusMT models and roughly [40 times](https://huggingface.co/spaces/quickmt/quickmt-vs-libretranslate) faster than [LibreTranslate](https://huggingface.co/spaces/quickmt/quickmt-vs-libretranslate)/[ArgosTranslate](github.com/argosopentech/argos-translate). 
-
-
-## *UPDATED VERSION!* 
-
-This model was trained with back-translated data and has improved translation quality!
-
-* https://huggingface.co/datasets/quickmt/madlad400-en-backtranslated-zh
-* https://huggingface.co/datasets/quickmt/newscrawl2024-en-backtranslated-zh
-
-
-## 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 seq2seq transformer
-* 32k separate Sentencepiece vocabs
-* Exported for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format
-* The pytorch model (for use with [`eole`](https://github.com/eole-nlp/eole)) is available in this repository in the `eole-model` folder
-
-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 -e ./quickmt/
-
-quickmt-model-download quickmt/quickmt-zh-en ./quickmt-zh-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-zh-en/", device="auto")
-
-# Translate - set beam size to 1 for faster speed (but lower quality)
-sample_text = '埃胡德·乌尔博士（新斯科舍省哈利法克斯市达尔豪西大学医学教授，加拿大糖尿病协会临床与科学部门教授）提醒，这项研究仍处在早期阶段。'
-
-t(sample_text, beam_size=5)
-```
-
-> 'Dr. Ehud Ur (Professor of Medicine, Dalhousie University, Halifax, Nova Scotia, and Professor of Clinical and Scientific Division, Canadian Diabetes Association) cautions that the study is still at an early stage.'
-
-```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 Elhoud (Professor of Medicine at Dalhousie University, Halifax, Nova Scotia, and professor of clinical and scientific Division of the Canadian Diabetes Association) cautions that the study is still at an early stage.'
-
-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) ("zho_Hans"->"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.
-
-
-|                                  |   bleu |   chrf2 |   comet22 |   Time (s) |
-|:---------------------------------|-------:|--------:|----------:|-----------:|
-| quickmt/quickmt-zh-en            |  30.0  |   58.42 |     86.72 |       1.10 |
-| Helsinki-NLP/opus-mt-zh-en       |  22.99 |   53.98 |     84.6  |       3.73 |
-| facebook/nllb-200-distilled-600M |  26.02 |   55.27 |     85.1  |      21.69 |
-| facebook/nllb-200-distilled-1.3B |  28.61 |   57.43 |     86.22 |      37.55 |
-| facebook/m2m100_418M             |  19.55 |   50.83 |     82.04 |      18.2  |
-| facebook/m2m100_1.2B             |  24.9  |   54.89 |     85.1  |      35.49 |
-

.ipynb_checkpoints/eole-config-checkpoint.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_small
-
-### Vocab
-src_vocab: zh.eole.vocab
-tgt_vocab: en.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.zh-en/zh
-        path_tgt: hf://quickmt/quickmt-train.zh-en/en
-        path_sco: hf://quickmt/quickmt-train.zh-en/sco
-        weight: 2
-    corpus_2:
-        path_src: hf://quickmt/newscrawl2024-en-backtranslated-zh/zh
-        path_tgt: hf://quickmt/newscrawl2024-en-backtranslated-zh/en
-        path_sco: hf://quickmt/newscrawl2024-en-backtranslated-zh/sco
-        weight: 1
-    corpus_3:
-        path_src: hf://quickmt/madlad400-en-backtranslated-zh/zh
-        path_tgt: hf://quickmt/madlad400-en-backtranslated-zh/en
-        path_sco: hf://quickmt/madlad400-en-backtranslated-zh/sco
-        weight: 2
-    valid:
-        path_src: valid.zh
-        path_tgt: valid.en
-
-transforms: [sentencepiece, filtertoolong]
-transforms_configs:
-  sentencepiece:
-    src_subword_model: "zh.spm.model"
-    tgt_subword_model: "en.spm.model"
-  filtertoolong:
-    src_seq_length: 256
-    tgt_seq_length: 256
-
-training:
-    # Run configuration
-    model_path: quickmt-zh-en-eole-model
-    keep_checkpoint: 4
-    train_steps: 200000
-    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: [20]
-    accum_steps: [0]
-
-    # Optimizer & Compute
-    compute_dtype: "fp16"
-    optim: "adamw"
-    #use_amp: False
-    learning_rate: 3.0
-    warmup_steps: 5000
-    decay_method: "noam"
-    adam_beta2: 0.998
-
-    # Data loading
-    bucket_size: 256000
-    num_workers: 4
-    prefetch_factor: 64
-    
-    # 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
-    add_estimator: false
-    add_ffnbias: true
-    add_qkvbias: false
-    layer_norm: standard
-    mlp_activation_fn: gelu
-    hidden_size: 768
-    encoder:
-        layers: 12
-    decoder:
-        layers: 2
-    heads: 8
-    transformer_ff: 4096
-    embeddings:
-        word_vec_size: 768
-        position_encoding_type: "SinusoidalInterleaved"
-