NVIDIA_03_encoder_task.ipynb

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        "<a href=\"https://colab.research.google.com/github/matiaszabal/argand/blob/master/NVIDIA_03_encoder_task.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4cpUy8MY9oki"
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      "source": [
        "<center><a href=\"https://www.nvidia.com/en-us/training/\"><img src=\"https://dli-lms.s3.amazonaws.com/assets/general/DLI_Header_White.png\" width=\"400\" height=\"186\" /></a></center>"
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        "id": "g2Dhs90vKb9d"
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      "source": [
        "# <font color=\"#76b900\">**3:** LLM Encoder Tasks</font>\n",
        "\n",
        "In the previous notebook, you went deeper into the HuggingFace &#x1F917; pipeline to consider how the LLMs are able to reason about natural language. In this notebook, we'll be pushing the BERT encoder to other potential tasks aside from the unmasking task we saw earlier.\n",
        "\n",
        "#### **Learning Objectives:**\n",
        "\n",
        "- Learn how to use task-specific pipelines that cover token-level, passage-level, and range-subsetting tasks.\n",
        "- Use the same abstractions to progress into zero-shot classification for classifying to arbitrary classes without re-training.\n",
        "\n",
        "-----"
      ]
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        "## 3.1. The Token Prediction Task Head\n",
        "\n",
        "Previously, we stumbled upon the unmasking pipeline and did a bit of exploration to see how it operated. Let's revisit it for a bit and see what's going on in the model:"
      ]
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          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n",
            "- This IS expected if you are initializing BertForMaskedLM 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 BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'score': 0.23866014182567596,\n",
              "  'token': 2183,\n",
              "  'token_str': 'going',\n",
              "  'sequence': 'hello, mr. bert! how is it going?'},\n",
              " {'score': 0.07178756594657898,\n",
              "  'token': 2017,\n",
              "  'token_str': 'you',\n",
              "  'sequence': 'hello, mr. bert! how is it you?'},\n",
              " {'score': 0.05827958881855011,\n",
              "  'token': 6230,\n",
              "  'token_str': 'happening',\n",
              "  'sequence': 'hello, mr. bert! how is it happening?'},\n",
              " {'score': 0.056334324181079865,\n",
              "  'token': 2651,\n",
              "  'token_str': 'today',\n",
              "  'sequence': 'hello, mr. bert! how is it today?'},\n",
              " {'score': 0.052870072424411774,\n",
              "  'token': 2085,\n",
              "  'token_str': 'now',\n",
              "  'sequence': 'hello, mr. bert! how is it now?'}]"
            ]
          },
          "metadata": {},
          "execution_count": 3
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      ],
      "source": [
        "# from transformers import pipeline\n",
        "# unmasker = pipeline('fill-mask', model='bert-base-uncased')\n",
        "\n",
        "from transformers import BertTokenizer, BertModel, FillMaskPipeline, AutoModelForMaskedLM\n",
        "\n",
        "unmasker = FillMaskPipeline(\n",
        "    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased'),\n",
        "    model = AutoModelForMaskedLM.from_pretrained(\"bert-base-uncased\")\n",
        ")\n",
        "unmasker(\"Hello, Mr. Bert! How is it [MASK]?\")"
      ]
    },
    {
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        "id": "8L6p4TKN_f_r"
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        "We previously discussed the strategies employed by the LLMs to perform their reasoning, namely by:\n",
        "- Learning an encoding for the tokens and sequence entries that captures semantic and positional information.\n",
        "- Providing a limited interface (i.e. attention) to allow some reasoning across the token boundary, while also loosely enforcing the token boundaries (i.e. with residual connections).\n",
        "\n",
        "This is the default utility of the **Transformer Encoder**; to take in a sequence, and spit out a latent sequence:\n",
        "\n",
        "- The sequence of tokens goes in.\n",
        "- The sequence propagates through the network.\n",
        "- A sequence of vectors comes out.\n",
        "\n",
        "With this in mind, the mechanism behind the unmasker should be rather intuitive: if the output of the transformer layers is semantically-rich with both its meaning and context, we can just pass each embedded entry through a dense net to perform **token-level prediction**!\n",
        "\n",
        "**When this prediction granularity is used to sort the tokens into classes, that's called [Token Classification](https://huggingface.co/tasks/token-classification)!** The variant we're observing in this notebook, [**mask filling**](https://huggingface.co/tasks/fill-mask), occurs when the output space is the space of tokens.\n",
        "\n",
        "<div><img src=\"imgs/task-token-classification.png\"\n",
        "     alt=\"Task as seen on https://huggingface.co/tasks/token-classification\"\n",
        "     width=\"800\"/></div>\n",
        "\n",
        "With that in mind, let's look at the `(cls)`, or the **classification head** component of our unmasking model:\n",
        "\n",
        "```python\n",
        "unmasker.model.cls\n",
        "```\n",
        "\n",
        "```python\n",
        "BertOnlyMLMHead(\n",
        "  (predictions): BertLMPredictionHead(\n",
        "    (transform): BertPredictionHeadTransform(\n",
        "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
        "      (transform_act_fn): GELUActivation()\n",
        "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
        "    )\n",
        "    (decoder): Linear(in_features=768, out_features=30522, bias=True)\n",
        "  )\n",
        ")\n",
        "```\n",
        "\n",
        "As always, feel free to look at [the source code](https://github.com/huggingface/transformers/blob/7a6efe1e9f756f585f2ffe5ada22cf6b15edd23b/src/transformers/models/bert/modeling_bert.py#L686) to see exactly how this is implemented, but the logic here is pretty simple:\n",
        "\n",
        "- Given BERT's 768-D output vectors, run each one through a dense layer with [GELU Activation](https://pytorch.org/docs/stable/generated/torch.nn.GELU.html) and [Layer Normalization](https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html).\n",
        "    - **GELU [Gaussian Error Linear Unit]:** Like ReLU, but a bit smoother.\n",
        "    - **LayerNorm:** Already discussed; Helps normalize output for smoother optimization.  \n",
        "- Feed that through one more dense layer at the end to go from the latent logits to a probability over the set of possible tokens.\n",
        "\n",
        "So in other words, give the classifier a bit of non-linear reasoning capabilities, and then finally predict the tokens for the final output. Not too bad, right? In theory, this should actually be sufficient to predict a new output token for each input token! To verify that the shapes are as expected, we can flesh out the pipeline a little more by dividing the forward pass and printing out the intermediate shapes:\n"
      ]
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n",
            "- This IS expected if you are initializing BertForMaskedLM 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 BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape from embedding into encoder: torch.Size([1, 10, 768])\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "Expected attn_mask dtype to be bool or float or to match query dtype, but got attn_mask.dtype: long int and  query.dtype: float instead.",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-4b5f1507687e>\u001b[0m in \u001b[0;36m<cell line: 34>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0munmasker\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMyFillMaskModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m \u001b[0munmasker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"I really wish [MASK] were an instrument!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-4-4b5f1507687e>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, string)\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'attention_mask'\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0minput_tensors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'attention_mask'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munmasker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbert\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Shape from encoder into cls:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'last_hidden_state'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    692\u001b[0m                 )\n\u001b[1;32m    693\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 694\u001b[0;31m                 layer_outputs = layer_module(\n\u001b[0m\u001b[1;32m    695\u001b[0m                     \u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    696\u001b[0m                     \u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1735\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1736\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1738\u001b[0m     \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1745\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1746\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1749\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[1;32m    582\u001b[0m         \u001b[0;31m# decoder uni-directional self-attention cached key/values tuple is at positions 1,2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m         \u001b[0mself_attn_past_key_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpast_key_value\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpast_key_value\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 584\u001b[0;31m         self_attention_outputs = self.attention(\n\u001b[0m\u001b[1;32m    585\u001b[0m             \u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    586\u001b[0m             \u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1735\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1736\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1738\u001b[0m     \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1745\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1746\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1749\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[1;32m    512\u001b[0m         \u001b[0moutput_attentions\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m     ) -> Tuple[torch.Tensor]:\n\u001b[0;32m--> 514\u001b[0;31m         self_outputs = self.self(\n\u001b[0m\u001b[1;32m    515\u001b[0m             \u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\u001b[0m             \u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1735\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1736\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1738\u001b[0m     \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1745\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1746\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1749\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[1;32m    437\u001b[0m         )\n\u001b[1;32m    438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m         attn_output = torch.nn.functional.scaled_dot_product_attention(\n\u001b[0m\u001b[1;32m    440\u001b[0m             \u001b[0mquery_layer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m             \u001b[0mkey_layer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Expected attn_mask dtype to be bool or float or to match query dtype, but got attn_mask.dtype: long int and  query.dtype: float instead."
          ]
        }
      ],
      "source": [
        "from transformers import BertTokenizer, BertModel, FillMaskPipeline, AutoModelForMaskedLM\n",
        "\n",
        "class MyFillMaskModel(FillMaskPipeline):\n",
        "    def __init__(self):\n",
        "        super().__init__(\n",
        "            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased'),\n",
        "            model = AutoModelForMaskedLM.from_pretrained(\"bert-base-uncased\")\n",
        "        )\n",
        "\n",
        "    def __call__(self, string):\n",
        "        # input_tensors = self.preprocess(string)\n",
        "        # output_tensors = self.forward(input_tensors)\n",
        "        # output = self.postprocess({**input_tensors, **output_tensors})\n",
        "\n",
        "        input_tensors = unmasker.preprocess(\"I really wish [MASK] were an instrument!\")\n",
        "\n",
        "        inputs = {'input_ids' : input_tensors['input_ids']}\n",
        "        x = unmasker.model.bert.embeddings.forward(**inputs)\n",
        "        print(\"Shape from embedding into encoder:\", x.shape)\n",
        "\n",
        "        inputs = {'attention_mask' : input_tensors['attention_mask']}\n",
        "        x = unmasker.model.bert.encoder.forward(x, **inputs)\n",
        "        print(\"Shape from encoder into cls:\", x['last_hidden_state'].shape)\n",
        "\n",
        "        x = unmasker.model.cls.forward(x['last_hidden_state'])\n",
        "        print(\"Shape from cls into postprocess:\", x.shape)\n",
        "\n",
        "        output = self.postprocess({**input_tensors, 'logits' : x})\n",
        "\n",
        "        return output\n",
        "\n",
        "\n",
        "unmasker = MyFillMaskModel()\n",
        "unmasker(\"I really wish [MASK] were an instrument!\")[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TDROSHMqlFb2"
      },
      "source": [
        "We can see here that the `postprocess` phase performs some extra function to make the pipeline work, and you are free to check out exactly how that happens in the [source code](https://github.com/huggingface/transformers/blob/95b374952dc27d8511541d6f5a4e22c9ec11fb24/src/transformers/pipelines/fill_mask.py#L105). Not surprisingly, postprocess just finds the first `[MASK]` instance in the string and sees which entries in the predicted probability vector have the highest values (known as **argmax-ing**, where the argument here is the index)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qpwsBjgUmDh0"
      },
      "source": [
        "## 3.2. Token-Level Prediction For Range Outputs\n",
        "\n",
        "The above task was an example of a natural token-in token-out task and conveniently maps to most BERT-like models' primary training objective:\n",
        "\n",
        "- [**Masked Language Modeling (MLM)**](https://huggingface.co/docs/transformers/main/tasks/masked_language_modeling)\n",
        "    - **Training Goal:** Recover the original tokens.\n",
        "    - **Augmentation:** Among the training data, replace some of the tokens with [MASK] and swap some more with random tokens.\n",
        "    - **Objective:** Gain bidirectional reasoning skills for per-token tasks.\n",
        "\n",
        "However, the BERT model is definitely not limited to this task (though your classifier may be)! The point of having pretrained LLM models is to use them as a language-understanding backbone, so we should be able to add different heads to the base model for other types of tasks.\n",
        "\n",
        "One simple token-level task that is only a little different in specification is the range prediction task! In this one, your classifier is trained to predict the start and end tokens of the model input to generate a subset as the model response. This feature of \"your-answer-is-a-substring-of-your-input\" is actually quite desirable to limit the reasoning space of smaller models, and for that it has become a popular choice for [Question Answering](https://huggingface.co/tasks/question-answering).\n",
        "\n",
        "<div><img src=\"imgs/task-qa.png\"\n",
        "     alt=\"Task as seen on https://huggingface.co/tasks/question-answering\"\n",
        "     width=\"800\"/></div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333,
          "referenced_widgets": [
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'score': 0.21171429753303528,\n",
              " 'start': 59,\n",
              " 'end': 84,\n",
              " 'answer': 'gives freedom to the user'}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline\n",
        "\n",
        "model_name = \"deepset/roberta-base-squad2\"\n",
        "\n",
        "## Example from https://huggingface.co/deepset/roberta-base-squad2\n",
        "nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)\n",
        "QA_input = {\n",
        "    'question': 'Why is model conversion important?',\n",
        "    'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'\n",
        "}\n",
        "nlp(QA_input)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GWz462iPmRBe"
      },
      "source": [
        "RoBERTa is another encoder-only model similar to BERT, so its expected inputs and outputs are the same. The main difference as far as we need to see is the fact that the classification head (fine-tuned on the [SQuAD2.0 question-answering dataset](https://huggingface.co/datasets/squad_v2)) only predicts two logits per token; a start probability and an end probability.\n",
        "\n",
        "You can verify this functionality by looking at the new classification head, which you may notice is a lot simpler than the unmasker:\n",
        "\n",
        "```python\n",
        "nlp.model.qa_outputs\n",
        "```\n",
        "> ```python\n",
        "(qa_outputs): Linear(in_features=768, out_features=2, bias=True)\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M8LvjMqI9okx"
      },
      "source": [
        "After this light $768 \\to 2$ transfer, the postprocessing just needs to figure out a desirable range to start and end the sequence (i.e. by maximizing the sum of the two predicted logits) and we have a substring prediction model!\n",
        "\n",
        "The same rough formulation can be applied for text summarization or whatever substring task you want, and the benefits/limitations are clear; the prediction is explicitly limited to be a subset of the inputs! This can be especially important when your application needs stability and risk aversion (i.e. for a public-facing application), but can also be viewed as a drawback since the restriction prevents conversational outputs."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-jxanUFvnDsa"
      },
      "source": [
        "## 3.3. The Sequence-Level Classification Head\n",
        "\n",
        "Now recall that in addition to the Masked Language Model task in BERT, the model was also trained on the NSP task:\n",
        "\n",
        "- **Next-Sentence Prediction (NSP)**\n",
        "    - **Training Goal:** Predict in if sentence A follows sentence B.\n",
        "    - **Augmentation:** Lump together sentence pairs, with 50% chance of A and B appearing in that order in the dataset.\n",
        "    - **Objective:** Enforce long-span reasoning and endow the first token with  general classification skills.\n",
        "\n",
        "The **MLM** objective is central to many BERT-like LLMs, but the **NSP** task has admittedly been contested since its incorporation in BERT. It does make sense that consolidating longer-spanning logic into specific parts of the model output would improve the model's language reasoning ability, but follow-up architectures have played around with dropping the objective (see [**RoBERTa**](https://huggingface.co/docs/transformers/model_doc/roberta)) or swapping it out for other flavors (see [**Albert**](https://huggingface.co/albert-base-v2), which uses sentence-order prediction, or SOP, instead). Regardless of which generalization techniques are used to train an encoder transformer model, the workflow for the classification head is usually quite consistent:\n",
        "\n",
        "> **Take just the a specific (i.e. 0th, `CLS`, etc.) output entry from the base model, and run it through a series of dense layers to form the output shape of choice.**\n",
        "\n",
        "**This is called [Text Classification](https://huggingface.co/tasks/text-classification)!**  (or more generally \"Sequence Classification\")\n",
        "\n",
        "<div><img src=\"imgs/task-text-classification.png\"\n",
        "     alt=\"Task as seen on https://huggingface.co/tasks/text-classification\"\n",
        "     width=\"800\"/></div>\n",
        "\n",
        "This formulation is great when you need passage-level reasoning, so let's pull in a popular emotion model to illustrate the point:"
      ]
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          "metadata": {}
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'label': 'love', 'score': 0.9212923049926758}]\n",
            "[{'label': 'curiosity', 'score': 0.3875223994255066}]\n",
            "[{'label': 'confusion', 'score': 0.7724329829216003}]\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "emo_model = pipeline('sentiment-analysis', 'SamLowe/roberta-base-go_emotions')\n",
        "\n",
        "print(emo_model(\"I love my old pillow?\"))\n",
        "print(emo_model(\"Why is it that every plant I touch dies within a few days?\"))\n",
        "print(emo_model(\"I'm so conflicted about these new instructions...\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_r21IpJontrY"
      },
      "source": [
        "As we can see, it works perfectly fine despite RoBERTa not relying on the next-sentence prediction task. During internet-scale training, it just manages to pick up on passage-level relationships throughout the network and just needs some supervision to pull it out via the classifier.\n",
        "\n",
        "Investigating the architecture, you might notice that the shift from token-level and sequence-level classification heads isn't that obvious:\n",
        "\n",
        "```python\n",
        "emo_model.model.classifier\n",
        "```\n",
        "> ```python\n",
        "RobertaClassificationHead(\n",
        "  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
        "  (dropout): Dropout(p=0.1, inplace=False)\n",
        "  (out_proj): Linear(in_features=768, out_features=28, bias=True)\n",
        ")\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qjvfpipBncpl"
      },
      "source": [
        "This looks nearly identical to the token-level prediction task we saw earlier. To actually see the subsetting in action, you really do need to see the source code for it! Luckily, the classifier printout does say that this logic is encompassed in the `RobertaClassificationHead`, so we can simply look [at the official source code](https://github.com/huggingface/transformers/blob/f26099e7b5cf579f99a42bab6ddd371bf2c8d548/src/transformers/models/roberta/modeling_roberta.py#L1510) and find out what's going on for ourselves:\n",
        "\n",
        "```python\n",
        "class RobertaForQuestionAnswering(RobertaPreTrainedModel):\n",
        "    # ...\n",
        "    def forward(\n",
        "        self,\n",
        "        input_ids: Optional[torch.LongTensor] = None,\n",
        "        attention_mask: Optional[torch.FloatTensor] = None,\n",
        "        token_type_ids: Optional[torch.LongTensor] = None,\n",
        "        position_ids: Optional[torch.LongTensor] = None,\n",
        "        head_mask: Optional[torch.FloatTensor] = None,\n",
        "        inputs_embeds: Optional[torch.FloatTensor] = None,\n",
        "        start_positions: Optional[torch.LongTensor] = None,\n",
        "        end_positions: Optional[torch.LongTensor] = None,\n",
        "        output_attentions: Optional[bool] = None,\n",
        "        output_hidden_states: Optional[bool] = None,\n",
        "        return_dict: Optional[bool] = None,\n",
        "    ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:\n",
        "        ## As you can see, the forward call starts off by passing\n",
        "        ## the inputs through the base model.\n",
        "        outputs = self.roberta(\n",
        "            input_ids,\n",
        "            attention_mask=attention_mask,\n",
        "            token_type_ids=token_type_ids,\n",
        "            position_ids=position_ids,\n",
        "            head_mask=head_mask,\n",
        "            inputs_embeds=inputs_embeds,\n",
        "            output_attentions=output_attentions,\n",
        "            output_hidden_states=output_hidden_states,\n",
        "            return_dict=return_dict,\n",
        "        )\n",
        "\n",
        "        ## Then, it just has to take the first sequence entry\n",
        "        ## from the model push it through the dense layers\n",
        "        ## for a single set of classifications.\n",
        "        sequence_output = outputs[0]   ### <- Interesting\n",
        "        logits = self.qa_outputs(sequence_output)\n",
        "        start_logits, end_logits = logits.split(1, dim=-1)\n",
        "        start_logits = start_logits.squeeze(-1).contiguous()\n",
        "        end_logits = end_logits.squeeze(-1).contiguous()\n",
        "        ## ...\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kRvWfME8opNA"
      },
      "source": [
        "Being able to look deeper into open-sourced projects like this is important when you want to see how people *really* make cutting-edge software, so keep that in mind going forward and always be on the lookout for points of confusion that you think warrant a brief stroll through the code base!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L92W2zBW9okz"
      },
      "source": [
        "## 3.4. Zero Shot Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "csG_AhDw9okz"
      },
      "source": [
        "So far, we've been talking about how we can modify the encoder formulation for various kinds of outputs **given a single call through the function**. It turns out, however, that we can start to break away from the `n->1` and `n->n` formulation **by allowing for multiple queries to our model**.\n",
        "\n",
        "In the subsequent notebooks, we'll be learning about decoders which use the transformer architecture to predict entire sequences autoregressively, one token at a time. These require special training and considerations, but for now we can play around with a different formulation:\n",
        "\n",
        "> **Predicting probabilities for classification, one class at a time, until you have a prediction for each of your desired classes!**\n",
        "\n",
        "**This is called [Zero-Shot Classification](https://huggingface.co/tasks/zero-shot-classification)!**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pe5QXyIz9ok0"
      },
      "source": [
        "<div><img src=\"imgs/task-zero-shot.png\"\n",
        "     alt=\"Zero-shot classification task as seen on https://huggingface.co/tasks/zero-shot-classification\"\n",
        "     width=\"800\"/></div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TGBWwyIH9ok0"
      },
      "source": [
        "The term zero-shot might seem unfimiliar if you're new to this space, so let's quickly define some key terms:\n",
        "> - **Zero-Shot Inference**: A model is asked to predict things that it was never specifically trained to predict.\n",
        "> - **Few-Shot Inference**: A model is asked to predict things that have come up in training (or are exemplified in the context), but the amount of training done on this category of data is aggressively limited.\n",
        "\n",
        "You'll notice that from this point on, most of the things we do in this course will be zero-shot in nature, where we're asking the model to do stuff it wasn't necessarily trained to do. It's able to do these things because it has some grasp of language and word meanings per our previous notebook, so there's an element of \"understanding\".\n",
        "\n",
        "#### **Task 1:** Zero-Shot Pipelines\n",
        "\n",
        "Skim through the task specification and import the model that's advertised ([`facebook/bart-large-mnli`](https://huggingface.co/facebook/bart-large-mnli))! Test it out with some examples, maybe starting with the example above and then finding some other classes that you're actually interested in."
      ]
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      ],
      "source": [
        "## Your Code Here:from transformers import pipeline\n",
        "from transformers import pipeline\n",
        "classifier = pipeline(\"zero-shot-classification\", model=\"facebook/bart-large-mnli\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example text and candidate labels\n",
        "text = \"I love coding in Python because it's so versatile and powerful.\"\n",
        "candidate_labels = [\"technology\", \"education\", \"hobby\", \"programming\", \"sports\"]\n",
        "\n",
        "# Running the classifier\n",
        "result = classifier(text, candidate_labels)\n",
        "print(result)"
      ],
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        },
        "id": "32YPQfCyBZXd",
        "outputId": "c8285540-3d70-4709-9968-53d8749d1799"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'sequence': \"I love coding in Python because it's so versatile and powerful.\", 'labels': ['programming', 'technology', 'hobby', 'education', 'sports'], 'scores': [0.5880739092826843, 0.3729352653026581, 0.023521948605775833, 0.008653370663523674, 0.006815571337938309]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Another example\n",
        "text = \"Blockchain technology is changing the financial industry.\"\n",
        "candidate_labels = [\"finance\", \"technology\", \"gaming\", \"environment\", \"health\"]\n",
        "\n",
        "result = classifier(text, candidate_labels)\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qf_t0b7nBlGO",
        "outputId": "c465984e-3a2e-45c3-e11b-c077e10de653"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'sequence': 'Blockchain technology is changing the financial industry.', 'labels': ['technology', 'finance', 'environment', 'health', 'gaming'], 'scores': [0.5998522043228149, 0.3971599340438843, 0.0015737003413960338, 0.0007135095656849444, 0.0007007023086771369]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9gga3hk19ok0"
      },
      "source": [
        "When you're done, hypothesize about how these values could have been generated, and see if you can't find any resources (maybe in the source code, maybe by replicating the pipeline yourself, or maybe somewhere else) that validate your assumption!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ORNFr9kv9ok0"
      },
      "source": [
        "**HINTS:**\n",
        "- Consider the task that this model is trained on: [MultiNLI](https://huggingface.co/datasets/multi_nli)...\n",
        "- Consider checking out the model card. Maybe the manual pytorch implementation helps...\n",
        "- If you get stuck trying to make the pipeline work, please feel free to check out the [`99_licenses.ipynb` notebook](extras_and_licenses/99_licenses.ipynb) which showcases some default API usage for the recommended models.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hypothesis on Probability Generation\n",
        "\n",
        "The facebook/bart-large-mnli model is a variant of BART (Bidirectional and Auto-Regressive Transformer) fine-tuned for Natural Language Inference (NLI), where the model was trained to determine the relationship between pairs of sentences (e.g., whether they entail, contradict, or are neutral to each other). In zero-shot classification, the process is modified by using the input sentence along with candidate labels formulated as hypothesis statements (e.g., \"This text is about technology\").\n",
        "\n",
        "For each label, the model computes a likelihood that the text \"entails\" the hypothesis. These entailment scores are converted into probabilities for the final classification output."
      ],
      "metadata": {
        "id": "Q5TQS7KxBvWH"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P-EfMhrrqI6i",
        "tags": []
      },
      "source": [
        "## 3.4. Wrapping Up\n",
        "\n",
        "At this point, we've seen how we can use a relatively-consistent transformer encoder architecture to execute on a few key types of tasks:\n",
        "- **Token-Level Predictions**\n",
        "    - Generate predictions for every entry in the sequence.\n",
        "    - Great for 1-to-1 token conversion or selection tasks, including **Range Prediction** for generating subsets.\n",
        "\n",
        "- **Sequence-Level Prediction**\n",
        "    - Pull passage-level data by taking the values from a specific sequence entry and pulling out insight from it.\n",
        "    - Great for semantic analysis and passage classification.\n",
        "    \n",
        "- **Multi-Query Prediction**\n",
        "    - Query the encoder architecture multiple times over to generate a number of sequence-level predictions.\n",
        "    - Useful for generating an unbounded number of realizations, one generation at a time.\n",
        "\n",
        "**In the next notebook, we will look at some other architectures that extend the multi-query prediction logic to generate entire ordered sequences of classes (so... natural language)!**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
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      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'status': 'ok', 'restart': True}"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "## Please Run When You're Done!\n",
        "import IPython\n",
        "app = IPython.Application.instance()\n",
        "app.kernel.do_shutdown(True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U6Sgwpj29ok1"
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        "<center><a href=\"https://www.nvidia.com/en-us/training/\"><img src=\"https://dli-lms.s3.amazonaws.com/assets/general/DLI_Header_White.png\" width=\"400\" height=\"186\" /></a></center>"
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    }
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