Upload 5 files

.gitattributes

@@ -53,3 +53,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+maltese_glove_10.txt filter=lfs diff=lfs merge=lfs -text
+mt_word2vec.txt filter=lfs diff=lfs merge=lfs -text
+ppmi_embeddings.txt filter=lfs diff=lfs merge=lfs -text

maltese_glove_10.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5b8e3e71dd1b3f65251ac3e6c877298107a6349a6d308bb3976daf7854575cda
+size 2037838610

mt_glove.sh

@@ -0,0 +1,57 @@
+#!/bin/bash
+set -e
+
+# Makes programs, downloads sample data, trains a GloVe model, and then evaluates it.
+# One optional argument can specify the language used for eval script: matlab, octave or [default] python
+
+make
+if [ ! -e text8 ]; then
+  if hash wget 2>/dev/null; then
+    wget http://mattmahoney.net/dc/text8.zip
+  else
+    curl -O http://mattmahoney.net/dc/text8.zip
+  fi
+  unzip text8.zip
+  rm text8.zip
+fi
+
+CORPUS=korpus_malti_tok.txt
+VOCAB_FILE=vocab.txt
+COOCCURRENCE_FILE=cooccurrence.bin
+COOCCURRENCE_SHUF_FILE=cooccurrence.shuf.bin
+BUILDDIR=build
+SAVE_FILE=maltese_glove_10
+VERBOSE=2
+MEMORY=4.0
+VOCAB_MIN_COUNT=5
+VECTOR_SIZE=300
+MAX_ITER=15
+WINDOW_SIZE=10
+BINARY=2
+NUM_THREADS=8
+X_MAX=10
+if hash python 2>/dev/null; then
+    PYTHON=python
+else
+    PYTHON=python3
+fi
+
+echo
+echo "$ $BUILDDIR/vocab_count -min-count $VOCAB_MIN_COUNT -verbose $VERBOSE < $CORPUS > $VOCAB_FILE"
+$BUILDDIR/vocab_count -min-count $VOCAB_MIN_COUNT -verbose $VERBOSE < $CORPUS > $VOCAB_FILE
+echo "$ $BUILDDIR/cooccur -memory $MEMORY -vocab-file $VOCAB_FILE -verbose $VERBOSE -window-size $WINDOW_SIZE < $CORPUS > $COOCCURRENCE_FILE"
+$BUILDDIR/cooccur -memory $MEMORY -vocab-file $VOCAB_FILE -verbose $VERBOSE -window-size $WINDOW_SIZE < $CORPUS > $COOCCURRENCE_FILE
+echo "$ $BUILDDIR/shuffle -memory $MEMORY -verbose $VERBOSE < $COOCCURRENCE_FILE > $COOCCURRENCE_SHUF_FILE"
+$BUILDDIR/shuffle -memory $MEMORY -verbose $VERBOSE < $COOCCURRENCE_FILE > $COOCCURRENCE_SHUF_FILE
+echo "$ $BUILDDIR/glove -save-file $SAVE_FILE -threads $NUM_THREADS -input-file $COOCCURRENCE_SHUF_FILE -x-max $X_MAX -iter $MAX_ITER -vector-size $VECTOR_SIZE -binary $BINARY -vocab-file $VOCAB_FILE -verbose $VERBOSE"
+$BUILDDIR/glove -save-file $SAVE_FILE -threads $NUM_THREADS -input-file $COOCCURRENCE_SHUF_FILE -x-max $X_MAX -iter $MAX_ITER -vector-size $VECTOR_SIZE -binary $BINARY -vocab-file $VOCAB_FILE -verbose $VERBOSE
+if [ "$CORPUS" = 'text8' ]; then
+   if [ "$1" = 'matlab' ]; then
+       matlab -nodisplay -nodesktop -nojvm -nosplash < ./eval/matlab/read_and_evaluate.m 1>&2 
+   elif [ "$1" = 'octave' ]; then
+       octave < ./eval/octave/read_and_evaluate_octave.m 1>&2
+   else
+       echo "$ $PYTHON eval/python/evaluate.py"
+       $PYTHON eval/python/evaluate.py
+   fi
+fi

mt_word2vec.py

@@ -0,0 +1,30 @@
+#!/usr/bin/env python
+from gensim.models import Word2Vec
+import gensim 
+
+def tokenize_sentence(sentence):
+    return sentence.split()
+
+with open('korpus_malti_tok.txt', 'r', encoding='utf-8') as file:
+    sentences = file.read().splitlines()
+
+data = [tokenize_sentence(sentence) for sentence in sentences]
+
+model = Word2Vec(data, 
+                     vector_size=300, 
+                     window=10, 
+                     min_count=20, 
+                     workers=16, 
+                     sample=1e-5, 
+                     alpha=0.03, 
+                     min_alpha=0.0007, 
+                     negative=20)
+
+model.build_vocab(data, progress_per=1000)
+print(model.corpus_count)
+
+model.train(data, total_examples=model.corpus_count, epochs=15)
+
+model.wv.save_word2vec_format('mt_word2vec_2.txt', binary=False)
+
+print('Émbeddings successfully trained!')

mt_word2vec.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9da95cfe3cd190183d04791e46250a6acb7304d4923d4d2a9e829efcd84ae82a
+size 5011712325

ppmi_embeddings.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c2443277bb1dca66914231951a196aa710e0d4f894e0802e7d1faf5557272ea
+size 102048632