text-classification bool 2 classes | text stringlengths 0 664k |
|---|---|
false |
# m2m3_fine_tuning_ref_cmbert_iob2
## Introduction
This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ref_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ref_cmbert_iob2)
* M3 : [nlpso/m3_hierarchical_ner_ref_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ref_cmbert_iob2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ref_cmbert_iob2")
|
false |
# m2m3_fine_tuning_ref_ptrn_cmbert_iob2
## Introduction
This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ref_ptrn_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ref_ptrn_cmbert_iob2)
* M3 : [nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_iob2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ref_ptrn_cmbert_iob2")
|
false |
# m0_qualitative_analysis_ref_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **flat NER task** using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
## Dataset parameters
* Approach : M0
* Dataset type : ground-truth
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned model : [nlpso/m0_flat_ner_ref_cmbert_io](https://huggingface.co/nlpso/m0_flat_ner_ref_cmbert_io)
## Entity types
Abbreviation|Description
-|-
O |Outside of a named entity
PER |Person or company name
ACT |Person or company professional activity
TITRE |Distinction
LOC |Street name
CARDINAL |Street number
FT |Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m0_qualitative_analysis_ref_cmbert_io")
|
false |
# m2m3_fine_tuning_ocr_cmbert_io
## Introduction
This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ocr_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ocr_cmbert_io)
* M3 : [nlpso/m3_hierarchical_ner_ocr_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_cmbert_io)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ocr_cmbert_io")
|
false |
# m0_qualitative_analysis_ref_ptrn_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **flat NER task** using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
## Dataset parameters
* Approach : M0
* Dataset type : ground-truth
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned model : [nlpso/m0_flat_ner_ref_ptrn_cmbert_io](https://huggingface.co/nlpso/m0_flat_ner_ref_ptrn_cmbert_io)
## Entity types
Abbreviation|Description
-|-
O |Outside of a named entity
PER |Person or company name
ACT |Person or company professional activity
TITRE |Distinction
LOC |Street name
CARDINAL |Street number
FT |Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m0_qualitative_analysis_ref_ptrn_cmbert_io")
|
false |
# m2m3_fine_tuning_ocr_ptrn_cmbert_io
## Introduction
This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ocr_ptrn_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ocr_ptrn_cmbert_io)
* M3 : [nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_io)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ocr_ptrn_cmbert_io")
|
false |
# m0_qualitative_analysis_ocr_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **flat NER task** using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
## Dataset parameters
* Approach : M0
* Dataset type : noisy (Pero OCR)
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned model : [nlpso/m0_flat_ner_ocr_cmbert_io](https://huggingface.co/nlpso/m0_flat_ner_ocr_cmbert_io)
## Entity types
Abbreviation|Description
-|-
O |Outside of a named entity
PER |Person or company name
ACT |Person or company professional activity
TITRE |Distinction
LOC |Street name
CARDINAL |Street number
FT |Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m0_qualitative_analysis_ocr_cmbert_io")
|
false |
# m2m3_fine_tuning_ocr_cmbert_iob2
## Introduction
This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ocr_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ocr_cmbert_iob2)
* M3 : [nlpso/m3_hierarchical_ner_ocr_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_cmbert_iob2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ocr_cmbert_iob2")
|
false |
# m0_qualitative_analysis_ocr_ptrn_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **flat NER task** using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
## Dataset parameters
* Approach : M0
* Dataset type : noisy (Pero OCR)
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned model : [nlpso/m0_flat_ner_ocr_ptrn_cmbert_io](https://huggingface.co/nlpso/m0_flat_ner_ocr_ptrn_cmbert_io)
## Entity types
Abbreviation|Description
-|-
O |Outside of a named entity
PER |Person or company name
ACT |Person or company professional activity
TITRE |Distinction
LOC |Street name
CARDINAL |Street number
FT |Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m0_qualitative_analysis_ocr_ptrn_cmbert_io")
|
false |
# m2m3_fine_tuning_ocr_ptrn_cmbert_iob2
## Introduction
This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ocr_ptrn_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ocr_ptrn_cmbert_iob2)
* M3 : [nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_iob2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ocr_ptrn_cmbert_iob2")
|
false |
# m1_qualitative_analysis_ref_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ref_cmbert_io_level_1](https://huggingface.co/nlpso/m1_ind_layers_ref_cmbert_io_level_1)
* Level 2 : [nlpso/m1_ind_layers_ref_cmbert_io_level_2](https://huggingface.co/nlpso/m1_ind_layers_ref_cmbert_io_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_qualitative_analysis_ref_cmbert_io")
|
false |
# m1_qualitative_analysis_ref_ptrn_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ref_ptrn_cmbert_io_level_1](https://huggingface.co/nlpso/m1_ind_layers_ref_ptrn_cmbert_io_level_1)
* Level 2 : [nlpso/m1_ind_layers_ref_ptrn_cmbert_io_level_2](https://huggingface.co/nlpso/m1_ind_layers_ref_ptrn_cmbert_io_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_qualitative_analysis_ref_ptrn_cmbert_io")
|
false |
# m1_qualitative_analysis_ref_cmbert_iob2
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ref_cmbert_iob2_level_1](https://huggingface.co/nlpso/m1_ind_layers_ref_cmbert_iob2_level_1)
* Level 2 : [nlpso/m1_ind_layers_ref_cmbert_iob2_level_2](https://huggingface.co/nlpso/m1_ind_layers_ref_cmbert_iob2_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_qualitative_analysis_ref_cmbert_iob2")
|
false |
# m1_qualitative_analysis_ref_ptrn_cmbert_iob2
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ref_ptrn_cmbert_iob2_level_1](https://huggingface.co/nlpso/m1_ind_layers_ref_ptrn_cmbert_iob2_level_1)
* Level 2 : [nlpso/m1_ind_layers_ref_ptrn_cmbert_iob2_level_2](https://huggingface.co/nlpso/m1_ind_layers_ref_ptrn_cmbert_iob2_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_qualitative_analysis_ref_ptrn_cmbert_iob2")
|
false |
# m1_qualitative_analysis_ocr_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ocr_cmbert_io_level_1](https://huggingface.co/nlpso/m1_ind_layers_ocr_cmbert_io_level_1)
* Level 2 : [nlpso/m1_ind_layers_ocr_cmbert_io_level_2](https://huggingface.co/nlpso/m1_ind_layers_ocr_cmbert_io_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_qualitative_analysis_ocr_cmbert_io")
|
false |
# m1_qualitative_analysis_ocr_ptrn_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_1](https://huggingface.co/nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_1)
* Level 2 : [nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_2](https://huggingface.co/nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_qualitative_analysis_ocr_ptrn_cmbert_io")
|
false |
# m1_qualitative_analysis_ocr_cmbert_iob2
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ocr_cmbert_iob2_level_1](https://huggingface.co/nlpso/m1_ind_layers_ocr_cmbert_iob2_level_1)
* Level 2 : [nlpso/m1_ind_layers_ocr_cmbert_iob2_level_2](https://huggingface.co/nlpso/m1_ind_layers_ocr_cmbert_iob2_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_qualitative_analysis_ocr_cmbert_iob2")
|
false |
# m1_qualitative_analysis_ocr_ptrn_cmbert_iob2
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ocr_ptrn_cmbert_iob2_level_1](https://huggingface.co/nlpso/m1_ind_layers_ocr_ptrn_cmbert_iob2_level_1)
* Level 2 : [nlpso/m1_ind_layers_ocr_ptrn_cmbert_iob2_level_2](https://huggingface.co/nlpso/m1_ind_layers_ocr_ptrn_cmbert_iob2_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_qualitative_analysis_ocr_ptrn_cmbert_iob2")
|
false |
# m2m3_qualitative_analysis_ref_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ref_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ref_cmbert_io)
* M3 : [nlpso/m3_hierarchical_ner_ref_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ref_cmbert_io)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_qualitative_analysis_ref_cmbert_io")
|
false |
# m2m3_qualitative_analysis_ref_ptrn_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ref_ptrn_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ref_ptrn_cmbert_io)
* M3 : [nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_io)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_qualitative_analysis_ref_ptrn_cmbert_io")
|
false |
# m2m3_qualitative_analysis_ref_cmbert_iob2
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ref_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ref_cmbert_iob2)
* M3 : [nlpso/m3_hierarchical_ner_ref_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ref_cmbert_iob2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_qualitative_analysis_ref_cmbert_iob2")
|
false |
# m2m3_qualitative_analysis_ref_ptrn_cmbert_iob2
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ref_ptrn_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ref_ptrn_cmbert_iob2)
* M3 : [nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_iob2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_qualitative_analysis_ref_ptrn_cmbert_iob2")
|
false |
# m2m3_qualitative_analysis_ocr_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ocr_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ocr_cmbert_io)
* M3 : [nlpso/m3_hierarchical_ner_ocr_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_cmbert_io)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_qualitative_analysis_ocr_cmbert_io")
|
false |
# m2m3_qualitative_analysis_ocr_cmbert_iob2
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ocr_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ocr_cmbert_iob2)
* M3 : [nlpso/m3_hierarchical_ner_ocr_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_cmbert_iob2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_qualitative_analysis_ocr_cmbert_iob2")
|
false | # vinbigdata_asr_vlsp_2020
- Source:
- Num examples: 46,494
- Language: Vietnamese
```python
from datasets import load_dataset
load_dataset("tdtunlp/vinbigdata_asr_vlsp_2020_vi")
``` |
false | # wikiquote_filtered
- Source: https://huggingface.co/datasets/bigscience-data/roots_en_wikiquote
- Num examples: 449
- Language: Vietnamese
```python
from datasets import load_dataset
load_dataset("tdtunlp/wikiquote_vi") |
false | # wikiquote_filtered
- Source: https://huggingface.co/datasets/bigscience-data/roots_en_wikiquote
- Num examples: 31,929
- Language: English
```python
from datasets import load_dataset
load_dataset("tdtunlp/wikiquote_en")
``` |
false | # wikivoyage_filtered
- Source: https://huggingface.co/datasets/bigscience-data/roots_en_wikivoyage
- Num examples: 24,838
- Language: English
```python
from datasets import load_dataset
load_dataset("tdtunlp/wikivoyage_en")
``` |
false | # wikivoyage_filtered
- Source: https://huggingface.co/datasets/bigscience-data/roots_en_wikivoyage
- Num examples: 1,527
- Language: Vietnamese
```python
from datasets import load_dataset
load_dataset("tdtunlp/wikivoyage_vi") |
false | # wikibooks_filtered
- Source: https://huggingface.co/datasets/bigscience-data/roots_en_wikibooks
- Num examples: 54,773
- Language: English
```python
from datasets import load_dataset
load_dataset("tdtunlp/wikibooks_en")
``` |
false | # wikibooks_filtered
- Source: https://huggingface.co/datasets/bigscience-data/roots_en_wikibooks
- Num examples: 3,832
- Language: Vietnamese
```python
from datasets import load_dataset
load_dataset("tdtunlp/wikibooks_vi")
``` |
false | # vinbigdata_monolingual_vlsp_2020
- Source:
- Num examples: 18,579,972
- Language: Vietnamese
```python
from datasets import load_dataset
load_dataset("tdtunlp/vinbigdata_monolingual_vlsp_2020_vi")
``` |
true | # Dataset Card for Dataset Name
## Dataset Description
- **Homepage:https://huggingface.co/datasets/jjiiaa/mj-prompts/
- **Repository:https://huggingface.co/datasets/jjiiaa/mj-prompts/
### Dataset Summary
adding soon
## Dataset Structure
adding soon
### Data Splits
adding soon
### Licensing Information
adding soon |
true | |
false | # wiki_lingua
- Source: https://huggingface.co/datasets/GEM/wiki_lingua
- Num examples: 6,616
- Language: Vietnamese
```python
from datasets import load_dataset
load_dataset("tdtunlp/wiki_lingua_vi")
``` |
false | # wiki_lingua
- Source: https://huggingface.co/datasets/GEM/wiki_lingua
- Num examples: 57,945
- Language: English
```python
from datasets import load_dataset
load_dataset("tdtunlp/wiki_lingua_en")
``` |
false |
# Dataset Card for UTS_Text
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
The UTS_Text dataset is a collection of 100,000 sentences sourced from various news articles.
Out of the 10,000 sentences in the dataset, 5,000 sentences have a length ranging from 50 to 150, while the other 5,000 sentences have a length ranging from 20 to 50. This distribution of sentence lengths provides a diverse range of text samples that can be used to train and test natural language processing models.
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
| name | train | validation | test |
|---------|--------:|-----------:|-------:|
| small | 1600 | 200 | 200 |
| base | 8000 | 1000 | 1000 |
| large | 95000 | 2500 | 2500 |
## Dataset Creation
### Curation Rationale
### Source Data
### Annotations
## Additional Information
### Licensing Information
The dataset is released under Apache 2.0.
### Citation Information
### Contributions
|
true | |
true | AG's News Topic Classification Dataset
Version 3, Updated 09/09/2015
ORIGIN
AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity. For more information, please refer to the link http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .
The AG's news topic classification dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the dataset above. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
DESCRIPTION
The AG's news topic classification dataset is constructed by choosing 4 largest classes from the original corpus. Each class contains 30,000 training samples and 1,900 testing samples. The total number of training samples is 120,000 and testing 7,600.
The file classes.txt contains a list of classes corresponding to each label.
The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 3 columns in them, corresponding to class index (1 to 4), title and description. The title and description are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".
CLASS NAME INFORMATION
1: World
2: Sports
3: Business
4: Sci/Tech
JSONL FORMAT
Instead of preserving csv format, I change the format to jsonl, which doesn't consider complicated rule about doublequote and escaping.
|
false |
# App Flow
This dataset consists of hourly maximum traffic flow for 128 systems deployed on 16 logic data centers, resulting in 1083 different time series in total.
The length of each series is more than 4 months. Each time series is divided into two segments for training and testing with a ratio of 32:1.
This dataset was collected at Ant Group and does not contain any Personal Identifiable Information and is desensitized and encrypted. |
true | # AutoTrain Dataset for project: stratefied-processing
## Dataset Description
This dataset has been automatically processed by AutoTrain for project stratefied-processing.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "dateline resources ltd",
"target": 0
},
{
"text": "dateline resources",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['BBG001S0S611', 'BBG001S112S8', 'BBG001S112Y1', 'BBG001S11324', 'BBG001S11351', 'BBG001S168Y3', 'BBG001S16908', 'BBG001S16971', 'BBG001S169N3', 'BBG001S169P1', 'BBG001S16LK9', 'BBG001S26WK3', 'BBG001S2CDJ0', 'BBG001S2CDW5', 'BBG001S2CK04', 'BBG001S2TZ30', 'BBG001S2WTG6', 'BBG001S2XSC1', 'BBG001S3F8M2', 'BBG001S3FBF3', 'BBG001S3RD02', 'BBG001S3RD11', 'BBG001S4BJY7', 'BBG001S4WVT3', 'BBG001S50HF1', 'BBG001S561K4', 'BBG001S561Q8', 'BBG001S561V2', 'BBG001S561Z8', 'BBG001S56277', 'BBG001S562F8', 'BBG001S562P7', 'BBG001S562W9', 'BBG001S56320', 'BBG001S56D46', 'BBG001S56G21', 'BBG001S56JK5', 'BBG001S56L11', 'BBG001S56P85', 'BBG001S56QM7', 'BBG001S56QV7', 'BBG001S56R18', 'BBG001S58BG5', 'BBG001S58L91', 'BBG001S5N8T1', 'BBG001S5N8V8', 'BBG001S5N964', 'BBG001S5N991', 'BBG001S5N9B8', 'BBG001S5N9D6', 'BBG001S5N9M6', 'BBG001S5N9P3', 'BBG001S5N9S0', 'BBG001S5NB14', 'BBG001S5NBJ5', 'BBG001S5NBN0', 'BBG001S5NBP8', 'BBG001S5NBQ7', 'BBG001S5NBT4', 'BBG001S5NBV1', 'BBG001S5NCC0', 'BBG001S5NCJ3', 'BBG001S5NCK1', 'BBG001S5NCQ5', 'BBG001S5NCZ5', 'BBG001S5ND38', 'BBG001S5ND65', 'BBG001S5ND74', 'BBG001S5ND92', 'BBG001S5NDG4', 'BBG001S5NDV7', 'BBG001S5NDW6', 'BBG001S5NF24', 'BBG001S5NF60', 'BBG001S5NFD2', 'BBG001S5NG22', 'BBG001S5NGJ4', 'BBG001S5NGN9', 'BBG001S5NGY7', 'BBG001S5NHB0', 'BBG001S5NHP5', 'BBG001S5NHS2', 'BBG001S5NJG1', 'BBG001S5NJV4', 'BBG001S5NJX2', 'BBG001S5NJZ0', 'BBG001S5NK14', 'BBG001S5NKC2', 'BBG001S5NKR6', 'BBG001S5NKV1', 'BBG001S5NKW0', 'BBG001S5NL21', 'BBG001S5NLG6', 'BBG001S5NLS3', 'BBG001S5NM29', 'BBG001S5NM65', 'BBG001S5NM92', 'BBG001S5NMM7', 'BBG001S5NMN6', 'BBG001S5NMX5', 'BBG001S5NN36', 'BBG001S5NN45', 'BBG001S5NN54', 'BBG001S5NN72', 'BBG001S5NNB7', 'BBG001S5NNL6', 'BBG001S5NNS9', 'BBG001S5NP04', 'BBG001S5NP86', 'BBG001S5NPF8', 'BBG001S5NPN9', 'BBG001S5NPQ6', 'BBG001S5NPR5', 'BBG001S5NQ11', 'BBG001S5NQ57', 'BBG001S5NQB0', 'BBG001S5NQH4', 'BBG001S5NR37', 'BBG001S5NRH2', 'BBG001S5NRY3', 'BBG001S5NS08', 'BBG001S5NS35', 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'BBG001SBJ5F4', 'BBG001SBJ7J6', 'BBG001SBJ859', 'BBG001SBJ8J4', 'BBG001SBJ8K2', 'BBG001SBJ975', 'BBG001SBJ9J2', 'BBG001SBJ9W7', 'BBG001SBJBY0', 'BBG001SBJHY7', 'BBG001SBJJ19', 'BBG001SBJKW2', 'BBG001SBJL05', 'BBG001SBJLL2', 'BBG001SBJMB1', 'BBG001SBJMT2', 'BBG001SBJQM0', 'BBG001SBJSG3', 'BBG001SBJW10', 'BBG001SBJY34', 'BBG001SBJZ04', 'BBG001SBJZY7', 'BBG001SBK0B7', 'BBG001SBK1V3', 'BBG001SBK232', 'BBG001SBK2J5', 'BBG001SBK704', 'BBG001SBK768', 'BBG001SBK7J4', 'BBG001SBK8N7', 'BBG001SBK8S2', 'BBG001SBKBP8', 'BBG001SBKCG6', 'BBG001SBKDL8', 'BBG001SBKFG9', 'BBG001SBKFK4', 'BBG001SBKFL3', 'BBG001SBKFS6', 'BBG001SBKG86', 'BBG001SBKHX6', 'BBG001SBKK32', 'BBG001SBKKW0', 'BBG001SBKLY6', 'BBG001SBKRV6', 'BBG001SBKS35', 'BBG001SBKSL5', 'BBG001SBKWH1', 'BBG001SBKWS9', 'BBG001SBKXT6', 'BBG001SBKYT4', 'BBG001SBL473', 'BBG001SBL4R1', 'BBG001SBL543', 'BBG001SBL623', 'BBG001SBL669', 'BBG001SBL687', 'BBG001SBL749', 'BBG001SBL892', 'BBG001SBL8C8', 'BBG001SBL9M5', 'BBG001SBLB13', 'BBG001SBLBQ6', 'BBG001SBLRT8', 'BBG001SBLS61', 'BBG001SBLS98', 'BBG001SBLT23', 'BBG001SBLZS1', 'BBG001SBM043', 'BBG001SBM1G8', 'BBG001SBM3X5', 'BBG001SBM4H1', 'BBG001SBM4S9', 'BBG001SBMB30', 'BBG001SBMBH5', 'BBG001SBMCG4', 'BBG001SBMT04', 'BBG001SBN0N1', 'BBG001SBNNC3', 'BBG001SBP2S0', 'BBG001SBPC35', 'BBG001SBPF83', 'BBG001SBPGW4', 'BBG001SBPLG1', 'BBG001SBPLT7', 'BBG001SBPRD1', 'BBG001SBQVL2', 'BBG001SBQVV1', 'BBG001SBQWT2', 'BBG001SBQWV9', 'BBG001SBQWW8', 'BBG001SBQY36', 'BBG001SBQYB7', 'BBG001SBQZC3', 'BBG001SBQZW1', 'BBG001SBR486', 'BBG001SBR547', 'BBG001SBR6X3', 'BBG001SBR743', 'BBG001SBR8H7', 'BBG001SBR8P8', 'BBG001SBRBG1', 'BBG001SBRBK6', 'BBG001SBRBM4', 'BBG001SBRBN3', 'BBG001SBRG07', 'BBG001SBRH69', 'BBG001SBRHJ5', 'BBG001SBRHK3', 'BBG001SBRHX9', 'BBG001SBRJY4', 'BBG001SBRK26', 'BBG001SBRK44', 'BBG001SBRK53', 'BBG001SBRK71', 'BBG001SBRK80', 'BBG001SBRKC5', 'BBG001SBRKD4', 'BBG001SBRKG1', 'BBG001SBRLC3', 'BBG001SBRLP9', 'BBG001SBRM22', 'BBG001SBRMZ6', 'BBG001SBRN75', 'BBG001SBRPM3', 'BBG001SBRR21', 'BBG001SBRRM9', 'BBG001SBRV22', 'BBG001SBRW11', 'BBG001SBRX64', 'BBG001SBRYW3', 'BBG001SBRZ32', 'BBG001SBRZB3', 'BBG001SBRZT4', 'BBG001SBS2H9', 'BBG001SBS2N2', 'BBG001SBS2R8', 'BBG001SBS4N8', 'BBG001SBS5H2', 'BBG001SBS644', 'BBG001SBS6S8', 'BBG001SBS706', 'BBG001SBS7C3', 'BBG001SBS7D2', 'BBG001SBS8V0', 'BBG001SBSCN0', 'BBG001SBSCZ7', 'BBG001SBSDZ5', 'BBG001SBSFM4', 'BBG001SBSKG0', 'BBG001SBSMH5', 'BBG001SBSNV7', 'BBG001SBSRN7', 'BBG001SBSTJ8', 'BBG001SBSTK6', 'BBG001SBSTN3', 'BBG001SBSTQ0', 'BBG001SBSV76', 'BBG001SBSWY4', 'BBG001SBSY34', 'BBG001SBSZ95', 'BBG001SBT1Y1', 'BBG001SBT2W1', 'BBG001SBT2X0', 'BBG001SBT652', 'BBG001SBT670', 'BBG001SBT7R6', 'BBG001SBT7X9', 'BBG001SBT830', 'BBG001SBT849', 'BBG001SBT867', 'BBG001SBT983', 'BBG001SBT9X5', 'BBG001SBTBG9', 'BBG001SBTBJ6', 'BBG001SBTC31', 'BBG001SBTHK1', 'BBG001SBTHX7', 'BBG001SBTK51', 'BBG001SBTNW5', 'BBG001SBTQ67', 'BBG001SBTR10', 'BBG001SBTRJ1', 'BBG001SBTRW6', 'BBG001SBTSF3', 'BBG001SBTTB5', 'BBG001SBTW91', 'BBG001SBTYG9', 'BBG001SBTZY6', 'BBG001SBV025', 'BBG001SBV1Z7', 'BBG001SBV5B4', 'BBG001SBV5W1', 'BBG001SBV8V6', 'BBG001SBV8X4', 'BBG001SBV9R9', 'BBG001SBVB21', 'BBG001SBVBM9', 'BBG001SBVC74', 'BBG001SBVCL8', 'BBG001SBVD90', 'BBG001SBVF13', 'BBG001SBVH37', 'BBG001SBVMW4', 'BBG001SBVMY2', 'BBG001SBVP84', 'BBG001SBVQF4', 'BBG001SBVQH2', 'BBG001SBVTD0', 'BBG001SBVWN2', 'BBG001SBVXF9', 'BBG001SBVY30', 'BBG001SBVY94', 'BBG001SBVYY6', 'BBG001SBVZ73', 'BBG001SBW569', 'BBG001SBW578', 'BBG001SBW5G8', 'BBG001SBW6S3', 'BBG001SBW7S1', 'BBG001SBW881', 'BBG001SBW8S9', 'BBG001SBWB48', 'BBG001SBWC19', 'BBG001SBWDB6', 'BBG001SBWDX2', 'BBG001SBWFH5', 'BBG001SBWFZ5', 'BBG001SBWG10', 'BBG001SBWGB9', 'BBG001SBWGV7', 'BBG001SBWJ96', 'BBG001SBWJZ7', 'BBG001SBWKK0', 'BBG001SBWN15', 'BBG001SBWR52', 'BBG001SBWRN2', 'BBG001SBWVD4', 'BBG001SBWVT7', 'BBG001SBWXC1', 'BBG001SBWXG7', 'BBG001SBWXS4', 'BBG001SBWY84', 'BBG001SBWZL6', 'BBG001SBX1Z5', 'BBG001SBX229', 'BBG001SBX238', 'BBG001SBX2Q3', 'BBG001SBX336', 'BBG001SBX345', 'BBG001SBX470', 'BBG001SBX4B5', 'BBG001SBX540', 'BBG001SBX728', 'BBG001SBX7C7', 'BBG001SBX7P3', 'BBG001SBX933', 'BBG001SBXB29', 'BBG001SBXCF3', 'BBG001SBXCM5', 'BBG001SBXCZ1', 'BBG001SBXDV3', 'BBG001SBXGW5', 'BBG001SBXK74', 'BBG001SBXKK9', 'BBG001SBXMD3', 'BBG001SBXNS5', 'BBG001SBXR88', 'BBG001SBXWD1', 'BBG001SBXY01', 'BBG001SBY086', 'BBG001SBY2M6', 'BBG001SBY2S0', 'BBG001SBY380', 'BBG001SBY647', 'BBG001SBY8P0', 'BBG001SBY8V3', 'BBG001SBYC53', 'BBG001SBYCQ0', 'BBG001SBYCR9', 'BBG001SBYCZ0', 'BBG001SBYDJ6', 'BBG001SBYF92', 'BBG001SBYFK9', 'BBG001SBYHB5', 'BBG001SBYJJ3', 'BBG001SBYK19', 'BBG001SBYK46', 'BBG001SBYK73', 'BBG001SBYKD6', 'BBG001SBYL17', 'BBG001SBYM42', 'BBG001SBYMW1', 'BBG001SBYQ16', 'BBG001SBYQ89', 'BBG001SBYQ98', 'BBG001SBYR23', 'BBG001SBYTL8', 'BBG001SBYVK4', 'BBG001SBYVV2', 'BBG001SBYVY9', 'BBG001SBYXM8', 'BBG001SBYZG0', 'BBG001SBYZM3', 'BBG001SBYZN2', 'BBG001SBZ0D9', 'BBG001SBZ2Z1', 'BBG001SBZ343', 'BBG001SBZ370', 'BBG001SBZ441', 'BBG001SBZ4W0', 'BBG001SBZ7Q0', 'BBG001SBZ8N1', 'BBG001SBZ8W1', 'BBG001SBZ8X0', 'BBG001SBZ9G7', 'BBG001SBZ9T3', 'BBG001SBZ9X8', 'BBG001SBZBQ1', 'BBG001SBZBZ1', 'BBG001SBZCT6', 'BBG001SBZDP8', 'BBG001SBZDW0', 'BBG001SBZF64', 'BBG001SBZFQ2', 'BBG001SBZG08', 'BBG001SBZGC5', 'BBG001SBZGP1', 'BBG001SBZH79', 'BBG001SBZH88', 'BBG001SBZHZ8', 'BBG001SBZJM8', 'BBG001SBZJT1', 'BBG001SBZK36', 'BBG001SBZKV5', 'BBG001SBZNN8', 'BBG001SBZNW8', 'BBG001SBZP17', 'BBG001SBZQV2', 'BBG001SBZVV1', 'BBG001SBZVZ7', 'BBG001SBZWH5', 'BBG001SBZWY6', 'BBG001SBZXY4', 'BBG001SBZZX0', 'BBG001SC0054', 'BBG001SC00J9', 'BBG001SC00P2', 'BBG001SC02D1', 'BBG001SC02L2', 'BBG001SC02Q7', 'BBG001SC0312', 'BBG001SC0358', 'BBG001SC0376', 'BBG001SC03W8', 'BBG001SC0447', 'BBG001SC04J1', 'BBG001SC04V7', 'BBG001SC05G1', 'BBG001SC05Q0', 'BBG001SC07V0', 'BBG001SC07Z6', 'BBG001SC08P5', 'BBG001SC0BC2', 'BBG001SC0BS5', 'BBG001SC0FW1', 'BBG001SC0G68', 'BBG001SC0G86', 'BBG001SC0HR3', 'BBG001SC0HS2', 'BBG001SC0K41', 'BBG001SC0MG4', 'BBG001SC0NL6', 'BBG001SC0WW4', 'BBG001SC0X34', 'BBG001SC0XF1', 'BBG001SC0XH9', 'BBG001SC0YF9', 'BBG001SC0ZM8', 'BBG001SC0ZS2', 'BBG001SC10W3', 'BBG001SC1160', 'BBG001SC1188', 'BBG001SC11K4', 'BBG001SC11L3', 'BBG001SC13J2', 'BBG001SC1678', 'BBG001SC1712', 'BBG001SC17F7', 'BBG001SC1DM6', 'BBG001SC1NV4', 'BBG001SC1PJ3', 'BBG001SC1ZS1', 'BBG001SC21T4', 'BBG001SC23N6', 'BBG001SC28R1', 'BBG001SC2D27', 'BBG001SC2MJ9', 'BBG001SC3M71', 'BBG001SC3S41', 'BBG001SC3Y75', 'BBG001SC40Y8', 'BBG001SC4719', 'BBG001SC4746', 'BBG001SC4755', 'BBG001SC47X4', 'BBG001SC4BT0', 'BBG001SC4CM5', 'BBG001SC4D52', 'BBG001SC4DT6', 'BBG001SC4F84', 'BBG001SC4JB2', 'BBG001SC4L63', 'BBG001SC4MN2', 'BBG001SC4MR8', 'BBG001SC4NZ7', 'BBG001SC4Q62', 'BBG001SC4TM8', 'BBG001SC4TR3', 'BBG001SC4W23', 'BBG001SC4WP8', 'BBG001SC4X67', 'BBG001SC4XZ5', 'BBG001SC4Y65', 'BBG001SC4YT0', 'BBG001SC5255', 'BBG001SC52R1', 'BBG001SC54H8', 'BBG001SC5601', 'BBG001SC5638', 'BBG001SC5647', 'BBG001SC56D7', 'BBG001SC59F9', 'BBG001SC5DP9', 'BBG001SC5F47', 'BBG001SC5F65', 'BBG001SC5GP2', 'BBG001SC5H89', 'BBG001SC5H98', 'BBG001SC5HJ7', 'BBG001SC5KN5', 'BBG001SC5KR1', 'BBG001SC5MN1', 'BBG001SC5P36', 'BBG001SC5P54', 'BBG001SC5P90', 'BBG001SC5PB7', 'BBG001SC5RM1', 'BBG001SC5SP6', 'BBG001SC5TT0', 'BBG001SC5X02', 'BBG001SC5XD8', 'BBG001SC5XW7', 'BBG001SC5YX4', 'BBG001SC6003', 'BBG001SC6405', 'BBG001SC64Y8', 'BBG001SC6548', 'BBG001SC68G9', 'BBG001SC6904', 'BBG001SC6CZ9', 'BBG001SC6F91', 'BBG001SC6FV6', 'BBG001SC6GK6', 'BBG001SC6GL5', 'BBG001SC6HQ8', 'BBG001SC6HT5', 'BBG001SC6JH4', 'BBG001SC6LZ9', 'BBG001SC6N58', 'BBG001SC6NK1', 'BBG001SC6NV9', 'BBG001SC6P08', 'BBG001SC6QX0', 'BBG001SC6TH2', 'BBG001SC6VG8', 'BBG001SC6VM1', 'BBG001SC6VN0', 'BBG001SC6W03', 'BBG001SC6W94', 'BBG001SC6XP4', 'BBG001SC6Y90', 'BBG001SC6YL6', 'BBG001SC75L8', 'BBG001SC76G2', 'BBG001SC76K7', 'BBG001SC7C15', 'BBG001SC7CN1', 'BBG001SC7DG7', 'BBG001SC7JZ3', 'BBG001SC7PX1', 'BBG001SC7Q14', 'BBG001SC7QW0', 'BBG001SC7R30', 'BBG001SC7RP6', 'BBG001SC7SZ3', 'BBG001SC7TN4', 'BBG001SC7TV5', 'BBG001SC7VZ6', 'BBG001SC7WK0', 'BBG001SC7ZJ5', 'BBG001SC80T0', 'BBG001SC82J7', 'BBG001SC8564', 'BBG001SC85H2', 'BBG001SC8724', 'BBG001SC8868', 'BBG001SC88P7', 'BBG001SC89R3', 'BBG001SC8JB8', 'BBG001SC8NG4', 'BBG001SC8YQ9', 'BBG001SC8YX1', 'BBG001SC90W5', 'BBG001SC92M2', 'BBG001SC93S4', 'BBG001SC9741', 'BBG001SC9B06', 'BBG001SC9C40', 'BBG001SC9C86', 'BBG001SC9FJ7', 'BBG001SC9GB3', 'BBG001SC9JW4', 'BBG001SC9KM2', 'BBG001SC9LN9', 'BBG001SC9MM8', 'BBG001SC9MR3', 'BBG001SC9QL0', 'BBG001SC9R65', 'BBG001SC9R83', 'BBG001SC9SB7', 'BBG001SC9T98', 'BBG001SCB089', 'BBG001SCB374', 'BBG001SCB3K9', 'BBG001SCB463', 'BBG001SCB551', 'BBG001SCB604', 'BBG001SCB980', 'BBG001SCB9W3', 'BBG001SCBBM9', 'BBG001SCBDN4', 'BBG001SCBDP2', 'BBG001SCBDR0', 'BBG001SCBF31', 'BBG001SCBGC9', 'BBG001SCBHD6', 'BBG001SCBHG3', 'BBG001SCBJG9', 'BBG001SCBJP9', 'BBG001SCBLC8', 'BBG001SCBLD7', 'BBG001SCBN16', 'BBG001SCBNK5', 'BBG001SCBSB4', 'BBG001SCBSL3', 'BBG001SCBVL6', 'BBG001SCBW07', 'BBG001SCBX23', 'BBG001SCBYZ5', 'BBG001SCC006', 'BBG001SCC1L1', 'BBG001SCC1T3', 'BBG001SCC2J2', 'BBG001SCC3T9', 'BBG001SCC649', 'BBG001SCC6N8', 'BBG001SCC774', 'BBG001SCC7R2', 'BBG001SCCBK0', 'BBG001SCCF30', 'BBG001SCCJ23', 'BBG001SCCJ50', 'BBG001SCCJ96', 'BBG001SCCJM1', 'BBG001SCCKH4', 'BBG001SCCMJ8', 'BBG001SCCNB4', 'BBG001SCCPJ1', 'BBG001SCCQ81', 'BBG001SCCQC6', 'BBG001SCCSS5', 'BBG001SCCV80', 'BBG001SCCVC5', 'BBG001SCCWT5', 'BBG001SCCYK0', 'BBG001SCCZ54', 'BBG001SCD3Z1', 'BBG001SCDB38', 'BBG001SCDDY0', 'BBG001SCDF75', 'BBG001SCDFC9', 'BBG001SCDFW7', 'BBG001SCDG73', 'BBG001SCDM52', 'BBG001SCDQP1', 'BBG001SCDRJ6', 'BBG001SCDSX8', 'BBG001SCDW32', 'BBG001SCDXM9', 'BBG001SCDXR4', 'BBG001SCDY38', 'BBG001SCDZ80', 'BBG001SCDZL5', 'BBG001SCF1B9', 'BBG001SCF2F3', 'BBG001SCF2M5', 'BBG001SCF2T8', 'BBG001SCF5V8', 'BBG001SCF7F2', 'BBG001SCFBQ1', 'BBG001SCFQV2', 'BBG001SCFRQ6', 'BBG001SCFRV0', 'BBG001SCFV14', 'BBG001SCFWF7', 'BBG001SCG0R3', 'BBG001SCG119', 'BBG001SCG164', 'BBG001SCG1F4', 'BBG001SCG1P3', 'BBG001SCG397', 'BBG001SCG510', 'BBG001SCG5V7', 'BBG001SCG8J5', 'BBG001SCG9N8', 'BBG001SCGCB4', 'BBG001SCGD40', 'BBG001SCGDF8', 'BBG001SCGJG4', 'BBG001SCGK08', 'BBG001SCGLJ6', 'BBG001SCGLY9', 'BBG001SCGR67', 'BBG001SCGRY6', 'BBG001SCGVP7', 'BBG001SCGX00', 'BBG001SCGX73', 'BBG001SCH0Q3', 'BBG001SCH270', 'BBG001SCH2H9', 'BBG001SCH369', 'BBG001SCH5J0', 'BBG001SCH7V2', 'BBG001SCHDC0', 'BBG001SCHDY6', 'BBG001SCHF99', 'BBG001SCHFK6', 'BBG001SCHFV4', 'BBG001SCHHD0', 'BBG001SCHKP0', 'BBG001SCHLC2', 'BBG001SCHTR9', 'BBG001SCHV30', 'BBG001SCHV85', 'BBG001SCHWG4', 'BBG001SCHX09', 'BBG001SCHYJ7', 'BBG001SCJ3Q5', 'BBG001SCJ3T2', 'BBG001SCJ937', 'BBG001SCJ946', 'BBG001SCJ9M6', 'BBG001SCJF24', 'BBG001SCJF60', 'BBG001SCJGQ6', 'BBG001SCJJ08', 'BBG001SCJJ53', 'BBG001SCJLG6', 'BBG001SCJM38', 'BBG001SCJNN4', 'BBG001SCJQ20', 'BBG001SCJTQ8', 'BBG001SCJVR2', 'BBG001SCJXR8', 'BBG001SCK0M3', 'BBG001SCK3P4', 'BBG001SCK846', 'BBG001SCK8H2', 'BBG001SCKBM9', 'BBG001SCKC47', 'BBG001SCKD81', 'BBG001SCKD90', 'BBG001SCKDG2', 'BBG001SCKGY5', 'BBG001SCKHG3', 'BBG001SCKJT5', 'BBG001SCKKF7', 'BBG001SCKMZ1', 'BBG001SCKPS2', 'BBG001SCKXX9', 'BBG001SCKYC0', 'BBG001SCL1T3', 'BBG001SCL2J2', 'BBG001SCL5C2', 'BBG001SCL5Z7', 'BBG001SCL658', 'BBG001SCL8F3', 'BBG001SCLBK0', 'BBG001SCLC46', 'BBG001SCLF03', 'BBG001SCLGS1', 'BBG001SCLJ96', 'BBG001SCLJD1', 'BBG001SCLJN0', 'BBG001SCLKQ4', 'BBG001SCLKR3', 'BBG001SCLKS2', 'BBG001SCLKT1', 'BBG001SCLLV6', 'BBG001SCLMK6', 'BBG001SCLP01', 'BBG001SCLSL2', 'BBG001SCLV26', 'BBG001SCLV80', 'BBG001SCLVD4', 'BBG001SCLXR5', 'BBG001SCLY66', 'BBG001SCLYN7', 'BBG001SCLZ36', 'BBG001SCLZJ9', 'BBG001SCLZN4', 'BBG001SCLZT8', 'BBG001SCM069', 'BBG001SCM3Z1', 'BBG001SCM4W2', 'BBG001SCM568', 'BBG001SCM9Y9', 'BBG001SCMCN4', 'BBG001SCMGW5', 'BBG001SCMKQ3', 'BBG001SCMM52', 'BBG001SCMM61', 'BBG001SCMPD6', 'BBG001SCMPL7', 'BBG001SCN111', 'BBG001SCN7J9', 'BBG001SCN8L4', 'BBG001SCN8R8', 'BBG001SCNDP9', 'BBG001SCNDY9', 'BBG001SCNF65', 'BBG001SCNHG0', 'BBG001SCNJW8', 'BBG001SCNKJ0', 'BBG001SCNKK8', 'BBG001SCNMF0', 'BBG001SCNQN2', 'BBG001SCNRQ7', 'BBG001SCNSC0', 'BBG001SCNST2', 'BBG001SCNVC3', 'BBG001SCNVY9', 'BBG001SCNW31', 'BBG001SCNXL9', 'BBG001SCNXT1', 'BBG001SCNXX6', 'BBG001SCNXY5', 'BBG001SCNY82', 'BBG001SCP208', 'BBG001SCP351', 'BBG001SCP486', 'BBG001SCP5M7', 'BBG001SCP5R2', 'BBG001SCP5X5', 'BBG001SCP6Y2', 'BBG001SCP6Z1', 'BBG001SCP8D1', 'BBG001SCP9P6', 'BBG001SCPB62', 'BBG001SCPBC5', 'BBG001SCPBP1', 'BBG001SCPBV4', 'BBG001SCPCG9', 'BBG001SCPDF8', 'BBG001SCPDW9', 'BBG001SCPGK5', 'BBG001SCPH78', 'BBG001SCPJ10', 'BBG001SCPKJ8', 'BBG001SCPKS8', 'BBG001SCPMJ4', 'BBG001SCPMM0', 'BBG001SCPMQ6', 'BBG001SCPN11', 'BBG001SCPP25', 'BBG001SCPQY8', 'BBG001SCPRX7', 'BBG001SCPT27', 'BBG001SCPT90', 'BBG001SCPV31', 'BBG001SCPX28', 'BBG001SCPXR1', 'BBG001SCPZ69', 'BBG001SCPZJ5', 'BBG001SCPZM1', 'BBG001SCPZR6', 'BBG001SCQ065', 'BBG001SCQ0Y4', 'BBG001SCQ1Z1', 'BBG001SCQ2H9', 'BBG001SCQ2X1', 'BBG001SCQ314', 'BBG001SCQ332', 'BBG001SCQ3J5', 'BBG001SCQ4G6', 'BBG001SCQ4S3', 'BBG001SCQ4T2', 'BBG001SCQ5L7', 'BBG001SCQ840', 'BBG001SCQB07', 'BBG001SCQCD1', 'BBG001SCQDY6', 'BBG001SCQF62', 'BBG001SCQG15', 'BBG001SCQH95', 'BBG001SCQHY7', 'BBG001SCQJ64', 'BBG001SCQMT2', 'BBG001SCQN56', 'BBG001SCQPX0', 'BBG001SCQRF6', 'BBG001SCQV30', 'BBG001SCQWF5', 'BBG001SCQWW6', 'BBG001SCQZY7', 'BBG001SCR2H8', 'BBG001SCR2T5', 'BBG001SCR4R3', 'BBG001SCR581', 'BBG001SCR6X1', 'BBG001SCR7R6', 'BBG001SCR885', 'BBG001SCR894', 'BBG001SCR9Q3', 'BBG001SCRDM8', 'BBG001SCRFD3', 'BBG001SCRFG0', 'BBG001SCRFK5', 'BBG001SCRJB7', 'BBG001SCRJK7', 'BBG001SCRLD0', 'BBG001SCRP78', 'BBG001SCRPV1', 'BBG001SCRSG2', 'BBG001SCRSZ1', 'BBG001SCRV39', 'BBG001SCRXC5', 'BBG001SCRZR4', 'BBG001SCS189', 'BBG001SCS205', 'BBG001SCS2R6', 'BBG001SCS303', 'BBG001SCS483', 'BBG001SCS795', 'BBG001SCS9H2', 'BBG001SCS9W5', 'BBG001SCSCZ5', 'BBG001SCSH57', 'BBG001SCSHS2', 'BBG001SCSN27', 'BBG001SCSN81', 'BBG001SCSPQ6', 'BBG001SCSQN7', 'BBG001SCSSJ8', 'BBG001SCST33', 'BBG001SCSVC8', 'BBG001SCT231', 'BBG001SCT3M8', 'BBG001SCT721', 'BBG001SCT7H5', 'BBG001SCT8G4', 'BBG001SCT8N6', 'BBG001SCT927', 'BBG001SCTCR3', 'BBG001SCTDQ2', 'BBG001SCTFM1', 'BBG001SCTFP8', 'BBG001SCTFW0', 'BBG001SCTG12', 'BBG001SCTGC0', 'BBG001SCTGZ5', 'BBG001SCTL48', 'BBG001SCTLL9', 'BBG001SCTPK1', 'BBG001SCTQB9', 'BBG001SCTQY4', 'BBG001SCTS25', 'BBG001SCTS61', 'BBG001SCTSN2', 'BBG001SCTSQ9', 'BBG001SCTT05', 'BBG001SCTT87', 'BBG001SCTW53', 'BBG001SCV1F7', 'BBG001SCV1W8', 'BBG001SCV2B9', 'BBG001SCV2N6', 'BBG001SCV513', 'BBG001SCV5Q6', 'BBG001SCV6N7', 'BBG001SCV7W5', 'BBG001SCVBM7', 'BBG001SCVJQ6', 'BBG001SCVMF1', 'BBG001SCVQ26', 'BBG001SCVQ99', 'BBG001SCVQG1', 'BBG001SCVQN3', 'BBG001SCVRZ8', 'BBG001SCVS22', 'BBG001SCVS59', 'BBG001SCVSS4', 'BBG001SCVV52', 'BBG001SCVWD1', 'BBG001SCVWW0', 'BBG001SCVXS3', 'BBG001SCVYG4', 'BBG001SCVYP4', 'BBG001SCVZ26', 'BBG001SCW228', 'BBG001SCW2F4', 'BBG001SCW488', 'BBG001SCW6R2', 'BBG001SCW8R8', 'BBG001SCW8V3', 'BBG001SCW9F9', 'BBG001SCWB00', 'BBG001SCWG18', 'BBG001SCWKB8', 'BBG001SCWN40', 'BBG001SCWN95', 'BBG001SCWNB2', 'BBG001SCWNN9', 'BBG001SCWP45', 'BBG001SCWP54', 'BBG001SCWVF0', 'BBG001SCX147', 'BBG001SCX192', 'BBG001SCX584', 'BBG001SCX7G1', 'BBG001SCX806', 'BBG001SCX8L3', 'BBG001SCXB36', 'BBG001SCXBG2', 'BBG001SCXBR0', 'BBG001SCXBS9', 'BBG001SCXBZ1', 'BBG001SCXC89', 'BBG001SCXDF9', 'BBG001SCXK90', 'BBG001SCXLD3', 'BBG001SCXLH9', 'BBG001SCXLV3', 'BBG001SCXRG7', 'BBG001SCXW21', 'BBG001SCXW30', 'BBG001SCXWP6', 'BBG001SCXY18', 'BBG001SCXZV2', 'BBG001SCY1L7', 'BBG001SCY1M6', 'BBG001SCY2P1', 'BBG001SCY716', 'BBG001SCY921', 'BBG001SCYBD4', 'BBG001SCYFJ9', 'BBG001SCYJR2', 'BBG001SCYLW1', 'BBG001SCYMJ4', 'BBG001SCYN75', 'BBG001SCYNN7', 'BBG001SCYR12', 'BBG001SCYW02', 'BBG001SCYWS2', 'BBG001SCYZ32', 'BBG001SCYZW0', 'BBG001SCZ0F5', 'BBG001SCZ0M7', 'BBG001SCZ118', 'BBG001SCZ181', 'BBG001SCZ2V3', 'BBG001SCZ341', 'BBG001SCZ3M1', 'BBG001SCZ555', 'BBG001SCZ7K4', 'BBG001SCZB89', 'BBG001SCZBR8', 'BBG001SCZCS5', 'BBG001SCZCV1', 'BBG001SCZDT2', 'BBG001SCZH13', 'BBG001SCZHX8', 'BBG001SCZKL4', 'BBG001SCZLH7', 'BBG001SCZLL2', 'BBG001SCZM76', 'BBG001SCZMK1', 'BBG001SCZQY7', 'BBG001SCZSG3', 'BBG001SCZSK8', 'BBG001SCZVC0', 'BBG001SCZVQ5', 'BBG001SCZZS4', 'BBG001SD01K3', 'BBG001SD0285', 'BBG001SD0659', 'BBG001SD06N9', 'BBG001SD0766', 'BBG001SD0800', 'BBG001SD08D6', 'BBG001SD08G3', 'BBG001SD0CF5', 'BBG001SD0CM7', 'BBG001SD0DF3', 'BBG001SD0DX3', 'BBG001SD0H46', 'BBG001SD0HL7', 'BBG001SD0PG5', 'BBG001SD0PL9', 'BBG001SD0QX4', 'BBG001SD0ST5', 'BBG001SD0ZQ2', 'BBG001SD1097', 'BBG001SD12S2', 'BBG001SD1408', 'BBG001SD14F2', 'BBG001SD14L5', 'BBG001SD1D44', 'BBG001SD1F76', 'BBG001SD1G38', 'BBG001SD1KZ4', 'BBG001SD1L64', 'BBG001SD1N79', 'BBG001SD1TR4', 'BBG001SD1X04', 'BBG001SD1XD0', 'BBG001SD1XQ6', 'BBG001SD1YR3', 'BBG001SD2041', 'BBG001SD2112', 'BBG001SD21P6', 'BBG001SD2318', 'BBG001SD2461', 'BBG001SD2666', 'BBG001SD2B10', 'BBG001SD2B74', 'BBG001SD2BB9', 'BBG001SD2DR8', 'BBG001SD2GJ0', 'BBG001SD2J40', 'BBG001SD2J68', 'BBG001SD2QH0', 'BBG001SD2QM4', 'BBG001SD2R79', 'BBG001SD2RB4', 'BBG001SD2RD2', 'BBG001SD2SB2', 'BBG001SD2SD0', 'BBG001SD2T57', 'BBG001SD2TR3', 'BBG001SD2TY5', 'BBG001SD2W32', 'BBG001SD2W96', 'BBG001SD2X12', 'BBG001SD2YP4', 'BBG001SD2Z08', 'BBG001SD2ZG1', 'BBG001SD3004', 'BBG001SD3013', 'BBG001SD3068', 'BBG001SD30D0', 'BBG001SD3148', 'BBG001SD31B0', 'BBG001SD31G5', 'BBG001SD31K0', 'BBG001SD31M8', 'BBG001SD31N7', 'BBG001SD31Q4', 'BBG001SD32N5', 'BBG001SD32Z2', 'BBG001SD33H0', 'BBG001SD33Z0', 'BBG001SD3530', 'BBG001SD3576', 'BBG001SD3692', 'BBG001SD36N6', 'BBG001SD36Q3', 'BBG001SD36W6', 'BBG001SD3709', 'BBG001SD3736', 'BBG001SD39D1', 'BBG001SD39J5', 'BBG001SD39Z7', 'BBG001SD3BG3', 'BBG001SD3BH2', 'BBG001SD3BP3', 'BBG001SD3BV6', 'BBG001SD3BW5', 'BBG001SD3CY1', 'BBG001SD3DY9', 'BBG001SD3DZ8', 'BBG001SD3F38', 'BBG001SD3F56', 'BBG001SD3F74', 'BBG001SD3FG4', 'BBG001SD3JR4', 'BBG001SD3L08', 'BBG001SD3M06', 'BBG001SD3M15', 'BBG001SD3N95', 'BBG001SD3PG2', 'BBG001SD3PL6', 'BBG001SD3PN4', 'BBG001SD3SG6', 'BBG001SD3SN8', 'BBG001SD3TS1', 'BBG001SD3XR3', 'BBG001SD3XZ4', 'BBG001SD3Y46', 'BBG001SD3YJ0', 'BBG001SD3ZX1', 'BBG001SD4085', 'BBG001SD4192', 'BBG001SD41P4', 'BBG001SD4245', 'BBG001SD4254', 'BBG001SD4290', 'BBG001SD42R0', 'BBG001SD42S9', 'BBG001SD42W4', 'BBG001SD43R8', 'BBG001SD43V3', 'BBG001SD43X1', 'BBG001SD45H4', 'BBG001SD46L7', 'BBG001SD46Q2', 'BBG001SD47H0', 'BBG001SD49H6', 'BBG001SD4BJ9', 'BBG001SD4BL6', 'BBG001SD4C43', 'BBG001SD4C89', 'BBG001SD4DZ7', 'BBG001SD4FQ2', 'BBG001SD4FZ2', 'BBG001SD4G08', 'BBG001SD4G53', 'BBG001SD4JP5', 'BBG001SD4JX6', 'BBG001SD4KQ1', 'BBG001SD4L34', 'BBG001SD4N12', 'BBG001SD4NM9', 'BBG001SD4NT2', 'BBG001SD4Q24', 'BBG001SD4Q88', 'BBG001SD4S66', 'BBG001SD4TH2', 'BBG001SD4TY3', 'BBG001SD4VV1', 'BBG001SD4W03', 'BBG001SD4W30', 'BBG001SD4W94', 'BBG001SD4WL0', 'BBG001SD4XG4', 'BBG001SD4Y36', 'BBG001SD4YL6', 'BBG001SD4YY2', 'BBG001SD4Z42', 'BBG001SD4Z97', 'BBG001SD50Y5', 'BBG001SD5137', 'BBG001SD5262', 'BBG001SD52B6', 'BBG001SD52Y1', 'BBG001SD54L1', 'BBG001SD5592', 'BBG001SD55D7', 'BBG001SD56D5', 'BBG001SD5CL3', 'BBG001SD5H41', 'BBG001SD5JX5', 'BBG001SD5K08', 'BBG001SD5MF8', 'BBG001SD5R03', 'BBG001SD5SK9', 'BBG001SD5TS9', 'BBG001SD5VL1', 'BBG001SD5WG5', 'BBG001SD61N4', 'BBG001SD64G6', 'BBG001SD68G7', 'BBG001SD69B0', 'BBG001SD69D8', 'BBG001SD69L9', 'BBG001SD6D76', 'BBG001SD6DW8', 'BBG001SD6GY9', 'BBG001SD6NF5', 'BBG001SD6S91', 'BBG001SD6SM6', 'BBG001SD6VB1', 'BBG001SD6Z04', 'BBG001SD6Z13', 'BBG001SD6ZW9', 'BBG001SD7135', 'BBG001SD73X8', 'BBG001SD7509', 'BBG001SD7536', 'BBG001SD75G2', 'BBG001SD77H7', 'BBG001SD77Z7', 'BBG001SD7901', 'BBG001SD79J1', 'BBG001SD79V7', 'BBG001SD7BP9', 'BBG001SD7D84', 'BBG001SD7FL4', 'BBG001SD7FZ9', 'BBG001SD7G05', 'BBG001SD7GP8', 'BBG001SD7H76', 'BBG001SD7H94', 'BBG001SD7HQ5', 'BBG001SD7J81', 'BBG001SD7MQ4', 'BBG001SD7PS5', 'BBG001SD7QK1', 'BBG001SD7RB9', 'BBG001SD7XS8', 'BBG001SD80P2', 'BBG001SD8214', 'BBG001SD82B3', 'BBG001SD8330', 'BBG001SD83T2', 'BBG001SD8517', 'BBG001SD86X0', 'BBG001SD8DD7', 'BBG001SD8DK9', 'BBG001SD8F15', 'BBG001SD8JJ8', 'BBG001SD8R91', 'BBG001SD8RV6', 'BBG001SD8VW6', 'BBG001SD8W72', 'BBG001SD8XK5', 'BBG001SD8Z84', 'BBG001SD9008', 'BBG001SD9213', 'BBG001SD94P3', 'BBG001SD9525', 'BBG001SD9561', 'BBG001SD96X9', 'BBG001SD9712', 'BBG001SD97Y6', 'BBG001SD9C75', 'BBG001SD9CQ4', 'BBG001SD9DW5', 'BBG001SD9H29', 'BBG001SD9N71', 'BBG001SD9P67', 'BBG001SD9RJ9', 'BBG001SD9RM5', 'BBG001SD9T14', 'BBG001SD9TG8', 'BBG001SD9TS5', 'BBG001SD9V55', 'BBG001SD9V73', 'BBG001SD9VH2', 'BBG001SD9WB6', 'BBG001SD9X60', 'BBG001SDB050', 'BBG001SDB087', 'BBG001SDB0L2', 'BBG001SDB176', 'BBG001SDB327', 'BBG001SDB363', 'BBG001SDB559', 'BBG001SDB5Z6', 'BBG001SDB791', 'BBG001SDB7B8', 'BBG001SDB7C7', 'BBG001SDBB01', 'BBG001SDBD34', 'BBG001SDBF48', 'BBG001SDBF84', 'BBG001SDBFQ4', 'BBG001SDBJR5', 'BBG001SDBJT3', 'BBG001SDBJZ6', 'BBG001SDBLX3', 'BBG001SDBN96', 'BBG001SDBP37', 'BBG001SDBP55', 'BBG001SDBRV2', 'BBG001SDBRW1', 'BBG001SDBSD0', 'BBG001SDBSR5', 'BBG001SDBZ80', 'BBG001SDC1W7', 'BBG001SDC273', 'BBG001SDC291', 'BBG001SDC380', 'BBG001SDC4D2', 'BBG001SDC4J6', 'BBG001SDC870', 'BBG001SDC8B5', 'BBG001SDC8L4', 'BBG001SDCC08', 'BBG001SDCF10', 'BBG001SDCFT0', 'BBG001SDCJ21', 'BBG001SDCK91', 'BBG001SDCL35', 'BBG001SDCM15', 'BBG001SDCPS9', 'BBG001SDCRT4', 'BBG001SDCST2', 'BBG001SDCV97', 'BBG001SDCWG7', 'BBG001SDCX75', 'BBG001SDCY73', 'BBG001SDCZ70', 'BBG001SDCZZ9', 'BBG001SDD1S1', 'BBG001SDD209', 'BBG001SDD3P0', 'BBG001SDD4T4', 'BBG001SDD6L7', 'BBG001SDD7S8', 'BBG001SDD7Z0', 'BBG001SDD824', 'BBG001SDD8F0', 'BBG001SDDBQ1', 'BBG001SDDC52', 'BBG001SDDD05', 'BBG001SDDDP8', 'BBG001SDDF82', 'BBG001SDDFM6', 'BBG001SDDFY3', 'BBG001SDDHJ6', 'BBG001SDDJH4', 'BBG001SDDMX9', 'BBG001SDDVQ7', 'BBG001SDDWL0', 'BBG001SDDWZ5', 'BBG001SDF181', 'BBG001SDF2N2', 'BBG001SDF582', 'BBG001SDF760', 'BBG001SDFF62', 'BBG001SDFFC5', 'BBG001SDFFK6', 'BBG001SDFG15', 'BBG001SDFHL1', 'BBG001SDFKP0', 'BBG001SDFP06', 'BBG001SDFPD2', 'BBG001SDFPL3', 'BBG001SDFR75', 'BBG001SDFRF6', 'BBG001SDFSM6', 'BBG001SDFV49', 'BBG001SDFX63', 'BBG001SDFY52', 'BBG001SDFYJ7', 'BBG001SDFYK5', 'BBG001SDFZS4', 'BBG001SDFZX8', 'BBG001SDG0H2', 'BBG001SDG572', 'BBG001SDG6C4', 'BBG001SDG7X9', 'BBG001SDG849', 'BBG001SDG885', 'BBG001SDG8T2', 'BBG001SDG8Y6', 'BBG001SDG965', 'BBG001SDG9D7', 'BBG001SDG9X5', 'BBG001SDGD75', 'BBG001SDGFR8', 'BBG001SDGFT6', 'BBG001SDGFV3', 'BBG001SDGHK1', 'BBG001SDGKP9', 'BBG001SDGNN5', 'BBG001SDGQB1', 'BBG001SDGR01', 'BBG001SDGSF3', 'BBG001SDGSH1', 'BBG001SDGTK5', 'BBG001SDGVT1', 'BBG001SDGWS0', 'BBG001SDGWV6', 'BBG001SDGXF2', 'BBG001SDGZB1', 'BBG001SDGZF7', 'BBG001SDH152', 'BBG001SDH1H9', 'BBG001SDH1V3', 'BBG001SDH250', 'BBG001SDH3R4', 'BBG001SDH6G9', 'BBG001SDH6W1', 'BBG001SDH7B2', 'BBG001SDH884', 'BBG001SDH8F6', 'BBG001SDH9Y3', 'BBG001SDHBB3', 'BBG001SDHBQ7', 'BBG001SDHC58', 'BBG001SDHD83', 'BBG001SDHF60', 'BBG001SDHGY7', 'BBG001SDHJS8', 'BBG001SDHJW3', 'BBG001SDHK32', 'BBG001SDHKH7', 'BBG001SDHNW4', 'BBG001SDHP95', 'BBG001SDHPH6', 'BBG001SDHSQ0', 'BBG001SDHSY1', 'BBG001SDHTQ8', 'BBG001SDHWD5', 'BBG001SDHWR0', 'BBG001SDJ285', 'BBG001SDJ347', 'BBG001SDJ365', 'BBG001SDJ3S1', 'BBG001SDJ4R0', 'BBG001SDJ6N9', 'BBG001SDJ7K0', 'BBG001SDJ882', 'BBG001SDJ9B6', 'BBG001SDJBK1', 'BBG001SDJBN8', 'BBG001SDJC38', 'BBG001SDJHP3', 'BBG001SDJJX0', 'BBG001SDJKQ5', 'BBG001SDJP11', 'BBG001SDJQ37', 'BBG001SDJSF0', 'BBG001SDJVG2', 'BBG001SDJX50', 'BBG001SDK0P8', 'BBG001SDK2P4', 'BBG001SDK336', 'BBG001SDK3J9', 'BBG001SDK3S9', 'BBG001SDK3V5', 'BBG001SDK6B0', 'BBG001SDK871', 'BBG001SDK915', 'BBG001SDKDB5', 'BBG001SDKJZ6', 'BBG001SDKMJ7', 'BBG001SDKNS5', 'BBG001SDKNY8', 'BBG001SDKPD6', 'BBG001SDKPT9', 'BBG001SDKS86', 'BBG001SDKTM8', 'BBG001SDKVB5', 'BBG001SDKXN8', 'BBG001SDKXT2', 'BBG001SDL1R3', 'BBG001SDL4X0', 'BBG001SDL5X7', 'BBG001SDL709', 'BBG001SDL7L6', 'BBG001SDL807', 'BBG001SDL9S5', 'BBG001SDLBT9', 'BBG001SDLC80', 'BBG001SDLD33', 'BBG001SDLD51', 'BBG001SDLDD2', 'BBG001SDLN68', 'BBG001SDLNC1', 'BBG001SDLP09', 'BBG001SDLQ34', 'BBG001SDLQH9', 'BBG001SDLQZ9', 'BBG001SDLSS3', 'BBG001SDLX20', 'BBG001SDLYG3', 'BBG001SDLYP3', 'BBG001SDLYX4', 'BBG001SDM0V9', 'BBG001SDM2J9', 'BBG001SDM307', 'BBG001SDM3D3', 'BBG001SDM3M3', 'BBG001SDM3W2', 'BBG001SDM4Q7', 'BBG001SDM673', 'BBG001SDMF46', 'BBG001SDMG62', 'BBG001SDMPG1', 'BBG001SDMRD0', 'BBG001SDMVC2', 'BBG001SDMX92', 'BBG001SDMXH3', 'BBG001SDN217', 'BBG001SDN912', 'BBG001SDN9T2', 'BBG001SDN9V9', 'BBG001SDN9W8', 'BBG001SDNB44', 'BBG001SDNBP1', 'BBG001SDNC79', 'BBG001SDNFQ1', 'BBG001SDNKB6', 'BBG001SDNNW7', 'BBG001SDNPJ7', 'BBG001SDNPY0', 'BBG001SDNSB9', 'BBG001SDNT81', 'BBG001SDNVN9', 'BBG001SDNYJ8', 'BBG001SDNYQ0', 'BBG001SDNYS8', 'BBG001SDP3B2', 'BBG001SDP3N9', 'BBG001SDP7H7', 'BBG001SDP7J5', 'BBG001SDP8P6', 'BBG001SDP9J1', 'BBG001SDPCV0', 'BBG001SDPDJ2', 'BBG001SDPDP5', 'BBG001SDPDR3', 'BBG001SDPFH9', 'BBG001SDPFY0', 'BBG001SDPG41', 'BBG001SDPGD1', 'BBG001SDPGZ7', 'BBG001SDPHZ5', 'BBG001SDPJC6', 'BBG001SDPLN9', 'BBG001SDPLQ6', 'BBG001SDPM66', 'BBG001SDPNY3', 'BBG001SDPPH7', 'BBG001SDPTN2', 'BBG001SDPZ76', 'BBG001SDQ2Q7', 'BBG001SDQ697', 'BBG001SDQ7F8', 'BBG001SDQ7N9', 'BBG001SDQ8V8', 'BBG001SDQ8W7', 'BBG001SDQB14', 'BBG001SDQC76', 'BBG001SDQF15', 'BBG001SDQJ17', 'BBG001SDQJG1', 'BBG001SDQNQ1', 'BBG001SDQNX3', 'BBG001SDQP59', 'BBG001SDQQD8', 'BBG001SDQR19', 'BBG001SDQRX4', 'BBG001SDQSB6', 'BBG001SDQSC5', 'BBG001SDQSW3', 'BBG001SDQV83', 'BBG001SDQWW4', 'BBG001SDR062', 'BBG001SDR7W8', 'BBG001SDR8R2', 'BBG001SDR8W6', 'BBG001SDRB22', 'BBG001SDRDX4', 'BBG001SDRG21', 'BBG001SDRGP6', 'BBG001SDRH74', 'BBG001SDRJ16', 'BBG001SDRJD3', 'BBG001SDRJX1', 'BBG001SDRL75', 'BBG001SDRL93', 'BBG001SDRLJ2', 'BBG001SDRLL9', 'BBG001SDRQM7', 'BBG001SDRQW6', 'BBG001SDRTR6', 'BBG001SDRVL7', 'BBG001SDRXY9', 'BBG001SDRZ29', 'BBG001SDRZL8', 'BBG001SDS178', 'BBG001SDS4K7', 'BBG001SDS5G9', 'BBG001SDS891', 'BBG001SDSDH1', 'BBG001SDSN07', 'BBG001SDST22', 'BBG001SDSV81', 'BBG001SDSVH1', 'BBG001SDSW61', 'BBG001SDSWX1', 'BBG001SDSWY0', 'BBG001SDSX14', 'BBG001SDSX32', 'BBG001SDSZP3', 'BBG001SDT523', 'BBG001SDT8M5', 'BBG001SDT961', 'BBG001SDTBP5', 'BBG001SDTCH2', 'BBG001SDTJY8', 'BBG001SDTLC7', 'BBG001SDTLV6', 'BBG001SDTN33', 'BBG001SDTNQ8', 'BBG001SDTNY9', 'BBG001SDTRN2', 'BBG001SDTSJ5', 'BBG001SDTVQ0', 'BBG001SDTZ45', 'BBG001SDV2Y2', 'BBG001SDV370', 'BBG001SDV3N2', 'BBG001SDV414', 'BBG001SDV5Z4', 'BBG001SDV6G3', 'BBG001SDV6R1', 'BBG001SDV780', 'BBG001SDV7X2', 'BBG001SDV8R7', 'BBG001SDVC34', 'BBG001SDVCH9', 'BBG001SDVFV6', 'BBG001SDVGN3', 'BBG001SDVKL6', 'BBG001SDVL07', 'BBG001SDVPN3', 'BBG001SDVS48', 'BBG001SDVSM8', 'BBG001SDVWG6', 'BBG001SDVXS1', 'BBG001SDVZP9', 'BBG001SDVZV2', 'BBG001SDW0C9', 'BBG001SDW128', 'BBG001SDW1S0', 'BBG001SDW4G7', 'BBG001SDW6Y2', 'BBG001SDW7L4', 'BBG001SDW7Q9', 'BBG001SDWBN3', 'BBG001SDWCT5', 'BBG001SDWCZ8', 'BBG001SDWD59', 'BBG001SDWJQ3', 'BBG001SDWMW9', 'BBG001SDWPS7', 'BBG001SDWRP6', 'BBG001SDWTD5', 'BBG001SDWVG7', 'BBG001SDWX19', 'BBG001SDX029', 'BBG001SDX207', 'BBG001SDX225', 'BBG001SDX252', 'BBG001SDX305', 'BBG001SDX4S3', 'BBG001SDX5H2', 'BBG001SDX6N3', 'BBG001SDX760', 'BBG001SDX840', 'BBG001SDX8R5', 'BBG001SDX8T3', 'BBG001SDX9V8', 'BBG001SDXBJ7', 'BBG001SDXC50', 'BBG001SDXFP1', 'BBG001SDXFT7', 'BBG001SDXGH8', 'BBG001SDXH04', 'BBG001SDXHV0', 'BBG001SDXJR1', 'BBG001SDXQD0', 'BBG001SDXT71', 'BBG001SDXTP1', 'BBG001SDY0X4', 'BBG001SDY2N1', 'BBG001SDY4R3', 'BBG001SDYBH8', 'BBG001SDYGQ7', 'BBG001SDYGS5', 'BBG001SDYMP5', 'BBG001SDYQL0', 'BBG001SDYZG6', 'BBG001SDYZM9', 'BBG001SDZ161', 'BBG001SDZ278', 'BBG001SDZ6G9', 'BBG001SDZ7B2', 'BBG001SDZ839', 'BBG001SDZ9K8', 'BBG001SDZBM1', 'BBG001SDZCF7', 'BBG001SDZCG6', 'BBG001SDZDD7', 'BBG001SDZGD0', 'BBG001SDZHG5', 'BBG001SDZHM8', 'BBG001SDZHP5', 'BBG001SDZHV8', 'BBG001SDZM29', 'BBG001SDZNG2', 'BBG001SDZPN9', 'BBG001SDZPR5', 'BBG001SDZPT3', 'BBG001SDZRB8', 'BBG001SDZSV4', 'BBG001SDZV65', 'BBG001SDZYX9', 'BBG001SF0100', 'BBG001SF01F4', 'BBG001SF01G3', 'BBG001SF02H0', 'BBG001SF06Q1', 'BBG001SF0CS6', 'BBG001SF0F27', 'BBG001SF0K17', 'BBG001SF0R30', 'BBG001SF0RN8', 'BBG001SF0T63', 'BBG001SF0VF8', 'BBG001SF0XN5', 'BBG001SF0Z23', 'BBG001SF11H1', 'BBG001SF1332', 'BBG001SF13S5', 'BBG001SF1449', 'BBG001SF1582', 'BBG001SF1617', 'BBG001SF1626', 'BBG001SF17M2', 'BBG001SF1BX1', 'BBG001SF1HK2', 'BBG001SF1NW6', 'BBG001SF1PH8', 'BBG001SF1R75', 'BBG001SF1SL7', 'BBG001SF1WN6', 'BBG001SF1XH1', 'BBG001SF1XY2', 'BBG001SF21Q0', 'BBG001SF2288', 'BBG001SF22H8', 'BBG001SF23V0', 'BBG001SF28J3', 'BBG001SF28W8', 'BBG001SF2DC9', 'BBG001SF2DY5', 'BBG001SF2GW0', 'BBG001SF2HF7', 'BBG001SF2JX3', 'BBG001SF2K06', 'BBG001SF2K42', 'BBG001SF2L95', 'BBG001SF2PW0', 'BBG001SF2VM8', 'BBG001SF2YK4', 'BBG001SF34K9', 'BBG001SF34Q3', 'BBG001SF3660', 'BBG001SF3946', 'BBG001SF3C76', 'BBG001SF3F60', 'BBG001SF3FX0', 'BBG001SF3J26', 'BBG001SF3JC5', 'BBG001SF3JF2', 'BBG001SF3JZ0', 'BBG001SF3K41', 'BBG001SF3K87', 'BBG001SF3KB3', 'BBG001SF3L85', 'BBG001SF3NM5', 'BBG001SF3P04', 'BBG001SF3P77', 'BBG001SF3Q66', 'BBG001SF3Q84', 'BBG001SF3TR7', 'BBG001SF3WY2', 'BBG001SF3ZQ4', 'BBG001SF4605', 'BBG001SF4B68', 'BBG001SF4KP7', 'BBG001SF4L48', 'BBG001SF4LP5', 'BBG001SF4NM4', 'BBG001SF4NY1', 'BBG001SF4RY2', 'BBG001SF4TP8', 'BBG001SF4TX9', 'BBG001SF4VS0', 'BBG001SF4XM2', 'BBG001SF5141', 'BBG001SF51N0', 'BBG001SF56T3', 'BBG001SF5953', 'BBG001SF5980', 'BBG001SF59H0', 'BBG001SF5DJ9', 'BBG001SF5FJ4', 'BBG001SF5H91', 'BBG001SF5HB8', 'BBG001SF5LT0', 'BBG001SF5LW6', 'BBG001SF5M09', 'BBG001SF5MB7', 'BBG001SF5MH1', 'BBG001SF5MS9', 'BBG001SF5MW4', 'BBG001SF5N07', 'BBG001SF5RK6', 'BBG001SF5RY1', 'BBG001SF5SC3', 'BBG001SF5SG9', 'BBG001SF5VZ1', 'BBG001SF60M2', 'BBG001SF62F6', 'BBG001SF62P5', 'BBG001SF6373', 'BBG001SF6596', 'BBG001SF65L2', 'BBG001SF65P8', 'BBG001SF6747', 'BBG001SF6961', 'BBG001SF69P0', 'BBG001SF6B93', 'BBG001SF6CM6', 'BBG001SF6DY1', 'BBG001SF6FR4', 'BBG001SF6G56', 'BBG001SF6HK7', 'BBG001SF6L46', 'BBG001SF6PZ3', 'BBG001SF6R07', 'BBG001SF6R25', 'BBG001SF6S05', 'BBG001SF6S96', 'BBG001SF6V71', 'BBG001SF6W88', 'BBG001SF6Y39', 'BBG001SF6YK0', 'BBG001SF6YV8', 'BBG001SF6Z45', 'BBG001SF6Z72', 'BBG001SF6ZZ1', 'BBG001SF7005', 'BBG001SF70S5', 'BBG001SF73F3', 'BBG001SF7416', 'BBG001SF74W2', 'BBG001SF74Y0', 'BBG001SF7522', 'BBG001SF7595', 'BBG001SF75B2', 'BBG001SF7657', 'BBG001SF76G5', 'BBG001SF7906', 'BBG001SF7BF5', 'BBG001SF7BW6', 'BBG001SF7C18', 'BBG001SF7C54', 'BBG001SF7C72', 'BBG001SF7CB7', 'BBG001SF7CH1', 'BBG001SF7CM5', 'BBG001SF7D61', 'BBG001SF7DQ9', 'BBG001SF7DX1', 'BBG001SF7FF6', 'BBG001SF7FT1', 'BBG001SF7G00', 'BBG001SF7H08', 'BBG001SF7HB6', 'BBG001SF7M43', 'BBG001SF7QG1', 'BBG001SF7R33', 'BBG001SF7RS6', 'BBG001SF7T02', 'BBG001SF7TV8', 'BBG001SF7V07', 'BBG001SF7V89', 'BBG001SF8255', 'BBG001SF82R1', 'BBG001SF83D4', 'BBG001SF83K6', 'BBG001SF8406', 'BBG001SF8460', 'BBG001SF84L3', 'BBG001SF85Y6', 'BBG001SF86D7', 'BBG001SF87V5', 'BBG001SF8807', 'BBG001SF8861', 'BBG001SF88Y0', 'BBG001SF8932', 'BBG001SF89W0', 'BBG001SF8DJ6', 'BBG001SF8F10', 'BBG001SF8FK9', 'BBG001SF8G27', 'BBG001SF8H07', 'BBG001SF8HM3', 'BBG001SF8L44', 'BBG001SF8LP1', 'BBG001SF8M33', 'BBG001SF8P45', 'BBG001SF8Q52', 'BBG001SF8W86', 'BBG001SF8WV0', 'BBG001SF8X02', 'BBG001SF8XY5', 'BBG001SF8XZ4', 'BBG001SF8YB8', 'BBG001SF8YW5', 'BBG001SF91V7', 'BBG001SF9272', 'BBG001SF93X1', 'BBG001SF93Z9', 'BBG001SF94V1', 'BBG001SF95L9', 'BBG001SF9744', 'BBG001SF97Y1', 'BBG001SF99G7', 'BBG001SF9B81', 'BBG001SF9DJ5', 'BBG001SF9F19', 'BBG001SF9FC7', 'BBG001SF9GV4', 'BBG001SF9H06', 'BBG001SF9H97', 'BBG001SF9HJ6', 'BBG001SF9J02', 'BBG001SF9J93', 'BBG001SF9JC9', 'BBG001SF9JJ2', 'BBG001SF9KP2', 'BBG001SF9KX3', 'BBG001SF9M14', 'BBG001SF9MK3', 'BBG001SF9ML2', 'BBG001SF9NK1', 'BBG001SF9NQ5', 'BBG001SF9QM2', 'BBG001SF9R68', 'BBG001SF9RT3', 'BBG001SF9RV0', 'BBG001SF9S57', 'BBG001SF9S75', 'BBG001SF9SX6', 'BBG001SF9W21', 'BBG001SF9W94', 'BBG001SF9WJ3', 'BBG001SF9WZ5', 'BBG001SF9X92', 'BBG001SF9XZ3', 'BBG001SF9YL6', 'BBG001SF9YT8', 'BBG001SF9YY2', 'BBG001SFB2S6', 'BBG001SFB6X1', 'BBG001SFB7R6', 'BBG001SFB7Z7', 'BBG001SFBBM2', 'BBG001SFBBS6', 'BBG001SFBD48', 'BBG001SFBDR3', 'BBG001SFBF25', 'BBG001SFBGZ7', 'BBG001SFBHY6', 'BBG001SFBJ27', 'BBG001SFBK60', 'BBG001SFBLL1', 'BBG001SFBM20', 'BBG001SFBNM6', 'BBG001SFBNQ2', 'BBG001SFBNY3', 'BBG001SFBP87', 'BBG001SFBPB3', 'BBG001SFBQ03', 'BBG001SFBQD9', 'BBG001SFBR01', 'BBG001SFBS27', 'BBG001SFBZN8', 'BBG001SFC349', 'BBG001SFC4M7', 'BBG001SFC4P4', 'BBG001SFC5F2', 'BBG001SFC7V0', 'BBG001SFC7W9', 'BBG001SFC7Y7', 'BBG001SFC7Z6', 'BBG001SFC802', 'BBG001SFC820', 'BBG001SFC9P3', 'BBG001SFC9S0', 'BBG001SFCBS5', 'BBG001SFCCH5', 'BBG001SFCH93', 'BBG001SFCJ35', 'BBG001SFCJK6', 'BBG001SFCJN3', 'BBG001SFCK78', 'BBG001SFCMH3', 'BBG001SFCMX5', 'BBG001SFCPS4', 'BBG001SFCR82', 'BBG001SFCS99', 'BBG001SFCSK6', 'BBG001SFCTL3', 'BBG001SFCTN1', 'BBG001SFCVJ1', 'BBG001SFCVW6', 'BBG001SFCW36', 'BBG001SFCXS7', 'BBG001SFCY78', 'BBG001SFCYN0', 'BBG001SFD0K6', 'BBG001SFD1Z8', 'BBG001SFD6Z7', 'BBG001SFD8J1', 'BBG001SFDBC1', 'BBG001SFDBF8', 'BBG001SFDF41', 'BBG001SFDFB3', 'BBG001SFDFV1', 'BBG001SFDHB9', 'BBG001SFDLD8', 'BBG001SFDM82', 'BBG001SFDT96', 'BBG001SFDTK3', 'BBG001SFDWV4', 'BBG001SFDYZ6', 'BBG001SFF0N1', 'BBG001SFF2W7', 'BBG001SFF4H0', 'BBG001SFF4S8', 'BBG001SFFB84', 'BBG001SFFDM4', 'BBG001SFFHL6', 'BBG001SFFL55', 'BBG001SFFLX4', 'BBG001SFFLY3', 'BBG001SFFYJ2', 'BBG001SFFZ63', 'BBG001SFFZF3', 'BBG001SFG452', 'BBG001SFG6Z4', 'BBG001SFG7G3', 'BBG001SFG8S8', 'BBG001SFGC09', 'BBG001SFGF84', 'BBG001SFGF93', 'BBG001SFGFG5', 'BBG001SFGH35', 'BBG001SFGJ31', 'BBG001SFGL81', 'BBG001SFGWG8', 'BBG001SFGWZ7', 'BBG001SFGX21', 'BBG001SFGXD9', 'BBG001SFGXG6', 'BBG001SFGXJ3', 'BBG001SFGXQ5', 'BBG001SFGXR4', 'BBG001SFGXS3', 'BBG001SFGXW8', 'BBG001SFGXZ5', 'BBG001SFGY10', 'BBG001SFGZL5', 'BBG001SFH0K2', 'BBG001SFH120', 'BBG001SFH1P5', 'BBG001SFH3T7', 'BBG001SFH5C0', 'BBG001SFH7D5', 'BBG001SFH7S9', 'BBG001SFH8P0', 'BBG001SFH950', 'BBG001SFH9C2', 'BBG001SFHGY2', 'BBG001SFHH43', 'BBG001SFHJL0', 'BBG001SFHKJ0', 'BBG001SFHKT9', 'BBG001SFHKW5', 'BBG001SFHP72', 'BBG001SFHPG2', 'BBG001SFHS85', 'BBG001SFHSV9', 'BBG001SFHVP9', 'BBG001SFHVZ8', 'BBG001SFHXR3', 'BBG001SFJ0F6', 'BBG001SFJ734', 'BBG001SFJ743', 'BBG001SFJF18', 'BBG001SFJFD5', 'BBG001SFJKF2', 'BBG001SFJL33', 'BBG001SFJMM0', 'BBG001SFJNH4', 'BBG001SFJQ69', 'BBG001SFJQS5', 'BBG001SFJQV1', 'BBG001SFJRV9', 'BBG001SFJXW5', 'BBG001SFJZ41', 'BBG001SFK0V6', 'BBG001SFK313', 'BBG001SFK4L9', 'BBG001SFK6C4', 'BBG001SFK9K9', 'BBG001SFK9N6', 'BBG001SFKCZ6', 'BBG001SFKF98', 'BBG001SFKFF1', 'BBG001SFKH21', 'BBG001SFKH49', 'BBG001SFKKH8', 'BBG001SFKN73', 'BBG001SFKP87', 'BBG001SFKPS5', 'BBG001SFKPX9', 'BBG001SFKRX5', 'BBG001SFKS45', 'BBG001SFKSH1', 'BBG001SFKTF1', 'BBG001SFKW19', 'BBG001SFKYQ8', 'BBG001SFKZ03', 'BBG001SFL125', 'BBG001SFL1C4', 'BBG001SFL1Q9', 'BBG001SFL1R8', 'BBG001SFL2F9', 'BBG001SFL3W8', 'BBG001SFL438', 'BBG001SFL642', 'BBG001SFL9K8', 'BBG001SFL9Y3', 'BBG001SFLB87', 'BBG001SFLC94', 'BBG001SFLCV9', 'BBG001SFLF15', 'BBG001SFLGF8', 'BBG001SFLGL1', 'BBG001SFLKN0', 'BBG001SFLL12', 'BBG001SFLN09', 'BBG001SFLP31', 'BBG001SFLPW9', 'BBG001SFLQM8', 'BBG001SFLS17', 'BBG001SFLS53', 'BBG001SFLSF2', 'BBG001SFLVP4', 'BBG001SFLYP8', 'BBG001SFM071', 'BBG001SFM2W9', 'BBG001SFM3D8', 'BBG001SFM3Q4', 'BBG001SFM6N0', 'BBG001SFMCT1', 'BBG001SFMFS5', 'BBG001SFMKS4', 'BBG001SFMSZ9', 'BBG001SFMT05', 'BBG001SFMVV6', 'BBG001SFMWH0', 'BBG001SFMX15', 'BBG001SFMXT5', 'BBG001SFMXZ8', 'BBG001SFN1L2', 'BBG001SFN347', 'BBG001SFN3T0', 'BBG001SFN5N1', 'BBG001SFN6V0', 'BBG001SFN819', 'BBG001SFN828', 'BBG001SFN837', 'BBG001SFNB21', 'BBG001SFNBB1', 'BBG001SFNBR4', 'BBG001SFNHB8', 'BBG001SFNK58', 'BBG001SFNL56', 'BBG001SFNTK2', 'BBG001SFNVQ1', 'BBG001SFNY12', 'BBG001SFP586', 'BBG001SFP5W9', 'BBG001SFP737', 'BBG001SFP8J8', 'BBG001SFPB56', 'BBG001SFPFL9', 'BBG001SFPFM8', 'BBG001SFPHB6', 'BBG001SFPML4', 'BBG001SFPMM3', 'BBG001SFPNM1', 'BBG001SFPRR7', 'BBG001SFPV25', 'BBG001SFPXM9', 'BBG001SFPXR4', 'BBG001SFPZ35', 'BBG001SFPZC5', 'BBG001SFPZZ0', 'BBG001SFQ0P7', 'BBG001SFQ2T9', 'BBG001SFQ3Y1', 'BBG001SFQ610', 'BBG001SFQ6S1', 'BBG001SFQ7D5', 'BBG001SFQCC5', 'BBG001SFQDT5', 'BBG001SFQHQ9', 'BBG001SFQJ76', 'BBG001SFQJF7', 'BBG001SFQJM9', 'BBG001SFQJX7', 'BBG001SFQL08', 'BBG001SFQL26', 'BBG001SFQL35', 'BBG001SFQL44', 'BBG001SFQL80', 'BBG001SFQL99', 'BBG001SFQM51', 'BBG001SFQM79', 'BBG001SFQMB4', 'BBG001SFQNN9', 'BBG001SFQPM5', 'BBG001SFQQ89', 'BBG001SFQQ98', 'BBG001SFQSF7', 'BBG001SFQWB2', 'BBG001SFQXG5', 'BBG001SFQXK0', 'BBG001SFQXM8', 'BBG001SFQXN7', 'BBG001SFR0N8', 'BBG001SFR4X9', 'BBG001SFR5G5', 'BBG001SFR6R1', 'BBG001SFR879', 'BBG001SFR888', 'BBG001SFRD87', 'BBG001SFRDG8', 'BBG001SFRF82', 'BBG001SFRHK4', 'BBG001SFRK81', 'BBG001SFRL43', 'BBG001SFRLH9', 'BBG001SFRPF2', 'BBG001SFRQ79', 'BBG001SFRR04', 'BBG001SFRR22', 'BBG001SFRT28', 'BBG001SFRVY8', 'BBG001SFRWG6', 'BBG001SFRWP6', 'BBG001SFRZW1', 'BBG001SFS1Q2', 'BBG001SFS3R7', 'BBG001SFS4P7', 'BBG001SFS5M7', 'BBG001SFS6T8', 'BBG001SFS7Q9', 'BBG001SFS805', 'BBG001SFS869', 'BBG001SFS896', 'BBG001SFSBH0', 'BBG001SFSC97', 'BBG001SFSHM1', 'BBG001SFSK62', 'BBG001SFSKW3', 'BBG001SFSMY7', 'BBG001SFSNV8', 'BBG001SFSQW0', 'BBG001SFSQX9', 'BBG001SFSR85', 'BBG001SFSSG4', 'BBG001SFSXD6', 'BBG001SFSXJ0', 'BBG001SFSY71', 'BBG001SFSZT4', 'BBG001SFT1Z1', 'BBG001SFT225', 'BBG001SFT2R8', 'BBG001SFT412', 'BBG001SFTBB5', 'BBG001SFTCR6', 'BBG001SFTCX9', 'BBG001SFTD76', 'BBG001SFTG51', 'BBG001SFTK61', 'BBG001SFTQW9', 'BBG001SFTQZ6', 'BBG001SFTRS2', 'BBG001SFTTS8', 'BBG001SFTVM9', 'BBG001SFTY52', 'BBG001SFV1C3', 'BBG001SFV1J6', 'BBG001SFV3R3', 'BBG001SFV6Q7', 'BBG001SFV721', 'BBG001SFV7B1', 'BBG001SFV7S3', 'BBG001SFV7W8', 'BBG001SFVCJ2', 'BBG001SFVD28', 'BBG001SFVF50', 'BBG001SFVF87', 'BBG001SFVJ52', 'BBG001SFVK86', 'BBG001SFVKJ4', 'BBG001SFVM00', 'BBG001SFVMP3', 'BBG001SFVN99', 'BBG001SFVNG1', 'BBG001SFVPN8', 'BBG001SFVPP6', 'BBG001SFVQ29', 'BBG001SFVQ83', 'BBG001SFVS07', 'BBG001SFVS98', 'BBG001SFVSP0', 'BBG001SFVT96', 'BBG001SFVTK3', 'BBG001SFVVN5', 'BBG001SFVVW5', 'BBG001SFVWR9', 'BBG001SFVYV0', 'BBG001SFW1V1', 'BBG001SFWCV7', 'BBG001SFWFL1', 'BBG001SFWHY3', 'BBG001SFWKK1', 'BBG001SFWLL8', 'BBG001SFWR17', 'BBG001SFWS51', 'BBG001SFWTN9', 'BBG001SFWTV0', 'BBG001SFWVX3', 'BBG001SFWWY0', 'BBG001SFWXN0', 'BBG001SFWYX7', 'BBG001SFWYY6', 'BBG001SFWZ82', 'BBG001SFWZR1', 'BBG001SFX104', 'BBG001SFX2D8', 'BBG001SFX3M6', 'BBG001SFX3P3', 'BBG001SFX5G8', 'BBG001SFX836', 'BBG001SFXCR1', 'BBG001SFXDX2', 'BBG001SFXG65', 'BBG001SFXJ96', 'BBG001SFXJR6', 'BBG001SFXK75', 'BBG001SFXLJ0', 'BBG001SFXMP1', 'BBG001SFXTV9', 'BBG001SFXVG1', 'BBG001SFXY66', 'BBG001SFXYR3', 'BBG001SFXZ36', 'BBG001SFY3K7', 'BBG001SFY5P7', 'BBG001SFYCM5', 'BBG001SFYHV4', 'BBG001SFYWL2', 'BBG001SFYZT7', 'BBG001SFZ086', 'BBG001SFZ2P3', 'BBG001SFZ3S8', 'BBG001SFZ7N4', 'BBG001SFZ9N0', 'BBG001SFZ9Y8', 'BBG001SFZBL7', 'BBG001SFZD60', 'BBG001SFZDJ6', 'BBG001SFZKN5', 'BBG001SFZL99', 'BBG001SFZLN3', 'BBG001SFZNJ4', 'BBG001SFZQJ7', 'BBG001SFZQL4', 'BBG001SFZRR6', 'BBG001SG00G3', 'BBG001SG03X8', 'BBG001SG0723', 'BBG001SG09V7', 'BBG001SG0B51', 'BBG001SG0F07', 'BBG001SG0GN0', 'BBG001SG0H12', 'BBG001SG0JW4', 'BBG001SG0K79', 'BBG001SG0M20', 'BBG001SG0M48', 'BBG001SG0TR8', 'BBG001SG0V75', 'BBG001SG0VF6', 'BBG001SG0X80', 'BBG001SG1571', 'BBG001SG15B6', 'BBG001SG17S4', 'BBG001SG17T3', 'BBG001SG1K14', 'BBG001SG1LH5', 'BBG001SG1LP6', 'BBG001SG1LY6', 'BBG001SG1ML8', 'BBG001SG1MZ3', 'BBG001SG1NR0', 'BBG001SG1WQ1', 'BBG001SG1XF1', 'BBG001SG1ZV8', 'BBG001SG23W7', 'BBG001SG24P3', 'BBG001SG2954', 'BBG001SG29F3', 'BBG001SG2BG7', 'BBG001SG2DQ2', 'BBG001SG2JJ7', 'BBG001SG2M46', 'BBG001SG2NJ8', 'BBG001SG2VR1', 'BBG001SG2X24', 'BBG001SG37B0', 'BBG001SG37M8', 'BBG001SG3CC8', 'BBG001SG3GG5', 'BBG001SG3HZ2', 'BBG001SG3JV2', 'BBG001SG3K30', 'BBG001SG3KP6', 'BBG001SG3L56', 'BBG001SG3M27', 'BBG001SG3MY2', 'BBG001SG3MZ1', 'BBG001SG3TN9', 'BBG001SG3WS7', 'BBG001SG3Y58', 'BBG001SG40N1', 'BBG001SG4195', 'BBG001SG4257', 'BBG001SG4275', 'BBG001SG4284', 'BBG001SG42D8', 'BBG001SG4435', 'BBG001SG46V9', 'BBG001SG47G4', 'BBG001SG4970', 'BBG001SG4B84', 'BBG001SG4DJ8', 'BBG001SG4DQ0', 'BBG001SG4DX2', 'BBG001SG4DY1', 'BBG001SG4F76', 'BBG001SG4F94', 'BBG001SG4FG6', 'BBG001SG4FM9', 'BBG001SG4FR4', 'BBG001SG4FT2', 'BBG001SG4FW8', 'BBG001SG4FX7', 'BBG001SG4G01', 'BBG001SG4G29', 'BBG001SG4G47', 'BBG001SG4G74', 'BBG001SG4G92', 'BBG001SG4GD7', 'BBG001SG4GF5', 'BBG001SG4GH3', 'BBG001SG4GJ1', 'BBG001SG4GR2', 'BBG001SG4GW6', 'BBG001SG4H18', 'BBG001SG4H63', 'BBG001SG4NL3', 'BBG001SG4NN1', 'BBG001SG4PF5', 'BBG001SG4PQ3', 'BBG001SG4WZ8', 'BBG001SG4XK2', 'BBG001SG4Z09', 'BBG001SG4Z27', 'BBG001SG53D5', 'BBG001SG5559', 'BBG001SG56C9', 'BBG001SG5DQ9', 'BBG001SG5DW2', 'BBG001SG5G91', 'BBG001SG5KF5', 'BBG001SG5P46', 'BBG001SG5PL7', 'BBG001SG5PR1', 'BBG001SG5RL3', 'BBG001SG5VG0', 'BBG001SG6175', 'BBG001SG6380', 'BBG001SG6567', 'BBG001SG65Q5', 'BBG001SG65Z5', 'BBG001SG6843', 'BBG001SG6DZ8', 'BBG001SG6K73', 'BBG001SG6LT7', 'BBG001SG6RL2', 'BBG001SG6ZX1', 'BBG001SG7986', 'BBG001SG7C98', 'BBG001SG7F64', 'BBG001SG7G71', 'BBG001SG7J11', 'BBG001SG7ML2', 'BBG001SG7SC9', 'BBG001SG80W7', 'BBG001SG82Y1', 'BBG001SG83T5', 'BBG001SG84J4', 'BBG001SG87L4', 'BBG001SG87Y0', 'BBG001SG88H7', 'BBG001SG8C24', 'BBG001SG8C60', 'BBG001SG8GH9', 'BBG001SG8GM3', 'BBG001SG8HX9', 'BBG001SG8KX2', 'BBG001SG8MN9', 'BBG001SG8MR5', 'BBG001SG8W48', 'BBG001SG8WT1', 'BBG001SG8Z87', 'BBG001SG95B8', 'BBG001SG98G7', 'BBG001SG98H6', 'BBG001SG9CL2', 'BBG001SG9D58', 'BBG001SG9FS8', 'BBG001SG9G79', 'BBG001SG9H22', 'BBG001SG9HF8', 'BBG001SG9HM0', 'BBG001SG9KH9', 'BBG001SG9PM2', 'BBG001SG9SZ2', 'BBG001SG9V85', 'BBG001SG9WK9', 'BBG001SG9YB5', 'BBG001SGB026', 'BBG001SGB1W1', 'BBG001SGB2G7', 'BBG001SGB2J4', 'BBG001SGB4T9', 'BBG001SGB561', 'BBG001SGB5Y0', 'BBG001SGB641', 'BBG001SGBBG7', 'BBG001SGBC02', 'BBG001SGBC48', 'BBG001SGBGW8', 'BBG001SGBJP0', 'BBG001SGBJW2', 'BBG001SGBLP5', 'BBG001SGBML7', 'BBG001SGBNY1', 'BBG001SGBNZ0', 'BBG001SGBPV9', 'BBG001SGBQ74', 'BBG001SGBQD7', 'BBG001SGBWR9', 'BBG001SGBXM2', 'BBG001SGBXP9', 'BBG001SGBYW9', 'BBG001SGC3C8', 'BBG001SGC5L3', 'BBG001SGC828', 'BBG001SGC8M6', 'BBG001SGC953', 'BBG001SGC9Z0', 'BBG001SGCFT3', 'BBG001SGCJY9', 'BBG001SGCKY6', 'BBG001SGCL92', 'BBG001SGCLB9', 'BBG001SGCRG1', 'BBG001SGCTD0', 'BBG001SGCVF3', 'BBG001SGCYR4', 'BBG001SGD499', 'BBG001SGD6C0', 'BBG001SGD7G4', 'BBG001SGD8Z1', 'BBG001SGDB57', 'BBG001SGDC64', 'BBG001SGDF49', 'BBG001SGDHN4', 'BBG001SGDJ23', 'BBG001SGDN51', 'BBG001SGDQF3', 'BBG001SGDR16', 'BBG001SGDV17', 'BBG001SGDX59', 'BBG001SGDXC1', 'BBG001SGF3N3', 'BBG001SGF5P6', 'BBG001SGF8F1', 'BBG001SGFG09', 'BBG001SGFMC3', 'BBG001SGFMK4', 'BBG001SGFR32', 'BBG001SGFTT0', 'BBG001SGFXV8', 'BBG001SGG5R3', 'BBG001SGG5W7', 'BBG001SGGDL2', 'BBG001SGGFG3', 'BBG001SGGK90', 'BBG001SGGKG2', 'BBG001SGGKT8', 'BBG001SGGP80', 'BBG001SGGQX0', 'BBG001SGGR22', 'BBG001SGH2P1', 'BBG001SGH2S8', 'BBG001SGH3R7', 'BBG001SGH8Q7', 'BBG001SGH967', 'BBG001SGH9N8', 'BBG001SGHBP1', 'BBG001SGHH14', 'BBG001SGHHZ7', 'BBG001SGHKN3', 'BBG001SGHPN2', 'BBG001SGHSS1', 'BBG001SGHSV7', 'BBG001SGHVD0', 'BBG001SGHYV4', 'BBG001SGJ1L5', 'BBG001SGJ4L9', 'BBG001SGJ5F3', 'BBG001SGJ714', 'BBG001SGJ8Q5', 'BBG001SGJ929', 'BBG001SGJD57', 'BBG001SGJG96', 'BBG001SGJGZ7', 'BBG001SGJH12', 'BBG001SGJHW8', 'BBG001SGJJB7', 'BBG001SGJJN4', 'BBG001SGJLB2', 'BBG001SGJPH7', 'BBG001SGJRC8', 'BBG001SGJTK5', 'BBG001SGJVC9', 'BBG001SGJYW1', 'BBG001SGK1B4', 'BBG001SGK758', 'BBG001SGKBW9', 'BBG001SGKBY7', 'BBG001SGKBZ6', 'BBG001SGKC20', 'BBG001SGKR63', 'BBG001SGKST6', 'BBG001SGKTJ5', 'BBG001SGKV64', 'BBG001SGL070', 'BBG001SGL0H9', 'BBG001SGL123', 'BBG001SGL1J5', 'BBG001SGL3Y4', 'BBG001SGL604', 'BBG001SGL6R5', 'BBG001SGL748', 'BBG001SGL8G3', 'BBG001SGL9Y1', 'BBG001SGLD63', 'BBG001SGLDS9', 'BBG001SGLF40', 'BBG001SGLH64', 'BBG001SGLHS0', 'BBG001SGLJ88', 'BBG001SGLMW4', 'BBG001SGLP11', 'BBG001SGLPS2', 'BBG001SGLQH2', 'BBG001SGLTC1', 'BBG001SGLW52', 'BBG001SGLXW0', 'BBG001SGLYM9', 'BBG001SGLZ37', 'BBG001SGM3F4', 'BBG001SGM5P8', 'BBG001SGM6M9', 'BBG001SGMBX6', 'BBG001SGMFJ3', 'BBG001SGMH90', 'BBG001SGMQ27', 'BBG001SGMVF2', 'BBG001SGMYJ2', 'BBG001SGN0C2', 'BBG001SGN0V1', 'BBG001SGN1L0', 'BBG001SGNC72', 'BBG001SGNL18', 'BBG001SGNMC4', 'BBG001SGNMK5', 'BBG001SGNNQ7', 'BBG001SGNPK8', 'BBG001SGNQG1', 'BBG001SGNX85', 'BBG001SGNYC8', 'BBG001SGNYQ3', 'BBG001SGP2V5', 'BBG001SGP2W4', 'BBG001SGP423', 'BBG001SGP7W3', 'BBG001SGP7Z0', 'BBG001SGPCF1', 'BBG001SGPF64', 'BBG001SGPKJ9', 'BBG001SGPQB4', 'BBG001SGPR59', 'BBG001SGPWZ5', 'BBG001SGPXJ1', 'BBG001SGPY09', 'BBG001SGQ002', 'BBG001SGQ2M4', 'BBG001SGQ4W9', 'BBG001SGQ6K7', 'BBG001SGQ9T2', 'BBG001SGQBL5', 'BBG001SGQD22', 'BBG001SGQFR0', 'BBG001SGQL06', 'BBG001SGQMB2', 'BBG001SGQND8', 'BBG001SGQQR6', 'BBG001SGQRM9', 'BBG001SGQWF6', 'BBG001SGQY62', 'BBG001SGQYB6', 'BBG001SGQYM4', 'BBG001SGR494', 'BBG001SGR5Q2', 'BBG001SGR6Q0', 'BBG001SGR859', 'BBG001SGRBK5', 'BBG001SGRMS3', 'BBG001SGRT26', 'BBG001SGRVF7', 'BBG001SGRW38', 'BBG001SGRY43', 'BBG001SGRZ86', 'BBG001SGS2Z8', 'BBG001SGSC04', 'BBG001SGSMW7', 'BBG001SGSP96', 'BBG001SGSRC8', 'BBG001SGSSF3', 'BBG001SGSZK1', 'BBG001SGT296', 'BBG001SGT4X5', 'BBG001SGT731', 'BBG001SGT8G5', 'BBG001SGTCH5', 'BBG001SGTCW8', 'BBG001SGTD56', 'BBG001SGTD74', 'BBG001SGTGK2', 'BBG001SGTPK2', 'BBG001SGTQB0', 'BBG001SGTVC8', 'BBG001SGTVM7', 'BBG001SGTWW4', 'BBG001SGTWZ1', 'BBG001SGV202', 'BBG001SGV417', 'BBG001SGV444', 'BBG001SGV4D4', 'BBG001SGV4P1', 'BBG001SGV569', 'BBG001SGVBF6', 'BBG001SGVD71', 'BBG001SGVH18', 'BBG001SGVHD5', 'BBG001SGVK75', 'BBG001SGVK84', 'BBG001SGVNJ6', 'BBG001SGVNZ8', 'BBG001SGVS78', 'BBG001SGVY93', 'BBG001SGW2S1', 'BBG001SGW3M5', 'BBG001SGW5F8', 'BBG001SGW6B0', 'BBG001SGW6F6', 'BBG001SGW6H4', 'BBG001SGWBH3', 'BBG001SGWCD5', 'BBG001SGWD25', 'BBG001SGWDS7', 'BBG001SGWK56', 'BBG001SGWWN0', 'BBG001SGWXQ5', 'BBG001SGX399', 'BBG001SGX3X2', 'BBG001SGX585', 'BBG001SGX6D7', 'BBG001SGXB64', 'BBG001SGXCQ0', 'BBG001SGXCY1', 'BBG001SGXKR1', 'BBG001SGXLB6', 'BBG001SGXNC1', 'BBG001SGXPS9', 'BBG001SGXPT8', 'BBG001SGXPX3', 'BBG001SGXPY2', 'BBG001SGXQ07', 'BBG001SGXVB4', 'BBG001SGXVD2', 'BBG001SGXZ16', 'BBG001SGY0F7', 'BBG001SGY138', 'BBG001SGY2X3', 'BBG001SGY325', 'BBG001SGY4N0', 'BBG001SGY6K8', 'BBG001SGY7M4', 'BBG001SGYD96', 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'BBG001SL2739', 'BBG001SL27N7', 'BBG001SL27R3', 'BBG001SL28N5', 'BBG001SL2C10', 'BBG001SL2CQ3', 'BBG001SL2D63', 'BBG001SL2FT3', 'BBG001SL2K30', 'BBG001SL2KB1', 'BBG001SL2L47', 'BBG001SL2Q82', 'BBG001SL30S6', 'BBG001SL3DN3', 'BBG001SL3GQ3', 'BBG001SL3HM5', 'BBG001SL3LN5', 'BBG001SL3LQ2', 'BBG001SL3TS3', 'BBG001SL3WS6', 'BBG001SL3ZH1', 'BBG001SL3ZY2', 'BBG001SL42X5', 'BBG001SL46L9', 'BBG001SL46X6', 'BBG001SL4LS9', 'BBG001SL4QM4', 'BBG001SL4TS2', 'BBG001SL4YJ1', 'BBG001SL4YT0', 'BBG001SL51D8', 'BBG001SL52N5', 'BBG001SL52Q2', 'BBG001SL56K9', 'BBG001SL57V5', 'BBG001SL5898', 'BBG001SL5DV2', 'BBG001SL5GB7', 'BBG001SL5GC6', 'BBG001SL5GH1', 'BBG001SL5GW4', 'BBG001SL5N13', 'BBG001SL5TM7', 'BBG001SL5Y46', 'BBG001SL64C2', 'BBG001SL64H7', 'BBG001SL6GN3', 'BBG001SL6GW3', 'BBG001SL6L70', 'BBG001SL6PR9', 'BBG001SL6PZ0', 'BBG001SL6QR7', 'BBG001SL6R95', 'BBG001SL6SX6', 'BBG001SL6T73', 'BBG001SL6WF7', 'BBG001SL6YH1', 'BBG001SL6Z15', 'BBG001SL72C5', 'BBG001SL72W3', 'BBG001SL7HR6', 'BBG001SL7HY8', 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'BBG001SLD9C3', 'BBG001SLD9D2', 'BBG001SLDC63', 'BBG001SLDJ59', 'BBG001SLDPD6', 'BBG001SLDQ80', 'BBG001SLF370', 'BBG001SLF4N0', 'BBG001SLF4P8', 'BBG001SLFB27', 'BBG001SLFCT6', 'BBG001SLFDF9', 'BBG001SLFDK3', 'BBG001SLFJB0', 'BBG001SLFKM5', 'BBG001SLFQ79', 'BBG001SLFVR6', 'BBG001SLFYY2', 'BBG001SLG5M7', 'BBG001SLG672', 'BBG001SLGGJ7', 'BBG001SLGHK3', 'BBG001SLGJ92', 'BBG001SLGM77', 'BBG001SLGR12', 'BBG001SLGV22', 'BBG001SLGXT9', 'BBG001SLH608', 'BBG001SLH7Q8', 'BBG001SLHHB2', 'BBG001SLHL96', 'BBG001SLHMT2', 'BBG001SLHNX5', 'BBG001SLHRS2', 'BBG001SLHSL7', 'BBG001SLHT35', 'BBG001SLHV21', 'BBG001SLHVV9', 'BBG001SLHZV0', 'BBG001SLJ198', 'BBG001SLJ624', 'BBG001SLJR19', 'BBG001SLJV56', 'BBG001SLJVN6', 'BBG001SLK0B5', 'BBG001SLK1R6', 'BBG001SLK3B9', 'BBG001SLK5M2', 'BBG001SLK8C7', 'BBG001SLKBJ3', 'BBG001SLKBL0', 'BBG001SLKBM9', 'BBG001SLKBR4', 'BBG001SLKD81', 'BBG001SLKDX3', 'BBG001SLKFL1', 'BBG001SLKGB0', 'BBG001SLKJK4', 'BBG001SLKK49', 'BBG001SLKL38', 'BBG001SLKNW2', 'BBG001SLKPP5', 'BBG001SLKS33', 'BBG001SLKS51', 'BBG001SLKYH5', 'BBG001SLKYJ3', 'BBG001SLL7N6', 'BBG001SLL934', 'BBG001SLLJ78', 'BBG001SLLLY3', 'BBG001SLLQV5', 'BBG001SLLS23', 'BBG001SLLWL3', 'BBG001SLLZM5', 'BBG001SLM3W4', 'BBG001SLM3Z1', 'BBG001SLM443', 'BBG001SLM522', 'BBG001SLMFB0', 'BBG001SLMK47', 'BBG001SLMK92', 'BBG001SLMKX5', 'BBG001SLMLM5', 'BBG001SLMNQ7', 'BBG001SLMP91', 'BBG001SLN031', 'BBG001SLN1H4', 'BBG001SLN3H0', 'BBG001SLN5S3', 'BBG001SLNBM6', 'BBG001SLNKV6', 'BBG001SLNQ61', 'BBG001SLNQD3', 'BBG001SLNSP6', 'BBG001SLNWJ4', 'BBG001SLNWP7', 'BBG001SLNXS2', 'BBG001SLP413', 'BBG001SLP431', 'BBG001SLP4P7', 'BBG001SLP5D7', 'BBG001SLP6Q1', 'BBG001SLPGH9', 'BBG001SLPHZ7', 'BBG001SLPMY7', 'BBG001SLPP25', 'BBG001SLPX37', 'BBG001SLQ109', 'BBG001SLQ6G1', 'BBG001SLQ6K6', 'BBG001SLQ6N3', 'BBG001SLQ8F8', 'BBG001SLQG97', 'BBG001SLQNX5', 'BBG001SLQPB4', 'BBG001SLQV49', 'BBG001SLQWW6', 'BBG001SLR359', 'BBG001SLR3C1', 'BBG001SLR625', 'BBG001SLR8B1', 'BBG001SLR8S3', 'BBG001SLRCD0', 'BBG001SLRCW9', 'BBG001SLRDN7', 'BBG001SLRDP5', 'BBG001SLRNY3', 'BBG001SLRWP3', 'BBG001SLS3T2', 'BBG001SLS4Q3', 'BBG001SLS606', 'BBG001SLS697', 'BBG001SLS7B2', 'BBG001SLS973', 'BBG001SLSBV1', 'BBG001SLSDV7', 'BBG001SLSF60', 'BBG001SLSHC9', 'BBG001SLSHZ4', 'BBG001SLSKN0', 'BBG001SLSN72', 'BBG001SLSQM8', 'BBG001SLSWH1', 'BBG001SLSXG0', 'BBG001SLSXK5', 'BBG001SLSXM3', 'BBG001SLT0M4', 'BBG001SLT758', 'BBG001SLTC75', 'BBG001SLTFJ5', 'BBG001SLTGB1', 'BBG001SLTPP6', 'BBG001SLTQD7', 'BBG001SLTSR8', 'BBG001SLTZG4', 'BBG001SLV078', 'BBG001SLV0G8', 'BBG001SLV0Q7', 'BBG001SLV4J7', 'BBG001SLVFY5', 'BBG001SLVJ95', 'BBG001SLVJZ6', 'BBG001SLVT11', 'BBG001SLVTL9', 'BBG001SLVXW8', 'BBG001SLW0V0', 'BBG001SLW3L5', 'BBG001SLW4C3', 'BBG001SLW549', 'BBG001SLW8L4', 'BBG001SLWD88', 'BBG001SLWGL6', 'BBG001SLWM79', 'BBG001SLWNS4', 'BBG001SLWQF1', 'BBG001SLWR05', 'BBG001SLWWP7', 'BBG001SLWWR5', 'BBG001SLWY37', 'BBG001SLX1D7', 'BBG001SLX1V7', 'BBG001SLX290', 'BBG001SLX2D5', 'BBG001SLX655', 'BBG001SLX7F2', 'BBG001SLX7G1', 'BBG001SLX9R5', 'BBG001SLXGZ0', 'BBG001SLXKF3', 'BBG001SLXRL1', 'BBG001SLXTB8', 'BBG001SLXTP3', 'BBG001SLXYK7', 'BBG001SLXYL6', 'BBG001SLY0X6', 'BBG001SLY155', 'BBG001SLY235', 'BBG001SLY244', 'BBG001SLY2K6', 'BBG001SLY2R9', 'BBG001SLY556', 'BBG001SLY7R8', 'BBG001SLY9X7', 'BBG001SLYQX9', 'BBG001SLYWD8', 'BBG001SLYY17', 'BBG001SLZ5Y3', 'BBG001SLZ671', 'BBG001SLZ724', 'BBG001SLZ8D0', 'BBG001SLZ939', 'BBG001SLZ9R3', 'BBG001SLZBJ7', 'BBG001SLZC23', 'BBG001SLZF44', 'BBG001SLZFT7', 'BBG001SLZLJ5', 'BBG001SM02X7', 'BBG001SM06F8', 'BBG001SM0819', 'BBG001SM08B8', 'BBG001SM08F4', 'BBG001SM0GX6', 'BBG001SM0HS0', 'BBG001SM0K30', 'BBG001SM0K49', 'BBG001SM0TL1', 'BBG001SM0W43', 'BBG001SM13G3', 'BBG001SM16W8', 'BBG001SM1DK6', 'BBG001SM1G56', 'BBG001SM1J50', 'BBG001SM1QQ1', 'BBG001SM1QT8', 'BBG001SM1RN2', 'BBG001SM1SS5', 'BBG001SM1T49', 'BBG001SM1TX7', 'BBG001SM1Z18', 'BBG001SM2112', 'BBG001SM21J3', 'BBG001SM2210', 'BBG001SM2390', 'BBG001SM23F3', 'BBG001SM24M3', 'BBG001SM28D4', 'BBG001SM29R7', 'BBG001SM2F48', 'BBG001SM2G73', 'BBG001SM2GJ0', 'BBG001SM2GK8', 'BBG001SM2GR1', 'BBG001SM2LG2', 'BBG001SM2MF1', 'BBG001SM2MK5', 'BBG001SM2PW5', 'BBG001SM2Q26', 'BBG001SM2S77', 'BBG001SM30Y7', 'BBG001SM31X6', 'BBG001SM32G3', 'BBG001SM3736', 'BBG001SM3BF4', 'BBG001SM3BQ2', 'BBG001SM3CC5', 'BBG001SM3DP9', 'BBG001SM3GY2', 'BBG001SM3KX4', 'BBG001SM3Q16', 'BBG001SM3VF0', 'BBG001SM3VS6', 'BBG001SM44F9', 'BBG001SM4539', 'BBG001SM4691', 'BBG001SM46B8', 'BBG001SM48F0', 'BBG001SM48J6', 'BBG001SM4986', 'BBG001SM49Z6', 'BBG001SM4HD2', 'BBG001SM4KY2', 'BBG001SM4P71', 'BBG001SM4PC5', 'BBG001SM4WF7', 'BBG001SM4WZ5', 'BBG001SM4XN6', 'BBG001SM4XW6', 'BBG001SM4YD5', 'BBG001SM4YK7', 'BBG001SM4Z60', 'BBG001SM51M6', 'BBG001SM51P3', 'BBG001SM51S0', 'BBG001SM51V6', 'BBG001SM53B4', 'BBG001SM54J4', 'BBG001SM5949', 'BBG001SM5958', 'BBG001SM5F27', 'BBG001SM5FW4', 'BBG001SM5H05', 'BBG001SM5JR2', 'BBG001SM5KZ0', 'BBG001SM5LL3', 'BBG001SM5MM0', 'BBG001SM5QS5', 'BBG001SM5TJ9', 'BBG001SM5XQ2', 'BBG001SM62K5', 'BBG001SM68G7', 'BBG001SM69P5', 'BBG001SM6C41', 'BBG001SM6FD4', 'BBG001SM6HR5', 'BBG001SM6JZ2', 'BBG001SM6LG8', 'BBG001SM6MG6', 'BBG001SM6PD2', 'BBG001SM6V94', 'BBG001SM6W65', 'BBG001SM6Y34', 'BBG001SM6Z59', 'BBG001SM70S0', 'BBG001SM70Y3', 'BBG001SM71W3', 'BBG001SM73S4', 'BBG001SM7B15', 'BBG001SM7B33', 'BBG001SM7BG9', 'BBG001SM7BJ6', 'BBG001SM7BN1', 'BBG001SM7HC0', 'BBG001SM7N73', 'BBG001SM7Q67', 'BBG001SM7XM4', 'BBG001SM7Y33', 'BBG001SM7Y60', 'BBG001SM7ZD9', 'BBG001SM80T8', 'BBG001SM81V3', 'BBG001SM82S5', 'BBG001SM85R9', 'BBG001SM8660', 'BBG001SM8BV1', 'BBG001SM8CY6', 'BBG001SM8D74', 'BBG001SM8F97', 'BBG001SM8JP1', 'BBG001SM8L58', 'BBG001SM8L67', 'BBG001SM8M10', 'BBG001SM8QX6', 'BBG001SM8WV5', 'BBG001SM8XW2', 'BBG001SM96T4', 'BBG001SM9794', 'BBG001SM99S9', 'BBG001SM99T8', 'BBG001SM99X3', 'BBG001SM9D19', 'BBG001SM9FD1', 'BBG001SM9G12', 'BBG001SM9GN8', 'BBG001SM9GY6', 'BBG001SM9H01', 'BBG001SM9LS2', 'BBG001SM9MD6', 'BBG001SM9NZ0', 'BBG001SM9P03', 'BBG001SM9QV7', 'BBG001SM9R18', 'BBG001SM9RC6', 'BBG001SM9WT7', 'BBG001SMB2L8', 'BBG001SMB2N6', 'BBG001SMB871', 'BBG001SMBB47', 'BBG001SMBC18', 'BBG001SMBD43', 'BBG001SMBJG7', 'BBG001SMBKM7', 'BBG001SMBT39', 'BBG001SMBVP0', 'BBG001SMBXG6', 'BBG001SMBY29', 'BBG001SMBYX5', 'BBG001SMBZR9', 'BBG001SMC1M8', 'BBG001SMC2J0', 'BBG001SMC4F0', 'BBG001SMC5N8', 'BBG001SMC5T2', 'BBG001SMC629', 'BBG001SMC6B9', 'BBG001SMC6F5', 'BBG001SMC7H1', 'BBG001SMC7S9', 'BBG001SMC9T4', 'BBG001SMCBQ2', 'BBG001SMCFB9', 'BBG001SMCHF1', 'BBG001SMCKB8', 'BBG001SMCN40', 'BBG001SMCRV1', 'BBG001SMCS49', 'BBG001SMCTP4', 'BBG001SMCWX8', 'BBG001SMCZH9', 'BBG001SMD0N8', 'BBG001SMD2X3', 'BBG001SMD307', 'BBG001SMD3Q9', 'BBG001SMD3T6', 'BBG001SMD6B8', 'BBG001SMDCC4', 'BBG001SMDCR8', 'BBG001SMDG62', 'BBG001SMDGS8', 'BBG001SMDMK3', 'BBG001SMDNB1', 'BBG001SMDPC5', 'BBG001SMDR68', 'BBG001SMDR77', 'BBG001SMF298', 'BBG001SMF7C3', 'BBG001SMF920', 'BBG001SMFCR6', 'BBG001SMFKS7', 'BBG001SMG1Q0', 'BBG001SMG9M7', 'BBG001SMG9V7', 'BBG001SMGG41', 'BBG001SMGGP8', 'BBG001SMGVG5', 'BBG001SMGW19', 'BBG001SMGWC7', 'BBG001SMH152', 'BBG001SMH2H7', 'BBG001SMH2R6', 'BBG001SMH2T4', 'BBG001SMH2W0', 'BBG001SMH456', 'BBG001SMH465', 'BBG001SMH4B9', 'BBG001SMH6Y9', 'BBG001SMH768', 'BBG001SMH7G7', 'BBG001SMH7Z6', 'BBG001SMH8K0', 'BBG001SMHBM1', 'BBG001SMHG59', 'BBG001SMHJ53', 'BBG001SMHLQ5', 'BBG001SMHPW9', 'BBG001SMHR46', 'BBG001SMHRF4', 'BBG001SMHRH2', 'BBG001SMHTF0', 'BBG001SMHW27', 'BBG001SMHX52', 'BBG001SMHY41', 'BBG001SMHY69', 'BBG001SMHYB3', 'BBG001SMJ3J1', 'BBG001SMJ5C3', 'BBG001SMJ5Y9', 'BBG001SMJ7S2', 'BBG001SMJ882', 'BBG001SMJB76', 'BBG001SMJC56', 'BBG001SMJCK9', 'BBG001SMJGS2', 'BBG001SMJL38', 'BBG001SMJMY2', 'BBG001SMJQ46', 'BBG001SMK0R6', 'BBG001SMK5P7', 'BBG001SMK666', 'BBG001SMK8X2', 'BBG001SMK906', 'BBG001SMK988', 'BBG001SMKDB5', 'BBG001SMKHL5', 'BBG001SMKJT3', 'BBG001SMKND1', 'BBG001SMKRM2', 'BBG001SMKV61', 'BBG001SMKX67', 'BBG001SMKXT2', 'BBG001SMKXY6', 'BBG001SMKZC5', 'BBG001SMKZP1', 'BBG001SML1C9', 'BBG001SML8F1', 'BBG001SMLC62', 'BBG001SMLFP4', 'BBG001SMLGK7', 'BBG001SMLHL4', 'BBG001SMLMJ6', 'BBG001SMLN40', 'BBG001SMLN77', 'BBG001SMLP54', 'BBG001SMLP90', 'BBG001SMLPK7', 'BBG001SMLRT4', 'BBG001SMLTL8', 'BBG001SMLVH8', 'BBG001SMLVL3', 'BBG001SMLVY9', 'BBG001SMLW04', 'BBG001SMLW13', 'BBG001SMLW77', 'BBG001SMLZ16', 'BBG001SMLZ34', 'BBG001SMM101', 'BBG001SMM1V7', 'BBG001SMM2J9', 'BBG001SMM7Q0', 'BBG001SMM806', 'BBG001SMM9J4', 'BBG001SMMCW2', 'BBG001SMMG26', 'BBG001SMMNF7', 'BBG001SMMW12', 'BBG001SMN155', 'BBG001SMN2X2', 'BBG001SMN4R5', 'BBG001SMN5W6', 'BBG001SMNB80', 'BBG001SMNBC5', 'BBG001SMNHG8', 'BBG001SMNMK2', 'BBG001SMNR49', 'BBG001SMNT90', 'BBG001SMP1N3', 'BBG001SMP1Y1', 'BBG001SMP466', 'BBG001SMP5L6', 'BBG001SMP6C4', 'BBG001SMP6Y0', 'BBG001SMPCQ6', 'BBG001SMPD11', 'BBG001SMPFZ9', 'BBG001SMPG23', 'BBG001SMPRZ3', 'BBG001SMPTF1', 'BBG001SMPX26', 'BBG001SMPXD4', 'BBG001SMPYN1', 'BBG001SMQ474', 'BBG001SMQ866', 'BBG001SMQ928', 'BBG001SMQD47', 'BBG001SMQD74', 'BBG001SMQD83', 'BBG001SMQF15', 'BBG001SMQJ35', 'BBG001SMQJ99', 'BBG001SMQJK6', 'BBG001SMQKD1', 'BBG001SMQKM1', 'BBG001SMRQL8', 'BBG001SMRV00', 'BBG001SMS1X9', 'BBG001SMTJ14', 'BBG001SMV110', 'BBG001SMX993', 'BBG001SMXWD7', 'BBG001SMY0Y3', 'BBG001SMYFR8', 'BBG001SMYH12', 'BBG001SMYJN4', 'BBG001SMYJW4', 'BBG001SMYMM8', 'BBG001SMYMP5', 'BBG001SMYY42', 'BBG001SMYYY9', 'BBG001SMZ5B6', 'BBG001SMZ5M4', 'BBG001SMZ5T7', 'BBG001SMZ857', 'BBG001SMZ955', 'BBG001SMZ9S0', 'BBG001SMZKH7', 'BBG001SMZNG2', 'BBG001SMZNP2', 'BBG001SMZP95', 'BBG001SMZQC9', 'BBG001SMZQS2', 'BBG001SMZQW7', 'BBG001SMZRH2', 'BBG001SMZRV6', 'BBG001SMZRY3', 'BBG001SN0434', 'BBG001SN0B92', 'BBG001SN0P46', 'BBG001SN0PJ0', 'BBG001SN0PQ2', 'BBG001SN0R60', 'BBG001SN0V52', 'BBG001SN0VJ7', 'BBG001SN0VM3', 'BBG001SN0Y92', 'BBG001SN0YW6', 'BBG001SN10M0', 'BBG001SN10Q6', 'BBG001SN11Y5', 'BBG001SN12G3', 'BBG001SN15J3', 'BBG001SN16N6', 'BBG001SN1825', 'BBG001SN18L4', 'BBG001SN1R78', 'BBG001SN1W22', 'BBG001SN1XQ4', 'BBG001SN2227', 'BBG001SN25H4', 'BBG001SN2619', 'BBG001SN29W9', 'BBG001SN2B54', 'BBG001SN2DT4', 'BBG001SN2NH5', 'BBG001SN2VH7', 'BBG001SN2YN4', 'BBG001SN2Z33', 'BBG001SN3HS5', 'BBG001SN3M04', 'BBG001SN3PM3', 'BBG001SN3PQ9', 'BBG001SN3Q23', 'BBG001SN3QG8', 'BBG001SN3RQ5', 'BBG001SN3V68', 'BBG001SN3WM8', 'BBG001SN3Z50', 'BBG001SN3Z78', 'BBG001SN4127', 'BBG001SN47D2', 'BBG001SN4L78', 'BBG001SN4LD1', 'BBG001SN4QP7', 'BBG001SN4SJ0', 'BBG001SN4T80', 'BBG001SN4TH0', 'BBG001SN4VK1', 'BBG001SN4XL6', 'BBG001SN4ZG7', 'BBG001SN4ZP7', 'BBG001SN52J6', 'BBG001SN5484', 'BBG001SN5929', 'BBG001SN59W6', 'BBG001SN5F07', 'BBG001SN5FL4', 'BBG001SN5MK0', 'BBG001SN5PR6', 'BBG001SN5ST8', 'BBG001SN5VB0', 'BBG001SN5XW3', 'BBG001SN60V5', 'BBG001SN60Z1', 'BBG001SN63X7', 'BBG001SN65J8', 'BBG001SN66N1', 'BBG001SN68L9', 'BBG001SN6GM0', 'BBG001SN6GN9', 'BBG001SN6GP7', 'BBG001SN6GS4', 'BBG001SN6GV0', 'BBG001SN6L03', 'BBG001SN74B8', 'BBG001SN7614', 'BBG001SN7C84', 'BBG001SN7CQ4', 'BBG001SN7DS0', 'BBG001SN7G49', 'BBG001SN7HP4', 'BBG001SN7ZL8', 'BBG001SN82H5', 'BBG001SN83G4', 'BBG001SN86J4', 'BBG001SN8FN9', 'BBG001SN8JL3', 'BBG001SN8NT6', 'BBG001SN8SK4', 'BBG001SN8XT4', 'BBG001SN91H6', 'BBG001SN93X4', 'BBG001SN9426', 'BBG001SN96C0', 'BBG001SN96V9', 'BBG001SN9872', 'BBG001SN99J7', 'BBG001SN9B75', 'BBG001SN9B84', 'BBG001SN9B93', 'BBG001SN9FQ5', 'BBG001SN9HR0', 'BBG001SN9L37', 'BBG001SN9NG9', 'BBG001SN9P56', 'BBG001SN9RF1', 'BBG001SN9RH9', 'BBG001SN9TC0', 'BBG001SN9TR4', 'BBG001SN9X86', 'BBG001SN9XB2', 'BBG001SNBF28', 'BBG001SNBF37', 'BBG001SNBK54', 'BBG001SNBK90', 'BBG001SNBKD5', 'BBG001SNBMJ5', 'BBG001SNBQ51', 'BBG001SNBR77', 'BBG001SNBTR1', 'BBG001SNC510', 'BBG001SNC8F9', 'BBG001SNC9N8', 'BBG001SNC9P6', 'BBG001SNCH50', 'BBG001SNCMR5', 'BBG001SNCNX6', 'BBG001SNCTZ1', 'BBG001SNCWV8', 'BBG001SNCYR9', 'BBG001SNCZG8', 'BBG001SNDBS7', 'BBG001SNDBX1', 'BBG001SNDHV0', 'BBG001SNDN29', 'BBG001SNDNZ3', 'BBG001SNDSG3', 'BBG001SNF0N4', 'BBG001SNF1V3', 'BBG001SNF3S3', 'BBG001SNF4L8', 'BBG001SNF6C3', 'BBG001SNFD38', 'BBG001SNFDG4', 'BBG001SNFHX6', 'BBG001SNFNL6', 'BBG001SNFP59', 'BBG001SNFRZ2', 'BBG001SNFXZ9', 'BBG001SNG0Y1', 'BBG001SNG6W0', 'BBG001SNG8P4', 'BBG001SNG9H1', 'BBG001SNGJ34', 'BBG001SNGPG6', 'BBG001SNGXJ6', 'BBG001SNGXL3', 'BBG001SNGY95', 'BBG001SNH2V9', 'BBG001SNH2X7', 'BBG001SNH4H1', 'BBG001SNH4N4', 'BBG001SNH5S6', 'BBG001SNH622', 'BBG001SNH6H6', 'BBG001SNH8Q2', 'BBG001SNH9L5', 'BBG001SNHDP2', 'BBG001SNHQJ0', 'BBG001SNHQP3', 'BBG001SNHR44', 'BBG001SNHR62', 'BBG001SNHVQ1', 'BBG001SNHXM1', 'BBG001SNHYZ5', 'BBG001SNJ3B7', 'BBG001SNJGK8', 'BBG001SNJTT1', 'BBG001SNJV52', 'BBG001SNJYP4', 'BBG001SNK4Z7', 'BBG001SNK6N5', 'BBG001SNK879', 'BBG001SNKC25', 'BBG001SNKCM3', 'BBG001SNKGQ0', 'BBG001SNKPZ0', 'BBG001SNKQ42', 'BBG001SNKXY4', 'BBG001SNL235', 'BBG001SNL4S4', 'BBG001SNL805', 'BBG001SNLCD2', 'BBG001SNLGR8', 'BBG001SNLHY8', 'BBG001SNLHZ7', 'BBG001SNLYV4', 'BBG001SNLZ23', 'BBG001SNM0H3', 'BBG001SNM387', 'BBG001SNM403', 'BBG001SNM5F4', 'BBG001SNM7S6', 'BBG001SNM8Q6', 'BBG001SNMN10', 'BBG001SNN4P5', 'BBG001SNN4Q4', 'BBG001SNN8R4', 'BBG001SNNBX0', 'BBG001SNNC86', 'BBG001SNNJN4', 'BBG001SNNVK0', 'BBG001SNNW37', 'BBG001SNNWL7', 'BBG001SNNWM6', 'BBG001SNNY88', 'BBG001SNP2B2', 'BBG001SNP936', 'BBG001SNPL84', 'BBG001SNPMH2', 'BBG001SNPP85', 'BBG001SNPRZ1', 'BBG001SNPT69', 'BBG001SNPVH2', 'BBG001SNPWX2', 'BBG001SNPY22', 'BBG001SNPYP7', 'BBG001SNQ2X7', 'BBG001SNQ2Z5', 'BBG001SNQ668', 'BBG001SNQ748', 'BBG001SNQ962', 'BBG001SNQ971', 'BBG001SNQFB2', 'BBG001SNQLG4', 'BBG001SNQLH3', 'BBG001SNQLM7', 'BBG001SNQXB3', 'BBG001SNQZ91', 'BBG001SNR159', 'BBG001SNR2K0', 'BBG001SNR391', 'BBG001SNR658', 'BBG001SNRN60', 'BBG001SNRRM3', 'BBG001SNRTC0', 'BBG001SNRVS8', 'BBG001SNRWN1', 'BBG001SNS069', 'BBG001SNS2B9', 'BBG001SNS461', 'BBG001SNS4R8', 'BBG001SNSCX3', 'BBG001SNSDQ9', 'BBG001SNSDZ9', 'BBG001SNSHN3', 'BBG001SNSLF3', 'BBG001SNSM07', 'BBG001SNSM70', 'BBG001SNSN87', 'BBG001SNST39', 'BBG001SNSYR2', 'BBG001SNSYW6', 'BBG001SNT0C1', 'BBG001SNT1R3', 'BBG001SNT4Q8', 'BBG001SNT6T0', 'BBG001SNT745', 'BBG001SNT7H1', 'BBG001SNT8T6', 'BBG001SNTDT5', 'BBG001SNTFW6', 'BBG001SNTNM0', 'BBG001SNTP63', 'BBG001SNTT01', 'BBG001SNTVT5', 'BBG001SNTWR5', 'BBG001SNTXY5', 'BBG001SNV0J1', 'BBG001SNV216', 'BBG001SNV3J5', 'BBG001SNV706', 'BBG001SNVCQ7', 'BBG001SNVCS5', 'BBG001SNVJN5', 'BBG001SNVQQ6', 'BBG001SNVT35', 'BBG001SNVWC8', 'BBG001SNVWN6', 'BBG001SNVWR2', 'BBG001SNVXC6', 'BBG001SNVXD5', 'BBG001SNVXP2', 'BBG001SNVYD3', 'BBG001SNW4H4', 'BBG001SNW8G6', 'BBG001SNW8K1', 'BBG001SNW8R4', 'BBG001SNWCK2', 'BBG001SNWCT3', 'BBG001SNWHL0', 'BBG001SNWNL7', 'BBG001SNWPR6', 'BBG001SNWRF5', 'BBG001SNWTM3', 'BBG001SNWVJ2', 'BBG001SNWVK0', 'BBG001SNWVT1', 'BBG001SNX0W4', 'BBG001SNX1Q9', 'BBG001SNX2X9', 'BBG001SNX2Y8', 'BBG001SNX349', 'BBG001SNX4Y4', 'BBG001SNXGW9', 'BBG001SNXN36', 'BBG001SNXQB0', 'BBG001SNXSQ0', 'BBG001SNXTW1', 'BBG001SNY6B3', 'BBG001SNY6R6', 'BBG001SNY7W8', 'BBG001SNYBH6', 'BBG001SNYFR6', 'BBG001SNYFT4', 'BBG001SNYRH1', 'BBG001SNYRK7', 'BBG001SNYTV1', 'BBG001SNZ1Q7', 'BBG001SNZ604', 'BBG001SNZ6P7', 'BBG001SNZ6Q6', 'BBG001SNZ926', 'BBG001SNZ9F2', 'BBG001SNZLM7', 'BBG001SNZPH4', 'BBG001SNZPY5', 'BBG001SNZTD0', 'BBG001SNZTS4', 'BBG001SNZWN2', 'BBG001SNZXJ5', 'BBG001SNZZK8', 'BBG001SP0625', 'BBG001SP07T4', 'BBG001SP0MG5', 'BBG001SP0QC0', 'BBG001SP0RF5', 'BBG001SP0S90', 'BBG001SP0YC3', 'BBG001SP0YQ8', 'BBG001SP11V3', 'BBG001SP14B9', 'BBG001SP19R1', 'BBG001SP1B69', 'BBG001SP1D10', 'BBG001SP1G40', 'BBG001SP1KL2', 'BBG001SP1LB1', 'BBG001SP1N63', 'BBG001SP1V65', 'BBG001SP1W09', 'BBG001SP1Y87', 'BBG001SP2482', 'BBG001SP24T9', 'BBG001SP29R0', 'BBG001SP2C20', 'BBG001SP2FG8', 'BBG001SP2H10', 'BBG001SP2HP4', 'BBG001SP2LL9', 'BBG001SP2M37', 'BBG001SP3016', 'BBG001SP3383', 'BBG001SP33S1', 'BBG001SP3K94', 'BBG001SP3KZ5', 'BBG001SP3MC6', 'BBG001SP3MD5', 'BBG001SP3NJ7', 'BBG001SP3QC7', 'BBG001SP3XJ5', 'BBG001SP4872', 'BBG001SP49Z9', 'BBG001SP4CD6', 'BBG001SP5023', 'BBG001SP50B3', 'BBG001SP57F4', 'BBG001SP58W3', 'BBG001SP5988', 'BBG001SP59T5', 'BBG001SP5B56', 'BBG001SP5H99', 'BBG001SP5JM0', 'BBG001SP5LC6', 'BBG001SP5R79', 'BBG001SP5TS2', 'BBG001SP5YH3', 'BBG001SP62C7', 'BBG001SP62F4', 'BBG001SP62Q2', 'BBG001SP66L8', 'BBG001SP68G0', 'BBG001SP6D06', 'BBG001SP6HB5', 'BBG001SP6NT3', 'BBG001SP6P27', 'BBG001SP6TY4', 'BBG001SP6Y91', 'BBG001SP6ZL4', 'BBG001SP76W5', 'BBG001SP77K6', 'BBG001SP7L61', 'BBG001SP7PB6', 'BBG001SP7PX2', 'BBG001SP7T00', 'BBG001SP7X10', 'BBG001SP7Z06', 'BBG001SP8208', 'BBG001SP8DR5', 'BBG001SP8DT3', 'BBG001SP8G34', 'BBG001SP8GP0', 'BBG001SP8JG4', 'BBG001SP8JP4', 'BBG001SP8JX5', 'BBG001SP8K26', 'BBG001SP8KW3', 'BBG001SP8M22', 'BBG001SP8MR5', 'BBG001SP8QM1', 'BBG001SP8RB1', 'BBG001SP8RR4', 'BBG001SP8RS3', 'BBG001SP8SS1', 'BBG001SP8TP2', 'BBG001SP8TQ1', 'BBG001SP8VG7', 'BBG001SP8VM0', 'BBG001SP8YD4', 'BBG001SP90J1', 'BBG001SP9591', 'BBG001SP95N5', 'BBG001SP98H6', 'BBG001SP9M85', 'BBG001SP9P33', 'BBG001SP9RS2', 'BBG001SP9TZ0', 'BBG001SP9WY4', 'BBG001SP9XM5', 'BBG001SP9XP2', 'BBG001SP9Z77', 'BBG001SPB1K4', 'BBG001SPBB86', 'BBG001SPBFS5', 'BBG001SPBG12', 'BBG001SPBRV5', 'BBG001SPBTG8', 'BBG001SPBTK3', 'BBG001SPBXV2', 'BBG001SPC3D7', 'BBG001SPC454', 'BBG001SPC6K2', 'BBG001SPCJ97', 'BBG001SPCJQ8', 'BBG001SPCRF2', 'BBG001SPCRL5', 'BBG001SPCSK4', 'BBG001SPCXW0', 'BBG001SPCY12', 'BBG001SPCYT2', 'BBG001SPD373', 'BBG001SPD3P3', 'BBG001SPD426', 'BBG001SPD4H0', 'BBG001SPDBC9', 'BBG001SPDGN6', 'BBG001SPDJ96', 'BBG001SPDND2', 'BBG001SPDRD3', 'BBG001SPDS41', 'BBG001SPDSF9', 'BBG001SPF068', 'BBG001SPF308', 'BBG001SPF5T2', 'BBG001SPF610', 'BBG001SPFKZ2', 'BBG001SPFMQ8', 'BBG001SPFRQ7', 'BBG001SPFX75', 'BBG001SPGCM3', 'BBG001SPGHF0', 'BBG001SPGV78', 'BBG001SPGVC2', 'BBG001SPH4P7', 'BBG001SPHCS6', 'BBG001SPHD86', 'BBG001SPHFR0', 'BBG001SPHFX3', 'BBG001SPHGX1', 'BBG001SPHKZ0', 'BBG001SPHL51', 'BBG001SPHL97', 'BBG001SPHLD2', 'BBG001SPHQJ5', 'BBG001SPHV22', 'BBG001SPHYW3', 'BBG001SPJ171', 'BBG001SPJ625', 'BBG001SPJ643', 'BBG001SPJ652', 'BBG001SPJ6C4', 'BBG001SPJH21', 'BBG001SPJN28', 'BBG001SPJN46', 'BBG001SPJPS5', 'BBG001SPJSD5', 'BBG001SPK017', 'BBG001SPK277', 'BBG001SPK286', 'BBG001SPK2B2', 'BBG001SPK6V1', 'BBG001SPKBJ4', 'BBG001SPKCM8', 'BBG001SPKCS2', 'BBG001SPKCV8', 'BBG001SPKDN5', 'BBG001SPKDQ2', 'BBG001SPKFB3', 'BBG001SPKHF5', 'BBG001SPKJS7', 'BBG001SPKMB8', 'BBG001SPKNS8', 'BBG001SPKP12', 'BBG001SPKRV5', 'BBG001SPKV19', 'BBG001SPKXV2', 'BBG001SPKYS4', 'BBG001SPKZC8', 'BBG001SPL025', 'BBG001SPL123', 'BBG001SPL5W1', 'BBG001SPLDY2', 'BBG001SPLNF1', 'BBG001SPLNK5', 'BBG001SPLWB5', 'BBG001SPM284', 'BBG001SPMFV9', 'BBG001SPMFY6', 'BBG001SPMJ05', 'BBG001SPMJ78', 'BBG001SPMJN0', 'BBG001SPMLR1', 'BBG001SPMQJ9', 'BBG001SPMVV4', 'BBG001SPMVX2', 'BBG001SPMW06', 'BBG001SPMXS4', 'BBG001SPMY48', 'BBG001SPN4X1', 'BBG001SPN997', 'BBG001SPNJT3', 'BBG001SPNK29', 'BBG001SPNL09', 'BBG001SPP343', 'BBG001SPP3S7', 'BBG001SPP986', 'BBG001SPPGX2', 'BBG001SPPL34', 'BBG001SPPQG9', 'BBG001SPPRR5', 'BBG001SPPWP6', 'BBG001SPQ770', 'BBG001SPQ798', 'BBG001SPQ823', 'BBG001SPQ8T4', 'BBG001SPQC15', 'BBG001SPQCB4', 'BBG001SPQCY9', 'BBG001SPQDD0', 'BBG001SPQFP2', 'BBG001SPQH05', 'BBG001SPQJW6', 'BBG001SPQLW1', 'BBG001SPQMZ6', 'BBG001SPQN39', 'BBG001SPQNH4', 'BBG001SPQVJ4', 'BBG001SPQXT9', 'BBG001SPR742', 'BBG001SPR760', 'BBG001SPR7B4', 'BBG001SPR886', 'BBG001SPR8H6', 'BBG001SPRC96', 'BBG001SPRG60', 'BBG001SPRJ28', 'BBG001SPRJ55', 'BBG001SPRNB9', 'BBG001SPRR39', 'BBG001SPRSJ0', 'BBG001SPRY52', 'BBG001SPS661', 'BBG001SPS723', 'BBG001SPSDJ2', 'BBG001SPSH76', 'BBG001SPSHC0', 'BBG001SPSJB7', 'BBG001SPSNV6', 'BBG001SPT287', 'BBG001SPT2Q7', 'BBG001SPT508', 'BBG001SPT5Z0', 'BBG001SPT6F0', 'BBG001SPT6G9', 'BBG001SPT759', 'BBG001SPT8K0', 'BBG001SPT8P5', 'BBG001SPT9T9', 'BBG001SPTB32', 'BBG001SPTB41', 'BBG001SPTB50', 'BBG001SPTB69', 'BBG001SPTB87', 'BBG001SPTBM1', 'BBG001SPTBN0', 'BBG001SPTBP8', 'BBG001SPTGD0', 'BBG001SPTQ39', 'BBG001SPTRF4', 'BBG001SPTX70', 'BBG001SPV1C1', 'BBG001SPV5M1', 'BBG001SPV854', 'BBG001SPVCQ2', 'BBG001SPVGM7', 'BBG001SPVKJ2', 'BBG001SPVL19', 'BBG001SPVM17', 'BBG001SPVN33', 'BBG001SPVXV0', 'BBG001SPW1R4', 'BBG001SPW5Q6', 'BBG001SPW700', 'BBG001SPWJZ6', 'BBG001SPWLV5', 'BBG001SPX5N8', 'BBG001SPX610', 'BBG001SPX870', 'BBG001SPX9K3', 'BBG001SPX9P8', 'BBG001SPXHX1', 'BBG001SPXL53', 'BBG001SPXLT7', 'BBG001SPXTJ1', 'BBG001SPXWN9', 'BBG001SPY030', 'BBG001SPY0W8', 'BBG001SPY0Z5', 'BBG001SPY4M1', 'BBG001SPY7K6', 'BBG001SPYB54', 'BBG001SPYFN5', 'BBG001SPYLH9', 'BBG001SPYN49', 'BBG001SPYN94', 'BBG001SPYP17', 'BBG001SPYSQ4', 'BBG001SPYVT4', 'BBG001SPYW58', 'BBG001SPZ5L8', 'BBG001SPZ9G6', 'BBG001SPZC33', 'BBG001SPZDL1', 'BBG001SQ0160', 'BBG001SQ0286', 'BBG001SQ04D6', 'BBG001SQ06K3', 'BBG001SQ08P4', 'BBG001SQ0C39', 'BBG001SQ0C48', 'BBG001SQ0DJ0', 'BBG001SQ0DL7', 'BBG001SQ0DN5', 'BBG001SQ0HM7', 'BBG001SQ0KL1', 'BBG001SQ0PM9', 'BBG001SQ0RS9', 'BBG001SQ1025', 'BBG001SQ11N0', 'BBG001SQ1882', 'BBG001SQ1RH0', 'BBG001SQ1Z91', 'BBG001SQ2300', 'BBG001SQ23K8', 'BBG001SQ25S5', 'BBG001SQ25W0', 'BBG001SQ2738', 'BBG001SQ2827', 'BBG001SQ2BX6', 'BBG001SQ2FS3', 'BBG001SQ2JP8', 'BBG001SQ2QL6', 'BBG001SQ2ZD5', 'BBG001SQ3GT9', 'BBG001SQ3K65', 'BBG001SQ3MC4', 'BBG001SQ3N96', 'BBG001SQ3NR6', 'BBG001SQ3Q08', 'BBG001SQ3QC5', 'BBG001SQ4923', 'BBG001SQ4996', 'BBG001SQ4DF0', 'BBG001SQ4DL3', 'BBG001SQ4DP9', 'BBG001SQ4ML3', 'BBG001SQ4NL1', 'BBG001SQ4P90', 'BBG001SQ4TD7', 'BBG001SQ4VN1', 'BBG001SQ4WV0', 'BBG001SQ4Y73', 'BBG001SQ4YC7', 'BBG001SQ50P6', 'BBG001SQ51M7', 'BBG001SQ52R0', 'BBG001SQ52W4', 'BBG001SQ52Y2', 'BBG001SQ5389', 'BBG001SQ5FD6', 'BBG001SQ5J84', 'BBG001SQ5M87', 'BBG001SQ5RM0', 'BBG001SQ6164', 'BBG001SQ62Q0', 'BBG001SQ6397', 'BBG001SQ67K5', 'BBG001SQ6887', 'BBG001SQ6F90', 'BBG001SQ6H05', 'BBG001SQ6V04', 'BBG001SQ7252', 'BBG001SQ77L3', 'BBG001SQ7H31', 'BBG001SQ7PD2', 'BBG001SQ83B2', 'BBG001SQ8714', 'BBG001SQ87R6', 'BBG001SQ87W0', 'BBG001SQ8CN9', 'BBG001SQ8K88', 'BBG001SQ8KV2', 'BBG001SQ8NB8', 'BBG001SQ8NH2', 'BBG001SQ8NS0', 'BBG001SQ8PW0', 'BBG001SQ8WP3', 'BBG001SQ8Y15', 'BBG001SQ9848', 'BBG001SQ9RJ0', 'BBG001SQ9SW3', 'BBG001SQ9WK7', 'BBG001SQ9XB5', 'BBG001SQB1N9', 'BBG001SQB382', 'BBG001SQB4C5', 'BBG001SQB9W2', 'BBG001SQBBX6', 'BBG001SQBFQ5', 'BBG001SQBGT0', 'BBG001SQBJT4', 'BBG001SQBJZ7', 'BBG001SQBK48', 'BBG001SQBK66', 'BBG001SQBKL9', 'BBG001SQBSD1', 'BBG001SQBSY8', 'BBG001SQBW15', 'BBG001SQBWB4', 'BBG001SQBZJ9', 'BBG001SQC1F7', 'BBG001SQC229', 'BBG001SQC2F5', 'BBG001SQC5P7', 'BBG001SQC5W9', 'BBG001SQC997', 'BBG001SQC9P9', 'BBG001SQCKV7', 'BBG001SQCLD5', 'BBG001SQCQC5', 'BBG001SQCRP9', 'BBG001SQD3M4', 'BBG001SQD5D9', 'BBG001SQD861', 'BBG001SQDFG4', 'BBG001SQDFT0', 'BBG001SQDK37', 'BBG001SQDKD6', 'BBG001SQDKG3', 'BBG001SQDKH2', 'BBG001SQDKJ0', 'BBG001SQF2F2', 'BBG001SQF805', 'BBG001SQF8Q7', 'BBG001SQFKK6', 'BBG001SQFP89', 'BBG001SQFQF9', 'BBG001SQFX64', 'BBG001SQG555', 'BBG001SQG9X6', 'BBG001SQGH95', 'BBG001SQGHS4', 'BBG001SQGTH0', 'BBG001SQGXG2', 'BBG001SQH8Q5', 'BBG001SQHLP7', 'BBG001SQHLR5', 'BBG001SQHMK0', 'BBG001SQHRC8', 'BBG001SQHV11', 'BBG001SQHVZ4', 'BBG001SQHWC7', 'BBG001SQHWP3', 'BBG001SQJ8T0', 'BBG001SQJ8X5', 'BBG001SQJ972', 'BBG001SQJHL8', 'BBG001SQJJ07', 'BBG001SQJKT3', 'BBG001SQJMZ2', 'BBG001SQJYV0', 'BBG001SQJZC8', 'BBG001SQK1G7', 'BBG001SQK2Y5', 'BBG001SQK3L7', 'BBG001SQK8Y2', 'BBG001SQK9P0', 'BBG001SQKG74', 'BBG001SQKGD7', 'BBG001SQKL19', 'BBG001SQKSL2', 'BBG001SQL1B1', 'BBG001SQLB47', 'BBG001SQLDM3', 'BBG001SQLFT1', 'BBG001SQLKK9', 'BBG001SQLKR2', 'BBG001SQLM25', 'BBG001SQLN69', 'BBG001SQLQT7', 'BBG001SQLV52', 'BBG001SQM5W8', 'BBG001SQM665', 'BBG001SQMG63', 'BBG001SQN1L8', 'BBG001SQN842', 'BBG001SQNDV1', 'BBG001SQNK54', 'BBG001SQNKR0', 'BBG001SQNQS6', 'BBG001SQNS57', 'BBG001SQNS66', 'BBG001SQNYQ1', 'BBG001SQP181', 'BBG001SQP3P8', 'BBG001SQP3W0', 'BBG001SQP5Z2', 'BBG001SQPL14', 'BBG001SQPNN6', 'BBG001SQPNV7', 'BBG001SQPR02', 'BBG001SQPSM6', 'BBG001SQPT71', 'BBG001SQQ4H4', 'BBG001SQQ5B7', 'BBG001SQQ5P2', 'BBG001SQQ6G0', 'BBG001SQQ8C0', 'BBG001SQQG69', 'BBG001SQQKX0', 'BBG001SQQVK0', 'BBG001SQQX44', 'BBG001SQR2X9', 'BBG001SQR6M2', 'BBG001SQR9C7', 'BBG001SQRCN8', 'BBG001SQRCY6', 'BBG001SQRDT0', 'BBG001SQRG86', 'BBG001SQRGC1', 'BBG001SQRHJ2', 'BBG001SQRHW7', 'BBG001SQRJS8', 'BBG001SQRMX5', 'BBG001SQRQ20', 'BBG001SQRWL6', 'BBG001SQS0X2', 'BBG001SQS6Z7', 'BBG001SQS7X7', 'BBG001SQS801', 'BBG001SQS9Y2', 'BBG001SQSBP7', 'BBG001SQSBX8', 'BBG001SQSCJ2', 'BBG001SQSDG3', 'BBG001SQSJX1', 'BBG001SQSKT3', 'BBG001SQSPF7', 'BBG001SQSRN4', 'BBG001SQSS43', 'BBG001SQST50', 'BBG001SQT2W8', 'BBG001SQTB21', 'BBG001SQTC10', 'BBG001SQTCC8', 'BBG001SQTF59', 'BBG001SQTTF8', 'BBG001SQTTP7', 'BBG001SQTYH5', 'BBG001SQTYK1', 'BBG001SQV3Z0', 'BBG001SQV7W4', 'BBG001SQVB91', 'BBG001SQVV42', 'BBG001SQW012', 'BBG001SQW4R6', 'BBG001SQWCZ9', 'BBG001SQWNQ5', 'BBG001SQWQC3', 'BBG001SQWTN5', 'BBG001SQWTR1', 'BBG001SQWVL2', 'BBG001SQWXQ3', 'BBG001SQX1T9', 'BBG001SQX4T3', 'BBG001SQX538', 'BBG001SQX565', 'BBG001SQX7D3', 'BBG001SQXCH8', 'BBG001SQXJL8', 'BBG001SQY3Y8', 'BBG001SQY4B1', 'BBG001SQY546', 'BBG001SQY555', 'BBG001SQYBG0', 'BBG001SQYBY0', 'BBG001SQYC14', 'BBG001SQYCG8', 'BBG001SQYNT0', 'BBG001SQYZC1', 'BBG001SQYZD0', 'BBG001SQZ7Z7', 'BBG001SQZ858', 'BBG001SQZ9W6', 'BBG001SQZCK2', 'BBG001SQZT52', 'BBG001SQZT98', 'BBG001SQZTC4', 'BBG001SQZTM3', 'BBG001SQZTR8', 'BBG001SQZW28', 'BBG001SR03L7', 'BBG001SR06G6', 'BBG001SR0CP3', 'BBG001SR0FM9', 'BBG001SR0GL8', 'BBG001SR0KH4', 'BBG001SR0KP5', 'BBG001SR0LL7', 'BBG001SR0LR1', 'BBG001SR0LS0', 'BBG001SR0MP1', 'BBG001SR0RT6', 'BBG001SR0WM2', 'BBG001SR0WP9', 'BBG001SR0YK0', 'BBG001SR1425', 'BBG001SR14H9', 'BBG001SR18T7', 'BBG001SR1G28', 'BBG001SR1J95', 'BBG001SR1VQ9', 'BBG001SR1Y29', 'BBG001SR22H2', 'BBG001SR25C0', 'BBG001SR27H1', 'BBG001SR2807', 'BBG001SR2J85', 'BBG001SR2JB1', 'BBG001SR2K91', 'BBG001SR2L71', 'BBG001SR2MN1', 'BBG001SR2MR7', 'BBG001SR2T01', 'BBG001SR2T29', 'BBG001SR2XP5', 'BBG001SR2Y28', 'BBG001SR33H9', 'BBG001SR34T4', 'BBG001SR3879', 'BBG001SR3BN4', 'BBG001SR3GT7', 'BBG001SR41D6', 'BBG001SR41Q2', 'BBG001SR4253', 'BBG001SR42Z0', 'BBG001SR4547', 'BBG001SR46J9', 'BBG001SR4F72', 'BBG001SR4GS7', 'BBG001SR4H14', 'BBG001SR4J92', 'BBG001SR4QH7', 'BBG001SR4TD5', 'BBG001SR4XN5', 'BBG001SR4XP3', 'BBG001SR4Z50', 'BBG001SR5092', 'BBG001SR5207', 'BBG001SR52L4', 'BBG001SR55Q2', 'BBG001SR57C3', 'BBG001SR5C50', 'BBG001SR5DY6', 'BBG001SR5KB5', 'BBG001SR5KK5', 'BBG001SR5QB2', 'BBG001SR5TY1', 'BBG001SR5X72', 'BBG001SR5XD5', 'BBG001SR5XF3', 'BBG001SR60L7', 'BBG001SR64K0', 'BBG001SR65P2', 'BBG001SR6D11', 'BBG001SR6GZ7', 'BBG001SR6H03', 'BBG001SR6H49', 'BBG001SR6H58', 'BBG001SR6H76', 'BBG001SR6H85', 'BBG001SR6H94', 'BBG001SR6HB1', 'BBG001SR6ZW8', 'BBG001SR70Y2', 'BBG001SR7508', 'BBG001SR77X8', 'BBG001SR7DC8', 'BBG001SR7DW6', 'BBG001SR7MB9', 'BBG001SR7NT8', 'BBG001SR80P1', 'BBG001SR82B2', 'BBG001SR8G12', 'BBG001SR8G21', 'BBG001SR8G30', 'BBG001SR8G49', 'BBG001SR8SJ7', 'BBG001SR8ZJ1', 'BBG001SR9230', 'BBG001SR92C0', 'BBG001SR97P5', 'BBG001SR9G39', 'BBG001SR9GR3', 'BBG001SR9JV2', 'BBG001SR9KR4', 'BBG001SR9LP4', 'BBG001SR9LY4', 'BBG001SR9N16', 'BBG001SR9QP3', 'BBG001SR9SC3', 'BBG001SR9SW1', 'BBG001SR9W89', 'BBG001SR9WR8', 'BBG001SRB4Z8', 'BBG001SRB5K1', 'BBG001SRB683', 'BBG001SRB9M1', 'BBG001SRBKC7', 'BBG001SRBKD6', 'BBG001SRBM33', 'BBG001SRBQZ9', 'BBG001SRBRP8', 'BBG001SRBRZ7', 'BBG001SRBTG4', 'BBG001SRBYW5', 'BBG001SRC0W8', 'BBG001SRC3F1', 'BBG001SRC8Y9', 'BBG001SRCB54', 'BBG001SRCFY3', 'BBG001SRCL43', 'BBG001SRCM50', 'BBG001SRCM78', 'BBG001SRCRZ6', 'BBG001SRCTT9', 'BBG001SRCV05', 'BBG001SRCVP8', 'BBG001SRCW21', 'BBG001SRCW94', 'BBG001SRCY63', 'BBG001SRD2X2', 'BBG001SRD8F9', 'BBG001SRD9B1', 'BBG001SRD9N8', 'BBG001SRDBM4', 'BBG001SRDBV4', 'BBG001SRDDF8', 'BBG001SRDDT3', 'BBG001SRDDY7', 'BBG001SRDF45', 'BBG001SRDF54', 'BBG001SRDT90', 'BBG001SRDTJ9', 'BBG001SRDTL6', 'BBG001SRDV40', 'BBG001SRDV59', 'BBG001SRDY53', 'BBG001SRF0K8', 'BBG001SRF3P7', 'BBG001SRF439', 'BBG001SRF803', 'BBG001SRF9K9', 'BBG001SRFK88', 'BBG001SRFL86', 'BBG001SRFLF8', 'BBG001SRFP14', 'BBG001SRFQX7', 'BBG001SRFYR7', 'BBG001SRG4B9', 'BBG001SRG5G1', 'BBG001SRG6K4', 'BBG001SRG6N1', 'BBG001SRG9W5', 'BBG001SRH3P5', 'BBG001SRHH47', 'BBG001SRHKF8', 'BBG001SRHKR5', 'BBG001SRHRS9', 'BBG001SRHS16', 'BBG001SRHTP8', 'BBG001SRJ0B4', 'BBG001SRJPJ1', 'BBG001SRJQL6', 'BBG001SRJR07', 'BBG001SRJWN1', 'BBG001SRJYJ2', 'BBG001SRJYP5', 'BBG001SRK4T5', 'BBG001SRK4W1', 'BBG001SRK7N4', 'BBG001SRK950', 'BBG001SRKB82', 'BBG001SRKCK6', 'BBG001SRKCM4', 'BBG001SRKDH8', 'BBG001SRKFW6', 'BBG001SRKGQ1', 'BBG001SRKHW2', 'BBG001SRKK73', 'BBG001SRKMN1', 'BBG001SRKV15', 'BBG001SRL6N5', 'BBG001SRL8Z8', 'BBG001SRLFL7', 'BBG001SRM6N4', 'BBG001SRM8D1', 'BBG001SRML88', 'BBG001SRMLH8', 'BBG001SRMY71', 'BBG001SRMY80', 'BBG001SRN591', 'BBG001SRN626', 'BBG001SRN7S6', 'BBG001SRN7W1', 'BBG001SRNBD3', 'BBG001SRNBK5', 'BBG001SRNBL4', 'BBG001SRNL96', 'BBG001SRNLF9', 'BBG001SRNLG8', 'BBG001SRNLL2', 'BBG001SRNQB2', 'BBG001SRNT99', 'BBG001SRNTH0', 'BBG001SRNTJ8', 'BBG001SRNVY6', 'BBG001SRNW74', 'BBG001SRNYN2', 'BBG001SRP223', 'BBG001SRPRX4', 'BBG001SRPT24', 'BBG001SRQ2D0', 'BBG001SRQ3S2', 'BBG001SRQ455', 'BBG001SRQ5C4', 'BBG001SRQ5K5', 'BBG001SRQ6K3', 'BBG001SRQSJ7', 'BBG001SRQVB8', 'BBG001SRQYB2', 'BBG001SRR9R9', 'BBG001SRRFC1', 'BBG001SRRFG7', 'BBG001SRRWL4', 'BBG001SRRWR8', 'BBG001SRRWS7', 'BBG001SRRXT4', 'BBG001SRS2P5', 'BBG001SRS4S8', 'BBG001SRS4V4', 'BBG001SRS550', 'BBG001SRS9Y0', 'BBG001SRSDF2', 'BBG001SRSL00', 'BBG001SRSPD7', 'BBG001SRSPW6', 'BBG001SRSTL0', 'BBG001SRTF75', 'BBG001SRTF84', 'BBG001SRTHL5', 'BBG001SRTK92', 'BBG001SRTND1', 'BBG001SRTS59', 'BBG001SRTVC4', 'BBG001SRTXL0', 'BBG001SRV422', 'BBG001SRV8J5', 'BBG001SRVGT6', 'BBG001SRVHT4', 'BBG001SRVP25', 'BBG001SRVQP8', 'BBG001SRVR58', 'BBG001SRVSL8', 'BBG001SRVWT1', 'BBG001SRVWZ4', 'BBG001SRVX19', 'BBG001SRVYB6', 'BBG001SRW2G0', 'BBG001SRW680', 'BBG001SRW699', 'BBG001SRW9J2', 'BBG001SRWCJ5', 'BBG001SRWF17', 'BBG001SRWGK4', 'BBG001SRWH77', 'BBG001SRWJ82', 'BBG001SRWJJ0', 'BBG001SRWNF5', 'BBG001SRWZJ4', 'BBG001SRX260', 'BBG001SRX4Z4', 'BBG001SRX527', 'BBG001SRXJ09', 'BBG001SRXPY8', 'BBG001SRXR92', 'BBG001SRXRB9', 'BBG001SRXRC8', 'BBG001SRXRD7', 'BBG001SRXRJ1', 'BBG001SRXRK9', 'BBG001SRXRL8', 'BBG001SRXRM7', 'BBG001SRXRN6', 'BBG001SRXTT6', 'BBG001SRXTW2', 'BBG001SRY1B5', 'BBG001SRY1V3', 'BBG001SRY722', 'BBG001SRY740', 'BBG001SRYH02', 'BBG001SRYH48', 'BBG001SRYJW3', 'BBG001SRZ669', 'BBG001SRZ8J1', 'BBG001SRZBZ6', 'BBG001SRZFZ7', 'BBG001SRZG76', 'BBG001SRZGP6', 'BBG001SRZGQ5', 'BBG001SRZGY6', 'BBG001SRZH47', 'BBG001SRZH92', 'BBG001SRZHC8', 'BBG001SRZHH3', 'BBG001SRZHJ1', 'BBG001SRZL39', 'BBG001SRZTG8', 'BBG001SRZX06', 'BBG001SRZXJ6', 'BBG001SRZXL3', 'BBG001SRZXS6', 'BBG001SRZXV2', 'BBG001SRZXW1', 'BBG001SRZXX0', 'BBG001SRZYS4', 'BBG001SRZYT3', 'BBG001SRZYV0', 'BBG001SRZZ29', 'BBG001SRZZ65', 'BBG001SS0950', 'BBG001SS0996', 'BBG001SS09B3', 'BBG001SS09C2', 'BBG001SS09D1', 'BBG001SS0DL3', 'BBG001SS0FH3', 'BBG001SS0FJ1', 'BBG001SS0HX1', 'BBG001SS0J76', 'BBG001SS0KV6', 'BBG001SS0LD4', 'BBG001SS0V24', 'BBG001SS0VB4', 'BBG001SS0VK4', 'BBG001SS0YX4', 'BBG001SS0ZZ9', 'BBG001SS1085', 'BBG001SS10F7', 'BBG001SS15H4', 'BBG001SS17H0', 'BBG001SS1B09', 'BBG001SS1JB0', 'BBG001SS1K90', 'BBG001SS1LR8', 'BBG001SS1NH5', 'BBG001SS1PV4', 'BBG001SS1PY1', 'BBG001SS1PZ0', 'BBG001SS1WH5', 'BBG001SS22W3', 'BBG001SS2BH0', 'BBG001SS2DG7', 'BBG001SS2F27', 'BBG001SS2WV8', 'BBG001SS30F5', 'BBG001SS30K9', 'BBG001SS31W4', 'BBG001SS35B8', 'BBG001SS36Q0', 'BBG001SS3BH9', 'BBG001SS3KF1', 'BBG001SS3L23', 'BBG001SS3M03', 'BBG001SS3M30', 'BBG001SS3PY9', 'BBG001SS3R20', 'BBG001SS3R48', 'BBG001SS3SK8', 'BBG001SS3WV7', 'BBG001SS3Y16', 'BBG001SS40Z2', 'BBG001SS4634', 'BBG001SS4BL3', 'BBG001SS4KZ8', 'BBG001SS4P23', 'BBG001SS5DH3', 'BBG001SS5KV1', 'BBG001SS5LM9', 'BBG001SS5QC9', 'BBG001SS5QV8', 'BBG001SS5VX5', 'BBG001SS6CP5', 'BBG001SS6T14', 'BBG001SS70P0', 'BBG001SS72L0', 'BBG001SS7463', 'BBG001SS74D5', 'BBG001SS78K8', 'BBG001SS78M6', 'BBG001SS78R1', 'BBG001SS7P93', 'BBG001SS80F0', 'BBG001SS81P7', 'BBG001SS81R5', 'BBG001SS8220', 'BBG001SS8248', 'BBG001SS8658', 'BBG001SS8685', 'BBG001SS86F7', 'BBG001SS86G6', 'BBG001SS86K1', 'BBG001SS86M9', 'BBG001SS8729', 'BBG001SS8R16', 'BBG001SS8XS4', 'BBG001SS94R8', 'BBG001SS9G64', 'BBG001SSB3T5', 'BBG001SSBCM2', 'BBG001SSBCY9', 'BBG001SSBKX2', 'BBG001SSBLS6', 'BBG001SSBTC6', 'BBG001SSBTK7', 'BBG001SSC5B8', 'BBG001SSCGC3', 'BBG001SSCPS6', 'BBG001SSCS82', 'BBG001SSCT17', 'BBG001SSCT99', 'BBG001SSCTP1', 'BBG001SSCTQ0', 'BBG001SSCTT7', 'BBG001SSD803', 'BBG001SSD812', 'BBG001SSD830', 'BBG001SSD849', 'BBG001SSD858', 'BBG001SSD876', 'BBG001SSD885', 'BBG001SSD894', 'BBG001SSD8B1', 'BBG001SSDCC1', 'BBG001SSDL86', 'BBG001SSDLM0', 'BBG001SSDLP7', 'BBG001SSDLR5', 'BBG001SSDLS4', 'BBG001SSDLT3', 'BBG001SSDLZ6', 'BBG001SSF7F7', 'BBG001SSF7H5', 'BBG001SSFN44', 'BBG001SSFN80', 'BBG001SSFX42', 'BBG001SSG0F1', 'BBG001SSG0N2', 'BBG001SSG5G9', 'BBG001SSG953', 'BBG001SSG980', 'BBG001SSGBL0', 'BBG001SSGF22', 'BBG001SSGGL9', 'BBG001SSGHJ0', 'BBG001SSGJB4', 'BBG001SSGJC3', 'BBG001SSGXZ7', 'BBG001SSGYL0', 'BBG001SSGYY6', 'BBG001SSH3K8', 'BBG001SSHFZ5', 'BBG001SSHNW1', 'BBG001SSHQR0', 'BBG001SSHRS7', 'BBG001SSHS05', 'BBG001SSHXP7', 'BBG001SSJ0C1', 'BBG001SSJ2K8', 'BBG001SSJ2X4', 'BBG001SSJCM4', 'BBG001SSJLN3', 'BBG001SSJSP6', 'BBG001SSJYS0', 'BBG001SSK0K0', 'BBG001SSP7N7', 'BBG001SSPFD0', 'BBG001SSPRJ8', 'BBG001SSPWL4', 'BBG001SSPXC2', 'BBG001SSPXQ7', 'BBG001SSQ3R1', 'BBG001SSQ4R9', 'BBG001SSQ4Z0', 'BBG001SSQDF2', 'BBG001SSQDH0', 'BBG001SSQDL5', 'BBG001SSQGT0', 'BBG001SSQLY3', 'BBG001SSQMH0', 'BBG001SSQW15', 'BBG001SSR9R7', 'BBG001SSRDW2', 'BBG001SSRFZ4', 'BBG001SSRG00', 'BBG001SSRHD4', 'BBG001SSRPL7', 'BBG001SSRPN5', 'BBG001SSRPP3', 'BBG001SSS022', 'BBG001SSSBF4', 'BBG001SSSG18', 'BBG001SSSMG9', 'BBG001SSSNF8', 'BBG001SSSNP7', 'BBG001SSSNQ6', 'BBG001SSSS94', 'BBG001SSSWR5', 'BBG001SSSWY7', 'BBG001SSSZS7', 'BBG001SST003', 'BBG001SST049', 'BBG001SST0B1', 'BBG001SST0D9', 'BBG001SST0J3', 'BBG001SST0Z5', 'BBG001SSTCF1', 'BBG001SSTCJ7', 'BBG001SSTCK5', 'BBG001SSTCL4', 'BBG001SSTCM3', 'BBG001SSTCQ9', 'BBG001SSTCR8', 'BBG001SSTCT6', 'BBG001SSTCW2', 'BBG001SSTCY0', 'BBG001SSTCZ9', 'BBG001SSTD05', 'BBG001SSTD14', 'BBG001SSTD23', 'BBG001SSTD41', 'BBG001SSTD50', 'BBG001SSTD69', 'BBG001SSTD78', 'BBG001SSTD87', 'BBG001SSTDB3', 'BBG001SSTDC2', 'BBG001SSTDD1', 'BBG001SSTH88', 'BBG001SSTNC0', 'BBG001SSTQ33', 'BBG001SSTRN9', 'BBG001SSV2L3', 'BBG001SSV4X6', 'BBG001SSV992', 'BBG001SSVBZ8', 'BBG001SSVGF9', 'BBG001SSVQM9', 'BBG001SSVT89', 'BBG001SSW232', 'BBG001SSW269', 'BBG001SSW2K3', 'BBG001SSW9F4', 'BBG001SSWNX3', 'BBG001SSXF96', 'BBG001SSXGC0', 'BBG001SSXJ16', 'BBG001SSXJF1', 'BBG001SSXKF8', 'BBG001SSXKV0', 'BBG001SSXKY7', 'BBG001SSXL11', 'BBG001SSXL39', 'BBG001SSXL57', 'BBG001SSXL66', 'BBG001SSXNN3', 'BBG001SSXNQ0', 'BBG001SSY5L3', 'BBG001SSY855', 'BBG001SSYFF8', 'BBG001SSYS60', 'BBG001SSYTZ6', 'BBG001SSYVX3', 'BBG001SSYWD3', 'BBG001SSZ024', 'BBG001SSZ088', 'BBG001SSZ097', 'BBG001SSZ0Q8', 'BBG001SSZ2K0', 'BBG001SSZB57', 'BBG001SSZB66', 'BBG001SSZBC9', 'BBG001SSZBH4', 'BBG001SSZBN7', 'BBG001SSZGH3', 'BBG001SSZKQ4', 'BBG001SSZNJ6', 'BBG001SSZNY9', 'BBG001SSZNZ8', 'BBG001SSZR43', 'BBG001SSZW79', 'BBG001SSZZ36', 'BBG001ST00J2', 'BBG001ST00L9', 'BBG001ST07H9', 'BBG001ST08X9', 'BBG001ST0D13', 'BBG001ST0MH6', 'BBG001ST0RW8', 'BBG001ST0XR1', 'BBG001ST0ZQ7', 'BBG001ST10K9', 'BBG001ST12P0', 'BBG001ST18J4', 'BBG001ST1GJ6', 'BBG001ST1PL3', 'BBG001ST1Y70', 'BBG001ST24L9', 'BBG001ST24Z4', 'BBG001ST2581', 'BBG001ST29Y4', 'BBG001ST2G69', 'BBG001ST2G96', 'BBG001ST2GK3', 'BBG001ST2GM1', 'BBG001ST2JV5', 'BBG001ST2M66', 'BBG001ST2NZ2', 'BBG001ST2V84', 'BBG001ST2XF2', 'BBG001ST32J5', 'BBG001ST38G5', 'BBG001ST39Q2', 'BBG001ST39R1', 'BBG001ST39S0', 'BBG001ST3FM2', 'BBG001ST4703', 'BBG001ST4D64', 'BBG001ST4D73', 'BBG001ST4HV7', 'BBG001ST4T05', 'BBG001ST4WN3', 'BBG001ST4X42', 'BBG001ST4X51', 'BBG001ST5FL1', 'BBG001ST5GJ2', 'BBG001ST5W34', 'BBG001ST5XW0', 'BBG001ST63J0', 'BBG001ST67S1', 'BBG001ST68N4', 'BBG001ST68P2', 'BBG001ST68Q1', 'BBG001ST68R0', 'BBG001ST68S9', 'BBG001ST68V5', 'BBG001ST68W4', 'BBG001ST68X3', 'BBG001ST68Z1', 'BBG001ST6916', 'BBG001ST6934', 'BBG001ST6943', 'BBG001ST6952', 'BBG001ST6961', 'BBG001ST6970', 'BBG001ST6998', 'BBG001ST69T6', 'BBG001ST6GG4', 'BBG001ST6GJ1', 'BBG001ST6HL6', 'BBG001ST6J87', 'BBG001ST6WX0', 'BBG001ST7087', 'BBG001ST7158', 'BBG001ST7265', 'BBG001ST7B10', 'BBG001ST7G55', 'BBG001ST7G73', 'BBG001ST7GB8', 'BBG001ST7M52', 'BBG001ST7MZ9', 'BBG001ST7PG3', 'BBG001ST82P3', 'BBG001ST82Y3', 'BBG001ST83R9', 'BBG001ST8DV2', 'BBG001ST8K37', 'BBG001ST8L99', 'BBG001ST8QH9', 'BBG001ST8WF8', 'BBG001ST96N5', 'BBG001ST96V6', 'BBG001ST98V2', 'BBG001ST99N9', 'BBG001ST9B63', 'BBG001ST9G35', 'BBG001ST9GV4', 'BBG001ST9K09', 'BBG001ST9LP0', 'BBG001STB2W1', 'BBG001STB5B7', 'BBG001STBC13', 'BBG001STBDB0', 'BBG001STBDG5', 'BBG001STBDR3', 'BBG001STBM75', 'BBG001STBR92', 'BBG001STBRD7', 'BBG001STBRF5', 'BBG001STBRG4', 'BBG001STBRH3', 'BBG001STBRR2', 'BBG001STC1D3', 'BBG001STF1M0', 'BBG001STFFC0', 'BBG001STFTT2', 'BBG001STFV35', 'BBG001STG611', 'BBG001STG620', 'BBG001STGMK5', 'BBG001STGSR5', 'BBG001STGYX5', 'BBG001STH175', 'BBG001STH2P3', 'BBG001STHBJ0', 'BBG001STHC62', 'BBG001STHCY1', 'BBG001STHCZ0', 'BBG001STHDJ6', 'BBG001STHGR0', 'BBG001STHGW4', 'BBG001STHSQ5', 'BBG001STHTL8', 'BBG001STJD04', 'BBG001STJDS4', 'BBG001STJFY2', 'BBG001STJJN6', 'BBG001STJJR2', 'BBG001STJXR1', 'BBG001STJXS0', 'BBG001STK4M8', 'BBG001STK554', 'BBG001STK7F9', 'BBG001STK894', 'BBG001STKCW9', 'BBG001STKCX8', 'BBG001STKCY7', 'BBG001STKCZ6', 'BBG001STKD11', 'BBG001STKGB3', 'BBG001STKPK3', 'BBG001STKSP2', 'BBG001STKXP1', 'BBG001STL5S8', 'BBG001STLMJ1', 'BBG001STLRP3', 'BBG001STLSK6', 'BBG001STLVB9', 'BBG001STLY96', 'BBG001STLYG8', 'BBG001STLZP5', 'BBG001STM1F0', 'BBG001STM838', 'BBG001STM9Y2', 'BBG001STMHG4', 'BBG001STMJG0', 'BBG001STMP49', 'BBG001STN3Y4', 'BBG001STN6W9', 'BBG001STNTP7', 'BBG001STNTY7', 'BBG001STNYV9', 'BBG001STNYY6', 'BBG001STP069', 'BBG001STP9N1', 'BBG001STPJW9', 'BBG001STPLG2', 'BBG001STPPK8', 'BBG001STPW14', 'BBG001STPW96', 'BBG001STQ0H6', 'BBG001STQ193', 'BBG001STQFY4', 'BBG001STQGL6', 'BBG001STQRH7', 'BBG001STR4S5', 'BBG001STR5N7', 'BBG001STR708', 'BBG001STRH79', 'BBG001STRP71', 'BBG001STRR86', 'BBG001STRS84', 'BBG001STRVL2', 'BBG001STRZ24', 'BBG001STS0K0', 'BBG001STS244', 'BBG001STSC42', 'BBG001STSKG1', 'BBG001STSMS4', 'BBG001STSMT3', 'BBG001STSXC7', 'BBG001STSZM1', 'BBG001STSZN0', 'BBG001STSZP8', 'BBG001STT6Q0', 'BBG001STT6T7', 'BBG001STTSL7', 'BBG001STTT99', 'BBG001STTZR5', 'BBG001STV3Q4', 'BBG001STV4H2', 'BBG001STV4X4', 'BBG001STV6N0', 'BBG001STV8J1', 'BBG001STV9K7', 'BBG001STV9L6', 'BBG001STV9N4', 'BBG001STVBR5', 'BBG001STVDG3', 'BBG001STVFX9', 'BBG001STW0V3', 'BBG001STW2Q5', 'BBG001STWCQ3', 'BBG001STWPZ4', 'BBG001STWVN4', 'BBG001STWVY2', 'BBG001STWW61', 'BBG001STX5B3', 'BBG001STX5R6', 'BBG001STX5S5', 'BBG001STX5Y8', 'BBG001STX5Z7', 'BBG001STX630', 'BBG001STX658', 'BBG001STXM62', 'BBG001STXPM7', 'BBG001STXSW0', 'BBG001STXVZ0', 'BBG001STXXW9', 'BBG001STXZS9', 'BBG001STXZV5', 'BBG001STXZX3', 'BBG001STY997', 'BBG001STYNV1', 'BBG001STYX58', 'BBG001STYZH0', 'BBG001STZBS0', 'BBG001STZCF2', 'BBG001STZD33', 'BBG001STZGL6', 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'BBG017GGDLW4', 'BBG017GGF1F5', 'BBG017GGPB44', 'BBG017GGPCC3', 'BBG017H2K588', 'BBG017H36J73', 'BBG017HC9SQ9', 'BBG017HTXNB2', 'BBG017J0G1T1', 'BBG017J18BH9', 'BBG017J18WC8', 'BBG017K67G77', 'BBG017K69RF2', 'BBG017K6FQS3', 'BBG017K6PR91', 'BBG017K8R3N2', 'BBG017K9G6P3', 'BBG017L17T79', 'BBG017L2B413', 'BBG017L35BZ5', 'BBG017NFDFJ6', 'BBG017NHSLG6', 'BBG017NK03Q8', 'BBG017NL7GG1', 'BBG017PH0BQ5', 'BBG017PH3Y28', 'BBG017PH48L3', 'BBG017PH4DL2', 'BBG017PHCJQ5', 'BBG017PHCX11', 'BBG017PHJP75', 'BBG017PHJT13', 'BBG017PHK4M3', 'BBG017QM86M0', 'BBG017QYQZ31', 'BBG017QYS205', 'BBG017RM8TP6', 'BBG017RMJCC5', 'BBG017RNTCM1', 'BBG017RPLKW5', 'BBG017RQJJH6', 'BBG017RRM615', 'BBG017SGKKW5', 'BBG017SJJK06', 'BBG017SK3NY7', 'BBG017SLCZW0', 'BBG017T7Q0G0', 'BBG017T7VX45', 'BBG017T7Z6K2', 'BBG017TFK4C4', 'BBG017TG0NB3', 'BBG017TG8NV3', 'BBG017TG9XV0', 'BBG017TGV7N1', 'BBG017TGXBH7', 'BBG017TH16P2', 'BBG017TH1FZ1', 'BBG017V5VKB6', 'BBG017VB97P2', 'BBG017VCFVK5', 'BBG017WPD5H8', 'BBG017WSP776', 'BBG017XJ21F4', 'BBG017Z2L9J7', 'BBG017Z2NGC6', 'BBG017ZFFLL5', 'BBG017ZJH3Y1', 'BBG017ZLG6K5', 'BBG017ZLXZ04', 'BBG017ZMZ7G5', 'BBG0180BK9Q6', 'BBG0180D7BT9', 'BBG01846SSX5', 'BBG0184FKT34', 'BBG0184H3201', 'BBG0184HP9D8', 'BBG018542C35', 'BBG018585342', 'BBG01858ZF15', 'BBG01859SRT5', 'BBG0185G2C56', 'BBG0185KW808', 'BBG0185KYXC8', 'BBG0185L0106', 'BBG0186GNBV5', 'BBG0186KPDB2', 'BBG0188WZ520', 'BBG0188ZYL92', 'BBG0189YGLW8', 'BBG018CD2W33', 'BBG018CNTHQ0', 'BBG018CPJHS4', 'BBG018CPTJQ1', 'BBG018CXW5M4', 'BBG018DZB6D9', 'BBG018G23S04', 'BBG018G2SPN8', 'BBG018GV5WV5', 'BBG018GZNDB0', 'BBG018K71HS4', 'BBG018K95469', 'BBG018KPNT22', 'BBG018KQ0LL1', 'BBG018KQC3P4', 'BBG018KX3R21', 'BBG018KX7W62', 'BBG018LYTVV9', 'BBG018MDWK53', 'BBG018MF5HB8', 'BBG018NBPT00', 'BBG018NNRQP1', 'BBG018PKX9G6', 'BBG018PM88H1', 'BBG018PW2QQ5', 'BBG018PWQ4S6', 'BBG018PWQF65', 'BBG018QSQ2M4', 'BBG018R2GN27', 'BBG018R3XY68', 'BBG018RBQT78', 'BBG018RBVFQ1', 'BBG018RBWQS4', 'BBG018RC2CT5', 'BBG018RC2JT0', 'BBG018RC3298', 'BBG018RDBGR6', 'BBG018RDD4P3', 'BBG018WBYMB3', 'BBG018XV13T7', 'BBG018Y7R936', 'BBG018Y8BQB4', 'BBG019097DP6', 'BBG01909W3R8', 'BBG0190B0VX9', 'BBG0190RVSQ5', 'BBG0190VW7P2', 'BBG0191FHR33', 'BBG0191G42V9', 'BBG0191G6FC9', 'BBG0191HDL54', 'BBG01920FMS1', 'BBG019229DL1', 'BBG01922FKG4', 'BBG01922G1T1', 'BBG01922X1R4', 'BBG0192379X7', 'BBG0192Y59N2', 'BBG0193BT054', 'BBG0193CN4X9', 'BBG0193D2FR2', 'BBG0193F22H7', 'BBG0193FZT19', 'BBG0193VFRN6', 'BBG0193YC043', 'BBG0193YGK09', 'BBG0193YH5W6', 'BBG0193YL3J1', 'BBG0193YQ1L7', 'BBG01958XYG8', 'BBG01959CJR0', 'BBG01959FB58', 'BBG01959G8T8', 'BBG0195MY622', 'BBG0195P5G61', 'BBG019624D23', 'BBG019624HF0', 'BBG0196FNPT7', 'BBG0196RJS01', 'BBG019BMSD45', 'BBG019C1WPK0', 'BBG019C1Y0V1', 'BBG019CHMV14', 'BBG019CHS8F4', 'BBG019DCJ9F2', 'BBG019FG0845', 'BBG019FGR8P3', 'BBG019FSVZ18', 'BBG019GV63H2', 'BBG019HFT4X1', 'BBG019HFVTM5', 'BBG019HFXY60', 'BBG019HFY7P7', 'BBG019J36L27', 'BBG019J36VW2', 'BBG019JBR2D6', 'BBG019JCKH66', 'BBG019JCL3F6', 'BBG019JCS8Z6', 'BBG019JCSKW2', 'BBG019JCWKY5', 'BBG019K3PSB9', 'BBG019K3SQ39', 'BBG019K5YGL9', 'BBG019KMQCY6', 'BBG019KN6Y14', 'BBG019LVZWW3', 'BBG019LXCGD0', 'BBG019M5GYX5', 'BBG019M5KJ90', 'BBG019M62R54', 'BBG019MBDD16', 'BBG019MBXTT9', 'BBG019MC9WF2', 'BBG019MCBX94', 'BBG019MCWB44', 'BBG019MCZ0Z1', 'BBG019MD2GB6', 'BBG019MD3FL6', 'BBG019MDH9Z0', 'BBG019MDHCC8', 'BBG019QV0D94', 'BBG019R0B346', 'BBG019R1PJ26', 'BBG019R1PPS4', 'BBG019R2Z783', 'BBG019RKWX52', 'BBG019RXP3Q5', 'BBG019RXTL36', 'BBG019STC653', 'BBG019T923G2', 'BBG019T9ZBN0', 'BBG019TB2P87', 'BBG019TC4TM9', 'BBG019V3VQF0', 'BBG019VDCV31', 'BBG019VFMNS6', 'BBG019VG2G74', 'BBG019VG3C45', 'BBG019VG86X1', 'BBG019VR3KN2', 'BBG019VRCYF1', 'BBG019WKMYV6', 'BBG019WLSRG0', 'BBG019WNHPH1', 'BBG019X2NLS6', 'BBG019X2ZF79', 'BBG019X35769', 'BBG019X38DF3', 'BBG019X3B9Z6', 'BBG019X46ZY3', 'BBG019XFYS46', 'BBG019XGH1M2', 'BBG019XQ3B07', 'BBG019XRCM29', 'BBG019XS5Y74', 'BBG019XS8NR3', 'BBG019XZXDB8', 'BBG019Z0CM44', 'BBG019Z2K9M0', 'BBG019ZLZL54', 'BBG01B07MBK7', 'BBG01B089FS3', 'BBG01B216BN3', 'BBG01B218Q67', 'BBG01B2TTFG6', 'BBG01B2TXTR9', 'BBG01B34NPM7', 'BBG01B34YSX7', 'BBG01B5GGC45', 'BBG01B5GNTH6', 'BBG01B5GQSR4', 'BBG01B5GT7V2', 'BBG01B6SGC20', 'BBG01B6VR0Y2', 'BBG01B6WZ6Y8', 'BBG01B77MVT7', 'BBG01B7B21C4', 'BBG01B7B3X24', 'BBG01B85WTY8', 'BBG01B8726R5', 'BBG01B8BLJB4', 'BBG01B8C7ZT3', 'BBG01B90HFN7', 'BBG01B92NHL4', 'BBG01B92RPD1', 'BBG01B92V9Y9', 'BBG01B92VTS2', 'BBG01B92W7S9', 'BBG01B9F3CJ1', 'BBG01B9G4424', 'BBG01B9L7B60', 'BBG01BBF3K64', 'BBG01BBHTVK2', 'BBG01BBJ5S85', 'BBG01BBJ74Y7', 'BBG01BBJHM03', 'BBG01BBJQ3B3', 'BBG01BCHMLH4', 'BBG01BCJMS85', 'BBG01BCKC066', 'BBG01BCKC3H8', 'BBG01BFHYK62', 'BBG01BFJ3TM5', 'BBG01BFJ8917', 'BBG01BG24837', 'BBG01BG3WNR5', 'BBG01BGVG000', 'BBG01BHJPST4', 'BBG01BHYXWN9', 'BBG01BJ1BGP6', 'BBG01BJ1DVS8', 'BBG01BJBL0Z7', 'BBG01BLRMW33', 'BBG01BM21GB6', 'BBG01BM23K43', 'BBG01BM2D7K3', 'BBG01BM2DMD8', 'BBG01BM2F1P9', 'BBG01BN5Z188', 'BBG01BN60P09', 'BBG01BN8Q306', 'BBG01BN8QHM1', 'BBG01BNNTQP2', 'BBG01BPH5MK3', 'BBG01BQ40S32', 'BBG01BTRTRF6', 'BBG01BTSPHM2', 'BBG01BTSR0M7', 'BBG01BTT0L11', 'BBG01BTTD0X8', 'BBG01BWBNB31', 'BBG01BWDFP53', 'BBG01BWDHVV9', 'BBG01BWDKSD2', 'BBG01BWRX8Q7', 'BBG01BWS8PR4', 'BBG01BWV1H47', 'BBG01BXXHCD6', 'BBG01BXXJCN3', 'BBG01BY3XQ06', 'BBG01BY3ZP60', 'BBG01BYD75L6'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 119505 |
| valid | 118419 |
|
true | # AutoTrain Dataset for project: i-bert-twitter-sentiment
## Dataset Description
This dataset has been automatically processed by AutoTrain for project i-bert-twitter-sentiment.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "Thanks\\u002c Dave! Great show tonight. Sorry\\u002c Craig. I\\u2019ve got to get to bed. I\\u2019ll catch you tomorrow. @user David Letterman",
"target": 2
},
{
"text": "\"I've been watching Gilmore Girls for the past 3 hours. Oops, happy Thursday!\"",
"target": 2
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['negative', 'neutral', 'positive'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 36491 |
| valid | 9124 |
|
false |
## Source:
Copied from the [original dataset](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic))
### Creators:
1. Dr. William H. Wolberg, General Surgery Dept.
University of Wisconsin, Clinical Sciences Center
Madison, WI 53792
wolberg '@' eagle.surgery.wisc.edu
2. W. Nick Street, Computer Sciences Dept.
University of Wisconsin, 1210 West Dayton St., Madison, WI 53706
street '@' cs.wisc.edu 608-262-6619
3. Olvi L. Mangasarian, Computer Sciences Dept.
University of Wisconsin, 1210 West Dayton St., Madison, WI 53706
olvi '@' cs.wisc.edu
### Donor:
Nick Street
## Data Set Information:
Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at [Web Link]
Separating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree Construction Via Linear Programming." Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.
The actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].
This database is also available through the UW CS ftp server:
ftp ftp.cs.wisc.edu
cd math-prog/cpo-dataset/machine-learn/WDBC/
### Attribute Information:
1) ID number
2) Diagnosis (M = malignant, B = benign)
3-32)
Ten real-valued features are computed for each cell nucleus:
a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
c) perimeter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry
j) fractal dimension ("coastline approximation" - 1) |
false |
# Dataset Card for Neon Isometric
Do i know what i'm doing? NO. XD So if anyone wants to help with this go for gold
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
false | TRAINING_CORPUS.txt:
The TRAINING_CORPUS is the collection of 12 books (The modern Prometheus, The liar of the white worm by bram Stoker, The Vampyre; a Tale, Nightmare Abbey; by Thomas Love Peacock', The History of Caliph Vathek by William Beckford The Lock and Key Library :Classic Mystery and Detectives Stories: Old Time, Caleb Williams; Or,Things as they are by William Godwin , The Private Memoirs and confessions of a justified sinner, Confessions of an English Opium Eater, The mysteries of udolpho, Wieland;Or,The Transformation: An American Tale by Charles Brocken Brown, The Castle of Otranto) which contains 1051518 Words and 6002980 characters from Project Gutenberg(https://www.gutenberg.org/), of the GOTHIC FICTION Genre. This text is fed as input to the PROJECT_GUTENBERG_GOTHIC_FICTION_TEXT_GENERATION_gpt2 model to perform the Text-Generation to get the Gothic Fiction style outputs.
TESTING_CORPPUS.txt
The TESTING_CORPPUS is the random text manually picked from the TRAINING_CORPPUS to evaluate the model. |
false | TRAINING_CORPUS.txt
The TRAINING_CORPUS is the collection of 12 books (The modern Prometheus, The liar of the white worm by bram Stoker, The Vampyre; a Tale, Nightmare Abbey; by Thomas Love Peacock', The History of Caliph Vathek by William Beckford The Lock and Key Library :Classic Mystery and Detectives Stories: Old Time, Caleb Williams; Or,Things as they are by William Godwin , The Private Memoirs and confessions of a justified sinner, Confessions of an English Opium Eater, The mysteries of udolpho, Wieland;Or,The Transformation: An American Tale by Charles Brocken Brown, The Castle of Otranto)
which contains 1051518 Words and 6002980 characters from Project Gutenberg(https://www.gutenberg.org/), of the GOTHIC FICTION Genre. This text is fed as input to the PROJECT_GUTENBERG_GOTHIC_FICTION_TEXT_GENERATION_gpt2
model to perform the Text-Generation to get the Gothic Fiction style outputs.
TESTING_CORPPUS.txt
The TESTING_CORPPUS is the random text manually picked from the TRAINING_CORPPUS to evaluate the model. |
false | # Vector store of embeddings for books
- **"1984" by George Orwell**
- **"The Almanac of Naval Ravikant" by Eric Jorgenson**
This is a [faiss](https://github.com/facebookresearch/faiss) vector store created with [instructor embeddings](https://github.com/HKUNLP/instructor-embedding) using [LangChain](https://langchain.readthedocs.io/en/latest/modules/indexes/examples/embeddings.html#instructembeddings) . Use it for similarity search, question answering or anything else that leverages embeddings! 😃
Creating these embeddings can take a while so here's a convenient, downloadable one 🤗
## How to use
1. Specify the book from one of the following:
- `"1984"`
- `"The Almanac of Naval Ravikant"`
3. Download data
4. Load to use with LangChain
```
pip install -qqq langchain InstructorEmbedding sentence_transformers faiss-cpu huggingface_hub
```
```python
import os
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.vectorstores.faiss import FAISS
from huggingface_hub import snapshot_download
# download the vectorstore for the book you want
BOOK="1984"
cache_dir=f"{book}_cache"
vectorstore = snapshot_download(repo_id="calmgoose/book-embeddings",
repo_type="dataset",
revision="main",
allow_patterns=f"books/{BOOK}/*", # to download only the one book
cache_dir=cache_dir,
)
# get path to the `vectorstore` folder that you just downloaded
# we'll look inside the `cache_dir` for the folder we want
target_dir = BOOK
# Walk through the directory tree recursively
for root, dirs, files in os.walk(cache_dir):
# Check if the target directory is in the list of directories
if target_dir in dirs:
# Get the full path of the target directory
target_path = os.path.join(root, target_dir)
# load embeddings
# this is what was used to create embeddings for the book
embeddings = HuggingFaceInstructEmbeddings(
embed_instruction="Represent the book passage for retrieval: ",
query_instruction="Represent the question for retrieving supporting texts from the book passage: "
)
# load vector store to use with langchain
docsearch = FAISS.load_local(folder_path=target_path, embeddings=embeddings)
# similarity search
question = "Who is big brother?"
search = docsearch.similarity_search(question, k=4)
for item in search:
print(item.page_content)
print(f"From page: {item.metadata['page']}")
print("---")
``` |
false |
## Source:
Creator:
David J. Slate
Odesta Corporation; 1890 Maple Ave; Suite 115; Evanston, IL 60201
Donor:
David J. Slate (dave '@' math.nwu.edu) (708) 491-3867
## Data Set Information:
The objective is to identify each of a large number of black-and-white rectangular pixel displays as one of the 26 capital letters in the English alphabet. The character images were based on 20 different fonts and each letter within these 20 fonts was randomly distorted to produce a file of 20,000 unique stimuli. Each stimulus was converted into 16 primitive numerical attributes (statistical moments and edge counts) which were then scaled to fit into a range of integer values from 0 through 15. We typically train on the first 16000 items and then use the resulting model to predict the letter category for the remaining 4000. See the article cited above for more details.
### Attribute Information:
1. x-box horizontal position of box (integer)
2. y-box vertical position of box (integer)
3. width width of box (integer)
4. high height of box (integer)
5. onpix total # on pixels (integer)
6. x-bar mean x of on pixels in box (integer)
7. y-bar mean y of on pixels in box (integer)
8. x2bar mean x variance (integer)
9. y2bar mean y variance (integer)
10. xybar mean x y correlation (integer)
11. x2ybr mean of x * x * y (integer)
12. xy2br mean of x * y * y (integer)
13. x-ege mean edge count left to right (integer)
14. xegvy correlation of x-ege with y (integer)
15. y-ege mean edge count bottom to top (integer)
16. yegvx correlation of y-ege with x (integer) |
true | |
true | # AutoTrain Dataset for project: tax_issues
## Dataset Description
This dataset has been automatically processed by AutoTrain for project tax_issues.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "How is Inheritance Tax calculated?",
"target": 10,
"feat_Unnamed: 2": null,
"feat_Unnamed: 3": null,
"feat_Unnamed: 4": null
},
{
"text": "What happens if I work part-time or have multiple jobs as an international student in the UK?",
"target": 13,
"feat_Unnamed: 2": null,
"feat_Unnamed: 3": null,
"feat_Unnamed: 4": null
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['Question1', 'Question10', 'Question11', 'Question12', 'Question13', 'Question14', 'Question15', 'Question16', 'Question17', 'Question18', 'Question19', 'Question2', 'Question20', 'Question21', 'Question22', 'Question23', 'Question24', 'Question25', 'Question26', 'Question27', 'Question28', 'Question29', 'Question3', 'Question30', 'Question31', 'Question32', 'Question33', 'Question34', 'Question35', 'Question36', 'Question37', 'Question38', 'Question39', 'Question4', 'Question40', 'Question41', 'Question42', 'Question43', 'Question44', 'Question45', 'Question46', 'Question47', 'Question49', 'Question5', 'Question50', 'Question6', 'Question7', 'Question8', 'Question9', 'question48'], id=None)",
"feat_Unnamed: 2": "Value(dtype='float64', id=None)",
"feat_Unnamed: 3": "Value(dtype='float64', id=None)",
"feat_Unnamed: 4": "Value(dtype='float64', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 2377 |
| valid | 622 |
|
false | |
false | # AutoTrain Dataset for project: klasifikasi-tutupan-lahan
## Dataset Description
This dataset has been automatically processed by AutoTrain for project klasifikasi-tutupan-lahan.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<64x64 RGB PIL image>",
"target": 8
},
{
"image": "<64x64 RGB PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['AnnualCrop', 'Forest', 'HerbaceousVegetation', 'Highway', 'Industrial', 'Pasture', 'PermanentCrop', 'Residential', 'River', 'SeaLake'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 400 |
| valid | 100 |
|
false |
Thanks and please support:
Ecigator is one of the well-known vape brands spun off from Giftsoar Technology Co., Ltd, it’s an ISO-certified [disposable vape manufacturer](https://ecigator.com/) for OEMs, ODMs, and OBM since 2010.
[https://ecigator.com/](https://ecigator.com/) |
false |
# Dataset Card for dev_mode-wtq
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [WikiTableQuestions homepage](https://nlp.stanford.edu/software/sempre/wikitable)
- **Repository:** [WikiTableQuestions repository](https://github.com/ppasupat/WikiTableQuestions)
- **Paper:** [Compositional Semantic Parsing on Semi-Structured Tables](https://arxiv.org/abs/1508.00305)
- **Leaderboard:** [WikiTableQuestions leaderboard on PaperWithCode](https://paperswithcode.com/dataset/wikitablequestions)
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The dev_mode-wtq dataset is a small-scale dataset for the task of question answering on semi-structured tables.
This data includes the `aggregation_label` and `answer_coordinates` to make it easy to train this model on any [TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas#usage-finetuning) based modles.
### Supported Tasks and Leaderboards
question-answering, table-question-answering
### Languages
en
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 27.91 MB
- **Size of the generated dataset:** 45.68 MB
- **Total amount of disk used:** 73.60 MB
An example of 'validation' looks as follows:
```
{
"id": "nt-0",
"question": "What is the duration for the last invocation?",
"answers": [
"340 ms"
],
"table": {
"header": [
"recent",
"type",
"spans",
"logs",
"errors",
"warnings",
"duration",
"resource"
],
"rows": [
[
"1",
"span",
"1",
"1",
"1",
"2",
"340 ms",
"aws-lambda-typescript-express-dev-express"
]
]
}
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `id`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a `list` of `string` feature.
- `answers_coordinates`: a `list` of `int,int` tuples.
- `aggregation_label`: a `string` feature.
- `table`: a dictionary feature containing:
- `header`: a `list` of `string` features.
- `rows`: a `list` of `list` of `string` features:
- `name`: a `string` feature.
### Data Splits
TBA
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
Panupong Pasupat and Percy Liang
### Licensing Information
Creative Commons Attribution Share Alike 4.0 International
### Citation Information
```
@inproceedings{pasupat-liang-2015-compositional,
title = "Compositional Semantic Parsing on Semi-Structured Tables",
author = "Pasupat, Panupong and Liang, Percy",
booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = jul,
year = "2015",
address = "Beijing, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P15-1142",
doi = "10.3115/v1/P15-1142",
pages = "1470--1480",
}
```
### Contributions
Thanks to [@SivilTaram](https://github.com/SivilTaram) for adding this dataset. |
false |
# Dataset Card for UTS_Dictionary
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
The UTS_Dictionary is an open source Vietnamese dictionary that provides users with an extensive collection of words and definitions in the Vietnamese language. It is a free resource that is available to anyone who wishes to use it, and it has been designed to be easily accessible and user-friendly.
The UTS_Dictionary is a collaborative project that has been developed by a team of passionate volunteers who are dedicated to promoting the Vietnamese language and culture. The project is aimed at providing an accurate and comprehensive dictionary that can be used by both native speakers and those who are learning the language.
With the UTS_Dictionary, users can search for words in Vietnamese and find their definitions in a matter of seconds. The dictionary includes a wide range of words, including technical terms, slang, idioms, and colloquialisms, making it a valuable resource for anyone who wants to understand the nuances of the Vietnamese language.
Overall, the UTS_Dictionary is a valuable resource for anyone who wants to learn or improve their Vietnamese language skills. Its open source nature allows for continuous improvement and expansion, making it an essential tool for anyone interested in the Vietnamese language and culture.
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
| name | train |
|---------|---------:|
| data | 72547 |
## Dataset Creation
### Curation Rationale
### Source Data
### Annotations
## Additional Information
### Licensing Information
The dataset is released under Apache 2.0.
### Citation Information
### Contributions
|
false | # Dataset Card for "ISSAI_KSC_335RS_v_1_1"
Kazakh Speech Corpus (KSC)
Identifier: SLR102
Summary: A crowdsourced open-source Kazakh speech corpus developed by ISSAI (330 hours)
Category: Speech
License: Attribution 4.0 International (CC BY 4.0)
Downloads (use a mirror closer to you):
ISSAI_KSC_335RS_v1.1_flac.tar.gz [19G] (speech, transcripts and metadata ) Mirrors: [US] [EU] [CN]
About this resource:
A crowdsourced open-source speech corpus for the Kazakh language. The KSC contains around 332 hours of transcribed audio comprising over 153,000 utterances spoken by participants from different regions and age groups, as well as both genders. It was carefully inspected by native Kazakh speakers to ensure high quality. The dataset is primarily intended to be used for training automatic speech recognition systems.
You can find more information about the dataset here.
To cite the dataset, please use the following BibTeX entry:
@inproceedings{khassanov-etal-2021-crowdsourced,
title = "A Crowdsourced Open-Source {K}azakh Speech Corpus and Initial Speech Recognition Baseline",
author={Yerbolat Khassanov and Saida Mussakhojayeva and Almas Mirzakhmetov and Alen Adiyev and Mukhamet Nurpeiissov and Huseyin Atakan Varol},
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.58",
doi = "10.18653/v1/2021.eacl-main.58",
pages = "697--706"
}
|
true |
# Dataset Card for Recept
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
false |
## Source
Source: [UCI](https://archive.ics.uci.edu/ml/datasets/BlogFeedback)
## Data Set Information:
This data originates from blog posts. The raw HTML-documents
of the blog posts were crawled and processed.
The prediction task associated with the data is the prediction
of the number of comments in the upcoming 24 hours. In order
to simulate this situation, we choose a basetime (in the past)
and select the blog posts that were published at most
72 hours before the selected base date/time. Then, we calculate
all the features of the selected blog posts from the information
that was available at the basetime, therefore each instance
corresponds to a blog post. The target is the number of
comments that the blog post received in the next 24 hours
relative to the basetime.
In the train data, the basetimes were in the years
2010 and 2011. In the test data the basetimes were
in February and March 2012. This simulates the real-world
situtation in which training data from the past is available
to predict events in the future.
The train data was generated from different basetimes that may
temporally overlap. Therefore, if you simply split the train
into disjoint partitions, the underlying time intervals may
overlap. Therefore, the you should use the provided, temporally
disjoint train and test splits in order to ensure that the
evaluation is fair.
## Attribute Information:
1...50:Average, standard deviation, min, max and median of them attributes 51...60 for the source of the current blog post. With source we mean the blog on which the post appeared.
For example, myblog.blog.org would be the source of the post myblog.blog.org/post_2010_09_10
51: Total number of comments before basetime
52: Number of comments in the last 24 hours before the
basetime
53: Let T1 denote the datetime 48 hours before basetime,
Let T2 denote the datetime 24 hours before basetime.
This attribute is the number of comments in the time period
between T1 and T2
54: Number of comments in the first 24 hours after the
publication of the blog post, but before basetime
55: The difference of Attribute 52 and Attribute 53
56...60:
The same features as the attributes 51...55, but
features 56...60 refer to the number of links (trackbacks),
while features 51...55 refer to the number of comments.
61: The length of time between the publication of the blog post
and basetime
62: The length of the blog post
63...262:
The 200 bag of words features for 200 frequent words of the
text of the blog post
263...269: binary indicator features (0 or 1) for the weekday
(Monday...Sunday) of the basetime
270...276: binary indicator features (0 or 1) for the weekday
(Monday...Sunday) of the date of publication of the blog
post
277: Number of parent pages: we consider a blog post P as a
parent of blog post B, if B is a reply (trackback) to
blog post P.
278...280:
Minimum, maximum, average number of comments that the
parents received
281: The target: the number of comments in the next 24 hours
(relative to basetime)
|
false | Concatenated and edited collection of fairy tales taken from Project Gutenberg.
Texts:
https://www.gutenberg.org/files/2591/2591-0.txt
https://www.gutenberg.org/files/503/503-0.txt
https://www.gutenberg.org/files/7277/7277-0.txt
https://www.gutenberg.org/cache/epub/35862/pg35862.txt
https://www.gutenberg.org/cache/epub/69739/pg69739.txt
https://www.gutenberg.org/files/2435/2435-0.txt
https://www.gutenberg.org/cache/epub/7871/pg7871.txt
https://www.gutenberg.org/files/8933/8933-0.txt
gutenberg.org/cache/epub/30834/pg30834.txt
https://www.gutenberg.org/cache/epub/68589/pg68589.txt
https://www.gutenberg.org/cache/epub/34453/pg34453.txt
gutenberg.org/cache/epub/8653/pg8653.txt |
false | # AutoTrain Dataset for project: guitarsproject
## Dataset Description
This dataset has been automatically processed by AutoTrain for project guitarsproject.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<1990x2520 RGB PIL image>",
"target": 1
},
{
"image": "<6000x4000 RGB PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['LesPaul', 'Stratocaster'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 80 |
| valid | 21 |
|
false | |
false | |
false |
## Source
https://www.kaggle.com/datasets/dhoogla/unswnb15?resource=download
## Dataset
This is an academic intrusion detection dataset. All the credit goes to the original authors: dr. Nour Moustafa and dr. Jill Slay.
Please cite their original paper and all other appropriate articles listed on the UNSW-NB15 page.
The full dataset also offers the pcap, BRO and Argus files along with additional documentation.
The modifications to the predesignated train-test sets are minimal and designed to decrease disk storage and increase performance & reliability.
Exploratory Data Analysis (EDA) through classification with very simple models to .877 AUROC. |
false | |
false | |
false | |
false | |
false | # Dataset Card
## Table of Contents
- [Dataset Card](#dataset-card)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** --
- **Repository:** [github.com/pstuerner/ukraine-liveblog-data](https://github.com/pstuerner/ukraine-liveblog-data)
- **Paper:** --
- **Leaderboard:** --
- **Point of Contact:** philipp.stuerner@web.de
### Dataset Summary
The "ukraine-liveblog" dataset contains a collection of news articles published on the liveblog of the popular German news website, tagesschau.de. The dataset covers the period from February 2022 to February 2023, and includes every news feed published during this time that covers the ongoing war in Ukraine.
### Supported Tasks and Leaderboards
--
### Languages
The language of the dataset is German.
## Dataset Structure
### Data Instances
Here is a JSON-formatted example of a typical instance in the "German Articles about the War in Ukraine" dataset:
This example consists of a headline and the corresponding text separated by a colon. The headline reads "Warum Waffenlieferungen in Ostdeutschland skeptisch gesehen werden" (Why Weapons Deliveries are Viewed Skeptically in East Germany), and the text provides additional details and analysis about the topic. This format is consistent across the dataset and allows for easy identification and extraction of key information.
```
{
"text": "Warum Waffenlieferungen in Ostdeutschland skeptisch gesehen werden: Die Debatten um Waffenlieferungen für die Ukraine stoßen in Ostdeutschland meist auf Ablehnung. Das lässt sich aber nicht allein mit Russlandfreundlichkeit erklären, sagt Politikwissenschaftlerin Sarah Pagung."
...
}
```
### Data Fields
The "ukraine-liveblog" dataset includes the following fields:
- `text`: The main body of the article, written in German. (string)
### Data Splits
The dataset has been split into two sets: a training set and a validation set. The training set contains 90% of the data, or 15,083 instances, and the validation set contains the remaining 10%, or 1,676 instances.
| | train | validation | test |
|-------------------------|------:|-----------:|-----:|
| Input Sentences | 15083 | 1676 | |
| Average Sentence Length | 768 | 674 | |
## Dataset Creation
### Curation Rationale
The creation of the dataset was motivated by a number of factors, such as the need to collect and analyze information about the conflict in Ukraine, understand how the conflict is being reported in German media, and provide a resource for NLP enthusiasts to fine-tune GPT2 on additional German data.
### Source Data
The liveblog on tagesschau.de about the war in Ukraine.
#### Initial Data Collection and Normalization
The dataset was built using a custom Python script that leverages the newspaper and beautifulsoup4 libraries. The script was designed to scrape data from the liveblog about the war in Ukraine on tagesschau.de, starting from the latest day of the liveblog and working backwards until it reaches the first day of the liveblog.
#### Who are the source language producers?
The articles were written by Tagesschau reporters.
### Annotations
--
#### Annotation process
--
#### Who are the annotators?
--
### Personal and Sensitive Information
All information is publicly available and doesn't include any personal or sensitive information.
## Considerations for Using the Data
### Social Impact of Dataset
--
### Discussion of Biases
--
### Other Known Limitations
--
## Additional Information
### Dataset Curators
--
### Licensing Information
--
### Citation Information
--
### Contributions
-- |
false | # Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
false |
# Dataset Card for semantic-domains-greek-lemmatized
## Dataset Description
- **Point of Contact:** https://huggingface.co/ryderwishart / https://github.com/ryderwishart
### Dataset Summary
Semantic domains aligned to tokens, broken down by sentences. Tokens have been lemmatized according to data in [Clear-Bible/macula-greek](https://github.com/Clear-Bible/macula-greek).
Domains are based on Louw and Nida's semantic domains for the Greek New Testament.
### Languages
Greek, Hellenistic Greek, Koine Greek, Greek of the New Testament
## Dataset Structure
### Data Instances
```
DatasetDict({
train: Dataset({
features: ['tokens', 'tags', 'labels'],
num_rows: 6408
})
test: Dataset({
features: ['tokens', 'tags', 'labels'],
num_rows: 801
})
eval: Dataset({
features: ['tokens', 'tags', 'labels'],
num_rows: 802
})
})
```
### Data Fields
`tokens`: plaintext words (only split by whitespace); e.g.,
```
['δέ', 'ὁ', 'ἀποκρίνομαι', 'εἷς', 'αὐτός', 'λέγω', 'ἑταῖρος', 'οὐ', 'ἀδικέω', 'σύ', 'οὐχί', 'δηνάριον', 'συμφωνέω', 'ἐγώ']
```
`tags`: integer IDs for each semantic domain (use these for training the model).
`labels`: label strings for each tag; e.g.,
```
['89.124', '92.24', '33.28', '92.22', '92.11', '33.69', '34.16', '69.3', '88.128 88.22', '92.6', '69.12', '6.75', '31.15', '92.1']
```
### Data Splits
Data split into train (75%), test (12.5%), and evaluation (12.5%) splits.
## Dataset Creation
Greek words are based on the Nestle1904 base text, which is in the public domain.
More information about the meanings of the semantic domain labels can be found online [here](https://www.laparola.net/greco/louwnida.php), or by consulting Louw and Nida's Lexicon.
## Considerations for Using the Data
### Social Impact of Dataset
This data may be used to further Christ's kingdom and glorify God.
### Other Known Limitations
Louw and Nida's semantic domains have some known limitations discussed [in this paper](https://academic.oup.com/ijl/article/31/4/394/5070421). |
true | Basic SQL Database that classifies claims as true (1) or false (0).
Cleaned FEVER and FEVEROUS dataset and scraped and cleaned politifact website into this DB file. |
false | |
false | # AutoTrain Dataset for project: auto_train
## Dataset Description
This dataset has been automatically processed by AutoTrain for project auto_train.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"source": "\u79fb\u5c45\u9999\u6e2f\u5f8c\uff0c\u60a8\u53ef\u4ee5\u524d\u5f80\u6211\u5011\u7684\u5176\u4e2d\u4e00\u5bb6\u5206\u884c\u7533\u8acb\u4fe1\u7528\u5361\u2014\u2014\u5728\u9019\u88e1\u627e\u5230\u60a8\u6700\u65b9\u4fbf\u7684\u5730\u9ede\u3002",
"target": "After you move to Hong Kong you can apply for a Credit Card by visiting one of our branches \u2013 find your most convenient location here."
},
{
"source": "\u79fb\u5c45\u9999\u6e2f\u5f8c\uff0c\u60a8\u53ef\u4ee5\u524d\u5f80\u6211\u5011\u7684\u5176\u4e2d\u4e00\u5bb6\u5206\u884c\u7533\u8acb\u5132\u84c4/\u652f\u7968\u8cec\u6236\u2014\u2014\u5728\u9019\u88e1\u627e\u5230\u60a8\u6700\u65b9\u4fbf\u7684\u5730\u9ede\u3002\u5982\u679c\u60a8\u9858\u610f\uff0c\u6211\u5011\u53ef\u4ee5\u5728\u60a8\u62b5\u9054\u5f8c\u70ba\u60a8\u5b89\u6392\u5728\u60a8\u9078\u64c7\u7684\u5206\u884c\u7684\u9810\u7d04\u8a0e\u8ad6\u60a8\u7684\u9280\u884c\u548c\u8ca1\u5bcc\u7ba1\u7406\u9700\u6c42\u3002\u8981\u5b89\u6392\u7d04\u6703\uff0c\u8acb\u806f\u7e6b\u60a8\u7576\u5730\u7684\u82b1\u65d7\u9280\u884c\u4ee3\u8868\u3002",
"target": "After you move to Hong Kong you can apply for a Savings / Checking Account by visiting one of our branches \u2013 find your most convenient location here.If you wish so, we can schedule an appointment for you in a Branch of your choice upon your arrival to discuss your banking and wealth management needs. To schedule an appointment, contact your local Citibank representative."
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"source": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 332 |
| valid | 83 |
|
false | |
false |
The blob dataset!
-----
This dataset consists of a collection of 100000 that contains randomly generated blobs over a random noise background. Each image has annotated its number of blobs and if they are large or small.
The task consists of learning at the same time a quantitable guess (number of blobs) and a qualitative one (its size). |
false | |
true | # Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
### Supported Tasks and Leaderboards
Multi-answer questioning, token classification
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
celex_id, input_ids, token_type_ids, attention_mask, labels
### Data Splits
validation samples
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
EURLEX dataset
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
true | # Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
false |
# Dataset Card for Star Villain Marvel Comics LoRa
## Dataset Description
- **https://duskfallcrew.carrd.co/:**
- **https://civitai.com/models/14831/star-ryan-ripley:**
- **https://civitai.com/models/14831/star-ryan-ripley**
# Data set for Duskfallcrew/Star_Marvel_comics_LoRa
Trained with: https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/main/kohya-LoRA-dreambooth.ipynb
# Where else can i find this ?
Both safetensors files are at: https://civitai.com/models/14831/star-ryan-ripley
# The outputs aren't comic format.
I haven't tested it in WEB UI yet, the scripted outputs largely rely on the actual model.
# YOU - YOU'RE A DATA SCRAPER!!!
And you're a booger picker! C'mon, work smarter, not harder. If you REALLY like this content, buy the comics. Seriously, or like find the artist, and buy their merch rather than supporting Disney.
# You do way too much is there a way to pay you in... anyway?
Yea, XD THROW ME PENNIES IN THE FOUNTAIN OF COFFEE!
https://ko-fi.com/DUSKFALLcrew
# What's the deal, is there a lisc on this?
Creative whatever it is, means that you basically just don't do this to do illegal crap. I'm not responsible for what you generate after you download this. I am not responsible for Disney coming after your ass for making Minnie kiss Deadpool. |
false | |
false | |
true | # AutoTrain Dataset for project: fake_news_fine_tuned_v4
## Dataset Description
This dataset has been automatically processed by AutoTrain for project fake_news_fine_tuned_v4.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "Many of us do not share Donald Trump s past values but we do love America In my opinion it is not about him It is about Who will preserve and a protect the constitution Who will protect the second amendment Who will not send your son or daughter into harms way unless there is a good reason Who does not support partial birth abortion Who wants to fix our inner cities and our infrastructure Who will improve our economy and bring jobs back to the USA Who is a nationalist and not a globalist Who will protect our vets from dying in the streets and on park benches and get them help when they need it Who supports our police firemen military men and women Who will protect our borders from criminal elements and terrorist Who will not leave Americans to die in foreign countries under the hands of terrorist Who is now a professed Christian and changed man according to many Christian leaders and pastors These are just a few reasons to vote for Donald Trump ",
"target": 1
},
{
"text": "Good morning Here s what you need to know President Trump is expected to order the construction of a Mexican border wall today and slash immigration of refugees from terror prone nations White House officials said He pressed automakers to increase jobs in the U S but a new forecast of a federal budget deficit that could expand to almost trillion over years could complicate his agenda to cut taxes and ramp up spending Mr Trump also revived two pipelines blocked under President Obama the Keystone XL pipeline the focus of years of debate over energy needs and climate change and the Dakota Access pipeline the target of Native American protests And tensions are rising between the U S and China Beijing after a challenge from a Trump spokesman insisted it had irrefutable sovereignty over disputed islands in the South China Sea China sees opportunities to extend its global reach with Mr Trump in power but it also sees a threat to its prized goal stability Israel discarding diplomatic restraint just a few days into the Trump administration approved the construction of housing units for Jews in West Bank settlements A month ago the Obama administration declined to veto a Security Council resolution condemning the settlements and last week a gathering of world leaders warned Israel to stop expanding them The office of Prime Minister Theresa May pledged to stay on its timetable for taking Britain out of the European Union despite a top court ruling requiring the approval of Parliament before the process can begin Political observers said it was unlikely that legislators despite being largely opposed to Brexit would try to block the will of the voters Three months after Prime Minister Narendra Modi abruptly banned percent of India s currency in a bid to fight black money hidden reserves that feed corruption unemployment is spreading and some people are going without fruit vegetables and milk This has actually hurt the poor enormously one business leader said Above a protest over cash shortages last week And this year s Oscar nominations are out Voters gave La La Land nominations a tie for the most in Academy Award history The academy also honored six black actors a record Here is the full list of nominees The Alibaba Group beat estimates with strong earnings The results showed how much the giant despite a growing global profile and its record I P O on Wall Street is still dependent on China Silver Lake an investment firm is leading a billion investment in Koubei an business that is part of what is increasingly known as the sector or O O President Trump s pick for the Federal Communications Commission Ajit Pai an from Kansas is expected to roll back rules that ban internet service providers from favoring certain websites and apps Nearly million Chinese citizens used their mobile phones to make payments last year an increase of percent from new government data shows Japan releases data on its imports and exports for December The country s economy has been struggling despite government efforts at stimulus South Korea and Taiwan release G D P figures Wall Street was up Here s a snapshot of global markets China s nuclear plant begun in the s was one of the country s most ambitious military projects Today the mountain has been reborn as a tourist attraction The New York Times Peter Thiel the American billionaire has taken New Zealand citizenship and quietly acquired a sprawling estate on Lake Wanaka New Zealand Herald Afghanistan ordered the arrest of nine bodyguards of Gen Abdul Rashid Dostum the more senior of the country s two vice presidents on accusations of the rape and torture of an Uzbek elder The New York Times A wave of smog is forecast to engulf four northern Chinese provinces as the Lunar New Year approaches Reuters Talks in Astana Kazakhstan may have done more to to firm up Russia s growing role in diplomacy over Syria than to create any progress toward peace The New York Times A proposal to ban the display of Vietnam s flag on city poles has divided the Vietnamese community of San Jose Calif The Mercury News Gambia s former president Yahya Jammeh didn t go into exile in Equatorial Guinea He left with two a and a plane full of luxury items The New York Times China s government has shut down of the country s golf courses since citing illegal land or water use Xinhua Scientists found the fossils of a huge nearly otter that roamed rivers and lakes in southwestern China about million years ago Reuters Doing just minutes of yoga can improve your bone health Vacations don t have to be all about relaxing Here s how to plan one with some good deeds along the way Recipe of the day Try this grilled cheese sandwich featuring mayonnaise for some midweek comfort food In China members of the Tanka people have survived on southern coastal waterways and on the margins of society since ancient times As big cities spread their floating way of life is disappearing Genetic research is taking some truly turns Ant researchers have identified the molecular and neural cues that help explain social behaviors including caring for young breeding even capturing and upstarts like tiny police officers And a pioneering scientist Maria Sibylla Merian captivated Europeans years ago with her studies of insects Now her findings are being celebrated again Grandstands filled with cheering fans perfectly plated vegetables and your country s name on the line this isn t your typical cooking competition This is the Bocuse d Or or the culinary version of the Olympics The competition which takes place this week in Lyon France was founded by the master French chef Paul Bocuse He was influential in establishing nouvelle cuisine which emphasizes ingredients and presentation At Mr Bocuse remains a king among chefs Long before became a trendy concept a Bocuse was learning how to butcher a cow for his first restaurant job He eventually joined his father at their family restaurant L Auberge du Pont de Collonges One year after he took over the restaurant won a Michelin star He recalled how early in his career he wowed a prominent food critic with fresh ingredients Mr Bocuse served haricots verts picked that morning lightly boiled and served with olive oil shallots and salt In The Times s Craig Claiborne described the restaurant outside of Lyon as one of the most elegant and proudest restaurants outside of Paris Mr Bocuse though once offered a less refined assessment of his skills Some men have mistresses Mr Bocuse once said I run a luxury restaurant Remy Tumin contributed reporting Your Morning Briefing is published weekday mornings What would you like to see here Contact us at asiabriefing nytimes com ",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['0', '1'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 7998 |
| valid | 2000 |
|
false | # AutoTrain Dataset for project: cylonix_summarize
## Dataset Description
This dataset has been automatically processed by AutoTrain for project cylonix_summarize.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "CAPE TOWN (Reuters) - Two men were injured in a drive-by shooting at South Africa s Cape Town International Airport, police said on Wednesday. Operations at the airport, the second busiest in the country and a tourism hub, were not affected, police spokesman Vish Naidoo said, adding the motive for the shooting was unknown. The airport is Africa s third busiest and handles around 10 million passengers a year, including tourists and businesspeople commuting to the economic hub Johannesburg. A man, about 50 years of age, was shot in a drive-by shooting and in the process a bystander, a 30-year-old man, was also hit, Naidoo said. The shooting took place in the early morning in a public area some distance from the airport s security gates, Naidoo said. Flights had not been affected, the airport s general manager Deon Cloete said in a statement. The shooting scene has been cordoned off while investigations continue, Cloete said. Local media reported that the shooting was gang-related, but Naidoo could not confirm that was the motive. ",
"target": "Two injured in shooting at South Africa's Cape Town airport"
},
{
"text": "WASHINGTON (Reuters) - The chairman of a U.S. Senate cyber security subcommittee said on Friday he planned to introduce sanctions legislation over \u201cRussia\u2019s cyber criminals\u201d after Washington accused Russia of political cyber attacks ahead of the Nov. 8 presidential election. Republican Senator Cory Gardner said his legislation would require the Obama administration to investigate those who have engaged in significant actions undermining cyber security and aggressively pursue sanctions when appropriate. Gardner is chairman of the Senate Foreign Relations Subcommittee on East Asia, the Pacific and International Cybersecurity. ",
"target": "U.S. lawmaker wants cyber sanctions on Russia after hacking charges"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 37 |
| valid | 10 |
|
false | # AutoTrain Dataset for project: size
## Dataset Description
This dataset has been automatically processed by AutoTrain for project size.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<990x990 RGB PIL image>",
"target": 0
},
{
"image": "<966x990 RGB PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['Large', 'Medium', 'Mosaic', 'Small'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 212 |
| valid | 54 |
|
true | # Movie Review Data
* Original source: sentence polarity dataset v1.0 http://www.cs.cornell.edu/people/pabo/movie-review-data/
* Seems to same as https://huggingface.co/datasets/rotten_tomatoes, but different split.
## Original README
=======
Introduction
This README v1.0 (June, 2005) for the v1.0 sentence polarity dataset comes
from the URL
http://www.cs.cornell.edu/people/pabo/movie-review-data .
=======
Citation Info
This data was first used in Bo Pang and Lillian Lee,
``Seeing stars: Exploiting class relationships for sentiment categorization
with respect to rating scales.'', Proceedings of the ACL, 2005.
@InProceedings{Pang+Lee:05a,
author = {Bo Pang and Lillian Lee},
title = {Seeing stars: Exploiting class relationships for sentiment
categorization with respect to rating scales},
booktitle = {Proceedings of the ACL},
year = 2005
}
=======
Data Format Summary
- rt-polaritydata.tar.gz: contains this readme and two data files that
were used in the experiments described in Pang/Lee ACL 2005.
Specifically:
* rt-polarity.pos contains 5331 positive snippets
* rt-polarity.neg contains 5331 negative snippets
Each line in these two files corresponds to a single snippet (usually
containing roughly one single sentence); all snippets are down-cased.
The snippets were labeled automatically, as described below (see
section "Label Decision").
Note: The original source files from which the data in
rt-polaritydata.tar.gz was derived can be found in the subjective
part (Rotten Tomatoes pages) of subjectivity_html.tar.gz (released
with subjectivity dataset v1.0).
=======
Label Decision
We assumed snippets (from Rotten Tomatoes webpages) for reviews marked with
``fresh'' are positive, and those for reviews marked with ``rotten'' are
negative.
## Preprocessing
To make csv with text and label field, we use the following script.
```python3
import csv
import random
# NOTE: The encoding of original file is "latin_1". We will change it to "utf8".
with open("rt-polarity.pos", encoding="latin_1") as f:
texts_pos = [line.strip() for line in f]
with open("rt-polarity.neg", encoding="latin_1") as f:
texts_neg = [line.strip() for line in f]
rows_pos = [{"text": text, "label": 1} for text in texts_pos]
rows_neg = [{"text": text, "label": 0} for text in texts_pos]
# NOTE: For fair validation, we split it into train and test. Also, for the research who wants to use different setting, we provide whole setting.
# NOTE: We follow the split setting in LM-BFF paper.
rows_whole = rows_pos + rows_neg
random.Random(42).shuffle(rows_whole)
rows_test, rows_train = rows_whole[:2000], rows_whole[2000:]
with open("whole.csv", "w", encoding="utf8") as f:
writer = csv.DictWriter(f, fieldnames=["text", "label"])
writer.writerows(rows_train)
with open("train.csv", "w", encoding="utf8") as f:
writer = csv.DictWriter(f, fieldnames=["text", "label"])
writer.writerows(rows_train)
with open("test.csv", "w", encoding="utf8") as f:
writer = csv.DictWriter(f, fieldnames=["text", "label"])
writer.writerows(rows_test)
```
|
true | # Dataset Card for "reklamation24_haus-reinigung-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
false | # CNN Dailymail
- Source: https://huggingface.co/datasets/cnn_dailymail
- Num examples:
- 287,113 (train)
- 13,368 (validation)
- 11,490 (test)
- Language: English
```python
from datasets import load_dataset
load_dataset("tdtunlp/cnn_dailymail_en")
```
- Format for summarization task
```python
import re
def clean_up_start(text):
text = re.split(r"\(CNN\) +--", text)[-1]
text = re.split(r"\(CNN\)", text[:100])[-1] + text[100:]
text = re.split(r".*UPDATED:\s+[0-9]{2}:[0-9]{2}.*[2011|2012|2013|2014|2015]", text)[-1]
text = text.replace("’", "'")
text = text.replace("‘", "'")
return text.strip()
def clean_up_highlights(line):
if line.startswith("NEW: "):
return line.split("NEW: ")[1].strip()
return line
def preprocess(sample):
article = clean_up_start(sample['article'])
summary = clean_up_highlights(sample['highlights'])
return {'text': f'<|startoftext|><|article|>{article}<|summary|>{summary}<|endoftext|>'}
"""
<|startoftext|><|article|>LONDON, England (Reuters) -- Harry Potter star Daniel Radcliffe gains access to a reported £20 million ($41.1 million) fortune as he turns 18 on Monday, but he insists the money won't cast a spell on him. Daniel Radcliffe as Harry Potter in "Harry Potter and the Order of the Phoenix" To the disappointment of gossip columnists around the world, the young actor says he has no plans to fritter his cash away on fast cars, drink and celebrity parties. "I don't plan to be one of those people who, as soon as they turn 18, suddenly buy themselves a massive sports car collection or something similar," he told an Australian interviewer earlier this month. "I don't think I'll be particularly extravagant. "The things I like buying are things that cost about 10 pounds -- books and CDs and DVDs." At 18, Radcliffe will be able to gamble in a casino, buy a drink in a pub or see the horror film "Hostel: Part II," currently six places below his number one movie on the UK box office chart. Details of how he'll mark his landmark birthday are under wraps. His agent and publicist had no comment on his plans. "I'll definitely have some sort of party," he said in an interview. "Hopefully none of you will be reading about it." Radcliffe's earnings from the first five Potter films have been held in a trust fund which he has not been able to touch. Despite his growing fame and riches, the actor says he is keeping his feet firmly on the ground. "People are always looking to say 'kid star goes off the rails,'" he told reporters last month. "But I try very hard not to go that way because it would be too easy for them." His latest outing as the boy wizard in "Harry Potter and the Order of the Phoenix" is breaking records on both sides of the Atlantic and he will reprise the role in the last two films. Watch I-Reporter give her review of Potter's latest » . There is life beyond Potter, however. The Londoner has filmed a TV movie called "My Boy Jack," about author Rudyard Kipling and his son, due for release later this year. He will also appear in "December Boys," an Australian film about four boys who escape an orphanage. Earlier this year, he made his stage debut playing a tortured teenager in Peter Shaffer's "Equus." Meanwhile, he is braced for even closer media scrutiny now that he's legally an adult: "I just think I'm going to be more sort of fair game," he told Reuters. E-mail to a friend . Copyright 2007 Reuters. All rights reserved.This material may not be published, broadcast, rewritten, or redistributed.<|summary|>Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday .
Young actor says he has no plans to fritter his cash away .
Radcliffe's earnings from first five Potter films have been held in trust fund .<|endoftext|>
"""
``` |
true | # AutoTrain Dataset for project: sentiment_analysis
## Dataset Description
This dataset has been automatically processed by AutoTrain for project sentiment_analysis.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "I grew up (b. 1965) watching and loving the Thunderbirds. All my mates at school watched. We played \"Thunderbirds\" before school, during lunch and after school. We all wanted to be Virgil or Scott. No one wanted to be Alan. Counting down from 5 became an art form. I took my children to see the movie hoping they would get a glimpse of what I loved as a child. How bitterly disappointing. The only high point was the snappy theme tune. Not that it could compare with the original score of the Thunderbirds. Thankfully early Saturday mornings one television channel still plays reruns of the series Gerry Anderson and his wife created. Jonatha Frakes should hand in his directors chair, his version was completely hopeless. A waste of film. Utter rubbish. A CGI remake may be acceptable but replacing marionettes with Homo sapiens subsp. sapiens was a huge error of judgment.",
"target": 0
},
{
"text": "When I put this movie in my DVD player, and sat down with a coke and some chips, I had some expectations. I was hoping that this movie would contain some of the strong-points of the first movie: Awsome animation, good flowing story, excellent voice cast, funny comedy and a kick-ass soundtrack. But, to my disappointment, not any of this is to be found in Atlantis: Milo's Return. Had I read some reviews first, I might not have been so let down. The following paragraph will be directed to those who have seen the first movie, and who enjoyed it primarily for the points mentioned.<br /><br />When the first scene appears, your in for a shock if you just picked Atlantis: Milo's Return from the display-case at your local videoshop (or whatever), and had the expectations I had. The music feels as a bad imitation of the first movie, and the voice cast has been replaced by a not so fitting one. (With the exception of a few characters, like the voice of Sweet). The actual drawings isnt that bad, but the animation in particular is a sad sight. The storyline is also pretty weak, as its more like three episodes of Schooby-Doo than the single adventurous story we got the last time. But dont misunderstand, it's not very good Schooby-Doo episodes. I didnt laugh a single time, although I might have sniggered once or twice.<br /><br />To the audience who haven't seen the first movie, or don't especially care for a similar sequel, here is a fast review of this movie as a stand-alone product: If you liked schooby-doo, you might like this movie. If you didn't, you could still enjoy this movie if you have nothing else to do. And I suspect it might be a good kids movie, but I wouldn't know. It might have been better if Milo's Return had been a three-episode series on a cartoon channel, or on breakfast TV.",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['0', '1'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 1497 |
| valid | 1497 |
|
false | |
false |
### Source Corpus
```
@misc{inel-kamas-1-0,
title={INEL Kamas Corpus},
DOI={10.25592/uhhfdm.9752},
author={Gusev, Valentin and Klooster, Tiina and Wagner-Nagy, Beáta},
year={2019},
month={Dec}
}
``` |
true | |
false | |
false |
# Dataset Card Creation Guide
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [http://noisy-text.github.io/2021/multi-lexnorm.html]()
- **Paper:** [https://aclanthology.org/2021.wnut-1.55/]()
### Dataset Summary
This is the huggingface version of the MultiLexnorm dataset.
I'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.
### Citation Information
```
@inproceedings{van-der-goot-etal-2021-multilexnorm,
title = "{M}ulti{L}ex{N}orm: A Shared Task on Multilingual Lexical Normalization",
author = {van der Goot, Rob and
Ramponi, Alan and
Zubiaga, Arkaitz and
Plank, Barbara and
Muller, Benjamin and
San Vicente Roncal, I{\~n}aki and
Ljube{\v{s}}i{\'c}, Nikola and
{\c{C}}etino{\u{g}}lu, {\"O}zlem and
Mahendra, Rahmad and
{\c{C}}olako{\u{g}}lu, Talha and
Baldwin, Timothy and
Caselli, Tommaso and
Sidorenko, Wladimir},
booktitle = "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wnut-1.55",
doi = "10.18653/v1/2021.wnut-1.55",
pages = "493--509",
abstract = "Lexical normalization is the task of transforming an utterance into its standardized form. This task is beneficial for downstream analysis, as it provides a way to harmonize (often spontaneous) linguistic variation. Such variation is typical for social media on which information is shared in a multitude of ways, including diverse languages and code-switching. Since the seminal work of Han and Baldwin (2011) a decade ago, lexical normalization has attracted attention in English and multiple other languages. However, there exists a lack of a common benchmark for comparison of systems across languages with a homogeneous data and evaluation setup. The MultiLexNorm shared task sets out to fill this gap. We provide the largest publicly available multilingual lexical normalization benchmark including 13 language variants. We propose a homogenized evaluation setup with both intrinsic and extrinsic evaluation. As extrinsic evaluation, we use dependency parsing and part-of-speech tagging with adapted evaluation metrics (a-LAS, a-UAS, and a-POS) to account for alignment discrepancies. The shared task hosted at W-NUT 2021 attracted 9 participants and 18 submissions. The results show that neural normalization systems outperform the previous state-of-the-art system by a large margin. Downstream parsing and part-of-speech tagging performance is positively affected but to varying degrees, with improvements of up to 1.72 a-LAS, 0.85 a-UAS, and 1.54 a-POS for the winning system.",
}
```
### Contributions
Thanks to [@larrylawl](https://github.com/larrylawl) for adding this dataset.
|
false | |
false | # AutoTrain Dataset for project: english-tokipona
## Dataset Description
This dataset has been automatically processed by AutoTrain for project english-tokipona.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"target": "mi kama jo e pali ante.",
"source": "I'll find another job."
},
{
"target": "tenpo pini weka la mi moku lon poka sina.",
"source": "It's been a while since we've had lunch together."
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"target": "Value(dtype='string', id=None)",
"source": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 16824 |
| valid | 4206 |
|
false | # AutoTrain Dataset for project: map_no_map_twitter_demo
## Dataset Description
This dataset has been automatically processed by AutoTrain for project map_no_map_twitter_demo.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<260x365 RGB PIL image>",
"target": 1,
"feat_created_at": "2023-03-08T13:56:12.283125Z",
"feat_lead_time": 0.768,
"feat_annotation_id": 23,
"feat_id": 24,
"feat_annotator": 1,
"feat_updated_at": "2023-03-08T13:56:12.283151Z"
},
{
"image": "<639x1298 RGB PIL image>",
"target": 0,
"feat_created_at": "2023-03-08T13:59:10.554628Z",
"feat_lead_time": 0.533,
"feat_annotation_id": 87,
"feat_id": 88,
"feat_annotator": 1,
"feat_updated_at": "2023-03-08T13:59:10.554650Z"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['map', 'no_map'], id=None)",
"feat_created_at": "Value(dtype='string', id=None)",
"feat_lead_time": "Value(dtype='float64', id=None)",
"feat_annotation_id": "Value(dtype='int64', id=None)",
"feat_id": "Value(dtype='int64', id=None)",
"feat_annotator": "Value(dtype='int64', id=None)",
"feat_updated_at": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 72 |
| valid | 19 |
|
false | |
false | # Dataset name: "modified_anthropic_convo_data"
# Dataset Card for Conversational AI Bot
## Dataset Description
- **Homepage:** https://huggingface.co/datasets/Anthropic/hh-rlhf
### Dataset Summary
- This dataset is the augmented version of the same dataset found here https://huggingface.co/datasets/Anthropic/hh-rlhf
- Two new columns have been added called ***human_speaker*** and ***assistant_speaker*** to make it easier to directly use the data for a causal langauage modeling task
- Currently only one pair of conversations have been picked, the dataset will be updated soon
### Contributions
Thanks to https://huggingface.co/datasets/Anthropic/hh-rlhf for adding this dataset. |
false | |
false |
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