111 lines
3.2 KiB
Markdown
111 lines
3.2 KiB
Markdown
|
|
---
|
|
language:
|
|
- en
|
|
thumbnail:
|
|
tags:
|
|
- translation
|
|
- facebook
|
|
- convAI
|
|
license: apache-2.0
|
|
datasets:
|
|
- blended_skill_talk
|
|
metrics:
|
|
- perplexity
|
|
---
|
|
|
|
# Blenderbot-3B
|
|
|
|
## Model description
|
|
|
|
|
|
+ [Paper](https://arxiv.org/abs/1907.06616).
|
|
+ [Original PARLAI Code]
|
|
|
|
The abbreviation FSMT stands for FairSeqMachineTranslation
|
|
|
|
All four models are available:
|
|
|
|
* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
|
|
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
|
|
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
|
|
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)
|
|
|
|
## Intended uses & limitations
|
|
|
|
#### How to use
|
|
|
|
```python
|
|
from transformers.tokenization_fsmt import FSMTTokenizer
|
|
from transformers.modeling_fsmt import FSMTForConditionalGeneration
|
|
mname = "facebook/wmt19-en-ru"
|
|
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) # Машинное обучение - это здорово, не так ли?
|
|
|
|
```
|
|
|
|
#### Limitations and bias
|
|
|
|
- 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)
|
|
|
|
## Training data
|
|
|
|
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).
|
|
|
|
## Eval results
|
|
|
|
pair | fairseq | transformers
|
|
-------|---------|----------
|
|
en-ru | [36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724) | 33.47
|
|
|
|
The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:
|
|
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
|
|
- re-ranking
|
|
|
|
The score was calculated using this code:
|
|
|
|
```bash
|
|
git clone https://github.com/huggingface/transformers
|
|
cd transformers
|
|
export PAIR=en-ru
|
|
export DATA_DIR=data/$PAIR
|
|
export SAVE_DIR=data/$PAIR
|
|
export BS=8
|
|
export NUM_BEAMS=15
|
|
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 $PAIR
|
|
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$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
|
|
```
|
|
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.
|
|
|
|
## Data Sources
|
|
|
|
- [training, etc.](http://www.statmt.org/wmt19/)
|
|
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)
|
|
|
|
|
|
### BibTeX entry and citation info
|
|
|
|
```bibtex
|
|
@inproceedings{...,
|
|
year={2020},
|
|
title={Facebook FAIR's WMT19 News Translation Task Submission},
|
|
author={Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey},
|
|
booktitle={Proc. of WMT},
|
|
}
|
|
```
|
|
|
|
|
|
## TODO
|
|
|
|
- port model ensemble (fairseq uses 4 model checkpoints)
|
|
|