Update README.md

README.md

@@ -71,4 +71,33 @@ We develop a new dataset named **Noise of Web (NoW)** for NCL. It contains **100
 
 ```
 
-Please note that since our raw data contains some sensitive business data, we only provide the **encoded image features** (\*_ims.npy) and the **token ids of the text tokenized**. For tokenizer, we provide [Tokenizers](https://github.com/huggingface/tokenizers) with [BPE](https://huggingface.co/docs/tokenizers/api/models#tokenizers.models.BPE) to produce \*_caps_bpe.txt, [BertTokenizer](https://huggingface.co/transformers/v3.0.2/model_doc/bert.html#berttokenizer) with [bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) pre-trained model to produce \*_caps_bert.txt, and [Jieba](https://github.com/fxsjy/jieba) to produce \*_caps_jieba.txt. **Our vocabulary size of BPETokenizer is 10,000, while BertTokenizer and JiebaTokenizer have a vocabulary size of 32,702 and 56,271 respectively.** (recorded in now100k_precomp_vocab\_\*.txt). \*_ids.txt records the data indexs in the original 500k dataset. In the future, we may process and make the original dataset public.
+Please note that since our raw data contains some sensitive business data, we only provide the **encoded image features** (\*_ims.npy) and the **token ids of the text tokenized**. For tokenizer, we provide [Tokenizers](https://github.com/huggingface/tokenizers) with [BPE](https://huggingface.co/docs/tokenizers/api/models#tokenizers.models.BPE) to produce \*_caps_bpe.txt, [BertTokenizer](https://huggingface.co/transformers/v3.0.2/model_doc/bert.html#berttokenizer) with [bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) pre-trained model to produce \*_caps_bert.txt, and [Jieba](https://github.com/fxsjy/jieba) to produce \*_caps_jieba.txt. **Our vocabulary size of BPETokenizer is 10,000, while BertTokenizer and JiebaTokenizer have a vocabulary size of 32,702 and 56,271 respectively.** (recorded in now100k_precomp_vocab\_\*.txt). \*_ids.txt records the data indexs in the original 500k dataset. In the future, we may process and make the original dataset public.
+
+### Usage
+
+```
+# data_path: your dataset name and path
+# data_split: {train,dev,test}
+# tokenizer: {bpe,bert,jieba}
+# vocabulary size of {bpe,bert,jieba} is {10000,32702,56271} 
+
+# captions
+with open(os.path.join(data_path, "{}_caps_{}.txt".format(data_split, tokenizer))) as f:
+    for line in f:
+        captions.append(line.strip())
+captions_token = []
+for index in range(len(captions)):
+  caption = captions[index]
+  tokens = caption.split(',')
+  caption = []
+  caption.append(vocab("<start>"))
+  caption.extend([int(token) for token in tokens if token])
+  caption.append(vocab("<end>"))
+  captions_token.append(caption)
+
+# images
+images = np.load(os.path.join(data_path, "%s_ims.npy" % data_split))
+
+return captions_token, images
+```
+Additionally, you can search for code snippets containing the string `now100k_precomp` in `co_train.py`, `data.py`, `evaluation.py`, and `run.py` in this repo and refer to them to process the NoW dataset for use in your own code.