How to run gauss markov mobility model on ns3 simulator? - ns-3

I am an absolute bigger in NS3 and also on stackoverflow. How do I run the gauss markov mobility model?
The command that I have in the terminal window is: ~/ns-allinone-3.31/ns-3.31$ ./waf --run src/mobility/model/gauss-markov-mobility-model
But somehow model is not found. I'm sure that I've keyed in the right folder locations and filename. For the examples folder, it is totally fine.
Can anybody provide guidance? I am unable to post pics as a new user.

From the Docs Gauss Markov Mobility Model
MobilityHelper mobility;
mobility.SetMobilityModel ("ns3::GaussMarkovMobilityModel",
"Bounds", BoxValue (Box (0, 150000, 0, 150000, 0, 10000)),
"TimeStep", TimeValue (Seconds (0.5)),
"Alpha", DoubleValue (0.85),
"MeanVelocity", StringValue ("ns3::UniformRandomVariable[Min=800|Max=1200]"),
"MeanDirection", StringValue ("ns3::UniformRandomVariable[Min=0|Max=6.283185307]"),
"MeanPitch", StringValue ("ns3::UniformRandomVariable[Min=0.05|Max=0.05]"),
"NormalVelocity", StringValue ("ns3::NormalRandomVariable[Mean=0.0|Variance=0.0|Bound=0.0]"),
"NormalDirection", StringValue ("ns3::NormalRandomVariable[Mean=0.0|Variance=0.2|Bound=0.4]"),
"NormalPitch", StringValue ("ns3::NormalRandomVariable[Mean=0.0|Variance=0.02|Bound=0.04]"));
mobility.SetPositionAllocator ("ns3::RandomBoxPositionAllocator",
"X", StringValue ("ns3::UniformRandomVariable[Min=0|Max=150000]"),
"Y", StringValue ("ns3::UniformRandomVariable[Min=0|Max=150000]"),
"Z", StringValue ("ns3::UniformRandomVariable[Min=0|Max=10000]"));
mobility.Install (wifiStaNodes);

Related

Intent chatbot with numeric and strings data

I am trying to build a chatbot using intents as json file, an example of the intent is
{"tag": "thanks",
"patterns": ["Thanks", "Thank you", "That's helpful"],
"responses": ["Happy to help!", "Any time!", "My pleasure"]
},
I have a lot of other tags but I want the chatbot to detect the response based on the input text and other factors for example speech intensity which will be a value ranges from 1-10.
The chatbot has been trained using tensorflow.
How can I modify the intent file and input to the chatbot a text together with some information.
Chatbot operates by this code
def chat():
print("Start talking with the bot (type quit to stop)!")
while True:
inp = input("You: ")
if inp.lower() == "quit":
break
results = model.predict([bag_of_words(inp, words)])
results_index = numpy.argmax(results)
tag = labels[results_index]
for tg in data["intents"]:
if tg['tag'] == tag:
responses = tg['responses']
print("Chatbot: ",random.choice(responses))
What I want is that for example speech intensity is 8 and the sentence is “what do you want” the response should be something like this
“Why are you nervous?”
You can add to the intent file a function that can do any purpose in each intent. for example
{"tag": "thanks",
"patterns": ["Thanks", "Thank you", "That's helpful"],
"responses": ["Happy to help!", "Any time!", "My pleasure"]
"action": getname()
},

How to use e.findAt for similar geometrical models?

I used Abaqus macro recording when defining a set: by edge (20 deg). My end goal is to run the script for different .STEP models/geometries which are identical in every aspect except for a few parametric values. For each model I am using the same procedure for selecting the highlighted edges: create a set from edge-by-angle-20.
However, when I run it for other geometric models, it selects an entirely different e.getSequenceFromMask or as I should say entirely different sets for e.findAt after running the commands for the similar edge.
session.journalOptions.replayGeometry
session.journalOptions.setValues(replayGeometry=INDEX)
I am aware that the program has restrictions over findAt :
findAt initially uses the ACIS tolerance of 1E-6. As a result, findAt returns any edge that is at the arbitrary point specified or at a distance of less than 1E-6 from the arbitrary point.
Edge to be selected in all models which are similar with a few variations in dimensions
I would appreciate it if anyone can suggest how to ensure the same edges are selected for different geometries when using the e.findAt command.
My code:
#Sample for the same edges selected from different dimensioned CAD models
#CAD Model_1
edges = e.getSequenceFromMask(mask=(
'[#63002104 #88110 #10000480 #20402 #480c802 #10420070 #c4411',
' #70104842 #10022000 #1e2002 #30 #80820010 #20003 #18144186',
' #b000 ]',), )
p.Set(edges=edges, name='Set-2')
#Set 2:
mdb.models['Model-1'].parts['Circular_knit - 2, 3, 10'].edges.findAt(((2.808153, 3.86272, -0.231317),),((3.959929, 2.669325, -0.231317),),((3.581888, 1.932068, -0.134932),),((1.501975, 3.782442, -0.134931),),((2.094736, 4.291667, -0.231317),),((2.135971, 3.464163, -0.134931),),((2.705066, 3.040627, -0.134931),),((3.436245, 3.316406, -0.231317),),((3.19197, 2.524704, -0.134932),),((4.363292, 1.941137, -0.231317),),((3.862971, 1.280728, -0.134932),),((-0.318814, 3.68708, 0.0),),((1.317672, 4.590214, -0.231317),),((4.764061, 0.331739, -0.231318),),((4.74929, -0.500571, -0.231318),),((4.590214, -1.317672, -0.231317),),((4.634078, 1.15397, -0.231318),),((-0.590217, 4.023581, -0.132766),),((-0.331245, 4.765369, -0.229231),),((0.822343, 3.985795, -0.134932),),((0.500571, 4.74929, -0.231318),),((4.02668, 0.590473, -0.134933),),((4.068041, -0.117724, -0.134933),),((4.291667, -2.094736, -0.231317),),((3.464163, -2.135971, -0.134931),),((3.86272, -2.808153, -0.231317),),((1.932068, -3.581888, -0.134932),),((1.280728, -3.862971, -0.134932),),((1.15397, -4.634078, -0.231318),),((0.590473, -4.02668, -0.134933),),((-0.117724, -4.068041, -0.134933),),((3.985795, -0.822343, -0.134932),),((0.117724, 4.068041, -0.134933),),((-1.15397, 4.634078, -0.231318),),((-1.280728, 3.862971, -0.134932),),((-2.524704, 3.19197, -0.134932),),((-2.669325, 3.959929, -0.231317),),((-1.941137, 4.363292, -0.231317),),((-1.932068, 3.581888, -0.134932),),((3.782442, -1.501975, -0.134931),),((3.040627, -2.705066, -0.134931),),((3.316406, -3.436245, -0.231317),),((2.524704, -3.19197, -0.134932),),((2.669325, -3.959929, -0.231317),),((1.941137, -4.363292, -0.231317),),((0.331739, -4.764061, -0.231318),),((-0.500571, -4.74929, -0.231318),),((-3.464163, 2.135971, -0.134931),),((-4.291667, 2.094736, -0.231317),),((-3.316406, 3.436245, -0.231317),),((-3.040627, 2.705066, -0.134931),),((-3.86272, 2.808153, -0.231317),),((-1.317672, -4.590214, -0.231317),),((-0.822343, -3.985795, -0.134932),),((-4.764061, -0.331739, -0.231318),),((-3.959929, -2.669325, -0.231317),),((-3.862971, -1.280728, -0.134932),),((-3.782442, 1.501975, -0.134931),),((-4.590214, 1.317672, -0.231317),),((-3.985795, 0.822343, -0.134932),),((-2.135971, -3.464163, -0.134931),),((-4.74929, 0.500571, -0.231318),),((-4.068041, 0.117724, -0.134933),),((-4.02668, -0.590473, -0.134933),),((-4.634078, -1.15397, -0.231318),),((-4.363292, -1.941137, -0.231317),),((-3.581888, -1.932068, -0.134932),),((-3.19197, -2.524704, -0.134932),),((-3.436245, -3.316406, -0.231317),),((-2.705066, -3.040627, -0.134931),),((-2.094736, -4.291667, -0.231317),),((-1.501975, -3.782442, -0.134931),),((-2.808153, -3.86272, -0.231317),),)
#CAD Model_2
edges = e.getSequenceFromMask(mask=('[#ffffffff:12 #3fffff ]', ), )
p.Set(edges=edges, name='Set-2')
#Set_2:
mdb.models['Model-1'].parts['Circular_knit - 2, 3, 9'].edges.findAt(((3.609683, 0.972388, 1e-06),),((3.573439, 0.962676, 0.012491),),((3.76173, 1.266747, -0.034239),),((4.345677, 1.895909, -0.106922),),((3.369331, 1.576662, 0.046191),),((3.59632, 1.879201, -0.101049),),((4.017677, 2.705061, -0.092682),),((3.044359, 2.137787, 0.046191),),((3.950435, 2.621725, -0.106922),),((4.319341, 2.493773, 0.001066),),((3.51041, 1.897727, -0.09198),),((4.38626, 1.924207, -0.137251),),((3.786618, 1.25932, -0.091981),),((4.653757, 1.133308, -0.137251),),((3.94777, 0.582649, -0.091981),),((4.779853, 0.307975, -0.137252),),((4.987546, 0.0, 0.001066),),((3.968123, -0.096234, -0.034239),),((3.706257, -0.319236, 0.046191),),((4.054105, -0.170725, -0.101049),),((4.831941, 0.333813, -0.092682),),((3.705386, 0.329198, 0.046191),),((3.686317, 0.32753, 0.012491),),((3.591928, 0.967631, 0.046191),),((4.608877, 1.112487, -0.106922),),((3.924549, 0.594285, -0.034239),),((4.732039, 0.295263, -0.106922),),((4.68676, 1.705841, 0.001066),),((3.351983, 1.568572, 0.012491),),((3.38599, 1.58443, 1e-06),),((4.426368, 1.966302, -0.092682),),((3.868004, 1.226157, -0.101049),),((4.700566, 1.167799, -0.092682),),((4.02216, 0.535857, -0.101049),),((3.723697, 0.3308, 1e-06),),((4.332897, 2.501599, 0.037363),),((3.215364, 2.475146, -0.101049),),((3.059416, 2.14833, 1e-06),),((3.028679, 2.126808, 0.012491),),((3.101617, 2.476941, -0.034239),),((3.435161, 3.267881, -0.106922),),((2.613351, 2.620422, 0.012491),),((2.649644, 2.983909, -0.091979),),((2.83651, 3.859543, -0.137251),),((2.134748, 3.057453, 1e-06),),((2.14551, 3.072822, 2e-06),),((2.850196, 3.916052, -0.092682),),((3.215994, 3.832672, 0.037363),),((3.205932, 3.820682, 0.001066),),((2.815512, 3.814744, -0.106922),),((2.129595, 3.050095, 0.046191),),((2.174904, 3.425595, -0.101049),),((2.501599, 4.332897, 0.037363),),((2.493773, 4.319341, 0.001066),),((2.067403, 3.388378, -0.034239),),((3.484612, 1.90072, -0.034239),),((3.985488, 2.656641, -0.137251),),((4.701469, 1.711195, 0.037363),),((4.927189, 0.868796, 0.037363),),((5.003198, 0.0, 0.037363),),((4.711421, -0.530933, -0.106922),),((3.891127, -0.78383, -0.034239),),((4.547648, -1.340996, -0.106922),),((3.695902, -1.447609, -0.034239),),((3.356209, -1.559509, 0.012491),),((3.710189, -1.469294, -0.091979),),((4.68676, -1.705841, 0.001066),),((4.816498, -0.510316, -0.092682),),((3.724569, -0.320838, 1e-06),),((3.687189, -0.317568, 0.012491),),((4.911774, 0.866078, 0.001066),),((3.48691, 3.361627, -0.092682),),((2.626886, 2.633957, 0.046191),),((3.127541, 2.478471, -0.091979),),((3.463619, 3.308353, -0.137251),),((2.639883, 2.646954, 1e-06),),((3.820682, 3.205932, 0.001066),),((3.832672, 3.215994, 0.037363),),((2.62438, 2.9779, -0.034239),),((2.118616, 3.034415, 0.012491),),((2.091239, 3.398682, -0.091979),),((2.736711, 2.995885, -0.101049),),((2.12688, 4.35149, -0.092682),),((1.547014, 3.751221, -0.101049),),((1.711195, 4.701469, 0.037363),),((1.469294, 3.710189, -0.091979),),((2.123214, 4.293462, -0.137251),),((2.110314, 4.245698, -0.106922),),((3.988971, -0.111726, -0.091981),),((4.911774, -0.866078, 0.001066),),((4.760715, -0.526717, -0.137252),),((3.594516, -0.957971, 0.046191),),((3.576027, -0.953017, 0.012491),),((3.908966, -0.802706, -0.09198),),((4.927189, -0.868796, 0.037363),),((3.373557, -1.567599, 0.046191),),((4.245698, -2.110314, -0.106922),),((4.319341, -2.493773, 0.001066),),((4.332897, -2.501599, 0.037363),),((4.35149, -2.12688, -0.092682),),((4.293462, -2.123214, -0.137251),),((3.390216, -1.575367, 1e-06),),((4.596926, -1.345405, -0.137251),),((3.612271, -0.962729, 1e-06),),((4.65471, -1.338939, -0.092682),),((3.751221, -1.547014, -0.101049),),((3.962868, -0.872119, -0.101049),),((1.567599, 3.373557, 0.046191),),((1.338939, 4.65471, -0.092682),),((0.872119, 3.962868, -0.101049),),((0.510316, 4.816498, -0.092682),),((0.321655, 3.733897, 2e-06),),((0.320019, 3.715207, 1e-06),),((0.111726, 3.988971, -0.091981),),((0.0, 4.987546, 0.001066),),((0.096234, 3.968123, -0.034239),),((0.530933, 4.711421, -0.106922),),((0.866078, 4.911774, 0.001066),),((0.526717, 4.760715, -0.137252),),((1.705841, 4.68676, 0.001066),),((1.345405, 4.596926, -0.137251),),((0.802706, 3.908966, -0.09198),),((1.571395, 3.381699, 1e-06),),((1.579324, 3.398703, 2e-06),),((1.447609, 3.695902, -0.034239),),((1.559509, 3.356209, 0.012491),),((3.388378, -2.067403, -0.034239),),((3.050095, -2.129595, 0.046191),),((3.065152, -2.140139, 1e-06),),((3.034415, -2.118616, 0.012491),),((3.398682, -2.091239, -0.091979),),((3.425595, -2.174904, -0.101049),),((4.701469, -1.711195, 0.037363),),((0.965152, 3.621316, 2e-06),),((0.957971, 3.594516, 0.046191),),((0.953017, 3.576027, 0.012491),),((0.868796, 4.927189, 0.037363),),((0.319236, 3.706257, 0.046191),),((0.170725, 4.054105, -0.101049),),((0.0, 5.003198, 0.037363),),((-0.3073, 4.782091, -0.136321),),((-0.320838, 3.724565, 1e-05),),((-0.323171, 3.73689, 1.3e-05),),((-0.324216, 3.705803, 0.046199),),((-0.593805, 3.921017, -0.033079),),((-0.866078, 4.911774, 0.001066),),((-1.133308, 4.653757, -0.137251),),((-0.972388, 3.609683, 1e-06),),((-0.967631, 3.591928, 0.046191),),((-1.112487, 4.608877, -0.106922),),((0.317568, 3.687189, 0.012491),),((-0.294772, 4.734331, -0.106051),),((0.78383, 3.891127, -0.034239),),((1.340996, 4.547648, -0.106922),),((0.960296, 3.603193, 1e-06),),((3.814744, -2.815512, -0.106922),),((3.820682, -3.205932, 0.001066),),((3.832672, -3.215994, 0.037363),),((3.916052, -2.850196, -0.092682),),((3.859543, -2.83651, -0.137251),),((-0.332828, 4.834011, -0.091687),),((-0.318814, 3.68708, 0.0),),((-0.325817, 3.724111, 1e-05),),((-0.536001, 4.018603, -0.099875),),((-0.868796, 4.927189, 0.037363),),((-0.319237, 3.706259, 0.046191),),((-0.322549, 3.68675, 0.0125),),((-0.31881, 3.687067, 0.05),),((-0.317568, 3.687186, 0.012495),),((-0.582378, 3.944173, -0.090789),),((-0.962676, 3.573439, 0.012491),),((-1.25932, 3.786618, -0.091981),),((-1.711195, 4.701469, 0.037363),),((-1.226157, 3.868004, -0.101049),),((-1.167799, 4.700566, -0.092682),),((2.9779, -2.62438, -0.034239),),((3.267881, -3.435161, -0.106922),),((2.476941, -3.101617, -0.034239),),((2.137787, -3.044359, 0.046191),),((2.475146, -3.215364, -0.101049),),((3.361627, -3.48691, -0.092682),),((2.633957, -2.626886, 0.046191),),((2.620422, -2.613351, 0.012491),),((2.983909, -2.649644, -0.091979),),((2.995885, -2.736711, -0.101049),),((2.646954, -2.639883, 1e-06),),((-1.266747, 3.76173, -0.034239),),((-1.705841, 4.68676, 0.001066),),((-1.924207, 4.38626, -0.137251),),((-1.58443, 3.38599, 1e-06),),((-1.966302, 4.426368, -0.092682),),((3.205932, -3.820682, 0.001066),),((2.478471, -3.127541, -0.091979),),((2.656641, -3.985488, -0.137251),),((2.493773, -4.319341, 0.001066),),((1.90072, -3.484612, -0.034239),),((1.576662, -3.369331, 0.046191),),((1.879201, -3.59632, -0.101049),),((2.705061, -4.017677, -0.092682),),((2.126808, -3.028679, 0.012491),),((2.14833, -3.059416, 1e-06),),((3.215994, 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-0.092682),),((-3.215994, 3.832672, 0.037363),),((1.897727, -3.51041, -0.09198),),((1.568572, -3.351983, 0.012491),),((1.266747, -3.76173, -0.034239),),((0.967631, -3.591928, 0.046191),),((0.972388, -3.609683, 1e-06),),((0.962676, -3.573439, 0.012491),),((1.112487, -4.608877, -0.106922),),((0.594285, -3.924549, -0.034239),),((0.329198, -3.705386, 0.046191),),((0.535857, -4.02216, -0.101049),),((1.167799, -4.700566, -0.092682),),((1.226157, -3.868004, -0.101049),),((1.711195, -4.701469, 0.037363),),((1.25932, -3.786618, -0.091981),),((-3.267881, 3.435161, -0.106922),),((-3.205932, 3.820682, 0.001066),),((-2.620422, 2.613351, 0.012491),),((-2.983909, 2.649644, -0.091979),),((-3.859543, 2.83651, -0.137251),),((-3.065152, 2.140139, 1e-06),),((-3.050095, 2.129595, 0.046191),),((-3.034415, 2.118616, 0.012491),),((-3.398682, 2.091239, -0.091979),),)+mdb.models['Model-1'].parts['Circular_knit - 2, 3, 9'].edges.findAt(((-4.319341, 2.493773, 0.001066),),((-4.245698, 2.110314, 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Do not use getSequenceFromMask
This method seems very appealing, however, Abaqus documentation does not cover how masks are formed, so it is not possible to artificially generate a mask for a set of objects.
Use coordinates (findAt()) or indeces instead
Most of the time if your geometry is complex and is generated without control over vertices/edges/etc numbering then the findAt() method is your only option (along with methods getByBoundingBox(...), getByBoundingCylinder(...) and getByBoundingSphere(...)).
If searching edge-by-edge is not very efficient and getByBounding... methods do not allow you to select the right array of edges then you can try: 1) to find the first edge of your sequence; 2) then use (as you already tried) the getEdgesByEdgeAngle(...) method to find all adjacent edges.
If you know for sure the numbering of your geometrical entities (vertices/edges/etc.) then you can access them by their index. So, using notations from your example, if edges of interest of your geometry have indexes from n to m then:
part = mdb.models['Model-1'].parts['Circular_knit - 2, 3, 10']
set2 = part.Set(name='Set-2', edges=part.edges[n:m+1])

Halcon - how to set white balance

I have this code to try around with halcon. Images are quite greenish, and cannot figure out how to set whitebalance. I cannot find it in the samples, in the documentation, on google, and in the parameters. How is whitebalance set on halcon?
* Image Acquisition 06: Code generated by Image Acquisition 06
* Image Acquisition 06: Attention: The initialization may fail in case parameters need to
* Image Acquisition 06: be set in a specific order (e.g., image resolution vs. offset).
open_framegrabber ('GigEVision', 0, 0, 0, 0, 0, 0, 'default', -1, 'default', 'GtlForceIP=00010dc465ce,10.5.5.144/24', 'false', 'default', 'S1204667', 0, -1, AcqHandle)
set_framegrabber_param (AcqHandle, 'Gain', 5.01187)
set_framegrabber_param (AcqHandle, 'BlackLevel', 240.0)
dev_open_window (0, 0, 500, 300, 'light gray', WindowHandleButton)
i := 0
create_bar_code_model ([], [], BarCodeHandle)
while (i < 100)
grab_image (Image, AcqHandle)
find_bar_code (Image, SymbolRegions, BarCodeHandle, 'auto', DecodedDataStrings)
get_bar_code_result (BarCodeHandle, 'all', 'decoded_types', BarCodeResults)
i:= i+1
endwhile
close_framegrabber (AcqHandle)
If you open your camera in HDevelop using Assistants -> Image Acquisition -> Connection tab there is a parameter for setting the color space. When I set it to "yuv" on one of my GigE cameras the image looks green. See if you can modify that value to "rgb" or "gray" or "default". There are also some advanced settings under the "Parameters" tab that you could play with by selecting "Guru" under the visibility settings. But I couldn't find white balance settings for my GigE camera under there. Usually the manufacturer of the camera will supply software to allow you to configure advanced parameters (IDS Camera Manager, Basler Pylon etc). You could try opening your camera under the manufacturers software to see if there are any settings for white balance.

Adding a Retokenize pipe while training NER model

I am currenly attempting to train a NER model centered around Property Descriptions. I could get a fully trained model to function to my liking however, I now want to add a retokenize pipe to the model so that I can set up the model to train other things.
From here, I am having issues getting the retokenize pipe to actually work. Here is the definition:
def retok(doc):
ents = [(ent.start, ent.end, ent.label) for ent in doc.ents]
with doc.retokenize() as retok:
string_store = doc.vocab.strings
for start, end, label in ents:
retok.merge(
doc[start: end],
attrs=intify_attrs({'ent_type':label},string_store))
return doc
i am adding it into my training like this:
nlp.add_pipe(retok, after="ner")
and I am adding it into the Language Factories like this:
Language.factories['retok'] = lambda nlp, **cfg: retok(nlp)
The issue I keep getting is "AttributeError: 'English' object has no attribute 'ents'". Now I am assuming I am getting this error because the parameter that is being passed through this function is not a doc but actually the NLP model itself. I am not really sure to get a doc to flow into this pipe during training. At this point I don't really know where to go from here to get the pipe to function the way I want.
Any help is appreciated, thanks.
You can potentially use the built-in merge_entities pipeline component: https://spacy.io/api/pipeline-functions#merge_entities
The example copied from the docs:
texts = [t.text for t in nlp("I like David Bowie")]
assert texts == ["I", "like", "David", "Bowie"]
merge_ents = nlp.create_pipe("merge_entities")
nlp.add_pipe(merge_ents)
texts = [t.text for t in nlp("I like David Bowie")]
assert texts == ["I", "like", "David Bowie"]
If you need to customize it further, the current implementation of merge_entities (v2.2) is a good starting point:
def merge_entities(doc):
"""Merge entities into a single token.
doc (Doc): The Doc object.
RETURNS (Doc): The Doc object with merged entities.
DOCS: https://spacy.io/api/pipeline-functions#merge_entities
"""
with doc.retokenize() as retokenizer:
for ent in doc.ents:
attrs = {"tag": ent.root.tag, "dep": ent.root.dep, "ent_type": ent.l
abel}
retokenizer.merge(ent, attrs=attrs)
return doc
P.S. You are passing nlp to retok() below, which is where the error is coming from:
Language.factories['retok'] = lambda nlp, **cfg: retok(nlp)
See a related question: Spacy - Save custom pipeline

How do I convert simple training style data to spaCy's command line JSON format?

I have the training data for a new NER type in the "Training an additional entity type" section of the spaCy documentation.
TRAIN_DATA = [
("Horses are too tall and they pretend to care about your feelings", {
'entities': [(0, 6, 'ANIMAL')]
}),
("Do they bite?", {
'entities': []
}),
("horses are too tall and they pretend to care about your feelings", {
'entities': [(0, 6, 'ANIMAL')]
}),
("horses pretend to care about your feelings", {
'entities': [(0, 6, 'ANIMAL')]
}),
("they pretend to care about your feelings, those horses", {
'entities': [(48, 54, 'ANIMAL')]
}),
("horses?", {
'entities': [(0, 6, 'ANIMAL')]
})
]
I want to train an NER model on this data using the spacy command line application. This requires data in spaCy's JSON format. How do I write the above data (i.e. text with labeled character offset spans) in this JSON format?
After looking at the documentation for that format, it's not clear to me how to manually write data in this format. (For example, do I have partition everything into paragraphs?) There is also a convert command line utility that converts from non-spaCy data formats to spaCy's format, but that doesn't take a spaCy format like the one above as input.
I understand the examples of NER training code that uses the "Simple training style", but I'd like to be able to use the command line utility for training. (Though as is apparent from my previous spaCy question, I'm unclear when you're supposed to use that style and when you're supposed to use the command line.)
Can someone show me an example of the above data in "spaCy's JSON format", or point to documentation that explains how to make this transformation.
There's a built in function to spaCy that will get you most of the way there:
from spacy.gold import biluo_tags_from_offsets
That takes in the "offset" type annotations you have there and converts them to the token-by-token BILOU format.
To put the NER annotations into the final training JSON format, you just need a bit more wrapping around them to fill out the other slots the data requires:
sentences = []
for t in TRAIN_DATA:
doc = nlp(t[0])
tags = biluo_tags_from_offsets(doc, t[1]['entities'])
ner_info = list(zip(doc, tags))
tokens = []
for n, i in enumerate(ner_info):
token = {"head" : 0,
"dep" : "",
"tag" : "",
"orth" : i[0].string,
"ner" : i[1],
"id" : n}
tokens.append(token)
sentences.append(tokens)
Make sure that you disable the non-NER pipelines before training with this data.
I've run into some issues using spacy train on NER-only data. See #1907 and also check out this discussion on the Prodigy forum for some possible workarounds.