RicardoRei
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WMT20 model
Browse files- README.md +1 -3
- checkpoints/model.ckpt +2 -2
- hparams.yaml +7 -7
README.md
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This is a [COMET](https://github.com/Unbabel/COMET) quality estimation model: It receives a source sentence and the respective translation and returns a score that reflects the quality of the translation.
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**NOTE:**
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- This model was recently replaced by an improved version [wmt22-cometkiwi-da](https://huggingface.co/Unbabel/wmt22-cometkiwi-da)
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- This model is equivalent as `wmt20-comet-qe-da-v2` from previous [COMET](https://github.com/Unbabel/COMET) versions (<2.0).
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# Paper
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This is a [COMET](https://github.com/Unbabel/COMET) quality estimation model: It receives a source sentence and the respective translation and returns a score that reflects the quality of the translation.
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**NOTE:** This model was recently replaced by an improved version [wmt22-cometkiwi-da](https://huggingface.co/Unbabel/wmt22-cometkiwi-da)
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# Paper
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checkpoints/model.ckpt
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:0dc381dfa76e78607d95f3ff8245e1b7e7010252fda43e6163802f67eba95732
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size 2277430715
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hparams.yaml
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activations: Tanh
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batch_size:
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class_identifier: referenceless_regression_metric
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dropout: 0.1
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encoder_learning_rate: 1.0e-05
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encoder_model: XLM-RoBERTa
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final_activation: null
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hidden_sizes:
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- 2048
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- 1024
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keep_embeddings_frozen: true
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layer: mix
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layerwise_decay: 0.95
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learning_rate: 3.
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load_weights_from_checkpoint: null
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optimizer: AdamW
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pool: avg
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pretrained_model: xlm-roberta-large
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train_data: data/
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validation_data: data/
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# Training Seed 3
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activations: Tanh
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batch_size: 2
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class_identifier: referenceless_regression_metric
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dropout: 0.1
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encoder_learning_rate: 1.0e-05
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encoder_model: XLM-RoBERTa
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hidden_sizes:
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- 2048
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- 1024
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keep_embeddings_frozen: true
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layer: mix
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layerwise_decay: 0.95
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learning_rate: 3.0e-05
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load_weights_from_checkpoint: null
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optimizer: Adam
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pool: avg
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pretrained_model: xlm-roberta-large
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train_data: data/scores_1719.csv
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validation_data: data/scores_1719.csv
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final_activation: "Sigmoid"
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