mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 05:10:06 +06:00
136 lines
4.7 KiB
Python
Executable File
136 lines
4.7 KiB
Python
Executable File
#!/usr/bin/env python
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# Usage:
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# ./gen-card-facebook-wmt19.py
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import os
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from pathlib import Path
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def write_model_card(model_card_dir, src_lang, tgt_lang):
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texts = {
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"en": "Machine learning is great, isn't it?",
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"ru": "Машинное обучение - это здорово, не так ли?",
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"de": "Maschinelles Lernen ist großartig, oder?",
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}
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# BLUE scores as follows:
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# "pair": [fairseq, transformers]
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scores = {
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"ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"],
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"en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"],
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"en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"],
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"de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"],
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}
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pair = f"{src_lang}-{tgt_lang}"
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readme = f"""
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---
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<!-- This file has been auto-generated by src/transformers/convert_fsmt_original_pytorch_checkpoint_to_pytorch.py - DO NOT EDIT or your changes will be lost -->
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language: {src_lang}, {tgt_lang}
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thumbnail:
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tags:
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- translation
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- wmt19
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license: Apache 2.0
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datasets:
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- http://www.statmt.org/wmt19/ ([test-set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561))
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metrics:
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- http://www.statmt.org/wmt19/metrics-task.html
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---
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# FSMT
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## Model description
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This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.
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For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).
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The abbreviation FSMT stands for FairSeqMachineTranslation
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All four models are available:
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* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
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* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
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* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
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* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)
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## Intended uses & limitations
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#### How to use
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```python
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from transformers.tokenization_fsmt import FSMTTokenizer
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from transformers.modeling_fsmt import FSMTForConditionalGeneration
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mname = "facebook/wmt19-{src_lang}-{tgt_lang}"
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tokenizer = FSMTTokenizer.from_pretrained(mname)
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model = FSMTForConditionalGeneration.from_pretrained(mname)
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input = "{texts[src_lang]}
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input_ids = tokenizer.encode(input, return_tensors="pt")
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outputs = model.generate(input_ids)
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decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(decoded) # {texts[tgt_lang]}
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```
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#### Limitations and bias
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- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)
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## Training data
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Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).
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## Eval results
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pair | fairseq | transformers
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-------|---------|----------
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{pair} | {scores[pair][0]} | {scores[pair][1]}
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The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:
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- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
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- re-ranking
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The score was calculated using this code:
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```bash
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git clone https://github.com/huggingface/transformers
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cd transformers
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export PAIR={pair}
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export DATA_DIR=data/$PAIR
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export SAVE_DIR=data/$PAIR
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export BS=8
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export NUM_BEAMS=15
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mkdir -p $DATA_DIR
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sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
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sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
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echo $PAIR
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PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
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```
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note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.
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## TODO
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- port model ensemble (fairseq uses 4 model checkpoints)
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"""
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os.makedirs(model_card_dir, exist_ok=True)
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path = os.path.join(model_card_dir, "README.md")
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print(f"Generating {path}")
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with open(path, "w", encoding="utf-8") as f:
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f.write(readme)
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# make sure we are under the root of the project
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repo_dir = Path(__file__).resolve().parent.parent.parent
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model_cards_dir = repo_dir / "model_cards"
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for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
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base, src_lang, tgt_lang = model_name.split("-")
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model_card_dir = model_cards_dir / "facebook" / model_name
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write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
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