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t5-political-evaluator

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1 Parent(s): 807357d

test

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Files changed (8) hide show
  1. generate_stats.py +2 -2
  2. generate_stats_various.py +116 -0
  3. stats_various/all.csv +101 -0
  4. stats_various/all.jsonl +100 -0
  5. stats_various/average_at_5000.csv +5 -0
  6. stats_various/average_at_5000.jsonl +4 -0
  7. stats_various/only_5000.csv +21 -0
  8. stats_various/only_5000.jsonl +20 -0
generate_stats.py CHANGED
@@ -12,8 +12,8 @@ bucket_name = "nb-t5x-us-central2"
12
  bucket = client.bucket(bucket_name)
13
 
14
 
15
- checkpoints=["exp1-t5-base-ul2-engvoc","exp2-t5-base-ul2-scandvoc","exp3-t5-base-span-engvoc","exp4-t5-base-span-scandvoc","exp5-t5-base-ul2-scandvoc-full","exp6-t5-base-span-scandvoc-full","exp7-t5-base-ul2-511-scandvoc","exp8-t5-base-span-511-scandvoc","exp9-t5-base-ul2-mt5voc","exp10-t5-base-span-mt5voc","exp11-t5-base-ul2-511-scandvoc-full","exp12-t5-base-span-511-scandvoc-full","exp13-t5-base-ul2-mt5voc-full","exp14-t5-base-span-mt5voc-full","exp15-t5-base-ul2-511-scandvoc-full-scratch","exp16-t5-base-span-511-scandvoc-full-scratch","exp17-t5-small-ul2-mt5voc-scratch","exp18-t5-small-span-mt5voc-scratch","exp19-t5-small-ul2-mt5voc","exp20-t5-small-span-mt5voc","exp21-t5-small-ul2-mt5voc-full","exp22-t5-small-span-mt5voc-full"]
16
- #checkpoints=["exp19-t5-small-ul2-mt5voc"]
17
 
18
  start=["100000","200000","300000","400000","500000","1000000","1100000","1184000","1200000","1204000","1284000","1300000","1384000","1400000","1484000","1500000"]
19
 
 
12
  bucket = client.bucket(bucket_name)
13
 
14
 
15
+ checkpoints=["exp1-t5-base-ul2-engvoc","exp2-t5-base-ul2-scandvoc","exp3-t5-base-span-engvoc","exp4-t5-base-span-scandvoc","exp5-t5-base-ul2-scandvoc-full","exp6-t5-base-span-scandvoc-full","exp7-t5-base-ul2-511-scandvoc","exp8-t5-base-span-511-scandvoc","exp9-t5-base-ul2-mt5voc","exp10-t5-base-span-mt5voc","exp11-t5-base-ul2-511-scandvoc-full","exp12-t5-base-span-511-scandvoc-full","exp13-t5-base-ul2-mt5voc-full","exp14-t5-base-span-mt5voc-full","exp15-t5-base-ul2-511-scandvoc-full-scratch","exp16-t5-base-span-511-scandvoc-full-scratch","exp17-t5-small-ul2-mt5voc-scratch","exp18-t5-small-span-mt5voc-scratch","exp19-t5-small-ul2-mt5voc","exp20-t5-small-span-mt5voc","exp21-t5-small-ul2-mt5voc-full","exp22-t5-small-span-mt5voc-full"]
16
+
17
 
18
  start=["100000","200000","300000","400000","500000","1000000","1100000","1184000","1200000","1204000","1284000","1300000","1384000","1400000","1484000","1500000"]
19
 
generate_stats_various.py ADDED
@@ -0,0 +1,116 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from google.cloud import storage
2
+ import pandas as pd
3
+ import json
4
+ import re
5
+ import sys
6
+
7
+ # Create a storage client
8
+ client = storage.Client()
9
+
10
+ # Get the bucket
11
+ bucket_name = "nb-t5x-us-central2"
12
+ bucket = client.bucket(bucket_name)
13
+
14
+
15
+ checkpoints=["exp100_mt5_small","exp101_north_t5_base_NCC","exp102_north_t5_base_NCC_lm","exp103_north_t5_base_scand3M","exp104_mt5_base","exp105_sab_base_2","exp106_sab_base_3","exp107_sab_base_4"]
16
+
17
+
18
+ start=["1500000","1600000","2000000","3000000","4000000"]
19
+
20
+ iterations=["1","2","3","4","5"]
21
+ file_names=[]
22
+
23
+ for i in iterations:
24
+ for c in checkpoints:
25
+ for s in start:
26
+ if "scand" in c:
27
+ name = f'finetuned/ul2test/eval_political_{c}_v{i}/inference_eval/parliament_max300_scand-metrics.jsonl'
28
+ elif "mt5" in c or "north" in c:
29
+ name = f'finetuned/ul2test/eval_political_{c}_v{i}/inference_eval/parliament_max300_mt5-metrics.jsonl'
30
+ else:
31
+ name = f'finetuned/ul2test/eval_political_{c}_v{i}/inference_eval/parliament_max300-metrics.jsonl'
32
+ file_names.append(name)
33
+
34
+
35
+ #list to store json files data
36
+ file_contents = []
37
+
38
+ downloaded = 0
39
+ not_downloaded = 0
40
+
41
+ #print(file_names)
42
+ #print(bucket)
43
+ #sys.exit(-1)
44
+
45
+ #iterate over the files
46
+ for file_name in file_names:
47
+ # Get the file
48
+ blob = bucket.get_blob(file_name)
49
+ print(f'gs://{bucket_name}/{file_name}')
50
+
51
+ if not blob:
52
+ #print(f"Unable to download {file_name}")
53
+ not_downloaded+=1
54
+ continue
55
+ else:
56
+ downloaded+=1
57
+
58
+ content = blob.download_as_string().decode("utf-8")
59
+
60
+ #print(file_name)
61
+ #print(content)
62
+
63
+ # Split the content by newline
64
+ lines = content.split("\n")
65
+
66
+ #iterate over the lines
67
+ for n,line in enumerate(lines):
68
+ if not line:
69
+ continue
70
+ #print(line)
71
+ #print(file_name)
72
+ data = json.loads(line)
73
+ data['base_file_name'] = file_name
74
+ pretraining_steps = 0 #re.search(r"(voc_|voc-full_|voc-full-scratch_|voc-scratch_)(.*?)(?=/)", file_name).group(2)
75
+ data['pretraining_steps'] = int(pretraining_steps)
76
+ data['finetuning_steps'] = int(str(data['step'])[-4:])
77
+ data['vocab'] = re.search(r"_(\w+?)-metric", file_name).group(1)
78
+ data['experiment'] = re.search(r"_exp(\w+?)_", file_name).group(1)
79
+ data['version'] = re.search(r"_v(\w+?)/", file_name).group(1)
80
+ data['experiment_name'] = re.search(r"exp\d+_(.*?)_v", file_name).group(1)
81
+ file_contents.append(data)
82
+
83
+ print(f"\nTotally {downloaded} files downloaded, {not_downloaded} files not downloaded")
84
+
85
+ df = pd.json_normalize(file_contents)
86
+ df = df.drop_duplicates(subset=['step','experiment','version']).reset_index()
87
+ only_5000 = df[df["finetuning_steps"] == 5000]
88
+ grouped_at_5000 = only_5000[["experiment_name","experiment","pretraining_steps", "accuracy", "f1_macro"]].groupby(["experiment","experiment_name","pretraining_steps"])
89
+ average_at_5000 = grouped_at_5000.mean().reset_index()
90
+ average_at_5000 = average_at_5000.assign(num_experiments=grouped_at_5000.size().values)
91
+ only_3000 = df[df["finetuning_steps"] == 3000]
92
+ grouped = only_3000[["experiment_name","experiment","pretraining_steps", "accuracy", "f1_macro"]].groupby(["experiment","experiment_name","pretraining_steps"])
93
+ average_at_3000 = grouped.mean().reset_index()
94
+ average_at_3000 = average_at_3000.assign(rows_count=grouped.size().values)
95
+
96
+ #print(average_at_3000.to_string(index=False))
97
+ print(average_at_5000.to_string(index=False))
98
+
99
+ print("\nNot complete:")
100
+ uncomplete = average_at_5000[average_at_5000['num_experiments'] != 5]
101
+ print(uncomplete)
102
+
103
+ df.to_json("stats_various/all.jsonl", orient="records", lines=True)
104
+ df.to_csv("stats_various/all.csv", index=False)
105
+
106
+ only_5000.to_json("stats_various/only_5000.jsonl", orient="records", lines=True)
107
+ only_5000.to_csv("stats_various/only_5000.csv", index=False)
108
+
109
+ average_at_5000.to_json("stats_various/average_at_5000.jsonl", orient="records", lines=True)
110
+ average_at_5000.to_csv("stats_various/average_at_5000.csv", index=False)
111
+
112
+
113
+ print(f"Files exported to stats")
114
+
115
+
116
+
stats_various/all.csv ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ index,step,accuracy,f1_macro,base_file_name,pretraining_steps,finetuning_steps,vocab,experiment,version,experiment_name
2
+ 0,1001000,74.66666666666667,74.60734660693453,finetuned/ul2test/eval_political_exp100_mt5_small_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,1000,max300_mt5,100,1,mt5_small
3
+ 1,1002000,72.41666666666666,72.3251667492313,finetuned/ul2test/eval_political_exp100_mt5_small_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,2000,max300_mt5,100,1,mt5_small
4
+ 2,1003000,70.83333333333334,70.8099001698586,finetuned/ul2test/eval_political_exp100_mt5_small_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,3000,max300_mt5,100,1,mt5_small
5
+ 3,1004000,69.41666666666667,69.36558803950199,finetuned/ul2test/eval_political_exp100_mt5_small_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,4000,max300_mt5,100,1,mt5_small
6
+ 4,1005000,69.41666666666667,69.37734632872348,finetuned/ul2test/eval_political_exp100_mt5_small_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,5000,max300_mt5,100,1,mt5_small
7
+ 25,1501000,76.58333333333334,76.57967390738136,finetuned/ul2test/eval_political_exp101_north_t5_base_NCC_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,1000,max300_mt5,101,1,north_t5_base_NCC
8
+ 26,1502000,80.58333333333333,80.57349866703352,finetuned/ul2test/eval_political_exp101_north_t5_base_NCC_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,2000,max300_mt5,101,1,north_t5_base_NCC
9
+ 27,1503000,84.0,83.97845992946074,finetuned/ul2test/eval_political_exp101_north_t5_base_NCC_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,3000,max300_mt5,101,1,north_t5_base_NCC
10
+ 28,1504000,85.0,84.99983333148147,finetuned/ul2test/eval_political_exp101_north_t5_base_NCC_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,4000,max300_mt5,101,1,north_t5_base_NCC
11
+ 29,1505000,85.08333333333333,85.06756094458106,finetuned/ul2test/eval_political_exp101_north_t5_base_NCC_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,5000,max300_mt5,101,1,north_t5_base_NCC
12
+ 50,1601000,73.66666666666667,73.3240286344604,finetuned/ul2test/eval_political_exp102_north_t5_base_NCC_lm_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,1000,max300_mt5,102,1,north_t5_base_NCC_lm
13
+ 51,1602000,77.08333333333334,76.87830491350034,finetuned/ul2test/eval_political_exp102_north_t5_base_NCC_lm_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,2000,max300_mt5,102,1,north_t5_base_NCC_lm
14
+ 52,1603000,80.33333333333333,80.3175326859618,finetuned/ul2test/eval_political_exp102_north_t5_base_NCC_lm_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,3000,max300_mt5,102,1,north_t5_base_NCC_lm
15
+ 53,1604000,80.83333333333333,80.82289246367466,finetuned/ul2test/eval_political_exp102_north_t5_base_NCC_lm_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,4000,max300_mt5,102,1,north_t5_base_NCC_lm
16
+ 54,1605000,82.0,81.92362732545003,finetuned/ul2test/eval_political_exp102_north_t5_base_NCC_lm_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,5000,max300_mt5,102,1,north_t5_base_NCC_lm
17
+ 75,1001000,75.0,74.96634363978238,finetuned/ul2test/eval_political_exp104_mt5_base_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,1000,max300_mt5,104,1,mt5_base
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+ 76,1002000,73.66666666666667,73.65019804044194,finetuned/ul2test/eval_political_exp104_mt5_base_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,2000,max300_mt5,104,1,mt5_base
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+ 77,1003000,74.08333333333333,74.07683453977005,finetuned/ul2test/eval_political_exp104_mt5_base_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,3000,max300_mt5,104,1,mt5_base
20
+ 78,1004000,73.16666666666667,73.16480311132719,finetuned/ul2test/eval_political_exp104_mt5_base_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,4000,max300_mt5,104,1,mt5_base
21
+ 79,1005000,73.83333333333333,73.79139957264957,finetuned/ul2test/eval_political_exp104_mt5_base_v1/inference_eval/parliament_max300_mt5-metrics.jsonl,0,5000,max300_mt5,104,1,mt5_base
22
+ 100,1001000,73.0,72.98919567827132,finetuned/ul2test/eval_political_exp100_mt5_small_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,1000,max300_mt5,100,2,mt5_small
23
+ 101,1002000,69.83333333333334,69.63902308105207,finetuned/ul2test/eval_political_exp100_mt5_small_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,2000,max300_mt5,100,2,mt5_small
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+ 102,1003000,69.58333333333333,69.52388374266188,finetuned/ul2test/eval_political_exp100_mt5_small_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,3000,max300_mt5,100,2,mt5_small
25
+ 103,1004000,68.25,67.95570429815494,finetuned/ul2test/eval_political_exp100_mt5_small_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,4000,max300_mt5,100,2,mt5_small
26
+ 104,1005000,68.41666666666667,68.33484302144639,finetuned/ul2test/eval_political_exp100_mt5_small_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,5000,max300_mt5,100,2,mt5_small
27
+ 125,1501000,77.25,77.24922584171267,finetuned/ul2test/eval_political_exp101_north_t5_base_NCC_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,1000,max300_mt5,101,2,north_t5_base_NCC
28
+ 126,1502000,80.58333333333333,80.54245898502062,finetuned/ul2test/eval_political_exp101_north_t5_base_NCC_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,2000,max300_mt5,101,2,north_t5_base_NCC
29
+ 127,1503000,81.66666666666667,81.5485775630703,finetuned/ul2test/eval_political_exp101_north_t5_base_NCC_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,3000,max300_mt5,101,2,north_t5_base_NCC
30
+ 128,1504000,85.33333333333334,85.30132288094072,finetuned/ul2test/eval_political_exp101_north_t5_base_NCC_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,4000,max300_mt5,101,2,north_t5_base_NCC
31
+ 129,1505000,86.41666666666666,86.40079147949793,finetuned/ul2test/eval_political_exp101_north_t5_base_NCC_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,5000,max300_mt5,101,2,north_t5_base_NCC
32
+ 150,1601000,74.5,74.28391343931648,finetuned/ul2test/eval_political_exp102_north_t5_base_NCC_lm_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,1000,max300_mt5,102,2,north_t5_base_NCC_lm
33
+ 151,1602000,79.0,78.98686679174484,finetuned/ul2test/eval_political_exp102_north_t5_base_NCC_lm_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,2000,max300_mt5,102,2,north_t5_base_NCC_lm
34
+ 152,1603000,80.08333333333333,80.06438065077145,finetuned/ul2test/eval_political_exp102_north_t5_base_NCC_lm_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,3000,max300_mt5,102,2,north_t5_base_NCC_lm
35
+ 153,1604000,79.83333333333333,79.77214406914248,finetuned/ul2test/eval_political_exp102_north_t5_base_NCC_lm_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,4000,max300_mt5,102,2,north_t5_base_NCC_lm
36
+ 154,1605000,81.16666666666667,81.12845178152426,finetuned/ul2test/eval_political_exp102_north_t5_base_NCC_lm_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,5000,max300_mt5,102,2,north_t5_base_NCC_lm
37
+ 175,1001000,73.91666666666666,73.91360513838082,finetuned/ul2test/eval_political_exp104_mt5_base_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,1000,max300_mt5,104,2,mt5_base
38
+ 176,1002000,72.58333333333333,72.43846300450075,finetuned/ul2test/eval_political_exp104_mt5_base_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,2000,max300_mt5,104,2,mt5_base
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+ 177,1003000,73.91666666666666,73.9114308357719,finetuned/ul2test/eval_political_exp104_mt5_base_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,3000,max300_mt5,104,2,mt5_base
40
+ 178,1004000,72.5,72.38331952499311,finetuned/ul2test/eval_political_exp104_mt5_base_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,4000,max300_mt5,104,2,mt5_base
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+ 179,1005000,73.33333333333333,73.27089116519426,finetuned/ul2test/eval_political_exp104_mt5_base_v2/inference_eval/parliament_max300_mt5-metrics.jsonl,0,5000,max300_mt5,104,2,mt5_base
42
+ 200,1001000,71.08333333333333,70.96378073331098,finetuned/ul2test/eval_political_exp100_mt5_small_v3/inference_eval/parliament_max300_mt5-metrics.jsonl,0,1000,max300_mt5,100,3,mt5_small
43
+ 201,1002000,67.41666666666667,66.98636604464721,finetuned/ul2test/eval_political_exp100_mt5_small_v3/inference_eval/parliament_max300_mt5-metrics.jsonl,0,2000,max300_mt5,100,3,mt5_small
44
+ 202,1003000,67.16666666666666,67.15352807789783,finetuned/ul2test/eval_political_exp100_mt5_small_v3/inference_eval/parliament_max300_mt5-metrics.jsonl,0,3000,max300_mt5,100,3,mt5_small
45
+ 203,1004000,66.91666666666667,66.80044516951355,finetuned/ul2test/eval_political_exp100_mt5_small_v3/inference_eval/parliament_max300_mt5-metrics.jsonl,0,4000,max300_mt5,100,3,mt5_small
46
+ 204,1005000,67.5,67.49990972197143,finetuned/ul2test/eval_political_exp100_mt5_small_v3/inference_eval/parliament_max300_mt5-metrics.jsonl,0,5000,max300_mt5,100,3,mt5_small
47
+ 225,1501000,75.58333333333334,75.24151145080096,finetuned/ul2test/eval_political_exp101_north_t5_base_NCC_v3/inference_eval/parliament_max300_mt5-metrics.jsonl,0,1000,max300_mt5,101,3,north_t5_base_NCC
48
+ 226,1502000,81.16666666666667,81.16666666666667,finetuned/ul2test/eval_political_exp101_north_t5_base_NCC_v3/inference_eval/parliament_max300_mt5-metrics.jsonl,0,2000,max300_mt5,101,3,north_t5_base_NCC
49
+ 227,1503000,84.5,84.49272019364645,finetuned/ul2test/eval_political_exp101_north_t5_base_NCC_v3/inference_eval/parliament_max300_mt5-metrics.jsonl,0,3000,max300_mt5,101,3,north_t5_base_NCC
50
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