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Unpack the following files:
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Run the following command to train the model for each dataset:
python3 src/train.py ... -
Evaluate on each seed of each dataset by using the following command:
python3 src/evaluate.py ...
Arguments for train.py:
| Argument | Type | Default Value | Description |
|---|---|---|---|
| --dataset_name | str | "fewrel/unseen_5" | Specifies the name of the dataset. This executes the training for all seeds as specified by the --seeds parameter. |
| --model_type | str | "bert-base-cased" | Specifies the type of model to be used. |
| --batch_size | int | 24 | Sets the batch size for training. |
| --num_workers | int | 2 | Number of worker processes for data loading. |
| --accumulate_grad_batches | int | 2 | Accumulates gradients over a specified number of batches. |
| --lr | float | 5e-5 | Learning rate for optimization. |
| --seeds | int, List | [0, 1, 2, 3, 4] | List of seeds of the dataset to train on. |
| --include_descriptions | store_true | False | Includes descriptions in the textual representation if this flag is present. |
| --include_types | store_true | False | Includes types in the textual if this flag is present. |
Arguments for evaluate.py:
| Argument | Type | Default Value | Required | Description |
|---|---|---|---|---|
| --model_checkpoint | str | - | Yes | Specifies the path to the model checkpoint. |
| --dataset_name | str | "fewrel/unseen_5_seed_0" | No | Specifies the name of the dataset with the corresponding seed. |
| --model_type | str | "bert-base-cased" | No | Specifies the type of model to be used. |
| --batch_size | int | 24 | No | Sets the batch size for training. |
| --num_workers | int | 2 | No | Number of worker processes for data loading. |
| --accumulate_grad_batches | int | 1 | No | Accumulates gradients over a specified number of batches. |
| --other_properties | int | 5 | No | Specifies the value for some other properties. |
| --hard_other_properties | int | 0 | No | Specifies the value for some other hard properties. |
| --include_descriptions | store_true | False | No | Includes descriptions in the textual representation if this flag is present. |
| --include_types | store_true | False | No | Includes types in the textual if this flag is present. |
| --use_predicted_candidates | store_true | False | No | Uses predicted candidates if this flag is present. |