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توضیحات

An awesome word alignment tool

ویژگی	مقدار
سیستم عامل	-
نام فایل	awesome-align-0.1.7
نام	awesome-align
نسخه کتابخانه	0.1.7
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	NeuLab
ایمیل نویسنده	zdou0830@gmail.com
آدرس صفحه اصلی	https://github.com/neulab/awesome-align
آدرس اینترنتی	https://pypi.org/project/awesome-align/
مجوز	BSD 3-Clause

## AWESOME: Aligning Word Embedding Spaces of Multilingual Encoders `awesome-align` is a tool that can extract word alignments from multilingual BERT (mBERT) [[Demo]](https://colab.research.google.com/drive/1205ubqebM0OsZa1nRgbGJBtitgHqIVv6?usp=sharing) and allows you to fine-tune mBERT on parallel corpora for better alignment quality (see [our paper](https://arxiv.org/abs/2101.08231) for more details). ### Dependencies First, you need to install the dependencies: ```bash pip install -r requirements.txt python setup.py install ``` ### Input format Inputs should be *tokenized* and each line is a source language sentence and its target language translation, separated by (` ||| `). You can see some examples in the `examples` folder. ### Extracting alignments Here is an example of extracting word alignments from multilingual BERT: ```bash DATA_FILE=/path/to/data/file MODEL_NAME_OR_PATH=bert-base-multilingual-cased OUTPUT_FILE=/path/to/output/file CUDA_VISIBLE_DEVICES=0 awesome-align \ --output_file=$OUTPUT_FILE \ --model_name_or_path=$MODEL_NAME_OR_PATH \ --data_file=$DATA_FILE \ --extraction 'softmax' \ --batch_size 32 ``` This produces outputs in the `i-j` Pharaoh format. A pair `i-j` indicates that the ith word (zero-indexed) of the source sentence is aligned to the jth word of the target sentence. You can set `--output_prob_file` if you want to obtain the alignment probability and set `--output_word_file` if you want to obtain the aligned word pairs (in the `src_word<sep>tgt_word` format). You can also set `--cache_dir` to specify where you want to cache multilingual BERT. You can also set `MODEL_NAME_OR_PATH` to the path of your fine-tuned model as shown below. ### Fine-tuning on parallel data If there is parallel data available, you can fine-tune embedding models on that data. Here is an example of fine-tuning mBERT that balances well between efficiency and effectiveness: ```bash TRAIN_FILE=/path/to/train/file EVAL_FILE=/path/to/eval/file OUTPUT_DIR=/path/to/output/directory CUDA_VISIBLE_DEVICES=0 awesome-train \ --output_dir=$OUTPUT_DIR \ --model_name_or_path=bert-base-multilingual-cased \ --extraction 'softmax' \ --do_train \ --train_tlm \ --train_so \ --train_data_file=$TRAIN_FILE \ --per_gpu_train_batch_size 2 \ --gradient_accumulation_steps 4 \ --num_train_epochs 1 \ --learning_rate 2e-5 \ --save_steps 4000 \ --max_steps 20000 \ --do_eval \ --eval_data_file=$EVAL_FILE ``` You can also fine-tune the model a bit longer with more training objectives for better quality: ```bash TRAIN_FILE=/path/to/train/file EVAL_FILE=/path/to/eval/file OUTPUT_DIR=/path/to/output/directory CUDA_VISIBLE_DEVICES=0 awesome-train \ --output_dir=$OUTPUT_DIR \ --model_name_or_path=bert-base-multilingual-cased \ --extraction 'softmax' \ --do_train \ --train_mlm \ --train_tlm \ --train_tlm_full \ --train_so \ --train_psi \ --train_data_file=$TRAIN_FILE \ --per_gpu_train_batch_size 2 \ --gradient_accumulation_steps 4 \ --num_train_epochs 1 \ --learning_rate 2e-5 \ --save_steps 10000 \ --max_steps 40000 \ --do_eval \ --eval_data_file=$EVAL_FILE ``` If you want high alignment recalls, you can turn on the `--train_co` option, but note that the alignment precisions may drop. You can set `--cache_dir` to specify where you want to cache multilingual BERT. ### Supervised settings In supervised settings where gold word alignments are available for your training data, you can incorporate the supervised signals into our self-training objective (`--train_so`) and here is an example command: ```bash TRAIN_FILE=/path/to/train/file TRAIN_GOLD_FILE=/path/to/train/gold/file OUTPUT_DIR=/path/to/output/directory CUDA_VISIBLE_DEVICES=0 awesome-train \ --output_dir=$OUTPUT_DIR \ --model_name_or_path=bert-base-multilingual-cased \ --extraction 'softmax' \ --do_train \ --train_so \ --train_data_file=$TRAIN_FILE \ --train_gold_file=$TRAIN_GOLD_FILE \ --per_gpu_train_batch_size 2 \ --gradient_accumulation_steps 4 \ --num_train_epochs 5 \ --learning_rate 1e-4 \ --save_steps 200 ``` See `examples/*.gold` for the example format of the gold alignments. You need to turn on the `--gold_one_index` option if the gold alignments are 1-indexed and you can turn on the `--ignore_possible_alignments` option if you want to ignore possible alignments. ### Model performance The following table shows the alignment error rates (AERs) of our models and popular statistical word aligners on five language pairs. The De-En, Fr-En, Ro-En datasets can be obtained following [this repo](https://github.com/lilt/alignment-scripts), the Ja-En data is from [this link](http://www.phontron.com/kftt/) and the Zh-En data is available at [this link](http://nlp.csai.tsinghua.edu.cn/~ly/systems/TsinghuaAligner/TsinghuaAligner.html). The best scores are in **bold**. | | De-En | Fr-En | Ro-En | Ja-En | Zh-En | | -| ------- | ------- | ------- | ------- | ------- | | [fast\_align](https://github.com/clab/fast_align) | 27.0 | 10.5 | 32.1 | 51.1 | 38.1 | | [eflomal](https://github.com/robertostling/eflomal) | 22.6 | 8.2 | 25.1 | 47.5 | 28.7 | | [Mgiza](https://github.com/moses-smt/mgiza) | 20.6 | 5.9 | 26.4 | 48.0 | 35.1 | | Ours (w/o fine-tuning, softmax) | 17.4 | 5.6 | 27.9 | 45.6 | 18.1 | | Ours (multilingually fine-tuned w/o `--train_co`, softmax) [[Download]](https://drive.google.com/file/d/1IcQx6t5qtv4bdcGjjVCwXnRkpr67eisJ/view?usp=sharing) | 15.2 | **4.1** | 22.6 | **37.4** | **13.4** | | Ours (multilingually fine-tuned w/ `--train_co`, softmax) [[Download]](https://drive.google.com/file/d/1IluQED1jb0rjITJtyj4lNMPmaRFyMslg/view?usp=sharing) | **15.1** | 4.5 | **20.7** | 38.4 | 14.5 | ### Citation If you use our tool, we'd appreciate if you cite the following paper: ``` @inproceedings{dou2021word, title={Word Alignment by Fine-tuning Embeddings on Parallel Corpora}, author={Dou, Zi-Yi and Neubig, Graham}, booktitle={Conference of the European Chapter of the Association for Computational Linguistics (EACL)}, year={2021} } ``` ### Acknowledgements Some of the code is borrowed from [HuggingFace Transformers](https://github.com/huggingface/transformers) licensed under [Apache 2.0](https://github.com/huggingface/transformers/blob/master/LICENSE) and the entmax implementation is from [this repo](https://github.com/deep-spin/entmax).

نیازمندی

مقدار	نام
>=0.5.2	tokenizers
>=1.2.0	torch
-	tqdm
-	numpy
-	boto3
-	filelock
-	requests

نحوه نصب

نصب پکیج whl awesome-align-0.1.7:

pip install awesome-align-0.1.7.whl

نصب پکیج tar.gz awesome-align-0.1.7:

pip install awesome-align-0.1.7.tar.gz