How to use Embeddings with numeric data

I have time-series dataset with different input types (numeric & ID’s/geographic grouping variables) that I want to put into the same model using embeddings and LSTM’s.

I tried using the tensorflow documentation here to build a dummy model with the model features that I want but am having issues with the training/validation input shapes.

import geopandas as gpd
import numpy as np
import pandas as pd
from urllib import request as rq
import tensorflow as tf
from tensorflow import keras
from keras import layers
from keras import Input

rq.urlretrieve("", "~boston_tracts.dbf")

bstn = gpd.read_file(filename="~boston_tracts.dbf")

bs2 = bstn.copy()
bs2[["CRIM", "ZN", "INDUS", "NOX", "RM", "AGE", "DIS", "RAD", "TAX", "PTRATIO", "LSTAT", "units", "cu5k", "c5_7_5", "C7_5_10", "C10_15", "C15_20", "C20_25", "C25_35", "C35_50", "co50k", "median", "BB", "POP", "LAT", "LON"]] = bs2[["CRIM", "ZN", "INDUS", "NOX", "RM", "AGE", "DIS", "RAD", "TAX", "PTRATIO", "LSTAT", "units", "cu5k", "c5_7_5", "C7_5_10", "C10_15", "C15_20", "C20_25", "C25_35", "C35_50", "co50k", "median", "BB", "POP", "LAT", "LON"]].apply(np.float32)
bs2 = pd.concat(objs=[bs2, pd.get_dummies(data=bs2.censored, prefix="censored")], axis=1)
bs2[["NOX_ID", "TOWNNO"]] = bs2[["NOX_ID", "TOWNNO"]].astype(int).astype(str)
bs2[["CHAS", "censored_left", "censored_right"]] = bs2[["CHAS", "censored_left", "censored_right"]].apply(np.int8)
bs2 = bs2.drop(labels=["censored", "censored_no", "LAT", "LON", "geometry", "poltract", "CMEDV", "MEDV", "TOWN", "B", "TRACT"], axis=1).dropna()

embedding_input1 = Input(shape=(None,), name="embedding_input1")
embedding_feat1 = layers.StringLookup(vocabulary=bs2.TOWNNO.unique(), name="embedding_stringlookup1")(embedding_input1)
embedding_feat1 = layers.Embedding(input_dim=len(bs2.TOWNNO.unique()), output_dim=10, name="embedding_layer1")(embedding_feat1)
embedding_feat1 = layers.LSTM(units=100, name="embedding_feat1")(embedding_feat1)

numeric_input = Input(shape=(None, 26), name="numeric_input")
numeric_feat = layers.LSTM(units=100, name="numeric_feat")(numeric_input)

x = layers.concatenate(inputs=[numeric_feat, embedding_feat1])
outputs = layers.Dense(units=1, name="output")(x)
model = keras.Model(inputs=[numeric_input, embedding_input1], outputs=outputs, name="multi_input_model")

keras.utils.plot_model(model, "multi_input_and_output_model.png", show_shapes=True)

multi-input model plot


history =
        "numeric_input":bs2.drop(columns=["median", "TOWNNO", "NOX_ID"]).values.reshape(-1, 489, 26),

ValueError: Training data contains 1 samples, which is not sufficient to split it into a validation and training set as specified by 'validation_split=0.2'. Either provide more data, or a different value for the 'validation_split' argument.