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---
dataset_info:
features:
- name: query
dtype: string
- name: positive_passages
sequence: string
- name: negative_passages
sequence: string
splits:
- name: train
num_bytes: 361146987
num_examples: 398398
- name: dev
num_bytes: 14493923
num_examples: 4030
- name: test
num_bytes: 10891808
num_examples: 6795
download_size: 153841910
dataset_size: 386532718
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
# Dataset Detail
this dataset is processed from 3 source of thai dataset consist of
- miracl/miracl
- facebook/xnli
- castorini/mr-tydi
- castorini/mr-tydi-corpus
## processing script
here is the precessing script I use
### miracl/miracl
```python
def create_miracl_datasets(datasets):
"""
nothing just extract texts
"""
datasets_ = {
'query': [],
'positive_passages': [],
'negative_passages': [],
}
for data in tqdm(datasets):
datasets_['query'].append(data['query'])
negative_passages = []
for negative_passage in data['negative_passages']:
negative_passages.append(negative_passage['text'])
datasets_['negative_passages'].append(negative_passages)
positive_passages = []
for positive_passage in data['positive_passages']:
positive_passages.append(positive_passage['text'])
datasets_['positive_passages'].append(positive_passages)
return Dataset.from_dict(datasets_)
```
ratio
```python
DatasetDict({
train: Dataset({
features: ['query', 'positive_passages', 'negative_passages'],
num_rows: 2972
})
eval: Dataset({
features: ['query', 'positive_passages', 'negative_passages'],
num_rows: 366
})
test: Dataset({
features: ['query', 'positive_passages', 'negative_passages'],
num_rows: 367
})
})
```
### facebook/xnli
```python
def create_xnli_datasets(datasets):
"""
transform format of ['premise', 'hypothesis', 'label'] to ['query', 'positive_passages', 'negative_passages']
using contradiction as negative passage pair and
neutral, entailment -> possitive passage pair
premise as passage (premise -> evidence)
hypothesis as query (hypothesis so called question so can be used as query)
"""
datasets_ = {
'query': [],
'positive_passages': [],
'negative_passages': []
}
for data in tqdm(datasets):
datasets_['query'].append(data['premise'])
if data['label'] == 'contradiction':
datasets_['positive_passages'].append([])
datasets_['negative_passages'].append([data['hypothesis']])
elif data['label'] == 'neutral' or 'entailment':
datasets_['positive_passages'].append([data['hypothesis']])
datasets_['negative_passages'].append([])
return Dataset.from_dict(datasets_)
```
ratio
```python
DatasetDict({
train: Dataset({
features: ['query', 'positive_passages', 'negative_passages'],
num_rows: 392702
})
eval: Dataset({
features: ['query', 'positive_passages', 'negative_passages'],
num_rows: 2490
})
test: Dataset({
features: ['query', 'positive_passages', 'negative_passages'],
num_rows: 5010
})
})
```
### castorini/mr-tydi
```python
def create_tydi_datasets(datasets, corpus, train = False):
"""
both dev, test set have only docid which may can be retrieve from the corpus
"""
cor_df = corpus.to_pandas()
datasets_ = {
'query': [],
'positive_passages': [],
'negative_passages': [],
}
for data in tqdm(datasets):
datasets_['query'].append(data['query'])
if train:
negative_passages = []
for negative_passage in data['negative_passages']:
negative_passages.append(negative_passage['text'])
datasets_['negative_passages'].append(negative_passages)
else:
datasets_['negative_passages'].append([])
positive_passages = []
for positive_passage in data['positive_passages']:
search_value = positive_passage['docid']
text = cor_df[cor_df["docid"] == search_value].text.values[0]
# if text.empty:
# continue
positive_passages.append(text)
datasets_['positive_passages'].append(positive_passages)
return Dataset.from_dict(datasets_)
```
ratio
```python
DatasetDict({
train: Dataset({
features: ['query_id', 'query', 'positive_passages', 'negative_passages'],
num_rows: 3319
})
dev: Dataset({
features: ['query_id', 'query', 'positive_passages', 'negative_passages'],
num_rows: 807
})
test: Dataset({
features: ['query_id', 'query', 'positive_passages', 'negative_passages'],
num_rows: 1190
})
})
``` |