[model cards] ported allenai Deep Encoder, Shallow Decoder models (#7153)

* [model cards] ported allenai Deep Encoder, Shallow Decoder models

* typo

* fix references

* add allenai/wmt19-de-en-6-6 model cards

* fill-in the missing info for the build script as provided by the searcher.

model_cards/allenai/wmt16-en-de-12-1/README.md

+
+---
+
+language: en, de
+thumbnail:
+tags:
+- translation
+- wmt16
+- allenai
+license: Apache 2.0
+datasets:
+- http://www.statmt.org/wmt16/ ([test-set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372))
+
+metrics:
+- http://www.statmt.org/wmt16/metrics-task.html
+---
+
+# FSMT
+
+## Model description
+
+This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for en-de.
+
+For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
+
+All 3 models are available:
+
+* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
+* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
+* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)
+
+```
+@misc{kasai2020deep,
+    title={Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation},
+    author={Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith},
+    year={2020},
+    eprint={2006.10369},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+from transformers.tokenization_fsmt import FSMTTokenizer
+from transformers.modeling_fsmt import FSMTForConditionalGeneration
+mname = "allenai/wmt16-en-de-12-1"
+tokenizer = FSMTTokenizer.from_pretrained(mname)
+model = FSMTForConditionalGeneration.from_pretrained(mname)
+
+input = "Machine learning is great, isn't it?"
+input_ids = tokenizer.encode(input, return_tensors="pt")
+outputs = model.generate(input_ids)
+decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(decoded) # Maschinelles Lernen ist großartig, nicht wahr?
+
+```
+
+#### Limitations and bias
+
+
+## Training data
+
+Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).
+
+## Eval results
+
+Here are the BLEU scores:
+
+model   | fairseq | transformers
+-------|---------|----------
+wmt16-en-de-12-1  | 26.9 | 25.75
+
+The score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.
+
+The score was calculated using this code:
+
+```bash
+git clone https://github.com/huggingface/transformers
+cd transformers
+export PAIR=en-de
+export DATA_DIR=data/$PAIR
+export SAVE_DIR=data/$PAIR
+export BS=8
+export NUM_BEAMS=5
+mkdir -p $DATA_DIR
+sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
+sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
+echo $PAIR
+PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/wmt16-en-de-12-1 $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
+```
+

model_cards/allenai/wmt16-en-de-dist-12-1/README.md

+
+---
+
+language: en, de
+thumbnail:
+tags:
+- translation
+- wmt16
+- allenai
+license: Apache 2.0
+datasets:
+- http://www.statmt.org/wmt16/ ([test-set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372))
+
+metrics:
+- http://www.statmt.org/wmt16/metrics-task.html
+---
+
+# FSMT
+
+## Model description
+
+This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for en-de.
+
+For more details, please see, [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
+
+All 3 models are available:
+
+* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
+* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
+* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)
+
+```
+@misc{kasai2020deep,
+    title={Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation},
+    author={Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith},
+    year={2020},
+    eprint={2006.10369},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+from transformers.tokenization_fsmt import FSMTTokenizer
+from transformers.modeling_fsmt import FSMTForConditionalGeneration
+mname = "allenai/wmt16-en-de-dist-12-1"
+tokenizer = FSMTTokenizer.from_pretrained(mname)
+model = FSMTForConditionalGeneration.from_pretrained(mname)
+
+input = "Machine learning is great, isn't it?"
+input_ids = tokenizer.encode(input, return_tensors="pt")
+outputs = model.generate(input_ids)
+decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(decoded) # Maschinelles Lernen ist großartig, nicht wahr?
+
+```
+
+#### Limitations and bias
+
+
+## Training data
+
+Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).
+
+## Eval results
+
+Here are the BLEU scores:
+
+model   | fairseq | transformers
+-------|---------|----------
+wmt16-en-de-dist-12-1  | 28.3 | 27.52
+
+The score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.
+
+The score was calculated using this code:
+
+```bash
+git clone https://github.com/huggingface/transformers
+cd transformers
+export PAIR=en-de
+export DATA_DIR=data/$PAIR
+export SAVE_DIR=data/$PAIR
+export BS=8
+export NUM_BEAMS=5
+mkdir -p $DATA_DIR
+sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
+sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
+echo $PAIR
+PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/wmt16-en-de-dist-12-1 $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
+```
+

model_cards/allenai/wmt16-en-de-dist-6-1/README.md

+
+---
+
+language: en, de
+thumbnail:
+tags:
+- translation
+- wmt16
+- allenai
+license: Apache 2.0
+datasets:
+- http://www.statmt.org/wmt16/ ([test-set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372))
+
+metrics:
+- http://www.statmt.org/wmt16/metrics-task.html
+---
+
+# FSMT
+
+## Model description
+
+This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for en-de.
+
+For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
+
+All 3 models are available:
+
+* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
+* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
+* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)
+
+```
+@misc{kasai2020deep,
+    title={Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation},
+    author={Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith},
+    year={2020},
+    eprint={2006.10369},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+from transformers.tokenization_fsmt import FSMTTokenizer
+from transformers.modeling_fsmt import FSMTForConditionalGeneration
+mname = "allenai/wmt16-en-de-dist-6-1"
+tokenizer = FSMTTokenizer.from_pretrained(mname)
+model = FSMTForConditionalGeneration.from_pretrained(mname)
+
+input = "Machine learning is great, isn't it?"
+input_ids = tokenizer.encode(input, return_tensors="pt")
+outputs = model.generate(input_ids)
+decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(decoded) # Maschinelles Lernen ist großartig, nicht wahr?
+
+```
+
+#### Limitations and bias
+
+
+## Training data
+
+Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).
+
+## Eval results
+
+Here are the BLEU scores:
+
+model   | fairseq | transformers
+-------|---------|----------
+wmt16-en-de-dist-6-1  | 27.4 | 27.11
+
+The score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.
+
+The score was calculated using this code:
+
+```bash
+git clone https://github.com/huggingface/transformers
+cd transformers
+export PAIR=en-de
+export DATA_DIR=data/$PAIR
+export SAVE_DIR=data/$PAIR
+export BS=8
+export NUM_BEAMS=5
+mkdir -p $DATA_DIR
+sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
+sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
+echo $PAIR
+PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/wmt16-en-de-dist-6-1 $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
+```
+

model_cards/allenai/wmt19-de-en-6-6-base/README.md

+
+---
+
+language: de, en
+thumbnail:
+tags:
+- translation
+- wmt19
+- allenai
+license: Apache 2.0
+datasets:
+- http://www.statmt.org/wmt19/ ([test-set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561))
+metrics:
+- http://www.statmt.org/wmt19/metrics-task.html
+---
+
+# FSMT
+
+## Model description
+
+This is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for de-en.
+
+For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
+
+2 models are available:
+
+* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
+* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)
+
+```
+@misc{kasai2020deep,
+    title={Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation},
+    author={Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith},
+    year={2020},
+    eprint={2006.10369},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+from transformers.tokenization_fsmt import FSMTTokenizer
+from transformers.modeling_fsmt import FSMTForConditionalGeneration
+mname = "allenai/wmt19-de-en-6-6-base"
+tokenizer = FSMTTokenizer.from_pretrained(mname)
+model = FSMTForConditionalGeneration.from_pretrained(mname)
+
+input = "Maschinelles Lernen ist großartig, nicht wahr?"
+input_ids = tokenizer.encode(input, return_tensors="pt")
+outputs = model.generate(input_ids)
+decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(decoded) # Machine learning is great, isn't it?
+
+```
+
+#### Limitations and bias
+
+
+## Training data
+
+Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).
+
+## Eval results
+
+Here are the BLEU scores:
+
+model   |  transformers
+-------|---------|----------
+wmt19-de-en-6-6-base  |  38.37
+
+The score was calculated using this code:
+
+```bash
+git clone https://github.com/huggingface/transformers
+cd transformers
+export PAIR=de-en
+export DATA_DIR=data/$PAIR
+export SAVE_DIR=data/$PAIR
+export BS=8
+export NUM_BEAMS=5
+mkdir -p $DATA_DIR
+sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
+sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
+echo $PAIR
+PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/wmt19-de-en-6-6-base $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
+```
+

model_cards/allenai/wmt19-de-en-6-6-big/README.md

+
+---
+
+language: de, en
+thumbnail:
+tags:
+- translation
+- wmt19
+- allenai
+license: Apache 2.0
+datasets:
+- http://www.statmt.org/wmt19/ ([test-set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561))
+metrics:
+- http://www.statmt.org/wmt19/metrics-task.html
+---
+
+# FSMT
+
+## Model description
+
+This is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for de-en.
+
+For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
+
+2 models are available:
+
+* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
+* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)
+
+```
+@misc{kasai2020deep,
+    title={Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation},
+    author={Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith},
+    year={2020},
+    eprint={2006.10369},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+from transformers.tokenization_fsmt import FSMTTokenizer
+from transformers.modeling_fsmt import FSMTForConditionalGeneration
+mname = "allenai/wmt19-de-en-6-6-big"
+tokenizer = FSMTTokenizer.from_pretrained(mname)
+model = FSMTForConditionalGeneration.from_pretrained(mname)
+
+input = "Maschinelles Lernen ist großartig, nicht wahr?"
+input_ids = tokenizer.encode(input, return_tensors="pt")
+outputs = model.generate(input_ids)
+decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(decoded) # Machine learning is great, isn't it?
+
+```
+
+#### Limitations and bias
+
+
+## Training data
+
+Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).
+
+## Eval results
+
+Here are the BLEU scores:
+
+model   |  transformers
+-------|---------|----------
+wmt19-de-en-6-6-big  |  39.9
+
+The score was calculated using this code:
+
+```bash
+git clone https://github.com/huggingface/transformers
+cd transformers
+export PAIR=de-en
+export DATA_DIR=data/$PAIR
+export SAVE_DIR=data/$PAIR
+export BS=8
+export NUM_BEAMS=5
+mkdir -p $DATA_DIR
+sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
+sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
+echo $PAIR
+PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/wmt19-de-en-6-6-big $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
+```
+

scripts/fsmt/gen-card-allenai-wmt19.py

@@ -42,13 +42,26 @@ metrics:
 
 ## Model description
 
-This is a ported version of fairseq-based wmt19 transformer created by [jungokasai]](https://github.com/jungokasai/) @ allenai for {src_lang}-{tgt_lang}.
+This is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.
+
+For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
 
 2 models are available:
 
 * [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
 * [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)
 
+```
+@misc{{kasai2020deep,
+    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
+    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
+    year={{2020}},
+    eprint={{2006.10369}},
+    archivePrefix={{arXiv}},
+    primaryClass={{cs.CL}}
+}}
+```
+
 ## Intended uses & limitations
 
 #### How to use
@@ -73,7 +86,7 @@ print(decoded) # {texts[tgt_lang]}
 
 ## Training data
 
-Pretrained weights were left identical to the original model released by the researcher.
+Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).
 
 ## Eval results