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/precomputed_pseudo_labels.md
### Saved Pseudo-Labels | |
These are the generations of various large models on various large **training** sets. All in all they took about 200 GPU hours to produce. | |
### Available Pseudo-labels | |
| Dataset | Model | Link | Rouge Scores | Notes | |
|---------|-----------------------------|----------------------------------------------------------------------------------------|--------------------|------------------------------------------------------------------------------------------------------------- | |
| XSUM | `facebook/bart-large-xsum` | [download](https://cdn-datasets.huggingface.co/pseudo/xsum/bart_xsum_pl.tgz) | 49.8/28.0/42.5 | | |
| XSUM | `google/pegasus-xsum` | [download](https://cdn-datasets.huggingface.co/pseudo/xsum/pegasus_xsum.tgz) | 53.3/32.7/46.5 | | |
| XSUM | `facebook/bart-large-xsum` | [download](https://cdn-datasets.huggingface.co/pseudo/xsum/xsum_pl2_bart.tgz) | | Bart pseudolabels filtered to those with Rouge2 > 10.0 w GT. | |
| CNN/DM | `sshleifer/pegasus-cnn-ft-v2` | [download](https://cdn-datasets.huggingface.co/pseudo/cnn_dm/pegasus_cnn_cnn_pls.tgz) | 47.316/26.65/44.56 | do not worry about the fact that train.source is one line shorter. | |
| CNN/DM | `facebook/bart-large-cnn` | [download](https://cdn-datasets.huggingface.co/pseudo/cnn_dm/cnn_bart_pl.tgz) | | 5K (2%) are missing, there should be 282173 | |
| CNN/DM | `google/pegasus-xsum` | [download](https://cdn-datasets.huggingface.co/pseudo/cnn_dm/pegasus_xsum_on_cnn.tgz) | 21.5/6.76/25 | extra labels for xsum distillation Used max_source_length=512, (and all other pegasus-xsum configuration). | |
| EN-RO | `Helsinki-NLP/opus-mt-en-ro` | [download](https://cdn-datasets.huggingface.co/pseudo/wmt_en_ro/opus_mt_en_ro.tgz) | | | |
| EN-RO | `facebook/mbart-large-en-ro` | [download](https://cdn-datasets.huggingface.co/pseudo/wmt_en_ro/mbart_large_en_ro.tgz) | | | |
(EN_RO = WMT 2016 English-Romanian). | |
Example Download Command: | |
```bash | |
curl -S https://cdn-datasets.huggingface.co/pseudo/xsum/bart_xsum_pl.tgz | tar -xvz -C . | |
``` | |
### Generating New Pseudolabels | |
Here is the command I used to generate the pseudolabels in the second row of the table, after downloading XSUM from [here](https://cdn-datasets.huggingface.co/summarization/xsum.tar.gz). | |
```bash | |
python -m torch.distributed.launch --nproc_per_node=8 run_distributed_eval.py \ | |
--model_name google/pegasus-xsum \ | |
--save_dir pegasus_xsum \ | |
--data_dir xsum \ | |
--bs 8 --sync_timeout 60000 \ | |
--max_source_length 512 \ | |
--type_path train | |
``` | |
+ These commands takes a while to run. For example, `pegasus_cnn_cnn_pls.tgz` took 8 hours on 8 GPUs. | |
+ Pegasus does not work in fp16 :(, Bart, mBART and Marian do. | |
+ Even if you have 1 GPU, `run_distributed_eval.py` is 10-20% faster than `run_eval.py` because it uses `SortishSampler` to minimize padding computation. | |
### Contributions | |
Feel free to contribute your own pseudolabels via PR. Add a row to this table with a new google drive link (or other command line downloadable link). | |