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pythia-12b-neuron-dataset-examples

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train · 47.2M rows

layer uint8 0 35	neuron uint16 0 20.5k	rank uint8 0 63	activation float32 -0 61.8	position uint8 0 15	text stringlengths 1 1.74k	id0 uint16 0 50.3k	id1 uint16 0 50.3k	id2 uint16 0 50.3k	id3 uint16 0 50.3k	id4 uint16 0 50.3k	id5 uint16 0 50.3k	id6 uint16 0 50.3k	id7 uint16 0 50.3k	id8 uint16 0 50.3k	id9 uint16 0 50.3k	id10 uint16 0 50.3k	id11 uint16 0 50.3k	id12 uint16 0 50.3k	id13 uint16 0 50.3k	id14 uint16 0 50.3k	id15 uint16 0 50.3k
35	20,389	4	2.974609	12	out more Model Engineer Exhibition 2016 The 2016	562	625	187	187	7,104	39,424	1,889	22,235	4,022	187	187	510	4,022	10,031	39,424	1,889
35	20,389	5	2.953125	7	- [Model Spotify](#	187	187	14	544	7,104	28,238	1,419	1,084	7,645	14	17,166	1,419	10	187	187	4,118
35	20,389	6	2.947266	12	), 3990 Model 6 (3990-6), and later	582	495	28,001	10,031	721	313	20	28,001	14	23	582	285	1,996	5,718	1,453	5,085
35	20,389	7	2.945313	11	out more Model Engineer Exhibition 2016 The	562	625	187	187	7,104	39,424	1,889	22,235	4,022	187	187	510	4,022	10,031	39,424	1,889
35	20,389	8	2.929688	11	multiple television sets of Ferguson (model 841T), Bush (	2,709	7,315	5,239	273	31,162	313	7,645	43,909	53	582	11,301	313	7,645	308	4,739	10
35	20,389	9	2.919922	15	space anymore. iPhone Model Differences The following chart describes the basic differences between various iPhone	2,317	10,542	15	13,971	10,031	33,968	380	1,563	8,326	8,631	253	5,044	3,910	875	2,710	13,971
35	20,389	10	2.917969	14	13) Model Magic and Watercolor Sculptures If you have	1,012	10	10,031	21,577	285	10,205	4,897	46,661	431	980	187	187	2,042	368	452	1,620
35	20,389	11	2.904297	15	that a helmet model introduced in 2000 provides better protection against concussion than an older	326	247	26,358	1,566	5,611	275	5,307	3,400	1,805	6,055	1,411	7,036	26,148	685	271	5,662
35	20,389	12	2.902344	14	from an NGO model, dependent from bigger institutions, to a social entrepreneurship	432	271	36,096	1,566	13	7,976	432	8,750	10,003	13	281	247	2,675	23,721	5,363	1,566
35	20,389	13	2.902344	15	); inputModel.Email = v.UserName; input	558	187	50,266	5,423	7,104	15	17,835	426	362	15	6,989	2,402	28	187	50,266	5,423
35	20,389	14	2.892578	15	ian War Axe (Model P) Ostian War Hammer (	757	3,660	329	7,096	313	7,104	367	10	187	187	48	296	757	3,660	44,591	313
35	20,389	15	2.859375	14	model.add(layers.Dropout(0.5))	187	7,645	15	1,911	9	33,990	15	24,475	483	9	17	15	22	1,228	187	187
35	20,389	16	2.847656	11	it is in model 0 if it is not in	352	310	275	1,566	187	50,263	17	604	352	310	417	275	1,566	187	50,270	32,488
35	20,389	17	2.822266	13	, is largely an interesting experiment in moving away from the ad-supported	13	310	8,127	271	4,722	3,368	275	4,886	1,977	432	253	519	14	19,391	2,136	1,566
35	20,389	18	2.820313	6	paternalistic model and recovery-oriented	38,629	2,531	1,566	285	7,355	14	21,085	1,566	984	273	187	33,530	2,338	6,095	326	2,770
35	20,389	19	2.814453	14	{ model.ContentParts = step.ContentParts;	92	187	50,262	7,645	15	8,590	49,563	426	3,213	15	8,590	49,563	28	187	50,262	7,645
35	20,389	20	2.806641	15	BrowserAdded(browser); TabStripModel* tab_strip_	34,228	33,988	9	28,197	558	535	50,276	10,297	998	7,417	7,104	11	10,334	64	25,763	64
35	20,389	21	2.806641	13	SList(initrootSList), currentModel(init	52	2,765	9	4,478	9,723	52	2,765	582	187	186	6,259	7,104	9	4,478	6,259	7,104
35	20,389	22	2.804688	15	is to make her own time easier by being a model prisoner. By being a	310	281	1,056	617	1,211	673	6,927	407	1,146	247	1,566	18,897	15	2,896	1,146	247
35	20,389	23	2.796875	15	presence and life events (Model 1), work related and psycho-social factors (	3,361	285	1,495	3,394	313	7,104	337	582	789	2,905	285	30,543	14	21,637	2,616	313
35	20,389	24	2.783203	14	a) Model 202 ($q_{\rm I}=1$), (b)	66	10	10,031	22,038	4,816	82	1,126	1,109	309	4,203	18	17,673	313	67	10	10,031
35	20,389	25	2.779297	10	Model Chapter Program The purpose of the	187	187	7,104	10,372	8,246	187	187	510	4,096	273	253	10,031	10,372	8,246	310	281
35	20,389	26	2.779297	14	(List Player) } initialModel : Model initial	313	2,765	20,929	10	187	50,274	94	535	187	19,078	7,104	1,163	10,031	187	19,078	7,104
35	20,389	27	2.763672	12	er. What is Model Mind Trainer?	254	15	187	187	1,276	310	10,031	18,296	29,712	254	32	187	187	7,104	18,296	29,712
35	20,389	28	2.746094	15	) : tab_strip_model_observer_(tab_strip_	10	187	50,274	27	10,334	64	25,763	64	7,645	64	44,094	18,284	8,476	64	25,763	64
35	20,389	29	2.744141	11	er. What is Model Mind Trainer?	254	15	187	187	1,276	310	10,031	18,296	29,712	254	32	187	187	7,104	18,296	29,712
35	20,389	30	2.728516	11	def testModelUpdates(self): """ Test	1,545	1,071	7,104	5,683	24,275	9	1,286	2,262	187	50,270	32,488	6,004	17,119	285	1,551	2,324
35	20,389	31	2.724609	14	of Modelio available here. Do you want to know more about	273	10,031	900	2,130	1,060	15	187	187	4,045	368	971	281	871	625	670	39,823
35	20,389	32	2.722656	15	Make(self.model.endImageSize.width / 3, self.	13,325	9	1,286	15	7,645	15	423	6,586	5,496	15	3,429	1,227	495	13	1,881	15
35	20,389	33	2.720703	14	SList(initrootSList), currentModel(initcurrent	52	2,765	9	4,478	9,723	52	2,765	582	187	186	6,259	7,104	9	4,478	6,259	7,104
35	20,389	34	2.708984	11	what seems to correspond to what then becomes a model. This	752	3,133	281	2,723	281	752	840	4,916	247	1,566	15	831	310	387	1,682	28,116
35	20,389	35	2.708984	14	maxVal = model()->maxValue(); const float minVal =	2,781	2,838	426	1,566	18,331	4,090	3,902	1,874	187	186	3,474	8,253	1,054	2,838	426	1,566
35	20,389	36	2.707031	12	Val = model()->minValue(); float maxVal =	2,838	426	1,566	18,331	1,222	3,902	1,874	187	186	9,599	2,781	2,838	426	1,566	18,331	4,090
35	20,389	37	2.697266	14	ptr<LaunchOptionsComboboxModel> launch_options_combobox_	4,773	29	41,803	10,976	26,180	48,577	7,104	31	8,027	64	10,121	64	17,890	48,577	64	7,645
35	20,389	38	2.693359	14	class]! testRoots \| model \|	2,437	62	2	4,888	187	2,566	16,560	84	2,379	186	93	1,566	1,040	2,379	186	7,645
35	20,389	39	2.683594	14	can be immense. Model engineering in society The aim of	476	320	24,878	15	187	187	7,104	11,369	275	5,948	187	187	510	4,388	273	1,566
35	20,389	40	2.673828	12	, ModelType.BOOKMARKS,	13	187	50,270	7,104	2,548	15	35	16,569	35,938	14,007	13	187	50,270	7,104	2,548	15
35	20,389	41	2.671875	12	ades (Model P) Ostian Repeaters (	3,355	313	7,104	367	10	187	187	48	296	757	37,780	398	313	7,104	367	10
35	20,389	42	2.664063	13	the 42mm Bluetooth model, or $350 for the 46mm Bluetooth	253	5,976	2,188	32,598	1,566	13	390	370	16,552	323	253	7,904	2,188	32,598	1,566	15
35	20,389	43	2.660156	13	ui; ExpertInfoModel expert_info_	475	4,113	28	535	50,274	1,672	8,292	6,074	7,104	11	6,485	64	5,374	64	7,645	17,540
35	20,389	44	2.652344	14	tourist attractions Model Railroad Layout: New addition to tourist attractions	22,777	31,154	187	187	7,104	31,606	38,870	27	1,457	1,635	281	22,777	31,154	187	187	46
35	20,389	45	2.650391	13	selectIntent(button.modelData.intent_category, button.	7,135	28,843	9	8,060	15	7,645	3,233	15	30,699	64	14,267	13	6,409	15	7,645	3,233
35	20,389	46	2.646484	13	(128)) model.add(Dense(64))	9	8,196	1,228	187	7,645	15	1,911	9	37	1,215	9	1,540	1,228	187	7,645	15
35	20,389	47	2.642578	9	completion_model_clear (self->completion_	45,634	64	7,645	64	8,250	313	1,286	1,168	45,634	64	7,645	558	535	50,276	11,316	64
35	20,389	48	2.626953	7	Model Chapter Program The	187	187	7,104	10,372	8,246	187	187	510	4,096	273	253	10,031	10,372	8,246	310	281
35	20,389	49	2.623047	7	The Model Window The	187	187	510	10,031	30,660	187	187	510	399	3,859	10,031	30,660	3,400	247	29,886	5,673
35	20,389	50	2.623047	12	that almost every web business defaulted to an advertising-based business	326	2,761	1,046	4,384	2,136	4,284	264	281	271	12,089	14	3,169	2,136	1,566	1,905	285
35	20,389	51	2.617188	12	“The South African model can be joined to the Tanzanian	187	1,628	510	3,684	8,118	1,566	476	320	7,416	281	253	39,924	44,316	2,793	17,580	3,918
35	20,389	52	2.615234	9	area, model.coordie.latitude,	12,879	13	1,566	15	29,309	466	15	13,324	3,396	13	1,566	15	29,309	466	15	5,056
35	20,389	53	2.615234	15	a modeline (because I won't get a correct output without a >	247	1,566	460	313	12,157	309	1,912	626	755	247	3,451	3,453	1,293	247	187	31
35	20,389	54	2.609375	13	a serial, process-oriented model to a dynamic, outcome-oriented	247	10,382	13	1,232	14	21,085	1,566	281	247	7,870	13	6,454	14	21,085	581	326
35	20,389	55	2.605469	15	geocentric model to give more accurate results than the old heliocentric	3,471	406	19,458	1,566	281	1,918	625	7,899	1,543	685	253	1,711	344	965	406	19,458
35	20,389	56	2.599609	14	, is largely an interesting experiment in moving away from the ad-supported business	13	310	8,127	271	4,722	3,368	275	4,886	1,977	432	253	519	14	19,391	2,136	1,566
35	20,389	57	2.599609	13	provide %v", len(model.Graph.Input), len(	2,085	2,462	87	995	8,472	9	7,645	15	15,499	15	8,982	582	8,472	9	78	1,767
35	20,389	58	2.595703	14	to start a model agency? Kirsten: Probably because although	281	1,265	247	1,566	6,757	32	187	187	44	712	257	27	27,692	984	3,738	627
35	20,389	59	2.59375	13	/MT+ Model: Left hemifieldV5/MT+	16	6,229	12	10,031	27	15,584	7,492	338	1,001	55	22	16	6,229	12	10,031	27
35	20,389	60	2.583984	10	companies who look for models with professional attributes. Once the	4,413	665	1,007	323	3,210	342	5,702	12,474	15	7,243	253	789	3,249	275	840	32,299
35	20,389	61	2.582031	10	than say painting one models arm blue and immediately painting another	685	1,333	13,497	581	3,210	4,430	4,797	285	4,745	13,497	1,529	3,210	9,246	2,502	15	329
35	20,389	62	2.578125	8	model = Sequential()	535	50,276	7,645	426	18,374	1,624	1,082	187	50,276	7,645	15	1,911	9	37	1,215	9
35	20,389	63	2.578125	15	20 models from the Stonemason catalogueCommunity Challenge Third Place : any 10	1,384	3,210	432	253	659	251	358	1,187	33,733	40,645	26,703	12,245	13,470	1,163	667	884
35	20,390	0	6.160156	13	Coffee Products Co. v. Cazarez, 9	190	187	36	23,520	23,910	2,434	15	362	15	330	1,370	46,793	13	898	1,787	322
35	20,390	1	6.128906	14	TextureCoordinatesShaderProgram; import org.andengine.openg	22,648	32,876	8,475	38,031	20,053	28	187	2,948	4,955	15	395	15,179	15	412	1,205	77
35	20,390	2	6.109375	12	import networkx as nx FG = nx.from	2,948	2,990	89	347	295	89	187	28,360	426	295	89	15	4,064	64	81	395
35	20,390	3	6.101563	7	<\|endoftext\|>package lime.graphics.openg	0	10,708	30,037	15	40,718	15	412	1,205	77	15	2,068	28	187	187	33	27
35	20,390	4	6.070313	15	.shader.ShaderProgram; import org.andengine.openg	15	1,200	6,475	15	38,031	20,053	28	187	2,948	4,955	15	395	15,179	15	412	1,205
35	20,390	5	6.003906	13	VertexBufferObjectAttributes; import org.andengine.openg	27,384	10,644	4,241	23,372	28	187	2,948	4,955	15	395	15,179	15	412	1,205	77	15
35	20,390	6	5.953125	14	.BitmapTextureAtlas; import org.andengine.openg	15	31,325	22,648	3,404	9,869	28	187	2,948	4,955	15	395	15,179	15	412	1,205	77
35	20,390	7	5.945313	9	; import org.andengine.openg	28	187	2,948	4,955	15	395	15,179	15	412	1,205	77	15	38,595	15	17,187	15
35	20,390	8	5.9375	15	euclidean'))\n", " G = nx.from	70	26,365	8	17,968	79	995	187	50,274	3	50,274	40	426	295	89	15	4,064
35	20,390	9	5.875	13	'))\n", " G = nx.from	8	17,968	79	995	187	50,274	3	50,274	40	426	295	89	15	4,064	64	79
35	20,390	10	5.816406	7	package org.andengine.openg	10,708	4,955	15	395	15,179	15	412	1,205	77	15	8,906	15	24,913	5,169	28	187
35	20,390	11	5.808594	8	import org.andengine.openg	187	2,948	4,955	15	395	15,179	15	412	1,205	77	15	8,906	15	5,990	5,443	28
35	20,390	12	5.808594	8	import org.andengine.openg	187	2,948	4,955	15	395	15,179	15	412	1,205	77	15	4,909	15	16,976	19,611	28
35	20,390	13	5.808594	8	import org.andengine.openg	187	2,948	4,955	15	395	15,179	15	412	1,205	77	15	4,909	15	3,801	834	28
35	20,390	14	5.808594	8	import org.andengine.openg	187	2,948	4,955	15	395	15,179	15	412	1,205	77	15	1,200	6,475	15	13,883	6,573
35	20,390	15	5.808594	8	import org.andengine.openg	187	2,948	4,955	15	395	15,179	15	412	1,205	77	15	87	2,399	15	15,810	15
35	20,390	16	5.796875	14	PyTorch). """ graph = nx.from	14,819	22,097	348	481	187	50,274	32,488	187	50,274	10,580	426	295	89	15	4,064	64
35	20,390	17	5.761719	7	brains.mps.internal.col	33,504	15	78	793	15	11,577	15	2,052	9,889	15	21,005	15	42	7,161	9,824	28
35	20,390	18	5.679688	5	with sess.graph.	50,274	3,113	46,796	15	10,580	15	284	64	6,986	14,850	187	50,270	64	426	657	6,435
35	20,390	19	5.554688	10	. Ellis, and T. Kott	15	575	28,578	261	13	285	308	15	575	44	1,519	375	13	4,111	15	3,555
35	20,390	20	5.464844	5	.andengine.openg	15	395	15,179	15	412	1,205	77	15	87	2,399	15	27,384	10,644	4,241	8,224	28
35	20,390	21	5.460938	6	the courage, like Eärend	253	15,838	13	751	444	1,392	5,047	293	13	281	8,778	1,411	253	13,862	1,561	13
35	20,390	22	5.429688	9	scheme]{}, Adv. Math. [35*]{} (	6,974	11,661	13,536	15	8,684	15	4,299	1,671	7,444	313	16,134	582	2,456	1,253	11,124	15
35	20,390	23	5.40625	12	ervesCFG(); AU.addRequired<AARes	19,085	34,949	1,874	187	50,274	16,408	15	1,911	28,463	29	34	1,277	265	3,628	27,757	12,161
35	20,390	24	5.363281	6	org.andengine.openg	4,955	15	395	15,179	15	412	1,205	77	15	38,595	15	22,648	10,976	28	187	2,948
35	20,390	25	5.363281	6	org.andengine.openg	4,955	15	395	15,179	15	412	1,205	77	15	38,595	15	255	9,869	15	2,713	4,251
35	20,390	26	5.339844	10	using Loon.Core.Graphics.Openg	187	5,302	418	3,508	15	13,745	15	37,430	15	12,535	1,205	77	28	187	5,302	418
35	20,390	27	5.332031	14	decl->updateActiveVertexAttrib( GFXGL->getOpeng	16,366	1,168	11,183	16,754	27,384	6,601	725	9	443	27,208	5,990	1,168	788	12,535	1,205	77
35	20,390	28	5.324219	15	GLES30_glBeginQuery(JNIEnv *__env, j	5,990	1,410	1,229	64	3,129	24,393	8,053	9	43	13,652	32,883	475	876	12,540	13	480
35	20,390	29	5.304688	7	import json import pyomo.en	2,948	14,113	187	2,948	7,239	19,216	15	257	2,411	347	7,239	80	187	2,948	7,239	19,216
35	20,390	30	5.246094	11	Commerce and Joint Development Authority<\|endoftext\|><\|endoftext\|>from pyomo.en	20,249	285	23,106	9,753	17,907	0	0	4,064	7,239	19,216	15	257	2,411	1,395	475	187
35	20,390	31	5.246094	13	static DxvkDeviceFeatures GetDeviceFeatures(const R	535	50,274	4,659	399	89	46,065	15,982	30,880	5,057	15,982	30,880	9	3,474	416	68	29
35	20,390	32	5.226563	13	ouvernement du Canada par l’entremise du ministère du	16,017	34,711	3,443	6,144	1,061	298	457	290	2,013	885	3,443	28,099	10,133	3,443	2,790	3,428
35	20,390	33	5.226563	5	glGetUniformLocation(g	1,289	3,633	44,804	11,930	9	72	64	38,031	16,722	13	346	22,648	3,287	187	50,274	72
35	20,390	34	5.210938	14	gouvernement du Canada par l’entremise du ministère du	305	16,017	34,711	3,443	6,144	1,061	298	457	290	2,013	885	3,443	28,099	10,133	3,443	2,790
35	20,390	35	5.207031	15	Id. Citing United States v. Gay, 774 F.2d	4,031	15	330	2,996	1,986	2,077	362	15	25,498	13	818	3,566	401	15	19	69
35	20,390	36	5.203125	4	andengine.openg	395	15,179	15	412	1,205	77	15	8,906	15	5,990	5,443	28	187	2,948	4,955	15
35	20,390	37	5.203125	4	andengine.openg	395	15,179	15	412	1,205	77	15	87	2,399	15	14,410	2,548	28	187	2,948	4,955
35	20,390	38	5.171875	6	pas incertains des aveug	7,222	1,485	797	1,550	711	41,096	814	868	13	14,078	40,999	247	23,162	24,821	1,162	866
35	20,390	39	5.167969	4	dx.graphics.g	9,665	15	40,718	15	72	19	69	15	22,648	26,330	28	187	2,948	389	15	14,367

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