text_classify_bert.ipynb

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      "execution_count": null,
      "metadata": {
        "id": "bEtnyz2A8RlO"
      },
      "outputs": [],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "PoqDNoIc8RlR"
      },
      "outputs": [],
      "source": [
        "from transformers import pipeline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifer = pipeline('sentiment-analysis')\n",
        "\n",
        "#this will download distilbert-based-uncased-finetuined-sst2-english\n",
        "\n",
        "# distilbert-base is our model\n",
        "# uncased- our dataset is alread trained\n",
        "# sst2-english:- our dataset"
      ],
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        "id": "fA5VL6kO9P0w",
        "outputId": "9cdbdd5a-b4e3-4e22-e599-5611bc67fedc"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
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    {
      "cell_type": "code",
      "source": [
        "# classifer('i am very happy about foods but DJ is not good')\n",
        "result = classifer(['foods is little bit ok with me.','but music is too noisy'])\n",
        "\n",
        "for x in result:\n",
        "  print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pvqnO5819Zg1",
        "outputId": "1bef24ad-5677-41a7-dd0f-6e6cee5ee1e2"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'label': 'POSITIVE', 'score': 0.9770360589027405}\n",
            "{'label': 'NEGATIVE', 'score': 0.9982159733772278}\n"
          ]
        }
      ]
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      "source": [
        "classifier = pipeline('sentiment-analysis', model=\"nlptown/bert-base-multilingual-uncased-sentiment\")\n"
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        {
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          "data": {
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          "metadata": {}
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            ],
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This is a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in\n",
        "# six languages: English, Dutch, German, French, Spanish, and Italian.\n",
        "# classifier('食物很好') #food is good\n",
        "\n",
        "classifier('食物不好') # food is not good"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tLlXYuu6FA4L",
        "outputId": "fe033a1a-6195-4602-b0b3-982acfdc509e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': '2 stars', 'score': 0.41226303577423096}]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenizer"
      ],
      "metadata": {
        "id": "-PWcJKMBIpWl"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification"
      ],
      "metadata": {
        "id": "UiHiuHysHUw5"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"nlptown/bert-base-multilingual-uncased-sentiment\"\n",
        "# This model only exists in PyTorch, so we use the `from_pt` flag to import that model in TensorFlow.\n",
        "# download models using tensorflow \"TFAutoModelForSequenceClassification\"\n",
        "model = TFAutoModelForSequenceClassification.from_pretrained(model_name, from_pt=True)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UBQUnW5eIvCh",
        "outputId": "6dad0369-b607-41b2-8304-50e459064492"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "All the weights of TFBertForSequenceClassification were initialized from the PyTorch model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertForSequenceClassification for predictions without further training.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier(\"I am a good boy\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kky5T-u8IvFq",
        "outputId": "fa10b657-346f-40d2-c7ad-a1aa702d196f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': '4 stars', 'score': 0.4229269027709961}]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# example of get another model and Tokenizer\n",
        "model_name = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "tf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ],
      "metadata": {
        "id": "SRSUvyqWIvJG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = tokenizer(\"We are very happy to show you the 🤗 Transformers library.\")"
      ],
      "metadata": {
        "id": "-T3yFn4CIvMt"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sHx--iz0LJB9",
        "outputId": "f92df7e9-fa23-4c2c-f55b-3a624ff70e48"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [101, 11312, 10320, 12495, 19308, 10114, 11391, 10855, 10103, 100, 58263, 13299, 119, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf_batch = tokenizer(\n",
        "    [\"We are very happy to show you the 🤗 Transformers library.\", \"We hope you don't hate it.\"],\n",
        "    padding=True,\n",
        "    truncation=True,\n",
        "    max_length=512,\n",
        "    return_tensors=\"tf\"\n",
        ")\n",
        "\n",
        "tf_batch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ToGTRDm1LO-u",
        "outputId": "015dcf74-3251-4024-bdb4-d733676cea38"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': <tf.Tensor: shape=(2, 14), dtype=int32, numpy=\n",
              "array([[  101, 11312, 10320, 12495, 19308, 10114, 11391, 10855, 10103,\n",
              "          100, 58263, 13299,   119,   102],\n",
              "       [  101, 11312, 18763, 10855, 11530,   112,   162, 39487, 10197,\n",
              "          119,   102,     0,     0,     0]], dtype=int32)>, 'token_type_ids': <tf.Tensor: shape=(2, 14), dtype=int32, numpy=\n",
              "array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(2, 14), dtype=int32, numpy=\n",
              "array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
              "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]], dtype=int32)>}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# using the model\n",
        "tf_outputs = model(tf_batch)\n",
        "tf_outputs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SOrEGk15Llck",
        "outputId": "2461fc9f-6f9e-4b93-80d6-6a223a6c4147"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TFSequenceClassifierOutput(loss=None, logits=<tf.Tensor: shape=(2, 5), dtype=float32, numpy=\n",
              "array([[-2.622201  , -2.7745326 , -0.8966623 ,  2.0137322 ,  3.3063855 ],\n",
              "       [ 0.0063578 , -0.12577416, -0.05034604, -0.16553028,  0.1328585 ]],\n",
              "      dtype=float32)>, hidden_states=None, attentions=None)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# apply softmax activation\n",
        "import tensorflow as tf\n",
        "tf_predictions = tf.nn.softmax(tf_outputs[0], axis=-1)"
      ],
      "metadata": {
        "id": "p71N32D2LlgK"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf_outputs = model(tf_batch, labels = tf.constant([1, 0]))"
      ],
      "metadata": {
        "id": "h2ErB2tZNKaX"
      },
      "execution_count": 29,
      "outputs": []
    },
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        "# Customizing the model\n",
        "from transformers import DistilBertConfig, DistilBertTokenizer, TFDistilBertForSequenceClassification\n",
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