Delete .ipynb_checkpoints

.ipynb_checkpoints/EADME-checkpoint.md

@@ -1,116 +0,0 @@
----
-language:
-- ar
-- en
-tags:
-- translation
-license: cc-by-4.0
-datasets:
-- quickmt/quickmt-train.ar-en
-- quickmt/madlad400-en-backtranslated-ar
-- quickmt/newscrawl2024-en-backtranslated-ar
-model-index:
-- name: quickmt-ar-en
-  results:
-  - task:
-      name: Translation arb-eng
-      type: translation
-      args: arb-eng
-    dataset:
-      name: flores101-devtest
-      type: flores_101
-      args: arb_Arab eng_Latn devtest
-    metrics:
-    - name: BLEU
-      type: bleu
-      value: 44.11
-    - name: CHRF
-      type: chrf
-      value: 67.96
-    - name: COMET
-      type: comet
-      value: 87.64
----
-
-
-# `quickmt-ar-en` Neural Machine Translation Model 
-
-`quickmt-ar-en` is a reasonably fast and reasonably accurate neural machine translation model for translation from `ar` 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!
-
-
-## 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-ar-en ./quickmt-ar-en
-```
-
-Finally use the model in python:
-
-```python
-from quickmt import Translator
-
-# Auto-detects GPU, set to "cpu" to force CPU inference
-mt = Translator("./quickmt-ar-en/", device="auto")
-
-# Translate - set beam size to 1 for faster speed (but lower quality)
-sample_text = 'نبه الدكتور إيهود أور -أستاذ الطب في جامعة دالهوزي في هاليفاكس، نوفا سكوتيا ورئيس الشعبة الطبية والعلمية في الجمعية الكندية للسكري- إلى أن البحث لا يزال في أيامه الأولى.'
-
-mt(sample_text, beam_size=5)
-```
-
-> 'Dr. Ehud Orr, a professor of medicine at Dalhousie University in Halifax, Nova Scotia and head of the medical and scientific division of the Canadian Diabetes Association, warned that the research is still in its early days.'
-
-```python
-# Get alternative translations by sampling
-# You can pass any cTranslate2 `translate_batch` arguments
-mt([sample_text], sampling_temperature=1.2, beam_size=1, sampling_topk=50, sampling_topp=0.9)
-```
-
-> 'Dr. Ehr, professor of medicine at Dalhousie University in Halifax, Nova Scotia and chief of the medical and scientific division at 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`. 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). `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-ar-en            |  44.11 |   67.96 |     87.64 |       1.11 |
-| Helsinki-NLP/opus-mt-ar-en       |  34.22 |   61.26 |     84.5  |       3.67 |
-| facebook/nllb-200-distilled-600M |  39.13 |   64.14 |     86.22 |      21.76 |
-| facebook/nllb-200-distilled-1.3B |  42.29 |   66.55 |     87.55 |      37.7  |
-| facebook/m2m100_418M             |  29.41 |   57.68 |     82.21 |      18.53 |
-| facebook/m2m100_1.2B             |  29.77 |   56.7  |     80.77 |      36.23 |

.ipynb_checkpoints/README-checkpoint.md

@@ -1,100 +0,0 @@
----
-language:
-- en
-- ko
-tags:
-- translation
-license: cc-by-4.0
-datasets:
-- quickmt/quickmt-train.ar-en
-model-index:
-- name: quickmt-ar-en
-  results:
-  - task:
-      name: Translation ara-eng
-      type: translation
-      args: ara-eng
-    dataset:
-      name: flores101-devtest
-      type: flores_101
-      args: ara_Arab eng_Latn devtest
-    metrics:
-    - name: CHRF
-      type: chrf
-      value: 66.98
-    - name: BLEU
-      type: bleu
-      value: 42.79
-    - name: COMET
-      type: comet
-      value: 87.4
----
-
-
-# `quickmt-ar-en` Neural Machine Translation Model 
-
-`quickmt-ar-en` is a reasonably fast and reasonably accurate neural machine translation model for translation from `ar` 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.ar-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-ar-en ./quickmt-ar-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-ar-en/", device="auto")
-
-# Translate - set beam size to 5 for higher quality (but slower speed)
-sample_text = 'نبه الدكتور إيهود أور -أستاذ الطب في جامعة دالهوزي في هاليفاكس، نوفا سكوتيا ورئيس الشعبة الطبية والعلمية في الجمعية الكندية للسكري- إلى أن البحث لا يزال في أيامه الأولى.'
-t(sample_text, beam_size=5)
-
-> 'Dr. Ehud Orr, professor of medicine at Dalhousie University in Halifax, Nova Scotia and head of the medical and scientific division of the Canadian Diabetes Association, warned 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)
-
-> 'Professor of Medicine at Dalhousie University in Halifax, Nova Scotia and chairman of the Medical and Scientific Division at the Canadian Diabetes Society, cautioned that the research was 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) ("kor_Hang"->"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-ar-en            |  42.79 |   66.98 |     87.4  |       0.88 |
-| Helsink-NLP/opus-mt-ar-en        |  34.22 |   61.26 |     84.5  |       3.78 |
-| facebook/nllb-200-distilled-600M |  39.13 |   64.14 |     86.22 |      21.58 |
-| facebook/nllb-200-distilled-1.3B |  42.29 |   66.55 |     87.55 |      37.7  |
-| facebook/m2m100_418M             |  29.41 |   57.68 |     82.21 |      18.5  |
-| facebook/m2m100_1.2B             |  29.77 |   56.7  |     80.77 |      36.23 |
-
-`quickmt-ar-en` is the fastest and highest quality.

.ipynb_checkpoints/eole-config-checkpoint.yaml

@@ -1,104 +0,0 @@
-## IO
-save_data: ar-en/data
-overwrite: True
-seed: 1234
-report_every: 100
-valid_metrics: ["BLEU"]
-tensorboard: true
-tensorboard_log_dir: tensorboard
-
-### Vocab
-src_vocab: ar.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.ar-en/ar
-        path_tgt: hf://quickmt/quickmt-train.ar-en/en
-        path_sco: hf://quickmt/quickmt-train.ar-en/sco
-    valid:
-        path_src: flores-dev.ar
-        path_tgt: flores-dev.en
-
-transforms: [sentencepiece, filtertoolong]
-transforms_configs:
-  sentencepiece:
-    src_subword_model: "ar.spm.model"
-    tgt_subword_model: "en.spm.model"
-    src_subword_alpha: 0.5
-    src_subword_nbest: -1
-  filtertoolong:
-    src_seq_length: 256
-    tgt_seq_length: 256
-
-training:
-    # Run configuration
-    model_path: model
-    train_from: model
-    keep_checkpoint: 4
-    save_checkpoint_steps: 1000
-    train_steps: 200000
-    valid_steps: 1000
-    
-    # Train on a single GPU
-    world_size: 1
-    gpu_ranks: [0]
-
-    # Batching
-    batch_type: "tokens"
-    batch_size: 8192
-    valid_batch_size: 8192
-    batch_size_multiple: 8
-    accum_count: [16]
-    accum_steps: [0]
-
-    # Optimizer & Compute
-    compute_dtype: "fp16"
-    #use_amp: true
-    optim: "pagedadamw8bit"
-    learning_rate: 2.0
-    warmup_steps: 5000
-    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]
-    max_grad_norm: 2
-    label_smoothing: 0.1
-    average_decay: 0.0001
-    param_init_method: xavier_uniform
-    normalization: "tokens"
-
-model:
-    architecture: "transformer"
-    layer_norm: standard
-    share_embeddings: false
-    share_decoder_embeddings: true
-    add_ffnbias: true
-    mlp_activation_fn: gelu
-    add_estimator: false
-    add_qkvbias: false
-    norm_eps: 1e-6
-    hidden_size: 1024
-    encoder:
-        layers: 8
-    decoder:
-        layers: 2
-    heads: 8
-    transformer_ff: 4096
-    embeddings:
-        word_vec_size: 1024
-        position_encoding_type: "SinusoidalInterleaved"
-