From 1f224ccd289b95e901b1cfaa6af441d72c2aeabb Mon Sep 17 00:00:00 2001 From: kpierzynski Date: Wed, 22 May 2024 02:48:52 +0200 Subject: [PATCH] fix missing correct files --- README.md | 2 +- dev-0/out.tsv | 21038 ++++++++++++++++++++++++------------------------ run.py | 77 +- trigram.ipynb | 187 +- 4 files changed, 10624 insertions(+), 10680 deletions(-) diff --git a/README.md b/README.md index 5d613d1..42dc741 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,3 @@ ## challenging-america-word-gap-prediction ### using simple trigram nn -calculated perplexity: 583.35 +calculated perplexity: 653.89 diff --git a/dev-0/out.tsv b/dev-0/out.tsv index d15a5c1..07d6c17 100644 --- a/dev-0/out.tsv +++ b/dev-0/out.tsv @@ -1,10519 +1,10519 @@ -the:0.06699013710021973 of:0.04036906361579895 and:0.030995916575193405 to:0.025135572999715805 a:0.019917039200663567 in:0.019255293533205986 that:0.009988833218812943 is:0.009045579470694065 it:0.007966709323227406 :0.7703358549624681 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031dfe9..376d2db 100644 --- a/run.py +++ b/run.py @@ -17,12 +17,12 @@ import torch from tqdm.notebook import tqdm -embed_size = 100 -vocab_size = 25_000 -num_epochs = 1 +embed_size = 30 +vocab_size = 10_000 +num_epochs = 2 device = 'cuda' -batch_size = 2048 -train_file_path = 'train/train.txt' +batch_size = 8192 +train_file_path = 'train/nano.txt' with open(train_file_path, 'r', encoding='utf-8') as file: total = len(file.readlines()) @@ -43,7 +43,7 @@ def get_words_from_line(line): def get_word_lines_from_file(file_name): limit = total * 2 with open(file_name, 'r', encoding='utf8') as fh: - for line in tqdm(fh, total=total): + for line in fh: limit -= 1 if not limit: break @@ -82,7 +82,6 @@ train_dataset = Trigrams(train_file_path, vocab_size) # In[3]: -# Neural network model for trigram language modeling class SimpleTrigramNeuralLanguageModel(nn.Module): def __init__(self, vocabulary_size, embedding_size): super(SimpleTrigramNeuralLanguageModel, self).__init__() @@ -93,48 +92,50 @@ class SimpleTrigramNeuralLanguageModel(nn.Module): self.embedding_size = embedding_size def forward(self, x): - embeds = self.embedding(x).view(-1, self.embedding_size * 2) - out = torch.relu(self.linear1(embeds)) + embeds = self.embedding(x).view(x.size(0), -1) + out = self.linear1(embeds) out = self.linear2(out) return self.softmax(out) -# Instantiate the model -model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size) +model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size).to(device) # In[4]: -model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size).to(device) data = DataLoader(train_dataset, batch_size=batch_size) optimizer = torch.optim.Adam(model.parameters()) -criterion = torch.nn.NLLLoss() +criterion = torch.nn.CrossEntropyLoss() + + +# In[5]: + model.train() step = 0 for _ in range(num_epochs): - for x1,x2,y in tqdm(data, total=total): - x = torch.cat((x1,x2), dim=0).to(device) - y = y.to(device) - optimizer.zero_grad() - ypredicted = model(x) - loss = criterion(torch.log(ypredicted), y) - if step % 5000 == 0: - print(step, loss) - step += 1 - loss.backward() - optimizer.step() - + for x1,x2,y in tqdm(data, desc="Train loop"): + y = y.to(device) + x = torch.cat((x1.unsqueeze(1),x2.unsqueeze(1)), dim=1).to(device) + optimizer.zero_grad() + ypredicted = model(x) + + loss = criterion(torch.log(ypredicted), y) + if step % 5000 == 0: + print(step, loss) + step += 1 + loss.backward() + optimizer.step() + step = 0 model.eval() -# In[10]: +# In[6]: def get_gap_candidates(words, n=10, vocab=train_dataset.vocab): - ixs = vocab.forward(words) - ixs = torch.tensor(ixs) - ixs = torch.cat(tuple([ixs]), dim=0).to(device) + ixs = vocab(words) + ixs = torch.tensor(ixs).unsqueeze(0).to(device) out = model(ixs) top = torch.topk(out[0], n) @@ -144,7 +145,7 @@ def get_gap_candidates(words, n=10, vocab=train_dataset.vocab): return list(zip(top_words, top_probs)) -# In[11]: +# In[7]: def clean(text): @@ -172,13 +173,7 @@ def predictor(prefix): return output -# In[12]: - - -predictor("I really bug") - - -# In[13]: +# In[8]: def generate_result(input_path, output_path='out.tsv'): @@ -191,14 +186,8 @@ def generate_result(input_path, output_path='out.tsv'): output_file.write(result + '\n') -# In[14]: +# In[9]: generate_result('dev-0/in.tsv', output_path='dev-0/out.tsv') - -# In[ ]: - - - - diff --git a/trigram.ipynb b/trigram.ipynb index b233c65..b3b0e76 100644 --- a/trigram.ipynb +++ b/trigram.ipynb @@ -10,11 +10,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "S:\\WENV\\Lib\\site-packages\\torchtext\\vocab\\__init__.py:4: UserWarning: \n", + "S:\\WENV_TORCHTEXT\\Lib\\site-packages\\torchtext\\vocab\\__init__.py:4: UserWarning: \n", "/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n", "Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n", " warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n", - "S:\\WENV\\Lib\\site-packages\\torchtext\\utils.py:4: UserWarning: \n", + "S:\\WENV_TORCHTEXT\\Lib\\site-packages\\torchtext\\utils.py:4: UserWarning: \n", "/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n", "Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n", " warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n" @@ -35,12 +35,12 @@ "\n", "from tqdm.notebook import tqdm\n", "\n", - "embed_size = 100\n", - "vocab_size = 25_000\n", - "num_epochs = 1\n", + "embed_size = 30\n", + "vocab_size = 10_000\n", + "num_epochs = 2\n", "device = 'cuda'\n", - "batch_size = 2048\n", - "train_file_path = 'train/train.txt'\n", + "batch_size = 8192\n", + "train_file_path = 'train/nano.txt'\n", "\n", "with open(train_file_path, 'r', encoding='utf-8') as file:\n", " total = len(file.readlines())" @@ -51,22 +51,7 @@ "execution_count": 2, "id": "40392665-79bc-4032-a5de-9d189545c9f7", "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5049d1e295954b7baf71ac05a793071a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/432022 [00:00)\n", - "5000 tensor(6.5147, device='cuda:0', grad_fn=)\n", - "10000 tensor(6.6747, device='cuda:0', grad_fn=)\n", - "15000 tensor(6.9061, device='cuda:0', grad_fn=)\n", - "20000 tensor(6.8899, device='cuda:0', grad_fn=)\n", - "25000 tensor(6.8373, device='cuda:0', grad_fn=)\n", - "30000 tensor(6.8942, device='cuda:0', grad_fn=)\n", - "35000 tensor(6.9564, device='cuda:0', grad_fn=)\n", - "40000 tensor(6.9709, device='cuda:0', grad_fn=)\n", - "45000 tensor(6.9592, device='cuda:0', grad_fn=)\n", - "50000 tensor(6.8195, device='cuda:0', grad_fn=)\n", - "55000 tensor(6.7074, device='cuda:0', grad_fn=)\n", - "60000 tensor(6.8755, device='cuda:0', grad_fn=)\n", - "65000 tensor(6.9605, device='cuda:0', grad_fn=)\n" + "0 tensor(9.2450, device='cuda:0', grad_fn=)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "044c2ea05e344306881002e34d89bd54", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Train loop: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 tensor(6.2669, device='cuda:0', grad_fn=)\n" ] }, { "data": { "text/plain": [ "SimpleTrigramNeuralLanguageModel(\n", - " (embedding): Embedding(25000, 100)\n", - " (linear1): Linear(in_features=200, out_features=100, bias=True)\n", - " (linear2): Linear(in_features=100, out_features=25000, bias=True)\n", + " (embedding): Embedding(10000, 30)\n", + " (linear1): Linear(in_features=60, out_features=30, bias=True)\n", + " (linear2): Linear(in_features=30, out_features=10000, bias=True)\n", " (softmax): Softmax(dim=1)\n", ")" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n", - "data = DataLoader(train_dataset, batch_size=batch_size)\n", - "optimizer = torch.optim.Adam(model.parameters())\n", - "criterion = torch.nn.NLLLoss()\n", - "\n", "model.train()\n", "step = 0\n", "for _ in range(num_epochs):\n", - " for x1,x2,y in tqdm(data, total=total):\n", - " x = torch.cat((x1,x2), dim=0).to(device)\n", - " y = y.to(device)\n", - " optimizer.zero_grad()\n", - " ypredicted = model(x)\n", - " loss = criterion(torch.log(ypredicted), y)\n", - " if step % 5000 == 0:\n", - " print(step, loss)\n", - " step += 1\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", + " for x1,x2,y in tqdm(data, desc=\"Train loop\"):\n", + " y = y.to(device)\n", + " x = torch.cat((x1.unsqueeze(1),x2.unsqueeze(1)), dim=1).to(device)\n", + " optimizer.zero_grad()\n", + " ypredicted = model(x)\n", + " \n", + " loss = criterion(torch.log(ypredicted), y)\n", + " if step % 5000 == 0:\n", + " print(step, loss)\n", + " step += 1\n", + " loss.backward()\n", + " optimizer.step()\n", + " step = 0\n", "model.eval()" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "id": "da4d116c-beec-436d-84d8-577282507226", "metadata": {}, "outputs": [], "source": [ "def get_gap_candidates(words, n=10, vocab=train_dataset.vocab):\n", - " ixs = vocab.forward(words)\n", - " ixs = torch.tensor(ixs)\n", - " ixs = torch.cat(tuple([ixs]), dim=0).to(device)\n", + " ixs = vocab(words)\n", + " ixs = torch.tensor(ixs).unsqueeze(0).to(device)\n", "\n", " out = model(ixs)\n", " top = torch.topk(out[0], n)\n", @@ -259,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "id": "0cafd70a-29b3-4a49-b40f-b8ce3143084a", "metadata": {}, "outputs": [], @@ -291,28 +275,7 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "2084fb5f-6405-4e44-a06f-953db852e526", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'the:0.07267232984304428 of:0.043321046978235245 and:0.032147664576768875 to:0.02692588046193123 a:0.020654045045375824 in:0.020213929936289787 that:0.010836434550583363 is:0.00959325022995472 it:0.008407277055084705 :0.755228141322732'" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predictor(\"I really bug\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "id": "965ebaf3-4c0b-4462-8ac5-4746ec9489ab", "metadata": {}, "outputs": [], @@ -329,21 +292,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "id": "80547ba7-9d01-4d2b-9e83-269919513de9", "metadata": {}, "outputs": [], "source": [ "generate_result('dev-0/in.tsv', output_path='dev-0/out.tsv')" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1d7d72b0-d629-487e-bcec-4756fa88ae49", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": {