Update README.md

README.md

@@ -242,41 +242,23 @@ See [here](https://github.com/FSoft-AI4Code/TheVault/blob/main/data/README.md) f
 
 ### Data Splits
 
-In this repo, The Vault is divided into 5 subsets, where three training versions are split based on size of the full training set, and the remains are validation set and test set (approximate 20,000 samples in each). The statistic for languages in each split set is illustrated in the following section.
-
-Before split, the dataset is deduplicated. There are 3 versions of training set that are small (5%), medium (20%) and large (100%).
+In this repo, the class level data is not split, and contained in only train set.
 
 ## Dataset Statistics
 
-- Compare to other benchmarks
-
-| Dataset                   | #Language | #Code-text pair |
-|:--------------------------|----------:|-----------------:|
-| PyMT5                     | 1         | ≈ 7,700,000      |
-| CoDesc                    | 1         | 4,211,516        |
-| CodeSearchNet             | 6         | 2,326,976        |
-| CodeSearchNet (CodeXGLUE) | 6         | 1,005,474        |
-| Deepcom                   | 1         | 424,028          |
-| CONCODE                   | 1         | 2,184,310        |
-| Funcom                    | 1         | 2,149,121        |
-| CodeT5                    | 8         | 3,158,313        |
-| **The Vault**             | **10**    | **34,098,775**   |
-
-- Statistic for split sets
-
-|            | train/small | train/medium | train/full | validation | test   | total         |
-|:-----------|------------:|-------------:|-----------:|-----------:|-------:|--------------:|
-|Python      |   370,657   |  1,952,110   | 7,772,647  | 30,992     | 21,652 | 7,825,291     |
-|Java        |   351,213   |  1,612,366   | 6,629,193  | 22,677     | 15,552 | 6,667,422     |
-|JavaScript  |    82,931   |    404,729   | 1,640,416  | 22,044     | 21,108 | 1,683,568     |
-|PHP         |   236,638   |  1,155,476   | 4,656,371  | 21,375     | 19,010 | 4,696,756     |
-|C           |   105,978   |    381,207   | 1,639,319  | 27,525     | 19,122 | 1,685,966     |
-|C#          |   141,090   |    783,166   | 3,305,891  | 24,787     | 19,638 | 3,350,316     |
-|C++         |    87,420   |    410,907   | 1,671,268  | 20,011     | 18,169 | 1,709,448     |
-|Go          |   267,535   |  1,319,547   | 5,109,020  | 19,102     | 25,314 | 5,153,436     |
-|Ruby        |    23,921   |    112,574   |   424,339  | 17,338     | 19,908 |   461,585     |
-|Rust        |    35,367   |    224,015   |   825,130  | 16,716     | 23,141 |   864,987     |
-|TOTAL       | 1,702,750   |  8,356,097   |33,673,594  |222,567     |202,614 |**34,098,775** |
+|Language    | Number of samples       |
+|:-----------|------------------------:|
+|Python      |     353,859             |
+|Java        |   4,069,174             |
+|JavaScript  |     236,525             |
+|PHP         |     969,667             |
+|C#          |   1,138,603             |
+|C++         |     150,530             |
+|Ruby        |      62,464             |
+|Rust        |     301,893             |
+|C           |       -                 |
+|Go          |       -                 |
+|TOTAL       | **7,282,715**           |
 
 ## Usage
 You can load The Vault dataset using datasets library: ```pip install datasets```
@@ -284,14 +266,14 @@ You can load The Vault dataset using datasets library: ```pip install datasets``
 ```python
 from datasets import load_dataset
 
-# Load full function level dataset (34M samples)
+# Load full class level dataset
 dataset = load_dataset("Fsoft-AIC/the-vault-class")
 
 # specific language (e.g. Python) 
-dataset = load_dataset("Fsoft-AIC/the-vault-class", split_set=["train"], languages=['Python'])
+dataset = load_dataset("Fsoft-AIC/the-vault-class", languages=['Python'])
 
 # dataset streaming
-data = load_dataset("Fsoft-AIC/the-vault-class", split_set= ["train"], streaming= True)
+data = load_dataset("Fsoft-AIC/the-vault-class", streaming= True)
 for sample in iter(data['train']): 
     print(sample)
 ```