GLUE-Benchmark

index int64 0 1.06k	sentence stringlengths 7 152
0	Bill whistled past the house.
1	The car honked its way down the road.
2	Bill pushed Harry off the sofa.
3	the kittens yawned awake and played.
4	I demand that the more John eats, the more he pay.
5	If John eats more, keep your mouth shut tighter, OK?
6	His expectations are always lower than mine are.
7	The sooner you call, the more carefully I will word the letter.
8	The more timid he feels, the more people he interviews without asking questions of.
9	Once Janet left, Fred became a lot crazier.
10	If you give every senator enough opportunity, he will succumb to corruption.
11	The more time that any senator spends with lobbyists, the more likely he is to succumb to corruption.
12	Which problem does the sooner you solve, the more easily you'll satisfy the folks up at corporate headquarters?
13	Which problem do you think that the sooner you solve, the quicker you'll be able to tell the folks up at corporate headquarters to buzz off?
14	Louise is not happy, is she?
15	He can just not have been working.
16	Which problem do you wonder whether John said Mary solved?
17	They could have left.
18	Have they could left?
19	John eats not chocolate.
20	Has John seen Mary?
21	Mickey looked it up.
22	Mickey looked up him and her.
23	He taught the children.
24	John proved to be a great linguist.
25	There is too likely to be a riot for there to be a serious discussion of the issues.
26	John is willing.
27	Bill seems to be obnoxious, but I don't think that Sam seems.
28	Bill seems to be obnoxious, but I don't think that Sam appears.
29	I believe John with all my heart is a fine person.
30	We would hate John to win.
31	I would want for John to win.
32	The chair abuts the wall.
33	The cup emptied of water.
34	the bottle drained empty of liquid.
35	The tub leaked water empty.
36	Spray the paint onto the whole wall completely.
37	What John did to the whole wall was hit it.
38	Yes, too much so.
39	I convinced John that it was late and Bill that it was early.
40	John is impressed as pompous.
41	The men were promised by Frank to leave.
42	The idea struck the men all as nonsense.
43	We proved Smith to the authorities to be the thief.
44	Us love them.
45	Mary reported him to have loved her.
46	I saw kissing Mary.
47	John wanted to leave the room happy and leave the room happy he did.
48	I only eat fish raw fresh.
49	I can't believe Holly won't eat cabbage.
50	You shouldn't play with rifles because it's dangerous to.
51	Mary claimed that eat cabbage, Holly wouldn't.
52	I visited every town I had to visit.
53	Joe likes his bar, and Sally does too.
54	Dulles suspected everyone who Angleton did it.
55	Dulles suspected everyone who Angleton did so.
56	Although Holly doesn't, Doc eats cabbage.
57	While Perry might switch the TV off, he won't on.
58	This is the book of which Bill approves, and this is the one of which he can't do so.
59	Wendy is eager to sail around the world and Bruce is eager to climb Kilimanjaro, but neither of them can because money is too tight.
60	Mary runs not the marathon.
61	The probable hostile German reaction is unfortunate.
62	What books does a critic think are readable?
63	Who reported that Max and which lady disappeared?
64	Everyone attended more than two seminars.
65	Old pictures of themselves usually strike the children as amusing.
66	Images of themselves seem to have frightened the children.
67	Lemons are always preferred to limes.
68	A gun went off which I had cleaned.
69	What did that Bill wore surprise everyone?
70	I know a boy who is mad at John.
71	He threw into the wastebasket the letter.
72	She asked whether it looked like rain a man who was near the window.
73	I know a man who John is taller than.
74	John is as tall as that man is.
75	What I said was that she was lying.
76	Jack is making the claim you won't need it.
77	John left, and he didn't even say goodbye.
78	I went to the movies and didn't pick up the shirts.
79	The boy works in a skyscraper and the girl works in a quonset hut.
80	That Peter showed up is a miracle and it is doubtful that he'll ever come again.
81	I claimed it that Bob was a nut.
82	What exploded when and who was hurt?
83	If you make yourself comfortable, I'll wash the dishes.
84	My sister arrived at a time when no buses were running and my brother arrived at one too.
85	Mike talked to my friends about politics yesterday.
86	Which hat do you believe she never wore?
87	The boy whose playing the piano loudly I disliked was a student.
88	Ivan figured it out that the bridge would hold.
89	That it was obvious is not true that Bob was lying.
90	It is possible that Sam didn't pick those packages up which are to be mailed tomorrow.
91	I promised that he would be there around midnight.
92	It is proud of him that I have never been.
93	It is beans that I don't think you'll be able to convince me Harry has ever tasted in his life.
94	That Sam sometimes didn't sleep must have pleased anybody.
95	John scratched his arm and so did Mary.
96	Harry believes it, although no one else believes that Sally is innocent.
97	Webster did it after Henry had touched a sword.
98	Webster did so after Henry had touched a sword.
99	These shoes won't fit into the trunk they're next to.

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CoLA Text Classification RCL Dataset

Overview

This dataset contains textual data structured explicitly for standard text classification tasks using Lumina AI's Random Contrast Learning (RCL) algorithm via the PrismRCL application. Unlike LLM datasets, standard text classification datasets contain individual .txt files organized by class.

Dataset Structure

The dataset structure for text classification training:

CoLA.Classification/
    train/
        [class_1]/
            sample_001.txt
            sample_002.txt
            ...
        [class_2]/
            sample_001.txt
            sample_002.txt
            ...
    test/
        [class_1]/
            sample_001.txt
            sample_002.txt
            ...
        [class_2]/
            sample_001.txt
            sample_002.txt
            ...

Classes: Folder names represent distinct text classes.
Text Samples: Each .txt file represents a single textual data sample.

Text Data Preparation

For standard text classification datasets, PrismRCL has specific preparation requirements:

Text samples must be in .txt format.
Each file should contain a single data sample.
File names must be unique across all class folders.

Usage (Text Classification-specific)

Use PrismRCL for training with text data:

C:\PrismRCL\PrismRCL.exe naivebayes rclticks=10 directional ^
  data=C:\path\to\CoLA.Classification\train testdata=C:\path\to\CoLA.Classification\test ^
  savemodel=C:\path\to\models\cola_text_model.classify ^
  log=C:\path\to\log_files stopwhendone

Explanation of Command

naivebayes: Specifies Naive Bayes as the evaluation method for text classification.
rclticks: Number of RCL iterations during training.
directional: Maintains the order of words, essential for text classification.
data & testdata: Paths to training and testing text datasets.
savemodel: Output path for the trained classification model.
log: Directory for storing log files.
stopwhendone: Automatically terminates the session after training completion.

Auto Optimize

PrismRCL includes an Auto Optimize feature designed to automatically identify optimal training parameters for your specific dataset, significantly streamlining the model training process. This feature removes the need for manual parameter tuning by systematically evaluating your data to determine the most effective settings for evaluation method, rclticks, boxdown, and other relevant parameters.

How to Use Auto Optimize:

Run the following command with your dataset:

C:\PrismRCL\PrismRCL.exe auto-optimize data=C:\path\to\your_dataset\train log=C:\path\to\log_files

Explanation:

auto-optimize: Initiates PrismRCL’s parameter optimization process.
data: Path to your training dataset.
log: Specifies the directory where PrismRCL will save a detailed summary file with optimal parameters determined by the optimization process.

After execution, PrismRCL generates an optimization summary file in your specified log directory (_optimize_summary_mm_dd_yy_hh_mm_ss.txt). This file will list the optimal parameters, which you should then apply in your training commands to achieve optimal model performance.

License

This dataset is licensed under the MIT License.

Original Source

Prepared explicitly by Lumina AI for RCL-based text classification training. Please credit Lumina AI when using this dataset in research or applications.

Additional Information

Refer to the PrismRCL Technical Documentation v2.6.2 for more detailed guidance on text classification data preparation and parameter specifications.

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