File size: 5,386 Bytes
d67e821 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | {
"retrieval": {
"SIGMA_B": {
"mrr10": 0.92306,
"r1": 0.8856,
"r5": 0.96956,
"r10": 0.97886,
"median_rank": 1.0,
"pool_size": 22046
},
"CodeBERT_FT": {
"mrr10": 0.96622,
"r1": 0.94484,
"r5": 0.99129,
"r10": 0.99356,
"median_rank": 1.0,
"pool_size": 22046
},
"delta_mrr": -0.04316
},
"generation": {
"sigma_gen_hist": {
"tr_loss": [
4.332911533272467,
3.513244101578118,
3.232151625534834,
3.0188788402276594,
2.876566329031201,
2.774567126666569,
2.7000688419086245,
2.6459983390151094,
2.61164868748713,
2.5937092144469105
],
"va_loss": [
4.333631096980431,
4.033719047488412,
3.887238296438657,
3.721125888444704,
3.652845814161021,
3.598194845310101,
3.560626884405384,
3.542548859641584,
3.5358788442590083,
3.5271108577827133
],
"va_ppl": [
76.2205489029526,
56.470537811754056,
48.77599550446955,
41.31087937681806,
38.58431374299519,
36.53222856246678,
35.18524731577227,
34.5548825857037,
34.32516793907655,
34.02552090105466
]
},
"cbert_gen_hist": {
"tr_loss": [
4.357259073945283,
3.5188005480055473,
3.1955701593955923,
2.988982406454519,
2.858495755219526,
2.7642144319277264,
2.69454125002109,
2.644378586365032,
2.6121866509032623,
2.5950699153240633
],
"va_loss": [
4.360623060350565,
4.023048199407739,
3.830441037275766,
3.7134811096919123,
3.6445059282146337,
3.6011066965343077,
3.5686047597707726,
3.551744504028521,
3.532704999526294,
3.5293681702964337
],
"va_ppl": [
78.30590853372148,
55.871152941678396,
46.08285801115232,
40.996270932086794,
38.263863085402065,
36.63876000351066,
35.46707352597844,
34.874102464608406,
34.216397886548734,
34.10241388827117
]
},
"gen_samples": [
{
"prompt": "Calculate the fibonacci number",
"sigma": "def get_best(of, x):\n \"\"\"Calculate the year of distribution.\"\"\"\n if len(x, t.shape):\n q = np.shape(cos_ind)\n for t in range(x, n):\n if s <= 0:\n ",
"codebert": "def rot(self, X):\n \"\"\"Return the increment and posterior of all details.\"\"\"\n return self.bits(np(\"%s\")\n return self.read_index_value(self._num)\n else:\n "
},
{
"prompt": "Sort a list using quicksort algorithm",
"sigma": "def _sqr_valid(self, p):\n \"\"\"Build the input a region of shape,\n a matrix in the grid(d = float)\n # Find the lines to make a point.\n # and '''\n\n\r\n if isinstance(value, x1):\r\n ",
"codebert": "def _rc_words(self, node):\n \"\"\"Returns the file when a dict.\"\"\"\n if isinstance(log(\" not necessary\") == 'f'):\n # self._match()\n if _swith(\"The tag\" in self.format(self._case):\n # Popon: d"
},
{
"prompt": "Read a file and return its contents as string",
"sigma": "def _format_to(path):\n \"\"\"Helper a file from the doi into `file`.\n Returns:\n \"\"\"\n\n # Note:\n return raw_file['test__', 'f'\n # Use ``object'\n return (arg.strip)\n # Runtime =",
"codebert": "def _parse_file(self, filename):\n \"\"\"\n Render a single file object\n \"\"\"\n try:\n for path in self.path['wargs']\n "
},
{
"prompt": "Convert a dictionary to a JSON string",
"sigma": "def _code(self, placeholder):\n idd\"\"\"' if a string and return None. \"\"\"\n if not None:\n return '{_list':\n return [int(node.join('OTO')\n info = 'name['join(node', str)\n data = '%s}\n ",
"codebert": "def _read_names(self, type):\n \"\"\"Convert a string with single style object.\n\n Returns the value of `line` object and if not be a\n \"\"\"\n\n if self.values:\n if not None:\n # type in se"
},
{
"prompt": "Find the maximum element in a list",
"sigma": "def _info_field(self, value):\n '''Returns a list for this is the packet\"\"\"\n if not None:\n return self._open(\n return raw_value)\n return not None:\n # we coerree of the child\n ",
"codebert": "def get_field(self, node):\n '''Return the raw tokens.\"\"\"\n for key in value in node.items:\n else = self._line_obj(index)\n else: ast.value(self._text)\n elif self.isfocol == 1:\n "
},
{
"prompt": "Calculate the factorial of a number",
"sigma": "def calculate_p(self, width=None):\n \"\"\"Calculate the water of a normalized time and\n '''\n\n n = np.term['sub1\n return np.scplot_vON(x, x)\n return self.fasttype(float)\n return self",
"codebert": "def compute(self):\n \"\"\"\n Updates the time-block over all one of a column\n \"\"\"\n s = self.build(self._coist, self.left_count)\n if self.start"
}
]
}
} |