QASC: incomplete training set 
Hi @danyaljj, thanks for reporting.

Unfortunately, I have not been able to reproduce your problem. My train split has 8134 examples:
```ipython
In [10]: ds["train"]
Out[10]:
Dataset({
    features: ['id', 'question', 'choices', 'answerKey', 'fact1', 'fact2', 'combinedfact', 'formatted_question'],
    num_rows: 8134
})

In [11]: ds["train"].shape
Out[11]: (8134, 8)
```
and the content of the last 5 examples is:
```ipython
In [12]: for i in range(8129, 8134):
    ...:     print(json.dumps(ds["train"][i]))
    ...:
{"id": "3KAKFY4PGU1LGXM77JAK2700NGCI3X", "question": "Chitin can be used for protection by whom?", "choices": {"text": ["Fungi", "People", "Man", "Fish", "trees", "Dogs", "animal", "Birds"], "label": ["A", "B",
 "C", "D", "E", "F", "G", "H"]}, "answerKey": "D", "fact1": "scales are used for protection by scaled animals", "fact2": "Fish scales are also composed of chitin.", "combinedfact": "Chitin can be used for prote
ction by fish.", "formatted_question": "Chitin can be used for protection by whom? (A) Fungi (B) People (C) Man (D) Fish (E) trees (F) Dogs (G) animal (H) Birds"}
{"id": "336YQZE83VDAQVZ26HW59X51JZ9M5M", "question": "Which type of animal uses plates for protection?", "choices": {"text": ["squids", "reptiles", "sea urchins", "fish", "amphibians", "Frogs", "mammals", "salm
on"], "label": ["A", "B", "C", "D", "E", "F", "G", "H"]}, "answerKey": "B", "fact1": "scales are used for protection by scaled animals", "fact2": "Reptiles have scales or plates.", "combinedfact": "Reptiles use
 their plates for protection.", "formatted_question": "Which type of animal uses plates for protection? (A) squids (B) reptiles (C) sea urchins (D) fish (E) amphibians (F) Frogs (G) mammals (H) salmon"}
{"id": "3WZ36BJEV3FGS66VGOOUYX0LN8GTBU", "question": "What are used for protection by fish?", "choices": {"text": ["scales", "fins", "streams.", "coral", "gills", "Collagen", "mussels", "whiskers"], "label": ["
A", "B", "C", "D", "E", "F", "G", "H"]}, "answerKey": "A", "fact1": "scales are used for protection by scaled animals", "fact2": "Fish are backboned aquatic animals.", "combinedfact": "scales are used for prote
ction by fish ", "formatted_question": "What are used for protection by fish? (A) scales (B) fins (C) streams. (D) coral (E) gills (F) Collagen (G) mussels (H) whiskers"}
{"id": "3Z2R0DQ0JHDKFAO2706OYIXGNA4E28", "question": "What are pangolins covered in?", "choices": {"text": ["tunicates", "Echinoids", "shells", "exoskeleton", "blastoids", "barrel-shaped", "protection", "white"
], "label": ["A", "B", "C", "D", "E", "F", "G", "H"]}, "answerKey": "G", "fact1": "scales are used for protection by scaled animals", "fact2": "Pangolins have an elongate and tapering body covered above with ov
erlapping scales.", "combinedfact": "Pangolins are covered in overlapping protection.", "formatted_question": "What are pangolins covered in? (A) tunicates (B) Echinoids (C) shells (D) exoskeleton (E) blastoids
 (F) barrel-shaped (G) protection (H) white"}
{"id": "3PMBY0YE272GIWPNWIF8IH5RBHVC9S", "question": "What are covered with protection?", "choices": {"text": ["apples", "trees", "coral", "clams", "roses", "wings", "hats", "fish"], "label": ["A", "B", "C", "D
", "E", "F", "G", "H"]}, "answerKey": "H", "fact1": "scales are used for protection by scaled animals", "fact2": "Fish are covered with scales.", "combinedfact": "Fish are covered with protection", "formatted_q
uestion": "What are covered with protection? (A) apples (B) trees (C) coral (D) clams (E) roses (F) wings (G) hats (H) fish"}
```

Could you please load again your dataset and print its shape, like this:
```python
ds = load_dataset("qasc", split="train)
print(ds.shape)
```
and confirm which is your output? Hmm .... it must have been a mistake on my side. Sorry for the hassle! 
## Describe the bug
The training instances are not loaded properly. 

## Steps to reproduce the bug
```python
from datasets import load_dataset

dataset = load_dataset("qasc", script_version='1.10.2')
 
def load_instances(split): 
    instances = dataset[split]
    print(f"split: {split} - size: {len(instances)}")
    for x in instances:
        print(json.dumps(x))


load_instances('test')
load_instances('validation')
load_instances('train')
```

##  results
For test and validation, we can see the examples in the output (which is good!): 
```
split: test - size: 920
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Anthax", "under water", "uterus", "wombs", "two", "moles", "live", "embryo"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What type of birth do therian mammals have? (A) Anthax (B) under water (C) uterus (D) wombs (E) two (F) moles (G) live (H) embryo", "id": "3C44YUNSI1OBFBB8D36GODNOZN9DPA", "question": "What type of birth do therian mammals have?"}
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Corvidae", "arthropods", "birds", "backbones", "keratin", "Jurassic", "front paws", "Parakeets."]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "By what time had mouse-sized viviparous mammals evolved? (A) Corvidae (B) arthropods (C) birds (D) backbones (E) keratin (F) Jurassic (G) front paws (H) Parakeets.", "id": "3B1NLC6UGZVERVLZFT7OUYQLD1SGPZ", "question": "By what time had mouse-sized viviparous mammals evolved?"}
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Reduced friction", "causes infection", "vital to a good life", "prevents water loss", "camouflage from consumers", "Protection against predators", "spur the growth of the plant", "a smooth surface"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What does a plant's skin do? (A) Reduced friction (B) causes infection (C) vital to a good life (D) prevents water loss (E) camouflage from consumers (F) Protection against predators (G) spur the growth of the plant (H) a smooth surface", "id": "3QRYMNZ7FYGITFVSJET3PS0F4S0NT9", "question": "What does a plant's skin do?"}
...
```
However, only a few instances are loaded for the training split, which is not correct. 

## Environment info
- `datasets` version: '1.10.2' 
- Platform: MaxOS 
- Python version:3.7
- PyArrow version: 3.0.0
