Debertav3_lsa.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jeaneigsi/Humain-/blob/main/Debertav3_lsa.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget http://alt.qcri.org/semeval2014/task4/data/uploads/semeval14-absa-base-eval-valid.zip\n",
        "!unzip semeval14-absa-base-eval-valid.zip\n",
        "!wget https://github.com/kmayutrisna/SemEval-Dataset/blob/main/SemEval%202014%20Task%204/Restaurants_Train.xml\n",
        "!wget https://github.com/kmayutrisna/SemEval-Dataset/blob/main/SemEval%202014%20Task%204/Laptops_Train.csv\n",
        "!wget https://github.com/kmayutrisna/SemEval-Dataset/blob/main/SemEval%202014%20Task%204/Laptops_Train.csv"
      ],
      "metadata": {
        "id": "iYwF3HcxkEXs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python SemEval2014.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EfRIKGlgq49Q",
        "outputId": "8541cc65-8542-438f-bc6f-f6aadcb2a57e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  File \"/content/SemEval2014.py\", line 38\n",
            "    <link rel=\"canonical\" href=\"https://huggingface.co/datasets/Yaxin/SemEval2014Task4Raw/blob/main/SemEval2014.py\">  <!-- HEAD_svelte-1oal594_START --><style>.blob-line-num::before {\n",
            "                                                                                                                                       ^\n",
            "SyntaxError: invalid decimal literal\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://github.com/kmayutrisna/SemEval-Dataset/blob/main/SemEval%202014%20Task%204/Laptops_Train.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fwwlBT8epvjt",
        "outputId": "fb307c04-8b23-486f-edf3-8f5b3632ccc2"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-09-06 09:25:21--  https://github.com/kmayutrisna/SemEval-Dataset/blob/main/SemEval%202014%20Task%204/Laptops_Train.csv\n",
            "Resolving github.com (github.com)... 140.82.112.4\n",
            "Connecting to github.com (github.com)|140.82.112.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [text/html]\n",
            "Saving to: ‘Laptops_Train.csv’\n",
            "\n",
            "Laptops_Train.csv       [ <=>                ]   1.20M  --.-KB/s    in 0.09s   \n",
            "\n",
            "2024-09-06 09:25:22 (13.7 MB/s) - ‘Laptops_Train.csv’ saved [1255035]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets"
      ],
      "metadata": {
        "id": "udKezGT0pI6g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prepocessing"
      ],
      "metadata": {
        "id": "B2y22__AogMH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from datasets import load_dataset\n",
        "\n"
      ],
      "metadata": {
        "id": "URnjUXvrojDp"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Restaurants_Train=load_dataset('alexcadillon/SemEval2014Task4','restaurants')\n",
        "Laptops_Train=load_dataset('alexcadillon/SemEval2014Task4','laptops')"
      ],
      "metadata": {
        "id": "kSqt9cPcpYCG"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Restaurants_df=Restaurants_Train['train'].to_pandas()\n",
        "laptops_df=Laptops_Train['train'].to_pandas()"
      ],
      "metadata": {
        "id": "jHWISpzssS4q"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#check correct\n",
        "Restaurants_df.isna().sum()\n",
        "laptops_df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "_KBwTbNGvQ6P",
        "outputId": "8eee7fda-1742-4120-a860-10cd6cafd622"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sentenceId     0\n",
              "text           0\n",
              "aspectTerms    0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sentenceId</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>text</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>aspectTerms</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Deberta-base LSA Report\n",
        "\n",
        "We will eval it on [here](https://github.com/kmayutrisna/SemEval-Dataset)"
      ],
      "metadata": {
        "id": "3FCWfW73IrLP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238,
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        },
        "id": "Zc1Lz9oARjWy",
        "outputId": "6bdfd4ef-9088-4164-9f7e-c5f0afb98403"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0d7adffa8a0c4f619812439f63327781"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/579 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "495789f26eb642b2bf478d6f294e6b05"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "spm.model:   0%|          | 0.00/2.46M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "69e1f1d8139b4827a570995d76c3d119"
            }
          },
          "metadata": {}
        },
        {
          "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",
            "/usr/local/lib/python3.10/dist-packages/transformers/convert_slow_tokenizer.py:551: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/371M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "60b1c1a725cb4721ae017e5691f27fc6"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModel\n",
        "\n",
        "tokenizer=AutoTokenizer.from_pretrained(\"microsoft/deberta-v3-base\")\n",
        "model=AutoModel.from_pretrained(\"microsoft/deberta-v3-base\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Normal inference"
      ],
      "metadata": {
        "id": "TYin1DLFwXE8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input='The service was good , but other is really bad'\n",
        "inputs=tokenizer(input, return_tensors='pt', padding=True,truncation=True,max_length=512)\n",
        "inputs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WGZcFXTka88Z",
        "outputId": "a39ac7c9-16aa-4228-f74f-17a78d61837d"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[  1, 279, 506, 284, 397, 366, 304, 340, 269, 431, 966,   2]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.decode(inputs['input_ids'][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "xN-r9XxhcBas",
        "outputId": "a4da9ded-49b3-40fc-a41f-a32eb3d80f42"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[CLS] The service was good, but other is really bad[SEP]'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "with torch.no_grad():\n",
        "  model(**inputs)\n",
        "  outputs=model(**inputs)"
      ],
      "metadata": {
        "id": "gQRwPrCJdu9V"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outputs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jiEo73YEegdx",
        "outputId": "090a804d-a878-402d-b6af-c3f7eef425e0"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BaseModelOutput(last_hidden_state=tensor([[[ 0.1389,  0.2316,  0.0410,  ..., -0.0660,  0.1466, -0.0018],\n",
              "         [ 0.1486, -0.1391,  0.1828,  ..., -1.0466,  0.1544, -0.1429],\n",
              "         [ 0.2820,  0.5533,  0.1707,  ..., -0.9439, -0.0039, -0.0498],\n",
              "         ...,\n",
              "         [ 0.1630,  0.2054,  0.1206,  ..., -0.0283,  0.0965,  0.0287],\n",
              "         [ 0.3194,  0.6675,  0.1107,  ...,  0.1889, -0.4547,  0.3727],\n",
              "         [ 0.2203,  0.2504, -0.0049,  ..., -0.0574,  0.1638,  0.0258]]]), hidden_states=None, attentions=None)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings=outputs.last_hidden_state\n",
        "embeddings.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qqc63fQxe_Nw",
        "outputId": "83d91053-0e80-4f41-d530-fbce8047b4b9"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 12, 768])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Tips** : The squeeze() function in PyTorch (or NumPy) is used to remove dimensions of size 1 from a tensor.\n",
        "Before: `embeddings.shape = (1, 512, 768) (batch size 1, 512 tokens, 768 dimensions).`   \n",
        "After: `embeddings.squeeze(0) gives embeddings.shape = (512, 768) (removing the batch size dimension).`"
      ],
      "metadata": {
        "id": "FjiVUBBrKZ0p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Appy LSa on last layer"
      ],
      "metadata": {
        "id": "5DrPNN972F0L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import TruncatedSVD\n",
        "import torch\n",
        "import numpy as np\n",
        "from transformers import Trainer, TrainingArguments\n",
        "from datasets import load_metric\n",
        "from sklearn.metrics import accuracy_score, recall_score, f1_score, confusion_matrix, precision_score"
      ],
      "metadata": {
        "id": "a88YyWkHDXH6"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_embeddings(text):\n",
        "  inputs=tokenizer(text, return_tensors='pt', padding=True,truncation=True,max_length=512)\n",
        "  # inference\n",
        "  with torch.no_grad():\n",
        "    output=model(**inputs)\n",
        "    hidden_states=output.last_hidden_state\n",
        " # apply lsa\n",
        "  embeddings=hidden_states.squeeze(0).numpy()\n",
        "  svd=TruncatedSVD(n_components=100)\n",
        "  reduced_embeddings=svd.fit_transform(embeddings)\n",
        "\n",
        "  return reduced_embeddings\n"
      ],
      "metadata": {
        "id": "8lxhynHWBqMI"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_embeddings(\" i love so much\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wzxBhEv0G8CL",
        "outputId": "4711f843-a187-4e88-9c37-78e0353997c0"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 6.40322971e+00, -1.04383526e+01,  1.46081886e+01,\n",
              "        -3.60971284e+00, -8.32375288e-01,  6.01719260e-01],\n",
              "       [ 2.82256145e+01,  2.25611649e+01,  8.18492699e+00,\n",
              "        -4.71714878e+00, -3.39641595e+00, -2.10821629e-03],\n",
              "       [ 3.81039734e+01, -8.05090666e-01,  4.67973328e+00,\n",
              "         1.45498085e+01,  2.89446163e+00,  6.34288788e-03],\n",
              "       [ 3.24927979e+01, -2.32613754e+00, -5.38651752e+00,\n",
              "        -8.83478355e+00,  9.36377716e+00, -3.49891186e-03],\n",
              "       [ 3.82714615e+01, -1.03871765e+01, -1.10041733e+01,\n",
              "        -2.34408164e+00, -8.05614662e+00, -3.75723839e-03],\n",
              "       [ 6.30585289e+00, -1.04935961e+01,  1.47938461e+01,\n",
              "        -3.38868546e+00, -7.97591925e-01, -5.99076629e-01]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def compute_metrics(eval_pred):\n",
        "  labels=eval_pred.label_ids\n",
        "  pred=np.argmax(eval_pred.predictions,axis=1)\n",
        "  accuracy=accuracy_score(labels,pred)\n",
        "  recall=recall_score(labels,pred,average='weighted')\n",
        "  f1=f1_score(labels,pred,average='weighted')\n",
        "  precision=precision_score(labels,pred,average='weighted')\n",
        "\n",
        "  return {'accuracy :' accuracy, 'recall :' recall, 'f1_score :': f1, 'precision :': precision}\n",
        "\n",
        "TrainingArguments= {\n",
        "    'output_dir':'./results',\n",
        "    'num-train_epochs':3,\n",
        "    'per_device_train_batch_size':8,\n",
        "    'per_device_eval_batch_size':8,\n",
        "    'warmup_steps':500,\n",
        "    'weight_decay':0.01,\n",
        "    'logging_dir':'./logs\n",
        "}\n",
        "\n",
        "trainer= Trainer{\n",
        "    model=get_embeddings,\n",
        "    args=TrainingArguments,\n",
        "    train_dataset=Restaurants_df,\n",
        "    eval_dataset=laptops_df,\n",
        "    compute_metrics=compute_metrics\n",
        "}"
      ],
      "metadata": {
        "id": "O5lsVVf7Dpvg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tgEqZknsF99k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clean = embeddings.squeeze(0).numpy()\n",
        "clean.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LMmymj8ogBQj",
        "outputId": "bb31ad24-3dd1-49e1-97be-9526542e76a0"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12, 768)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Instruct ABASE ATE inference**\n",
        "(https://huggingface.co/liujiawu/Llama-3-8b-review-absa-4bit-v0.1/tree/main)\n",
        "\n"
      ],
      "metadata": {
        "id": "opoqLAr9IxGD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer,AutoModelForSeq2SeqLM\n",
        "\n",
        "tokenizer=AutoTokenizer.from_pretrained(\"kevinscaria/ate_tk-instruct-base-def-pos-neg-neut-combined\")\n",
        "model=AutoModelForSeq2SeqLM.from_pretrained(\"kevinscaria/ate_tk-instruct-base-def-pos-neg-neut-combined\")"
      ],
      "metadata": {
        "id": "DQO6SYuYXLgn"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs=tokenizer(\"the food was amazing\", return_tensors='pt', padding=True,truncation=True,max_length=512)"
      ],
      "metadata": {
        "id": "jZmOM9t0Xo7w"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RF6FKFh5jPqh",
        "outputId": "3dbf18ec-f6b8-4b27-ee75-cf1d419305ba"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[   8,  542,   47, 1237,    1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1]])}"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.generate(inputs['input_ids'])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PgT_YNhRiXa5",
        "outputId": "e1e444f2-fc36-4881-c6c6-475533c5ac39"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1258: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[   0,   11,    8,  313,   47, 1287,    5,   37,  542,   47, 1237,   11,\n",
              "            8,  313,   47, 1287,    5,    1]])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output=tokenizer.batch_decode(model.generate(inputs['input_ids']), skip_special_tokens=True)"
      ],
      "metadata": {
        "id": "UEfIOkTSjV9u"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mOJQC1JRjeG0",
        "outputId": "f40e8299-dadc-4cc5-a581-798d57a8f9f9"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['and the service was excellent. The food was amazing and the service was excellent.']"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.decode(inputs['input_ids'][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "El3yLgbqiPlp",
        "outputId": "b02fa17f-4abd-49f4-d125-9edae2930202"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'the food was amazing</s>'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hB2zAfs-hiwj",
        "outputId": "b918996c-aadc-4ce1-fa70-c13990d886d4"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[   8,  542,   47, 1237,    1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1]])}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs=tokenizer(\"The service was really good\", return_tensors='pt', padding=True,truncation=True,max_length=512)\n",
        "inputs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tk5zp3mKX2Nb",
        "outputId": "1112717b-d1d4-49ee-8c96-93289a4119fd"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[ 37, 313,  47, 310, 207,   1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1]])}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/kevinscaria/InstructABSA.git\n",
        "%cd InstructABSA\n",
        "!pip install -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iNcWz4nRla-w",
        "outputId": "bb066a10-2e0f-4724-c0fb-301b3aaeecd4"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'InstructABSA'...\n",
            "remote: Enumerating objects: 562, done.\u001b[K\n",
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            "remote: Total 562 (delta 123), reused 197 (delta 106), pack-reused 338 (from 1)\u001b[K\n",
            "Receiving objects: 100% (562/562), 1.58 MiB | 11.11 MiB/s, done.\n",
            "Resolving deltas: 100% (320/320), done.\n",
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            "\u001b[?25hInstalling collected packages: xxhash, pyarrow, dill, multiprocess, datasets, evaluate\n",
            "  Attempting uninstall: pyarrow\n",
            "    Found existing installation: pyarrow 14.0.2\n",
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            "      Successfully uninstalled pyarrow-14.0.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-2.21.0 dill-0.3.8 evaluate-0.4.2 multiprocess-0.70.16 pyarrow-17.0.0 xxhash-3.5.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pyarrow"
                ]
              },
              "id": "3dac2c92ba054f3e9d73bdc5a881cab1"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/InstructABSA\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zIowrPILliEj",
        "outputId": "09d74982-d8a7-4e05-eee9-52b61209b43c"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/InstructABSA\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://huggingface.co/kevinscaria/ate_tk-instruct-base-def-pos-neg-neut-combined\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oDt4FDffmlgz",
        "outputId": "c7f12933-fde1-4a64-a447-a9b7061ef2fb"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'ate_tk-instruct-base-def-pos-neg-neut-combined'...\n",
            "remote: Enumerating objects: 23, done.\u001b[K\n",
            "remote: Total 23 (delta 0), reused 0 (delta 0), pack-reused 23 (from 1)\u001b[K\n",
            "Unpacking objects: 100% (23/23), 607.16 KiB | 5.14 MiB/s, done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python run_model.py -mode cli -task ate \\\n",
        "-model_checkpoint kevinscaria/ate_tk-instruct-base-def-pos-neg-neut-combined \\\n",
        "-test_input 'The cab ride was amazing but the service was pricey'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6bpdYkxTm6KX",
        "outputId": "607f49aa-fee7-4f85-a941-430efd440956"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2024-09-06 18:32:37.021412: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-09-06 18:32:37.398238: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-09-06 18:32:37.495353: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-09-06 18:32:38.100946: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2024-09-06 18:32:40.970666: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "Task:  ate\n",
            "Mode set to:  Individual sample inference\n",
            "Running inference on input:  The cab ride was amazing but the service was pricey\n",
            "1\n",
            "Model loaded from:  kevinscaria/ate_tk-instruct-base-def-pos-neg-neut-combined\n",
            "Model output:  cab ride, service\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DBERTA"
      ],
      "metadata": {
        "id": "RhExRpAZpG5V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "iNbL5lAfpF89"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"yangheng/deberta-v3-large-absa-v1.1\")\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"yangheng/deberta-v3-large-absa-v1.1\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302,
          "referenced_widgets": [
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        },
        "id": "UVf8dJOEpEfX",
        "outputId": "f5da507b-7c0a-4661-8bd8-17d72488a6d4"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/397 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "54a3816b4be44c91b03436b0afcd6e11"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "spm.model:   0%|          | 0.00/2.46M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8dd57094280b4fd3b2df375341b4a7a8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/18.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70ec28a2f0a14bb6898f400eb4004942"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/156 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dfb189b08a5542a88791f5f65a8b7f4f"
            }
          },
          "metadata": {}
        },
        {
          "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",
            "/usr/local/lib/python3.10/dist-packages/transformers/convert_slow_tokenizer.py:551: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.03k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "68e35358f72042c69f848858e0c391a8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.74G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dbe62f96cfb847879550701034c326e2"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs=tokenizer(\"The service was really good\", return_tensors='pt', padding=True,truncation=True,max_length=512)\n"
      ],
      "metadata": {
        "id": "X1p5c0mhpMr3"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad():\n",
        "  outputs=model(**inputs)\n",
        "\n"
      ],
      "metadata": {
        "id": "Wbp5jYaJqDXK"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example sentence and aspect\n",
        "sentence = \"The battery life of this phone is fantastic, but the camera is disappointing.\"\n",
        "aspect = \"camera\"  # You focus on the sentiment towards the camera aspect."
      ],
      "metadata": {
        "id": "zHHxxUW9wpqH"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_text = f\"{aspect} [SEP] {sentence}\"\n",
        "\n",
        "# Tokenize the input with both the aspect and the sentence\n",
        "inputs = tokenizer(input_text, return_tensors=\"pt\", truncation=True, padding=True)\n",
        "\n",
        "# Run inference to get logits\n",
        "with torch.no_grad():\n",
        "    outputs = model(**inputs)\n",
        "\n",
        "# Extract logits\n",
        "logits = outputs.logits\n",
        "\n",
        "# Convert logits to probabilities\n",
        "probs = torch.softmax(logits, dim=-1)\n",
        "\n",
        "# Get the predicted sentiment label (e.g., positive, neutral, negative)\n",
        "predicted_label = torch.argmax(probs, dim=-1).item()\n",
        "\n",
        "# Print the result\n",
        "print(f\"Aspect: {aspect}\")\n",
        "print(f\"Predicted sentiment label: {predicted_label}\")\n",
        "print(f\"Probabilities: {probs}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1AeUEJiqPRP",
        "outputId": "054da173-6c51-4e4e-da71-60bf953c431c"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Aspect: camera\n",
            "Predicted sentiment label: 0\n",
            "Probabilities: tensor([[0.9704, 0.0261, 0.0035]])\n"
          ]
        }
      ]
    }
  ]
}