SATP_12.20

1. Annotation pipeline

1_annotation_parallel_2000_to_2020.ipynb

2. Prepare our training/ testing data

2_Preprocess-Data-12.27.ipynb

3. BiLSTM Baseline Deep Learning Model

3_BiLSTM.ipynb

4. BERT

4_transformer_reduced_bert.ipynb

5. Baselines: Traditional Machine learning models (SVM, LR) using 2-step pipeline.

5_Baselines_reduced-2-step.ipynb

6. HAN

6.1 Create word vector and dataloader from train.csv.

python create_input_files.py

6.2 Train and eval HAN model

python HAN_end2end.py

Result:

	Accuracy	Precision	Recall	F1	Exact Matching Ratio
One vs Rest	26.07	87.19	27.40	28.04	82.20
Binary Relevance	26.65	86.08	28.29	28.40	82.47
Class Chain	27.93	83.47	29.78	29.32	82.87
Label Powerset	28.86	85.53	30.51	30.05	83.14
BiLSTM	37.11	53.81	58.51	42.12	76.23
HAN	52.98	65.64	74.82	55.78	83.07
BERT	62.52	74.73	80.01	64.59	87.23

Name		Name	Last commit message	Last commit date
Latest commit History 54 Commits
.idea		.idea
Data_TagTog_Annotated		Data_TagTog_Annotated
Data_TagTog_NotAnnotated		Data_TagTog_NotAnnotated
outdata		outdata
1_annotation_get_their_annotation_back-final.ipynb		1_annotation_get_their_annotation_back-final.ipynb
1_annotation_parallel_2000_to_2020.ipynb		1_annotation_parallel_2000_to_2020.ipynb
2_Preprocess-Data-12.27.ipynb		2_Preprocess-Data-12.27.ipynb
3.BiLSTM.ipynb		3.BiLSTM.ipynb
4. transformer_reduced_bert.ipynb		4. transformer_reduced_bert.ipynb
5. Baselines_reduced-2-step.ipynb		5. Baselines_reduced-2-step.ipynb
README.md		README.md
create_input_files.py		create_input_files.py
datasets.py		datasets.py
han_end2end.py		han_end2end.py
model.py		model.py
utils.py		utils.py

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

SATP_12.20

1. Annotation pipeline

2. Prepare our training/ testing data

3. BiLSTM Baseline Deep Learning Model

4. BERT

5. Baselines: Traditional Machine learning models (SVM, LR) using 2-step pipeline.

6. HAN

6.1 Create word vector and dataloader from train.csv.

6.2 Train and eval HAN model

Result:

About

Releases

Packages

Languages

snowood1/SATP

Folders and files

Latest commit

History

Repository files navigation

SATP_12.20

1. Annotation pipeline

2. Prepare our training/ testing data

3. BiLSTM Baseline Deep Learning Model

4. BERT

5. Baselines: Traditional Machine learning models (SVM, LR) using 2-step pipeline.

6. HAN

6.1 Create word vector and dataloader from train.csv.

6.2 Train and eval HAN model

Result:

About

Resources

Stars

Watchers

Forks

Releases

Packages 0

Languages

Packages