HuggingFace_Internship_Test.ipynb

{
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      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Vonewman/Hugginface-course/blob/main/HuggingFace_Internship_Test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/"
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        "id": "fzh1knlTnt5i",
        "outputId": "05ae0fe2-d62c-44f4-ea89-e9bce62d2954"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "\u001b[?25hInstalling collected packages: urllib3, xxhash, tokenizers, responses, multiprocess, huggingface-hub, transformers, sentencepiece, datasets, evaluate\n",
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          ]
        }
      ],
      "source": [
        "!pip install datasets evaluate transformers[sentencepiece]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AtcV-xqfsoIl"
      },
      "source": [
        "# Internship HuggingFace (Speech)\n",
        "\n",
        "We're going to use the wikitext (link) dataset with the distilbert-base-cased (link) model checkpoint.\n",
        "\n",
        "Start by loading the wikitext-2-raw-v1 version of that dataset, and take the 11th example (index 10) of the train split.\n",
        "We'll tokenize this using the appropriate tokenizer, and we'll mask the sixth token (index 5) the sequence.\n",
        "\n",
        "When using the distilbert-base-cased checkpoint to unmask that (sixth token, index 5) token, what is the most probable predicted token (please provide the decoded token, and not the ID)?\n",
        "\n",
        "Tips:\n",
        "- You might find the transformers docs (link) useful.\n",
        "- You might find the datasets docs (link) useful.\n",
        "- You might also be interested in the Hugging Face course (link)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wjQVK2YynnL5",
        "outputId": "594a1843-d478-4acb-e1e2-355f768ed956"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:datasets.builder:Found cached dataset wikitext (/root/.cache/huggingface/datasets/wikitext/wikitext-2-raw-v1/1.0.0/a241db52902eaf2c6aa732210bead40c090019a499ceb13bcbfa3f8ab646a126)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text'],\n",
              "    num_rows: 36718\n",
              "})"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "raw_datasets = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\", split=\"train\")\n",
        "raw_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MxCo5mIHsZ6T",
        "outputId": "04a49dfb-fd6d-4a34-bfcd-585dd594d757"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'text': ' The game \\'s battle system , the BliTZ system , is carried over directly from Valkyira Chronicles . During missions , players select each unit using a top @-@ down perspective of the battlefield map : once a character is selected , the player moves the character around the battlefield in third @-@ person . A character can only act once per @-@ turn , but characters can be granted multiple turns at the expense of other characters \\' turns . Each character has a field and distance of movement limited by their Action Gauge . Up to nine characters can be assigned to a single mission . During gameplay , characters will call out if something happens to them , such as their health points ( HP ) getting low or being knocked out by enemy attacks . Each character has specific \" Potentials \" , skills unique to each character . They are divided into \" Personal Potential \" , which are innate skills that remain unaltered unless otherwise dictated by the story and can either help or impede a character , and \" Battle Potentials \" , which are grown throughout the game and always grant boons to a character . To learn Battle Potentials , each character has a unique \" Masters Table \" , a grid @-@ based skill table that can be used to acquire and link different skills . Characters also have Special Abilities that grant them temporary boosts on the battlefield : Kurt can activate \" Direct Command \" and move around the battlefield without depleting his Action Point gauge , the character Reila can shift into her \" Valkyria Form \" and become invincible , while Imca can target multiple enemy units with her heavy weapon . \\n'}"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "example = raw_datasets[10]\n",
        "example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qgQ5QugOt0Mc",
        "outputId": "72e5a583-9f88-4fb5-c544-173239fff134"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'text': Value(dtype='string', id=None)}"
            ]
          },
          "execution_count": 29,
          "metadata": {},
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      ],
      "source": [
        "raw_datasets.features"
      ]
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          "metadata": {},
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      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "bert_fill_mask = pipeline(\"fill-mask\", model=\"distilbert-base-cased\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rjlMS5jrqMz9"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForMaskedLM\n",
        "\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-cased\")\n",
        "model = AutoModelForMaskedLM.from_pretrained(\"distilbert-base-cased\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nPROZX4uqgiK",
        "outputId": "5351e39f-74f6-4ed5-8e04-acfe25034d35"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "PreTrainedTokenizerFast(name_or_path='distilbert-base-cased', vocab_size=28996, model_max_len=512, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'})"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JfOXyuVVs_X6",
        "outputId": "493b96e0-09ef-42d3-e266-5c5b17ca2e7d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DistilBertForMaskedLM(\n",
              "  (activation): GELUActivation()\n",
              "  (distilbert): DistilBertModel(\n",
              "    (embeddings): Embeddings(\n",
              "      (word_embeddings): Embedding(28996, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (transformer): Transformer(\n",
              "      (layer): ModuleList(\n",
              "        (0): TransformerBlock(\n",
              "          (attention): MultiHeadSelfAttention(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (ffn): FFN(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (activation): GELUActivation()\n",
              "          )\n",
              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "        (1): TransformerBlock(\n",
              "          (attention): MultiHeadSelfAttention(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (ffn): FFN(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (activation): GELUActivation()\n",
              "          )\n",
              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "        (2): TransformerBlock(\n",
              "          (attention): MultiHeadSelfAttention(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (ffn): FFN(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (activation): GELUActivation()\n",
              "          )\n",
              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "        (3): TransformerBlock(\n",
              "          (attention): MultiHeadSelfAttention(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (ffn): FFN(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (activation): GELUActivation()\n",
              "          )\n",
              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "        (4): TransformerBlock(\n",
              "          (attention): MultiHeadSelfAttention(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (ffn): FFN(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (activation): GELUActivation()\n",
              "          )\n",
              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "        (5): TransformerBlock(\n",
              "          (attention): MultiHeadSelfAttention(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (ffn): FFN(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (activation): GELUActivation()\n",
              "          )\n",
              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (vocab_transform): Linear(in_features=768, out_features=768, bias=True)\n",
              "  (vocab_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "  (vocab_projector): Linear(in_features=768, out_features=28996, bias=True)\n",
              "  (mlm_loss_fct): CrossEntropyLoss()\n",
              ")"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "tF2SZGoYqhTe"
      },
      "outputs": [],
      "source": [
        "tokens = tokenizer.encode(example[\"text\"], return_tensors='pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "v2AgHaPPs-ev"
      },
      "outputs": [],
      "source": [
        "# Mask the sixth token (index 5) in the sequence\n",
        "tokens[:, 5] = tokenizer.mask_token_id"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "# Generate predictions for the unmasked token\n",
        "predictions = model(tokens)[0]\n",
        "\n",
        "# Determine the predicted token with the highest probability\n",
        "predicted_token_id = torch.argmax(predictions[:, 5]).item()\n",
        "\n",
        "# Decode the predicted token\n",
        "predicted_token = tokenizer.decode(predicted_token_id)\n",
        "\n",
        "print(f'The most probable predicted token is: {predicted_token}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "omD0e0KVvNtt",
        "outputId": "e02e7d1d-f51a-46c5-c69e-79aacb4960d5"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The most probable predicted token is: combat\n"
          ]
        }
      ]
    },
    {
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      "source": [],
      "metadata": {
        "id": "STYxY_VKvYnl"
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      "execution_count": null,
      "outputs": []
    }
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