Comments (4)
There is a mismatch between unbabel-comet==1.1.3
and the current master branch.
If you are using version 1.1.3 you can't pass a list of training files.. the config is just:
ranking_metric:
class_path: comet.models.RankingMetric
init_args:
nr_frozen_epochs: 0.3
keep_embeddings_frozen: True
optimizer: AdamW
encoder_learning_rate: 5.0e-06
learning_rate: 1.5e-05
layerwise_decay: 0.95
encoder_model: XLM-RoBERTa
pretrained_model: xlm-roberta-base
pool: avg
layer: mix
dropout: 0.1
batch_size: 4
train_data: /MT-work/COMET/data/apequest/train.csv
validation_data:
- /MT-work/COMET/data/apequest/test.csv
trainer: /MT-work/COMET/configs/trainer.yaml
early_stopping: /MT-work/COMET/configs/early_stopping.yaml
model_checkpoint: /MT-work/COMET/configs/model_checkpoint.yaml
from comet.
Hi @ricardorei
Thanks for the explanation.
I just pulled the latest version.
git clone https://github.com/Unbabel/COMET
The error has not changed
from comet.
Hi @sdlmw I just tested the code on master and everything is working fine.
Here is my configs:
ranking_metric:
class_path: comet.models.RankingMetric
init_args:
nr_frozen_epochs: 0.3
keep_embeddings_frozen: True
optimizer: AdamW
encoder_learning_rate: 1.0e-06
learning_rate: 1.5e-05
layerwise_decay: 0.95
encoder_model: XLM-RoBERTa
pretrained_model: xlm-roberta-base
pool: avg
layer: mix
layer_transformation: sparsemax
layer_norm: False
dropout: 0.1
batch_size: 4
train_data:
- tests/data/ranking_data.csv
validation_data:
- tests/data/ranking_data.csv
trainer: ../trainer.yaml
early_stopping: ../early_stopping.yaml
model_checkpoint: ../model_checkpoint.yaml
and for the trainer.yaml:
class_path: pytorch_lightning.trainer.trainer.Trainer
init_args:
accelerator: gpu
devices: 1
accumulate_grad_batches: 4
amp_backend: native
amp_level: null
auto_lr_find: False
auto_scale_batch_size: False
auto_select_gpus: False
benchmark: null
check_val_every_n_epoch: 1
default_root_dir: null
deterministic: False
fast_dev_run: False
gradient_clip_val: 1.0
gradient_clip_algorithm: norm
limit_train_batches: 1.0
limit_val_batches: 1.0
limit_test_batches: 1.0
limit_predict_batches: 1.0
log_every_n_steps: 50
profiler: null
overfit_batches: 0
plugins: null
precision: 16
max_epochs: 4
min_epochs: 1
max_steps: -1
min_steps: null
max_time: null
num_nodes: 1
num_sanity_val_steps: 10
reload_dataloaders_every_n_epochs: 0
replace_sampler_ddp: True
sync_batchnorm: False
detect_anomaly: False
tpu_cores: null
track_grad_norm: -1
val_check_interval: 1.0
enable_model_summary: True
move_metrics_to_cpu: True
multiple_trainloader_mode: max_size_cycle
from comet.
note that the data I am using is in the tests folder. Make sure that the data you are using for the ranking model is in the same shape
from comet.
Related Issues (20)
- [QUESTION] default model is not update? HOT 2
- Version 2.0 HOT 2
- Specifying GPU ID for inference HOT 4
- Models not accessible HOT 2
- Inefficient _layer_norm implementation in layerwise_attention.py HOT 1
- [QUESTION]__init__.py generates a wrong path for hparams.yaml in Windows HOT 6
- tensor_lru_cache is limited to tensors with at least 2-Dimensions HOT 5
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- Can't reproduce Cometinho model scores HOT 3
- Do system scores above 100 really "differ"? HOT 4
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- Support pandas 2
- [QUESTION] How to finetune `wmt22-comet-da` and have results scaled to 0-1 range HOT 1
- UnifiedMetric test failing: test_multitask_with_references HOT 1
- Segmentation error when tring to reproduce wmt22 results HOT 1
- How to reproduce Unbabel/wmt22-comet-da model HOT 2
- Evaluate lines with newline characters
- [QUESTION] Does COMET support Scoring multiple refs like scarebleu? HOT 2
- [QUESTION] How are the older models supposed to be used?
- v1.x and v2.x have different scores for wmt20-comet-qe-da model HOT 2
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