Fix typos (#215)

* Update graph.jl

* Update smsspamcollection.jl

* Update ptblm.jl

* Update faust.jl

* Update index.md

Author: pitmonticone

docs/src/index.md

@@ -135,7 +135,7 @@ julia> iris.targets
 
 ## MLUtils compatibility
 
-MLDatasets.jl garuantees compatibility with the [getobs](https://juliaml.github.io/MLUtils.jl/dev/api/#MLUtils.getobs) and [numobs](https://juliaml.github.io/MLUtils.jl/dev/api/#MLUtils.numobs) interface defined in [MLUtils.jl](https://github.com/JuliaML/MLUtils.jl).
+MLDatasets.jl guarantees compatibility with the [getobs](https://juliaml.github.io/MLUtils.jl/dev/api/#MLUtils.getobs) and [numobs](https://juliaml.github.io/MLUtils.jl/dev/api/#MLUtils.numobs) interface defined in [MLUtils.jl](https://github.com/JuliaML/MLUtils.jl).
 In practice, applying `getobs` and `numobs` on datasets is equivalent to applying indexing and `length`.
 
 ## Conditional module loading
@@ -181,7 +181,7 @@ overwrite this on a case by case basis, it is possible to specify
 a data directory directly in the dataset constructor (e.g. `MNIST(dir = <directory>)`).
 
 In order to download datasets without having to manually confirm the download, 
-you can set to true the following enviromental variable:
+you can set to true the following environmental variable:
 
 ```julia
 ENV["DATADEPS_ALWAYS_ACCEPT"] = true

src/datasets/meshes/faust.jl

@@ -143,7 +143,7 @@ julia> label = dataset.labels[1] # The ground-truth/labels for each vertices in
  0
 ```
 
-# Refereneces
+# References
 
 1. [MPI Faust Website](http://faust.is.tue.mpg.de/)
 

src/datasets/text/ptblm.jl

@@ -26,7 +26,7 @@ end
 The PTBLM dataset consists of Penn Treebank sentences
 for language modeling, available from https://github.com/tomsercu/lstm.
 The unknown words are replaced with <unk> so that the
-total vocaburary size becomes 10000.
+total vocabulary size becomes 10000.
 """
 struct PTBLM <: SupervisedDataset
     metadata::Dict{String, Any}

src/datasets/text/smsspamcollection.jl

@@ -21,7 +21,7 @@ end
 
 The SMS Spam Collection v.1 (hereafter the corpus) is a set of SMS tagged messages 
 that have been collected for SMS Spam research. It contains one set of SMS messages
-in English of 5,574 messages, tagged acording being ham (legitimate) or spam.
+in English of 5,574 messages, tagged according being ham (legitimate) or spam.
 The corpus has a total of 4,827 SMS legitimate messages (86.6%) and a total of 747 (13.4%) spam messages.
 
 The corpus has been collected by Tiago Agostinho de Almeida (http://www.dt.fee.unicamp.br/~tiago) 

src/graph.jl

@@ -135,10 +135,10 @@ Nodes are indexed in `1:num_nodes`.
 - `num_edges`: Dictionary containing the number of edges for each relation.
 - `edge_indices`: Dictionary containing the `edge_index` for each edge relation. An `edge_index` is a tuple containing two vectors with length equal to the number of edges for the relation.
     The first vector contains the list of the source nodes of each edge, the second contains the target nodes.
-- `node_data`: node-related data. Can be `nothing`, Dictionary of a dictionary of arrays. Data of a speific type of node can be accessed 
+- `node_data`: node-related data. Can be `nothing`, Dictionary of a dictionary of arrays. Data of a specific type of node can be accessed 
             using node_data[node_type].The array's last dimension size should be equal to the number of nodes.
             Default `nothing`.
-- `edge_data`: Can be `nothing`, Dictionary of a dictionary of arrays. Data of a speific type of edge can be accessed 
+- `edge_data`: Can be `nothing`, Dictionary of a dictionary of arrays. Data of a specific type of edge can be accessed 
             using edge_data[edge_type].The array's last dimension size should be equal to the number of nodes.
             Default `nothing`.
 """