Upload flan_t5_large의_그래머_검사기.ipynb

flan_t5_large의_그래머_검사기.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <center> demo for `pszemraj/flan-t5-large-grammar-synthesis`\n",
+        "\n",
+        "\n",
+        "- this notebook runs on CPU by default. use google or chatGPT to figure out how to change that if you want to run on GPU\n",
+        "- some details on usage\n",
+        "    - this model was trained on several (1-8) sentences at a time. \n",
+        "    - by default, it **will not** work well for super low token counts (like 4) or super long texts\n",
+        "    - I would recommend using it in batches of 4-128 tokens at a time\n",
+        "    - an **extension** of this notebook for **batch inference** on longer texts is available [here](https://colab.research.google.com/gist/pszemraj/6e961b08970f98479511bb1e17cdb4f0/batch-grammar-check-correct-demo.ipynb)\n",
+        "- [link to model card](https://huggingface.co/pszemraj/flan-t5-large-grammar-synthesis)\n",
+        "\n",
+        "\n",
+        "---\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "LEpomcp9mMno"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "LrDWdEzv3LaX"
+      },
+      "outputs": [],
+      "source": [
+        "#@markdown add auto-Colab formatting with `IPython.display`\n",
+        "from IPython.display import HTML, display\n",
+        "# colab formatting\n",
+        "def set_css():\n",
+        "    display(\n",
+        "        HTML(\n",
+        "            \"\"\"\n",
+        "  <style>\n",
+        "    pre {\n",
+        "        white-space: pre-wrap;\n",
+        "    }\n",
+        "  </style>\n",
+        "  \"\"\"\n",
+        "        )\n",
+        "    )\n",
+        "\n",
+        "get_ipython().events.register(\"pre_run_cell\", set_css)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lIYdn1woOS1n",
+        "outputId": "fba7842b-269d-4bfa-991c-714c8764281e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.8/6.8 MB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m215.3/215.3 KB\u001b[0m \u001b[31m15.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.8/199.8 KB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m18.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "pip install -U -q transformers accelerate"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from transformers import pipeline\n",
+        "\n",
+        "corrector = pipeline(\n",
+        "    \"text2text-generation\",\n",
+        "    \"pszemraj/flan-t5-large-grammar-synthesis\",\n",
+        ")\n"
+      ],
+      "metadata": {
+        "id": "8ZfUqTVJl8Zw",
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+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)lve/main/config.json:   0%|          | 0.00/892 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading pytorch_model.bin:   0%|          | 0.00/3.13G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
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+          },
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+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)okenizer_config.json:   0%|          | 0.00/2.56k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
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+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
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+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b01d8f96a17548aaaf82920c85f0bd13"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "29bc49c6f76043d1bbb254a920a9e5af"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "raw_text = \"Jamie 2the store yesturday to bye some food. I needd milk, bread, andafew otter things. The $$tore was reely crowed and I had a hard time finding everyting I needed. I finaly madeit t0 dacheck 0ut line and payed for my stuff.\" #@param {type:\"string\"}\n",
+        "print(f\"input text:\\n\\t{raw_text}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "789e5665-809f-4f69-e5d9-2e247e8cbf0a",
+        "id": "Js4cJhFA-S6L"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "input text:\n",
+            "\tJamie 2the store yesturday to bye some food. I needd milk, bread, andafew otter things. The $$tore was reely crowed and I had a hard time finding everyting I needed. I finaly madeit t0 dacheck 0ut line and payed for my stuff.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "params = {\n",
+        "    'max_length':64,\n",
+        "    'repetition_penalty':1.05,\n",
+        "    'early_stopping':True,\n",
+        "    'num_beams':4\n",
+        "}"
+      ],
+      "metadata": {
+        "id": "FjWFn4577hvw"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%%time\n",
+        "\n",
+        "results = corrector(raw_text, **params)\n",
+        "print(results[0]['generated_text'], \"\\n\"*2)"
+      ],
+      "metadata": {
+        "id": "wt5T_MrzmAOI",
+        "outputId": "a4239e36-3241-490e-bb12-453e0e499f8f",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Jamie went to the store yesterday to buy some food. I needed milk, bread, and a few other things. The store was very busy and I had a hard time finding everything I needed. I finally made it to the cashier and paid for my stuff. \n",
+            "\n",
+            "\n",
+            "CPU times: user 26 s, sys: 1.95 ms, total: 26 s\n",
+            "Wall time: 28.6 s\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "it's much faster on GPU + the inference parameters can be adjusted (less beams = faster, but worse quality).\n",
+        "\n",
+        "There is also now an [XL model here](https://huggingface.co/pszemraj/flan-t5-xl-grammar-synthesis)"
+      ],
+      "metadata": {
+        "id": "5yWmlkLxArnV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "TA6meSTdmztt"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
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