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        "<a href=\"https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/Huggingface_pipelines_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Demo of [Huggingface Transformers](https://github.com/huggingface/transformers) pipelines\n",
        "\n",
        "New in version `v2.3`: `Pipeline` are high-level objects which automatically handle tokenization, running your data through a transformers model\n",
        "and outputting the result in a structured object.\n",
        "\n",
        "You can create `Pipeline` objects for the following down-stream tasks:\n",
        "\n",
        " - `feature-extraction`: Generates a tensor representation for the input sequence\n",
        " - `ner`: Generates named entity mapping for each word in the input sequence.\n",
        " - `sentiment-analysis`: Gives the polarity (positive / negative) of the whole input sequence.\n",
        " - `question-answering`: Provided some context and a question refering to the context, it will extract the answer to the question\n",
        " in the context.\n",
        "\n",
        " > Colab creator: [Manuel Romero](https://twitter.com/mrm8488)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2YTuVJUv1_tM",
        "colab_type": "code",
        "outputId": "15079c21-7a24-4958-d36c-d0bd2fcec9b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "%tensorflow_version 2.x"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KvF2APhH1Zii",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "bbbbc2a1-39df-4108-c828-0b544d533be6"
      },
      "source": [
        "!pip install -q transformers"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 481kB 8.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 3.1MB 17.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 870kB 24.7MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.0MB 24.2MB/s \n",
            "\u001b[?25h  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9mZJLpVC1N_T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from transformers import pipeline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LvXGY6iz5kHJ",
        "colab_type": "text"
      },
      "source": [
        "## 1. Sentiment Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jnJmxKJj5LVW",
        "colab_type": "code",
        "outputId": "b2f19c88-7721-4af2-f494-f9edb3a72132",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184,
          "referenced_widgets": [
            "1fbe5532bf55445e8b17c8adf46c8dea",
            "266c428521204d3fb20f2695905bc1c0",
            "9556afd193814517b68dff28b5e45b42",
            "09a5987830254e3a9600635013a28edf",
            "933c4d434fd6403690a4696b35c58ae6",
            "495021e9ae8549ce9ab613ba14726fb7",
            "79c47f082aa04078b415ad9e10424656",
            "59179c42a1574b76be7de3583b74aeb4",
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            "6b444e6781c74243a89126a51a09f04f",
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            "eff04c498fa04311817f6fe56c6c0493",
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            "ccff25e4324544c69ae3222dda00996d",
            "305f28a237314a71b4429602ab0e2cec",
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            "c70485841098458ab3a416a949ddccc6",
            "d4404b39cd6a46e284c5a030d8c125f8",
            "29ceafb282f346d09a8806aaae41cb66"
          ]
        }
      },
      "source": [
        "nlp_sentiment_analysis = pipeline(\"sentiment-analysis\")\n",
        "text_sentiment = \"We are very happy to include pipeline into the transformers repository\"\n",
        "nlp_sentiment_analysis(text_sentiment)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1fbe5532bf55445e8b17c8adf46c8dea",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Downloading', max=686, style=ProgressStyle(description_width=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "419e94b0851842cb84350aafbcd6db81",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Downloading', max=230, style=ProgressStyle(description_width=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e29b7b9c654b4dc39ebe4a7b3477d02a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Downloading', max=267844284, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'POSITIVE', 'score': 0.99687505}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BAoHGP8F5oPg",
        "colab_type": "text"
      },
      "source": [
        "## 2. Question Answering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "laT8e0BT5xkT",
        "colab_type": "code",
        "outputId": "d0b31f1e-1846-4e8d-b7aa-678a4c36de0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372,
          "referenced_widgets": [
            "a4c4035fffa34964b5478da3fadfcb25",
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            "ab31d908fcb343dfb7b6ab471acebbc6",
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            "0e3ad81df466401998875397e0da9f50",
            "ec494a7b22634a52a898a5ead00f99ae",
            "444a8a526b5d4e1aa22c9861a03e48d9",
            "81fef48a27e7447787ff08a773f7bd2a",
            "a8ec51cdb7ea444a94af18e094cce124",
            "ff631fc29ace4df883fc501d274314fe",
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            "b372a7e723de4ebe9da57955d84ce6b0"
          ]
        }
      },
      "source": [
        "nlp_qa = pipeline(\"question-answering\")\n",
        "context = \"Pipeline have been included in the huggingface/transformers repository\"\n",
        "question = \"What is the name of the repository?\"\n",
        "nlp_qa({\n",
        "    'question': question,\n",
        "    'context': context\n",
        "})"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a4c4035fffa34964b5478da3fadfcb25",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Downloading', max=492, style=ProgressStyle(description_width=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e39a71947a934247870ee73c390336ab",
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              "version_major": 2
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              "HBox(children=(IntProgress(value=0, description='Downloading', max=231508, style=ProgressStyle(description_wid…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1614de04fb0f47249a63bc16fe5baf58",
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ab31d908fcb343dfb7b6ab471acebbc6",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(IntProgress(value=0, description='Downloading', max=492, style=ProgressStyle(description_width=…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ec494a7b22634a52a898a5ead00f99ae",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Downloading', max=265481570, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "convert squad examples to features: 100%|██████████| 1/1 [00:00<00:00, 269.56it/s]\n",
            "add example index and unique id: 100%|██████████| 1/1 [00:00<00:00, 2832.08it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'answer': 'huggingface/transformers',\n",
              " 'end': 59,\n",
              " 'score': 0.2875610410939,\n",
              " 'start': 35}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FStvE_ls6V51",
        "colab_type": "text"
      },
      "source": [
        "## 3. NER"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "74R1YM5x6nnQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102,
          "referenced_widgets": [
            "48833f92454947ed976eb7df5b3c3d01",
            "827c3d19de4e4a6fb9c642f50b56bc97",
            "813715ea0e0f436bbe9e7edfac07e300",
            "71740825341d4bc6a007a11cc1c6b6d4",
            "81df2e984bf2436ba3546fb5b2e5fc41",
            "3370e60cb7724e1ebe238d5da3f29fc8",
            "9623f5896e97480fb96c6c6b609bd8e5",
            "4198fcc0bc734f338e99f0bc3be717d4"
          ]
        },
        "outputId": "205b7c23-270f-44cd-f0cd-dbe5401d28b3"
      },
      "source": [
        "nlp_ner = pipeline(\"ner\")\n",
        "text_ner = \"European authorities fined Google a record $5.1 billion on Wednesday for abusing its power in the mobile phone market and ordered the company to alter its practices\"\n",
        "nlp_ner(text_ner)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "48833f92454947ed976eb7df5b3c3d01",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Downloading', max=230, style=ProgressStyle(description_width=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'entity': 'I-MISC', 'score': 0.9980936050415039, 'word': 'European'},\n",
              " {'entity': 'I-ORG', 'score': 0.9990612864494324, 'word': 'Google'}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_wXZbCq162ka",
        "colab_type": "text"
      },
      "source": [
        "## 4. Feature Extraction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZdThaEG6-2s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "nlp_fe = pipeline(\"feature-extraction\")\n",
        "text_fe = \"We are very happy to include pipeline into the transformers repository\"\n",
        "nlp_fe(text_fe)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HM22GQsb7Nrz",
        "colab_type": "text"
      },
      "source": [
        "## 5. Bonus Forms (sentiment-analysis, ner, feature-extraction)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iiJ0-z8B7ROd",
        "colab_type": "code",
        "cellView": "form",
        "outputId": "d6e0a9c3-ee2d-462f-8157-b5b5443039b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "1230ccd90884496a9647e1ad8791f665",
            "c845d8da269b4f748e4019730960414b",
            "5df7b3f6e80047afba2d589f96386e76",
            "ece3ebb441374ec081b09c54a8b41fd5",
            "78c587cc578d48858ea2133e85ee42b4",
            "1d278602101a4e909e097df4d0d27ea3",
            "c38f004688a64653b97ef37015c5f666",
            "878fa04ef47143d09dd46755e4f5d2ad"
          ]
        }
      },
      "source": [
        "#@title Choose a pipeline and write a text { run: \"auto\" }\n",
        "task = 'sentiment-analysis' #@param [\"sentiment-analysis\", \"ner\", \"feature-extraction\"]\n",
        "text = 'We are very happy to include pipeline into the transformers repository.' #@param {type:\"string\"}\n",
        "nlp = pipeline(task)\n",
        "nlp(text)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1230ccd90884496a9647e1ad8791f665",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Downloading', max=230, style=ProgressStyle(description_width=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'POSITIVE', 'score': 0.99781936}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sfI-QbZh7yCR",
        "colab_type": "text"
      },
      "source": [
        "## question-answering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pn2J9RXq73-E",
        "colab_type": "code",
        "outputId": "1a865b47-243f-495a-dacf-4ce69eb8aa84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173,
          "referenced_widgets": [
            "74552d9ccf91409cb83150c776149aee",
            "9d18e3881de5454b85c8377689a7b7d9",
            "a3021d4a0eb24566955fdde0a41101e0",
            "4160d61b26c44322880c490085870aa0",
            "f0dd15bd25b44fbca4e1ceb221ad54df",
            "2a8f46a01c254520940f80d2065e9eba",
            "e8d571a274cf4f11838a9183f4e9622c",
            "4fb5144fcdc94c4a97a3ec256718dcad"
          ]
        },
        "cellView": "form"
      },
      "source": [
        "#@title Write a context and a question { run: \"auto\" }\n",
        "context = 'Bitcoin[a] (\\u20BF) is a cryptocurrency. It is a decentralized digital currency without a central bank or single administrator that can be sent from user to user on the peer-to-peer bitcoin network without the need for intermediaries.[8]  Transactions are verified by network nodes through cryptography and recorded in a public distributed ledger called a blockchain. Bitcoin was invented in 2008 by an unknown person or group of people using the name Satoshi Nakamoto[15] and started in 2009[16] when its source code was released as open-source software.[7]:ch. 1 Bitcoins are created as a reward for a process known as mining. They can be exchanged for other currencies, products, and services.[17] Research produced by University of Cambridge estimates that in 2017, there were 2.9 to 5.8 million unique users using a cryptocurrency wallet, most of them using bitcoin.[18]  Bitcoin has been criticized for its use in illegal transactions, its high electricity consumption, price volatility, and thefts from exchanges. Some economists, including several Nobel laureates, have characterized it as a speculative bubble. Bitcoin has also been used as an investment, although several regulatory agencies have issued investor alerts about bitcoin.[19][20]' #@param {type:\"string\"}\n",
        "question = 'How are Bitcoins created?' #@param {type:\"string\"}\n",
        "nlp_qa = pipeline(\"question-answering\")\n",
        "nlp_qa({\n",
        "    'question': question,\n",
        "    'context': context\n",
        "})"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "74552d9ccf91409cb83150c776149aee",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Downloading', max=230, style=ProgressStyle(description_width=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "convert squad examples to features: 100%|██████████| 1/1 [00:00<00:00, 43.99it/s]\n",
            "add example index and unique id: 100%|██████████| 1/1 [00:00<00:00, 2896.62it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'answer': 'as a reward for a process known as mining.',\n",
              " 'end': 623,\n",
              " 'score': 0.5793338079010653,\n",
              " 'start': 581}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hK7kGyrd1pWG",
        "colab_type": "text"
      },
      "source": [
        "## 6. BONUS (II) Mask filling with **umBERTo**, an Italian <img alt=\"🇮🇹\" draggable=\"false\" src=\"https://abs-0.twimg.com/emoji/v2/svg/1f1ee-1f1f9.svg\" width=\"32\" height=\"32\"> Language Model trained with Whole Word Masking.\n",
        "\n",
        "Source code: https://github.com/musixmatchresearch/umberto)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ANlnLMjy3b5J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "e5f99741-0675-4be7-a96a-74484009089d"
      },
      "source": [
        "nlp_fill_mask_ita = pipeline(\n",
        "\t\"fill-mask\",\n",
        "\tmodel=\"Musixmatch/umberto-commoncrawl-cased-v1\",\n",
        "\ttokenizer=\"Musixmatch/umberto-commoncrawl-cased-v1\"\n",
        ")\n",
        "\n",
        "nlp_fill_mask_ita(\"Umberto Eco è <mask> un grande scrittore\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model name 'Musixmatch/umberto-commoncrawl-cased-v1' was not found in model name list (bert-base-uncased, bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, bert-base-multilingual-cased, bert-base-chinese, bert-base-german-cased, bert-large-uncased-whole-word-masking, bert-large-cased-whole-word-masking, bert-large-uncased-whole-word-masking-finetuned-squad, bert-large-cased-whole-word-masking-finetuned-squad, bert-base-cased-finetuned-mrpc, bert-base-german-dbmdz-cased, bert-base-german-dbmdz-uncased, bert-base-japanese, bert-base-japanese-whole-word-masking, bert-base-japanese-char, bert-base-japanese-char-whole-word-masking, bert-base-finnish-cased-v1, bert-base-finnish-uncased-v1, bert-base-dutch-cased, openai-gpt, transfo-xl-wt103, gpt2, gpt2-medium, gpt2-large, gpt2-xl, distilgpt2, ctrl, xlnet-base-cased, xlnet-large-cased, xlm-mlm-en-2048, xlm-mlm-ende-1024, xlm-mlm-enfr-1024, xlm-mlm-enro-1024, xlm-mlm-tlm-xnli15-1024, xlm-mlm-xnli15-1024, xlm-clm-enfr-1024, xlm-clm-ende-1024, xlm-mlm-17-1280, xlm-mlm-100-1280, roberta-base, roberta-large, roberta-large-mnli, distilroberta-base, roberta-base-openai-detector, roberta-large-openai-detector, distilbert-base-uncased, distilbert-base-uncased-distilled-squad, distilbert-base-german-cased, distilbert-base-multilingual-cased, distilbert-base-uncased-finetuned-sst-2-english, albert-base-v1, albert-large-v1, albert-xlarge-v1, albert-xxlarge-v1, albert-base-v2, albert-large-v2, albert-xlarge-v2, albert-xxlarge-v2, camembert-base, umberto-commoncrawl-cased-v1, umberto-wikipedia-uncased-v1, t5-small, t5-base, t5-large, t5-3b, t5-11b, xlm-roberta-base, xlm-roberta-large, xlm-roberta-large-finetuned-conll02-dutch, xlm-roberta-large-finetuned-conll02-spanish, xlm-roberta-large-finetuned-conll03-english, xlm-roberta-large-finetuned-conll03-german, flaubert-small-cased, flaubert-base-uncased, flaubert-base-cased, flaubert-large-cased). We assumed 'https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-commoncrawl-cased-v1/modelcard.json' was a path or url to a model card file named modelcard.json or a directory containing such a file but couldn't find any such file at this path or url.\n",
            "Creating an empty model card.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'score': 0.1859990507364273,\n",
              "  'sequence': '<s> Umberto Eco è considerato un grande scrittore</s>',\n",
              "  'token': 5032},\n",
              " {'score': 0.17816734313964844,\n",
              "  'sequence': '<s> Umberto Eco è stato un grande scrittore</s>',\n",
              "  'token': 471},\n",
              " {'score': 0.16565516591072083,\n",
              "  'sequence': '<s> Umberto Eco è sicuramente un grande scrittore</s>',\n",
              "  'token': 2654},\n",
              " {'score': 0.09329013526439667,\n",
              "  'sequence': '<s> Umberto Eco è indubbiamente un grande scrittore</s>',\n",
              "  'token': 17908},\n",
              " {'score': 0.05470135435461998,\n",
              "  'sequence': '<s> Umberto Eco è certamente un grande scrittore</s>',\n",
              "  'token': 5269}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    }
  ]
}