1798 lines
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1798 lines
59 KiB
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|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"with open(\"stopwords.txt\", \"r+\") as f:\n",
|
|||
|
|
" stop_words = f.read().split(\"\\n\")"
|
|||
|
|
],
|
|||
|
|
"execution_count": 7,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "7hDKry1k46ZJ"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"def clean_text(text):\n",
|
|||
|
|
" split = text.lower().split(\" \")\n",
|
|||
|
|
"\n",
|
|||
|
|
" # removing punctuation\n",
|
|||
|
|
" clean = []\n",
|
|||
|
|
" for token in split:\n",
|
|||
|
|
" token = re.sub(r'[^\\w\\s]', '', token)\n",
|
|||
|
|
" if token:\n",
|
|||
|
|
" clean.append(token)\n",
|
|||
|
|
" return clean"
|
|||
|
|
],
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "EZSzAKY8ALMK"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"def prepare_corpus(texts, min_count=1, min_word_len=1):\n",
|
|||
|
|
" corpus = {}\n",
|
|||
|
|
" counters = defaultdict(lambda: 0)\n",
|
|||
|
|
" idx_counter = 0\n",
|
|||
|
|
" for text in texts:\n",
|
|||
|
|
"\n",
|
|||
|
|
" # add to corpus\n",
|
|||
|
|
" for token in text:\n",
|
|||
|
|
" if len(token) < min_word_len or token in stop_words:\n",
|
|||
|
|
" continue\n",
|
|||
|
|
" counters[token] += 1\n",
|
|||
|
|
" if token not in corpus and counters[token] == min_count:\n",
|
|||
|
|
" corpus[token] = idx_counter\n",
|
|||
|
|
" idx_counter += 1\n",
|
|||
|
|
" return corpus"
|
|||
|
|
],
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "7Mpm7weQANWX"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"counters = defaultdict(lambda: 0)\n",
|
|||
|
|
"\n",
|
|||
|
|
"class WordCorpus:\n",
|
|||
|
|
" def __init__(self, corpus=None, texts=None, min_count=1, min_word_len=1):\n",
|
|||
|
|
" if corpus:\n",
|
|||
|
|
" self.corpus = corpus\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" self.corpus = prepare_corpus(texts, min_count, min_word_len)\n",
|
|||
|
|
"\n",
|
|||
|
|
" def get_word_idx(self, token):\n",
|
|||
|
|
" token = token.lower()\n",
|
|||
|
|
" token = re.sub(r'[^\\w\\s]', '', token)\n",
|
|||
|
|
"\n",
|
|||
|
|
" return self.corpus.get(token, None)\n",
|
|||
|
|
"\n",
|
|||
|
|
" def get_embedding(self, token, encode=False):\n",
|
|||
|
|
" embedding = np.zeros(len(self.corpus), dtype=np.int32)\n",
|
|||
|
|
" if encode:\n",
|
|||
|
|
" token_idx = token\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" token = token.lower()\n",
|
|||
|
|
" token = re.sub(r'[^\\w\\s]', '', token)\n",
|
|||
|
|
" if not token or token not in self.corpus:\n",
|
|||
|
|
" return embedding\n",
|
|||
|
|
"\n",
|
|||
|
|
" token_idx = self.corpus[token]\n",
|
|||
|
|
" embedding[token_idx] = 1\n",
|
|||
|
|
" return embedding\n",
|
|||
|
|
"\n",
|
|||
|
|
" def get_bow(self, text, encode=False):\n",
|
|||
|
|
" if encode:\n",
|
|||
|
|
" embeddings = [\n",
|
|||
|
|
" self.get_embedding(token, encode) for token in text\n",
|
|||
|
|
" ]\n",
|
|||
|
|
"\n",
|
|||
|
|
" return np.sum(embeddings, axis=0)\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" bow = np.zeros(len(self.corpus), dtype=np.int32)\n",
|
|||
|
|
" for token in text:\n",
|
|||
|
|
" bow[token] += 1\n",
|
|||
|
|
" return bow"
|
|||
|
|
],
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "IjOoR5qyAQSS"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"def load_train_data(train_path):\n",
|
|||
|
|
" texts = []\n",
|
|||
|
|
" with open(train_path, \"r+\") as file:\n",
|
|||
|
|
" while True:\n",
|
|||
|
|
" line = file.readline()\n",
|
|||
|
|
" if not line:\n",
|
|||
|
|
" break\n",
|
|||
|
|
"\n",
|
|||
|
|
" _, _, _, _, text, *_ = line.split(\"\\t\")\n",
|
|||
|
|
" texts.append(clean_text(text))\n",
|
|||
|
|
" print(f\"Loaded {len(texts)} texts from train_set.\")\n",
|
|||
|
|
" return texts"
|
|||
|
|
],
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "oYgV8yPJGqq3"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"class LanguageNeuralModel(nn.Module):\n",
|
|||
|
|
" def __init__(self, corpus_size, hidden_size):\n",
|
|||
|
|
" super().__init__()\n",
|
|||
|
|
" self.input = nn.Linear(corpus_size, hidden_size)\n",
|
|||
|
|
" self.hidden = nn.Linear(hidden_size, hidden_size)\n",
|
|||
|
|
" self.output = nn.Linear(hidden_size, corpus_size)\n",
|
|||
|
|
"\n",
|
|||
|
|
" def forward(self, x):\n",
|
|||
|
|
" x = self.input(x)\n",
|
|||
|
|
" x = F.relu(x)\n",
|
|||
|
|
" x = self.hidden(x)\n",
|
|||
|
|
" x = F.relu(x)\n",
|
|||
|
|
"\n",
|
|||
|
|
" x = self.output(x)\n",
|
|||
|
|
" return x"
|
|||
|
|
],
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "vAmveHcBGtrf"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"def get_random_word_with_contexts(text, context_size):\n",
|
|||
|
|
" allowed_indexes = np.arange(context_size, len(text) - context_size)\n",
|
|||
|
|
" if not len(allowed_indexes):\n",
|
|||
|
|
" return None, None\n",
|
|||
|
|
" word_idx = np.random.choice(allowed_indexes)\n",
|
|||
|
|
" word = text[word_idx]\n",
|
|||
|
|
" context = text[(word_idx - context_size):word_idx] + text[(word_idx + 1):(word_idx + 1 + context_size)]\n",
|
|||
|
|
" return word, context"
|
|||
|
|
],
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "6N72wcXIIPFu",
|
|||
|
|
"outputId": "01634aee-baa5-48cc-ecd3-420859bb1e76"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"a = clean_text(\"Ala ma kota , kot pije mleko\")\n",
|
|||
|
|
"get_random_word_with_contexts(a, 2)"
|
|||
|
|
],
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"('kota', ['ala', 'ma', 'kot', 'pije'])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"execution_count": 14
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "5cgusynnIQa7",
|
|||
|
|
"outputId": "8928fb1a-244a-4afa-d98b-8b2b1e790b77"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"train_texts = load_train_data(\"drive/MyDrive/train.tsv\")"
|
|||
|
|
],
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Loaded 107471 texts from train_set.\n"
|
|||
|
|
],
|
|||
|
|
"name": "stdout"
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "qvOgmYr1KM10"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"corpus = WordCorpus(texts=train_texts, min_count=20, min_word_len=5)"
|
|||
|
|
],
|
|||
|
|
"execution_count": 17,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "u56IlKZuju9d",
|
|||
|
|
"outputId": "c3caf546-0cfe-4458-e710-a9d8b13f2b21"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"len(corpus.corpus)"
|
|||
|
|
],
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"111418"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"execution_count": 18
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "0GO307zuHYLm"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"def remove_words_outside_corpus_and_encode(text, corpus):\n",
|
|||
|
|
" return [corpus.get_word_idx(token) for token in text if token in corpus.corpus]"
|
|||
|
|
],
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "zFlfsgR3IQqX"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"train_texts = [remove_words_outside_corpus_and_encode(text, corpus) for text in train_texts]"
|
|||
|
|
],
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "NTOCBZssKwci"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"BATCH_SIZE = 96\n",
|
|||
|
|
"CONTEXT_SIZE = 15"
|
|||
|
|
],
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "AtuP4xv7LF26"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"import time\n",
|
|||
|
|
"\n",
|
|||
|
|
"def get_batch(texts):\n",
|
|||
|
|
" X, y = [], []\n",
|
|||
|
|
" size = len(texts)\n",
|
|||
|
|
" for _ in range(BATCH_SIZE):\n",
|
|||
|
|
" word_idx = None\n",
|
|||
|
|
" while word_idx is None:\n",
|
|||
|
|
" text_idx = np.random.randint(size)\n",
|
|||
|
|
" text = texts[text_idx]\n",
|
|||
|
|
" word_idx, context = get_random_word_with_contexts(text, CONTEXT_SIZE)\n",
|
|||
|
|
" bow = corpus.get_bow(context, encode=False)\n",
|
|||
|
|
" X.append(bow)\n",
|
|||
|
|
" y.append(word_idx)\n",
|
|||
|
|
" r = (np.array(X) / (CONTEXT_SIZE * 2)).astype(np.float32), np.array(y).astype(np.int64)\n",
|
|||
|
|
" return r"
|
|||
|
|
],
|
|||
|
|
"execution_count": 22,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "0LrDHSC-MF2g"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"model = LanguageNeuralModel(len(corpus.corpus), 250)"
|
|||
|
|
],
|
|||
|
|
"execution_count": 31,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "JhswE-B4MMBw"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"model = model.to(device)"
|
|||
|
|
],
|
|||
|
|
"execution_count": 32,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "6jsfrzQMOJHs",
|
|||
|
|
"outputId": "9b73e201-62b9-4904-e5d0-cd3f256c4bba"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"model.train()"
|
|||
|
|
],
|
|||
|
|
"execution_count": 33,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"LanguageNeuralModel(\n",
|
|||
|
|
" (input): Linear(in_features=111418, out_features=250, bias=True)\n",
|
|||
|
|
" (hidden): Linear(in_features=250, out_features=250, bias=True)\n",
|
|||
|
|
" (output): Linear(in_features=250, out_features=111418, bias=True)\n",
|
|||
|
|
")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"execution_count": 33
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "wgVAoHOjOZEP"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"criterion = nn.CrossEntropyLoss().to(device)\n",
|
|||
|
|
"optimizer = torch.optim.RMSprop(model.parameters(), lr=0.001)"
|
|||
|
|
],
|
|||
|
|
"execution_count": 34,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "32o0ZAtkOwzY"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"import tqdm"
|
|||
|
|
],
|
|||
|
|
"execution_count": 35,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 817,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"9c76638985c94701a2d86bb525efc606",
|
|||
|
|
"67d9046a45cb462d91eb2dd14259a21b",
|
|||
|
|
"75d3a999f54647d69bad407b015b3de1",
|
|||
|
|
"68c7c73101124e039210b5077ac36d5b",
|
|||
|
|
"8e7cbdb93fb2490ea24f616db532648b",
|
|||
|
|
"ef225bfda78b40bd84dc81dd2b64a215",
|
|||
|
|
"43a31b9b301c454db0a359ce13172883",
|
|||
|
|
"633e30a42efd458c9b6899552f5473d1"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"id": "0zYz4HDuO3mC",
|
|||
|
|
"outputId": "9b2f238c-8577-4e38-bcde-84251b3021c4"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"running_loss = 0.0\n",
|
|||
|
|
"\n",
|
|||
|
|
"for i in tqdm.tqdm_notebook(range(20000)):\n",
|
|||
|
|
" X, y = get_batch(train_texts)\n",
|
|||
|
|
" X, y = torch.from_numpy(X).to(device), torch.from_numpy(y).to(device)\n",
|
|||
|
|
"\n",
|
|||
|
|
" optimizer.zero_grad()\n",
|
|||
|
|
"\n",
|
|||
|
|
" outputs = model(X)\n",
|
|||
|
|
" loss = criterion(outputs, y)\n",
|
|||
|
|
"\n",
|
|||
|
|
" loss.backward()\n",
|
|||
|
|
" optimizer.step()\n",
|
|||
|
|
"\n",
|
|||
|
|
" running_loss += loss.item()\n",
|
|||
|
|
" if i % 500 == 499:\n",
|
|||
|
|
" torch.save(model.state_dict(), \"model.pth\")\n",
|
|||
|
|
" print('[%d, %5d] loss: %.3f' %\n",
|
|||
|
|
" (1, i + 1, running_loss / 500))\n",
|
|||
|
|
" running_loss = 0.0"
|
|||
|
|
],
|
|||
|
|
"execution_count": 36,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
|
|||
|
|
"Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
|
|||
|
|
" This is separate from the ipykernel package so we can avoid doing imports until\n"
|
|||
|
|
],
|
|||
|
|
"name": "stderr"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"model_id": "9c76638985c94701a2d86bb525efc606",
|
|||
|
|
"version_minor": 0,
|
|||
|
|
"version_major": 2
|
|||
|
|
},
|
|||
|
|
"text/plain": [
|
|||
|
|
"HBox(children=(FloatProgress(value=0.0, max=20000.0), HTML(value='')))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"[1, 500] loss: 10.873\n",
|
|||
|
|
"[1, 1000] loss: 10.559\n",
|
|||
|
|
"[1, 1500] loss: 10.505\n",
|
|||
|
|
"[1, 2000] loss: 10.437\n",
|
|||
|
|
"[1, 2500] loss: 10.371\n",
|
|||
|
|
"[1, 3000] loss: 10.371\n",
|
|||
|
|
"[1, 3500] loss: 10.336\n",
|
|||
|
|
"[1, 4000] loss: 10.338\n",
|
|||
|
|
"[1, 4500] loss: 10.325\n",
|
|||
|
|
"[1, 5000] loss: 10.325\n",
|
|||
|
|
"[1, 5500] loss: 10.335\n",
|
|||
|
|
"[1, 6000] loss: 10.366\n",
|
|||
|
|
"[1, 6500] loss: 10.366\n",
|
|||
|
|
"[1, 7000] loss: 10.377\n",
|
|||
|
|
"[1, 7500] loss: 10.392\n",
|
|||
|
|
"[1, 8000] loss: 10.422\n",
|
|||
|
|
"[1, 8500] loss: 10.477\n",
|
|||
|
|
"[1, 9000] loss: 10.525\n",
|
|||
|
|
"[1, 9500] loss: 10.562\n",
|
|||
|
|
"[1, 10000] loss: 10.593\n",
|
|||
|
|
"[1, 10500] loss: 10.657\n",
|
|||
|
|
"[1, 11000] loss: 10.711\n",
|
|||
|
|
"[1, 11500] loss: 10.706\n",
|
|||
|
|
"[1, 12000] loss: 10.781\n",
|
|||
|
|
"[1, 12500] loss: 10.799\n",
|
|||
|
|
"[1, 13000] loss: 10.875\n",
|
|||
|
|
"[1, 13500] loss: 10.882\n",
|
|||
|
|
"[1, 14000] loss: 10.921\n",
|
|||
|
|
"[1, 14500] loss: 10.946\n",
|
|||
|
|
"[1, 15000] loss: 10.979\n",
|
|||
|
|
"[1, 15500] loss: 11.001\n",
|
|||
|
|
"[1, 16000] loss: 11.032\n",
|
|||
|
|
"[1, 16500] loss: 11.069\n",
|
|||
|
|
"[1, 17000] loss: 11.090\n",
|
|||
|
|
"[1, 17500] loss: 11.112\n",
|
|||
|
|
"[1, 18000] loss: 11.119\n",
|
|||
|
|
"[1, 18500] loss: 11.132\n",
|
|||
|
|
"[1, 19000] loss: 11.212\n",
|
|||
|
|
"[1, 19500] loss: 11.188\n",
|
|||
|
|
"[1, 20000] loss: 11.213\n",
|
|||
|
|
"\n"
|
|||
|
|
],
|
|||
|
|
"name": "stdout"
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "Yoe-By2iQANV",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"outputId": "db1aea22-e3d3-4023-b07c-31a1df139c7e"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"model.eval()"
|
|||
|
|
],
|
|||
|
|
"execution_count": 37,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"LanguageNeuralModel(\n",
|
|||
|
|
" (input): Linear(in_features=111418, out_features=250, bias=True)\n",
|
|||
|
|
" (hidden): Linear(in_features=250, out_features=250, bias=True)\n",
|
|||
|
|
" (output): Linear(in_features=250, out_features=111418, bias=True)\n",
|
|||
|
|
")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"execution_count": 37
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "LX9xmKXwQdd7"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"sets_to_eval = [\"drive/MyDrive/dev0/\", \"drive/MyDrive/dev1/\", \"drive/MyDrive/test/\"]"
|
|||
|
|
],
|
|||
|
|
"execution_count": 50,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "OmPYkEsHQ_QL"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"def load_test_data(test_path, corpus):\n",
|
|||
|
|
" texts = []\n",
|
|||
|
|
" with open(test_path, \"r+\") as file:\n",
|
|||
|
|
" while True:\n",
|
|||
|
|
" line = file.readline()\n",
|
|||
|
|
" if not line:\n",
|
|||
|
|
" break\n",
|
|||
|
|
"\n",
|
|||
|
|
" _, _, left, right, *_ = line.split(\"\\t\")\n",
|
|||
|
|
" texts.append(\n",
|
|||
|
|
" (\n",
|
|||
|
|
" remove_words_outside_corpus_and_encode(clean_text(left), corpus),\n",
|
|||
|
|
" remove_words_outside_corpus_and_encode(clean_text(right), corpus)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" print(f\"Loaded {len(texts)} texts from train_set.\")\n",
|
|||
|
|
" return texts"
|
|||
|
|
],
|
|||
|
|
"execution_count": 39,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "6j2QUhPWSXyL",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 320,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"1438b4d8e39948be9d41796cea1bf35e",
|
|||
|
|
"fa644d0ae27b4f4985bb537115ffa5e4",
|
|||
|
|
"7e966f59f546486595f8a18986a18b33",
|
|||
|
|
"cd063db3bf0a48909dbc59ce83417556",
|
|||
|
|
"43a63a37e4504b08bcef619d49a0c283",
|
|||
|
|
"e102711dd88e409abbac9b3658469811",
|
|||
|
|
"97d72b38ec5c4381b1e125030132739c",
|
|||
|
|
"b603a738fa0249a2b45e5a8c725f7385",
|
|||
|
|
"90ea9fd32df14868b7286d00193e65f5",
|
|||
|
|
"4f154dd9265f413ab629ed6683080abd",
|
|||
|
|
"7f7f5bdd0e5f4201a13baf81681e51ec",
|
|||
|
|
"9c49b3c0da8f4b13a7686525a180d873",
|
|||
|
|
"dadf258368454db4a3a5cb31d24d6217",
|
|||
|
|
"735dc0020f43400a8189f14549c3d259",
|
|||
|
|
"361e57bbb4e34a57ad67dff4b0d50406",
|
|||
|
|
"6c39888179824232afe573d81aca2aca",
|
|||
|
|
"7fd3d8803eaf44dabe06fd7c8a1e3569",
|
|||
|
|
"4b6ba75be39846b891d6cb04b9110734",
|
|||
|
|
"72b4b1bb0ad14fc38a4e5a9d47ac4f27",
|
|||
|
|
"47cdc9134a934e5997e0274e0aa51ed5",
|
|||
|
|
"cda7970d110c43d5b9626a917445c272",
|
|||
|
|
"95942678b94f44eb954b6e93295f54c1",
|
|||
|
|
"23d2f634959c4211b44bda5de65860e0",
|
|||
|
|
"2b7d89e4271f4722ae8aab078f55e21c"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"outputId": "3a93de12-bb84-43e9-e26b-a6c92d789a77"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"words = list(corpus.corpus)\n",
|
|||
|
|
"\n",
|
|||
|
|
"with torch.no_grad():\n",
|
|||
|
|
" for path in sets_to_eval:\n",
|
|||
|
|
" data = load_test_data(path + \"in.tsv\", corpus)\n",
|
|||
|
|
" results = []\n",
|
|||
|
|
" batch = []\n",
|
|||
|
|
" for left, right in tqdm.tqdm_notebook(data):\n",
|
|||
|
|
" if len(batch) < BATCH_SIZE:\n",
|
|||
|
|
" context = left[-CONTEXT_SIZE:] + right[:CONTEXT_SIZE]\n",
|
|||
|
|
" context = corpus.get_bow(context, encode=False)\n",
|
|||
|
|
" batch.append(context)\n",
|
|||
|
|
" continue\n",
|
|||
|
|
" batch = (np.array(batch) / (2*CONTEXT_SIZE)).astype(np.float32)\n",
|
|||
|
|
" X = torch.from_numpy(batch).to(device)\n",
|
|||
|
|
" out = F.softmax(model(X)).tolist()[0]\n",
|
|||
|
|
"\n",
|
|||
|
|
" indexes = list(range(len(corpus.corpus)))\n",
|
|||
|
|
" indexes = sorted(indexes, key=lambda x: out[x], reverse=True)\n",
|
|||
|
|
"\n",
|
|||
|
|
" res = \"\"\n",
|
|||
|
|
" prob0 = 1.\n",
|
|||
|
|
" for idx in indexes[:10000]:\n",
|
|||
|
|
" prob0 -= out[idx]\n",
|
|||
|
|
" res += f\"{words[idx]}:{np.log(out[idx])} \"\n",
|
|||
|
|
" res += f\":{np.log(prob0)}\"\n",
|
|||
|
|
" results.append(res)\n",
|
|||
|
|
" batch = []\n",
|
|||
|
|
" with open(path + \"out.tsv\", \"w+\") as f:\n",
|
|||
|
|
" f.write(\"\\n\".join(results))"
|
|||
|
|
],
|
|||
|
|
"execution_count": 54,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Loaded 19986 texts from train_set.\n"
|
|||
|
|
],
|
|||
|
|
"name": "stdout"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
|
|||
|
|
"Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
|
|||
|
|
" \n"
|
|||
|
|
],
|
|||
|
|
"name": "stderr"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"model_id": "1438b4d8e39948be9d41796cea1bf35e",
|
|||
|
|
"version_minor": 0,
|
|||
|
|
"version_major": 2
|
|||
|
|
},
|
|||
|
|
"text/plain": [
|
|||
|
|
"HBox(children=(FloatProgress(value=0.0, max=19986.0), HTML(value='')))"
|
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]
|
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|
},
|
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|
"metadata": {
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|
"tags": []
|
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|
}
|
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|
|
},
|
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|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:16: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
|
|||
|
|
" app.launch_new_instance()\n"
|
|||
|
|
],
|
|||
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|
"name": "stderr"
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|
|
},
|
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{
|
|||
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|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\n",
|
|||
|
|
"Loaded 11628 texts from train_set.\n"
|
|||
|
|
],
|
|||
|
|
"name": "stdout"
|
|||
|
|
},
|
|||
|
|
{
|
|||
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|
"output_type": "display_data",
|
|||
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|
"data": {
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|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"model_id": "90ea9fd32df14868b7286d00193e65f5",
|
|||
|
|
"version_minor": 0,
|
|||
|
|
"version_major": 2
|
|||
|
|
},
|
|||
|
|
"text/plain": [
|
|||
|
|
"HBox(children=(FloatProgress(value=0.0, max=11628.0), HTML(value='')))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\n",
|
|||
|
|
"Loaded 14132 texts from train_set.\n"
|
|||
|
|
],
|
|||
|
|
"name": "stdout"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
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|
|
"model_id": "7fd3d8803eaf44dabe06fd7c8a1e3569",
|
|||
|
|
"version_minor": 0,
|
|||
|
|
"version_major": 2
|
|||
|
|
},
|
|||
|
|
"text/plain": [
|
|||
|
|
"HBox(children=(FloatProgress(value=0.0, max=14132.0), HTML(value='')))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\n"
|
|||
|
|
],
|
|||
|
|
"name": "stdout"
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "ZKLc8SZLt171"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
""
|
|||
|
|
],
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": []
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
}
|