Richard Wong
1b9c4323c3
- added analysis for overall statistics |
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.. | ||
class_number_tokens | ||
classification_bert_complete_desc | ||
classification_bert_complete_desc_unit | ||
classification_bert_complete_desc_unit_name | ||
classification_bert_pattern_desc | ||
classification_bert_pattern_desc_unit | ||
classification_t5_complete | ||
classification_t5_complete_with_constrastive | ||
classification_t5_mdm_with_contrastive | ||
frozen_t5_decoder | ||
frozen_t5_encoder | ||
hybrid_t5_complete_desc_unit | ||
hybrid_t5_pattern_desc_unit | ||
mapping_baseline | ||
mapping_t5-base_desc | ||
mapping_t5-base_desc_unit | ||
mapping_t5_1e4 | ||
mapping_t5_complete_desc | ||
mapping_t5_complete_desc_unit | ||
mapping_t5_complete_desc_unit_name | ||
mapping_t5_final_delivery_model | ||
mapping_t5_pattern_desc_unit | ||
modified_t5_decoder_1_layers | ||
modified_t5_decoder_2_layers | ||
modified_t5_decoder_4_layers | ||
modified_t5_decoder_8_layers | ||
random_t5_encoder | ||
README.md | ||
predict.bash | ||
train.bash |
README.md
Train
What is this folder
Here contains the code for training and mapping evaluation.
Each folder contains a training variation.
After training, each folder contains the checkpoint files for each fold.
The folders are named with the following convention:
<method_type>
_<model_type>
_<prediction_type>
_<included_fields>
e.g.
"classification_bert_complete_desc_unit",
which means: folder to perform classification using the bert model, predicting for the complete thing+property output using description and unit
To train, just run python train.py
The inference code is within a folder classification_prediction
or
mapping_prediction
.
Note: the classification_t5 folders are depracated in favor of the BERT-based classification models.