02-done

02.ipynb

@@ -551,6 +551,46 @@
     "## Zadanie 6\n",
     "Spróbuj porównać czy jedno zdanie następuje po drugim."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForNextSentencePrediction: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.bias']\n",
+      "- This IS expected if you are initializing BertForNextSentencePrediction from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing BertForNextSentencePrediction from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Kolejne zdanie jest losowe: False\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import BertTokenizer, BertForNextSentencePrediction\n",
+    "import torch\n",
+    "\n",
+    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\n",
+    "model = BertForNextSentencePrediction.from_pretrained(\"bert-base-uncased\")\n",
+    "\n",
+    "prompt = \"In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.\"\n",
+    "next_sentence = \"In other cases pizza may be sliced.\"\n",
+    "encoding = tokenizer(prompt, next_sentence, return_tensors=\"pt\")\n",
+    "\n",
+    "outputs = model(**encoding, labels=torch.LongTensor([1]))\n",
+    "logits = outputs.logits\n",
+    "\n",
+    "sentenceWasRandom = logits[0, 0] < logits[0, 1]\n",
+    "print(\"Kolejne zdanie jest losowe: \" + str(sentenceWasRandom.item()))"
+   ]
   }
  ],
  "metadata": {