add usage

README.md

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+see [read_pyarrow.py](https://gist.github.com/csarron/df712e53c9e0dcaad4eb6843e7a3d51c#file-read_pyarrow-py) for how to read one pyarrow file.
+
+example PyTorch dataset:
+
+```python
+from torch.utils.data import Dataset
+
+class ImageCaptionArrowDataset(Dataset):
+    def __init__(
+        self,
+        dataset_file,
+        tokenizer,
+    ):
+
+        import pyarrow as pa
+
+        data = [pa.ipc.open_file(pa.memory_map(f, "rb")).read_all() for f in glob.glob(dataset_file)]
+        self.data = pa.concat_tables(data)
+        # do other initialization, like init image preprocessing fn, 
+
+    def __getitem__(self, index):
+        # item_id = self.data["id"][index].as_py()
+        text = self.data["text"][index].as_py() # get text
+        if isinstance(text, list):
+            text = random.choice(text)
+
+        img_bytes = self.data["image"][index].as_py() # get image bytes
+        
+        # do some processing with image and text, return the features
+        
+        
+        # img_feat = self.image_bytes_to_tensor(img_bytes)
+        # inputs = self.tokenizer(
+        #     text,
+        #     padding="max_length",
+        #     max_length=self.max_text_len,
+        #     truncation=True,
+        #     return_token_type_ids=True,
+        #     return_attention_mask=True,
+        #     add_special_tokens=True,
+        #     return_tensors="pt",
+        # )
+        # input_ids = inputs.input_ids.squeeze(0)
+        # attention_mask = inputs.attention_mask.squeeze(0)
+        # return {
+        #     # "item_ids": item_id,
+        #     "text_ids": input_ids,
+        #     "input_ids": input_ids,
+        #     "text_masks": attention_mask,
+        #     "pixel_values": img_feat,
+        # }
+    def __len__(self):
+        return len(self.data)
+
+
+```