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\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m418.7/418.7 kB\u001b[0m \u001b[31m29.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ], + "source": [ + "!pip install stanza pandas -q" + ] + }, + { + "cell_type": "code", + "source": [ + "import stanza\n", + "import pandas as pd\n", + "\n", + "stanza.download('id') # sekali saja, ukuran sekitar puluhan MB\n", + "nlp = stanza.Pipeline(lang='id', processors='tokenize,pos')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 612, + "referenced_widgets": [ + "fe82999a4acd4b3b9131a15d337be964", + "f5c72f21a24142cb990fd9f3aab38e0e", + "d17bb6ac391742958ecfdd67ebcce570", + "56a7943e619f4c8c9a2244cee1dac974", + "f35972f9684b4deda19fa13e56556cc1", + "f0acabd2ea8143518400129fea3d9030", + "7f065c54df5a47e5a3f3d28008bc607f", + "09add7a0086a475189e034f9f098dacc", + "51a8032c2b7e4f8688f7daab4456b7dd", + "8e7676f0849b476c96591ef53f5c60eb", + "1425b197ade140e5827a1dbb4e3447ab", + "fce35d073c7542738eb3763867f831f6", + "1f79bdfc48104d3b8ec0f905c407cbb3", + "dfa28fa4c1144b6fb7a8c35d273f260d", + "8bb5bf942de144d4bf7ec7012eae2ffb", + "971544429cee461281c630b540082475", + "8247be54de1b4f5594055ce1a52635f8", + "96e02e8a54f34b39b8db597e1a65f03e", + "e7d0aaf51e9f4489949700a65e257e3a", + "e84a124c34804891a66f9c6fe9f75080", + "f08d218dbce24d2699d3fd5576a8de74", + "93f4c9779a0347efa24af8d8945bcc3f", + "b908ee625ada4f3e94c9b4514a243436", + "d617ccc7637345cdbae658e79f5cd525", + "6d811545d04a49768147a4c2209ad4e5", + "2405a7cbbc2d4beea762f0d6792c6910", + "f8725dc1470c47d28966a39d331b1e8e", + "d16d1d5df69d4762b0df8757f567c88e", + "efdc90803b2b47ccb4198bbaf39f1c63", + "984df37148fb4f2082c4d41814bcdaa1", + "d950c32a9e4948168c06037a7bb04f74", + "e593348e44124e5d8eb2c10d695ed088", + "64be1981b16e488dbdf016c0b1a6b864", + "4bfa16b1767744fbbf60d04fc2411fbe", + "39e3aae17551441190cd4eba24fb5205", + "f7fb8b219fd746edad70e43b2eb0ba18", + "321a09bc05ca45a3b4565b31ee91b46e", + "866dc5ca94294c1aaf69fd7d9a5bae0f", + "9b54e305eff74ec0b6445d6bffb1763e", + "e33e0cadbcab44f08874d9ba3dce35d2", + "a9b44329d830440988767c3352ec8825", + "77fdf18439a3460687982910f3db06e1", + "05c553cebe0547eaa6fe1b96fd9a61b1", + "128046df199e4ab18266fd6e77d7f086" + ] + }, + "id": "_7Ezv8LS8JJh", + "outputId": "c9c78966-cdda-4ecd-fede-8eea83818d79" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/main/resources_1.15.0.json: 0%| …" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "fe82999a4acd4b3b9131a15d337be964" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "INFO:stanza:Downloaded file to /root/.cache/stanza/1.15.0/resources/resources.json\n", + "INFO:stanza:Downloading default packages for language: id (Indonesian) ...\n", + "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n", + "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "models/default.zip: reconstructing file: 0%| | 0.00B / 394MB " + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "fce35d073c7542738eb3763867f831f6" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "models/default.zip: downloading bytes: | 0.00B " + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "b908ee625ada4f3e94c9b4514a243436" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "INFO:stanza:Downloaded file to /root/.cache/stanza/1.15.0/resources/id/default.zip\n", + "INFO:stanza:Finished downloading models and saved to /root/.cache/stanza/1.15.0/resources\n", + "INFO:stanza:Checking for updates to resources.json in case models have been updated. Note: this behavior can be turned off with download_method=None or download_method=DownloadMethod.REUSE_RESOURCES\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/main/resources_1.15.0.json: 0%| …" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "4bfa16b1767744fbbf60d04fc2411fbe" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "INFO:stanza:Downloaded file to /root/.cache/stanza/1.15.0/resources/resources.json\n", + "WARNING:stanza:Language id package default expects mwt, which has been added\n", + "INFO:stanza:Loading these models for language: id (Indonesian):\n", + "============================\n", + "| Processor | Package |\n", + "----------------------------\n", + "| tokenize | gsd_nocharlm |\n", + "| mwt | gsd |\n", + "| pos | gsd_charlm |\n", + "============================\n", + "\n", + "INFO:stanza:Using device: cpu\n", + "INFO:stanza:Loading: tokenize\n", + "INFO:stanza:Loading: mwt\n", + "INFO:stanza:Loading: pos\n", + "INFO:stanza:Done loading processors!\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "def pos_tagging(teks):\n", + " doc = nlp(teks)\n", + " hasil = []\n", + " for no_kalimat, kalimat in enumerate(doc.sentences, start=1):\n", + " for w in kalimat.words:\n", + " hasil.append({\n", + " \"kalimat\": no_kalimat,\n", + " \"kata\": w.text,\n", + " \"UPOS\": w.upos,\n", + " \"XPOS\": w.xpos\n", + " })\n", + " return pd.DataFrame(hasil)" + ], + "metadata": { + "id": "Ckhw-nfe8NS0" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "kalimat = \"Dendi sedang belanja ke pasar\"\n", + "df = pos_tagging(kalimat)\n", + "df" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "id": "9GRZQ6tm8PnB", + "outputId": "bd5bb0b8-7b95-4f06-ba79-1cb9c63e9e88" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " kalimat kata UPOS XPOS\n", + "0 1 Dendi PROPN X--\n", + "1 1 sedang AUX D--\n", + "2 1 belanja VERB VSA\n", + "3 1 ke ADP R--\n", + "4 1 pasar NOUN NSD" + ], + "text/html": [ + "\n", + "
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51JawaPROPNNSD
61TengahPROPNNSD
71padaADPR--
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122sepanjangADPR--
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152ituDETB--
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172dapatAUXVSA
182memperlancarVERBVSA
192arusNOUNNSD
202barangNOUNNSD
212danCCONJH--
222mengurangiVERBVSA
232kemacetanNOUNNSD
242diADPR--
252jalurNOUNNSD
262panturaNOUNX--
272.PUNCTZ--
283WargaNOUNNSD
293setempatADJASP
303menyambutVERBVSA
313baikADJASP
323pembangunanNOUNNSD
333tersebutDETB--
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "df_berita", + "summary": "{\n \"name\": \"df_berita\",\n \"rows\": 35,\n \"fields\": [\n {\n \"column\": \"kalimat\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 1,\n \"max\": 3,\n \"num_unique_values\": 3,\n \"samples\": [\n 1,\n 2,\n 3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"kata\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 32,\n \"samples\": [\n \"baik\",\n \"itu\",\n \"jalur\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"UPOS\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"AUX\",\n \"VERB\",\n \"PUNCT\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"XPOS\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"H--\",\n \"VSA\",\n \"CC-\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "code", + "source": [ + "rekap = df_berita[\"UPOS\"].value_counts()\n", + "print(rekap)\n", + "\n", + "rekap.plot(kind=\"bar\", title=\"Distribusi POS Tag\", figsize=(8,4))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 665 + }, + "id": "VrMaEUZ28U76", + "outputId": "1eb653e8-f14f-40d0-e07e-72375ffb3abd" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "UPOS\n", + "NOUN 11\n", + "VERB 5\n", + "PROPN 4\n", + "ADP 4\n", + "ADJ 3\n", + "PUNCT 3\n", + "DET 2\n", + "NUM 1\n", + "AUX 1\n", + "CCONJ 1\n", + "Name: count, dtype: int64\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 6 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "doc = nlp(paragraf)\n", + "for i, kal in enumerate(doc.sentences, 1):\n", + " print(f\"Kalimat {i}:\", \" \".join(f\"{w.text}/{w.upos}\" for w in kal.words))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ipft74Do8W-9", + "outputId": "bdc213c3-9f06-4a17-d9fd-c823fc95d768" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Kalimat 1: Presiden/PROPN meresmikan/VERB jembatan/NOUN baru/ADJ di/ADP Jawa/PROPN Tengah/PROPN pada/ADP Senin/PROPN pagi/NOUN ./PUNCT\n", + "Kalimat 2: Jembatan/NOUN sepanjang/ADP 500/NUM meter/NOUN itu/DET diharapkan/VERB dapat/AUX memperlancar/VERB arus/NOUN barang/NOUN dan/CCONJ mengurangi/VERB kemacetan/NOUN di/ADP jalur/NOUN pantura/NOUN ./PUNCT\n", + "Kalimat 3: Warga/NOUN setempat/ADJ menyambut/VERB baik/ADJ pembangunan/NOUN tersebut/DET ./PUNCT\n" + ] + } + ] + } + ] +} \ No newline at end of file