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README.md

@@ -29,3 +29,144 @@ configs:
   - 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
+    })
+})
+```