notebooks/setfit-absa-fiqa.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "faV2oTCDWfOP"
      },
      "source": [
        "# **SetFitABSA vs. BloombergGPT**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rx8Ty07xyZSb"
      },
      "source": [
        "### **BloombergGPT**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tYr4RIcVpp6X"
      },
      "source": [
        "In their paper [BloombergGPT: A Large Language Model for Finance](https://arxiv.org/pdf/2303.17564.pdf), Bloomberg presents the development of **BloombergGPT**, LLM which is specialized for the financial domain.<br>\n",
        "\n",
        "The model is a **50B** parameters language model, trained on a wide range of financial data:\n",
        "\n",
        "*   **363 billion** tokens dataset based on Bloomberg's extensive data sources\n",
        "*   **345 billion** tokens from general purpose datasets\n",
        "\n",
        "The model was evaluated on various financial NLP tasks, including Aspect-Based Sentiment Analysis (**ABSA**). Bloomberg evaluated ABSA in an in-context manner using the [FiQA_SA](https://huggingface.co/datasets/AdaptLLM/finance-tasks/viewer/FiQA_SA). This dataset contains a test set of 235 finance related sentences, each is prefixed by 5 tagged sentences, which overall comprise an input prompt to the model. Every tagged sentence in the prompt contains a known aspect and a question whether its corresponding polarity is Positive, Negative or Neutral, and the answer (the correct polarity towards the mentioned aspect) is written at the end. BloombergGPT is expected to predict the polarity of a given aspect in the test sentence based on the 5 tagged example sentences. This is an **SB2 ABSA task**, which means that the aspect is already given and the model just needs to predict the corresponding polarity.\n",
        "\n",
        "The evaluation score achieved by this model is **weighted F1-score=75.07**, as reported in table 8 in the paper."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MGvd4lhLOY6D"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7aO6BJTKzDPz"
      },
      "source": [
        "### **SetFitABSA**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LjzpXKMZP3Lv"
      },
      "source": [
        "[SetFitABSA](https://huggingface.co/blog/setfit-absa) is a framework for **few-shot** training of domain-specific ABSA models. It provides an efficient and accurate technique to detect sentiment towards specific aspects within the text. The following graph shows evaluation results of a SetFitABSA model that was trained with just few k finance related sentences and tested on the same 235 FiQA_SA test sentences that were used used to evaluate BloombergGPT. The size of the model is based on its underlying sentence transformer and is usually considerably smaller compared to SOTA LLMs.\n",
        "\n",
        "Specifically, in this work, we used the **paraphrase-mpnet-base-v2** sentence transformer which contains only **110M** params compared to **50B** of BloombergGPT.<br>\n",
        "\n",
        "We separated from each prompt in [FiQA_SA](https://huggingface.co/datasets/AdaptLLM/finance-tasks/viewer/FiQA_SA) the tagged and non-tagged sentences, to create [train and test sets](https://huggingface.co/datasets/ronenlap/SetFitAbsa_FiQA) correspondingly. The train set contains 646 unique sentences overall.<br>This graph summarizes the evaluation results. It presents the weighted F1-score achieved by SetFitABSA as a function of k, the number of sampled training sentences. The result for each k is averaged over 5 seeds. We can see that with only 50 tranining sentences SetFitABSA exceeds the BloombergGPT score. BTW, if we use the full training set for training the SetFitABSA model we get a weighted F1-score of over 86."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RP52Iu84mpni"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lIYXB0_WsS9v"
      },
      "source": [
        "# **Demo Code**\n",
        "The following code demonstrates the training of a SetFitABSA model and its evaluation over the **FiQA_SA** financial dataset.\n",
        "\n",
        "Specifically:\n",
        "\n",
        "*   We sample **k=24** sentences from the training set using an arbitrary seed\n",
        "*   We trained the SetFitABSA model using only these **k** samples\n",
        "*   We evaluate the trained model over the entire test set which is exactly the same set that was used to evalute BloombergGPT model. The evaluated task is **SB2** - aspects are given and the model needs to predict their corresponding polarity\n",
        "\n",
        "With just 24 training sentences we get a weighted F1-score which is on par or better than the 75.07 score achieved by BloombergGPT (try it yourself!)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CKhJf7WGmbE7"
      },
      "source": [
        "### Install setfit with **SetFitABSA option**\n",
        "We have to install SetFit as well as download a spaCy model for doing the initial aspect span candidate selection. We will download `en_core_web_lg`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vMlfBkqRPBh5",
        "outputId": "7fc3451c-104c-481d-ab45-15261e75c181"
      },
      "outputs": [],
      "source": [
        "!pip install -U \"setfit[absa]\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P44W5eNik2Hi",
        "outputId": "64420ddd-8f1c-43d5-af4d-3347832b4a51"
      },
      "outputs": [],
      "source": [
        "!python -m spacy download en_core_web_lg"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lTpFKfgWUc0T"
      },
      "source": [
        "### Import required packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "G8McUhomj__u"
      },
      "outputs": [],
      "source": [
        "from setfit import AbsaTrainer, TrainingArguments, AbsaModel\n",
        "from datasets import load_dataset\n",
        "from sklearn.metrics import f1_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ECeJLDYi1E9"
      },
      "source": [
        "### Load the **FiQA_SA** dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336,
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          ]
        },
        "id": "cDDyzGgUPmyS",
        "outputId": "86adc620-2ce1-43a0-d558-e90ef357f407"
      },
      "outputs": [],
      "source": [
        "dataset = load_dataset(\"ronenlap/SetFitAbsa_FiQA\")\n",
        "train_ds = dataset[\"train\"]\n",
        "test_ds = dataset[\"test\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "_eqmbNT-W_EY"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['sentence', 'aspect', 'polarity', 'ordinal'],\n",
              "    num_rows: 670\n",
              "})"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_ds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'sentence': '#Apple breaks major support, here are some levels to watch - http://stks.co/jRmW $AAPL $QQQ',\n",
              " 'aspect': 'QQQ',\n",
              " 'polarity': 'Negative',\n",
              " 'ordinal': 0}"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_ds[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_pGtvlgeoETT"
      },
      "source": [
        "### Simulate the few-shot regime by sampling **k** text reviews for training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "2QioIQtf6R49"
      },
      "outputs": [],
      "source": [
        "k = 24\n",
        "seed = 35"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "WiWN392BhTNb"
      },
      "outputs": [],
      "source": [
        "experiment_ds = train_ds.shuffle(seed=seed).select(range(k))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['sentence', 'aspect', 'polarity', 'ordinal'],\n",
              "    num_rows: 24\n",
              "})"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "experiment_ds"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yo-AQvyEjDSp"
      },
      "source": [
        "### **Training a SetFitABSA Model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iuQd_TN-ko0H"
      },
      "source": [
        "#### Initialize an ABSA model\n",
        "We'll initialize an AbsaModel using the strong [paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) base model."
      ]
    },
    {
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      "execution_count": 9,
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          ]
        },
        "id": "_IGAjO9mjIZa",
        "outputId": "b2cb3a00-6f3f-44fa-d31e-e9deb0e65384"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "model_head.pkl not found on HuggingFace Hub, initialising classification head with random weights. You should TRAIN this model on a downstream task to use it for predictions and inference.\n",
            "model_head.pkl not found on HuggingFace Hub, initialising classification head with random weights. You should TRAIN this model on a downstream task to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "model = AbsaModel.from_pretrained(\n",
        "    \"sentence-transformers/paraphrase-mpnet-base-v2\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RhLd40H1ltYv"
      },
      "source": [
        "#### Setting the training arguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZXrk2_m3j32r",
        "outputId": "1e9f5b92-a6d9-49ef-b7ce-6edefa76601c"
      },
      "outputs": [],
      "source": [
        "args = TrainingArguments(\n",
        "    num_epochs=1,\n",
        "    batch_size=4,\n",
        "    num_iterations=20,\n",
        "    save_strategy=\"no\",\n",
        "    report_to=\"none\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mrEK4zCmmVzo"
      },
      "source": [
        "#### Creating a trainer object and executing the SetFitABSA model \n",
        "We need to apply a column mapping as the AbsaTrainer expects `\"text\"`, `\"span\"`, `\"label\"` and `\"ordinal\"` columns. The text refers to the sentence, whereas span is an aspect span in the sentence. The label is the corresponding label (e.g. \"positive\"), while ordinal is used to distinguish spans if they occur multiple times in the sentence. For example, if `\"stock\"` is the current aspect span, and it occurs 3 times in the sentence, an ordinal of `0` indicates that the sample is referring to the first occurrence."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
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        "colab": {
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        "id": "UQhELf4kmgdi",
        "outputId": "a3828fd6-2767-40cc-c4ec-61c7f63376f2"
      },
      "outputs": [
        {
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              "Map:   0%|          | 0/97 [00:00<?, ? examples/s]"
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          "text": [
            "***** Running training *****\n",
            "  Num examples = 970\n",
            "  Num epochs = 1\n",
            "  Total optimization steps = 970\n",
            "  Total train batch size = 4\n"
          ]
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            "{'aspect_embedding_loss': 0.435, 'learning_rate': 2.061855670103093e-07, 'epoch': 0.0}\n",
            "{'aspect_embedding_loss': 0.23, 'learning_rate': 1.0309278350515464e-05, 'epoch': 0.05}\n",
            "{'aspect_embedding_loss': 0.3388, 'learning_rate': 1.9931271477663232e-05, 'epoch': 0.1}\n",
            "{'aspect_embedding_loss': 0.3288, 'learning_rate': 1.8785796105383734e-05, 'epoch': 0.15}\n",
            "{'aspect_embedding_loss': 0.0135, 'learning_rate': 1.764032073310424e-05, 'epoch': 0.21}\n",
            "{'aspect_embedding_loss': 0.0006, 'learning_rate': 1.6494845360824743e-05, 'epoch': 0.26}\n",
            "{'aspect_embedding_loss': 0.0006, 'learning_rate': 1.534936998854525e-05, 'epoch': 0.31}\n",
            "{'aspect_embedding_loss': 0.0002, 'learning_rate': 1.4203894616265753e-05, 'epoch': 0.36}\n",
            "{'aspect_embedding_loss': 0.0003, 'learning_rate': 1.3058419243986255e-05, 'epoch': 0.41}\n",
            "{'aspect_embedding_loss': 0.0007, 'learning_rate': 1.191294387170676e-05, 'epoch': 0.46}\n",
            "{'aspect_embedding_loss': 0.0002, 'learning_rate': 1.0767468499427262e-05, 'epoch': 0.52}\n",
            "{'aspect_embedding_loss': 0.0001, 'learning_rate': 9.621993127147768e-06, 'epoch': 0.57}\n",
            "{'aspect_embedding_loss': 0.0004, 'learning_rate': 8.47651775486827e-06, 'epoch': 0.62}\n",
            "{'aspect_embedding_loss': 0.0001, 'learning_rate': 7.331042382588775e-06, 'epoch': 0.67}\n",
            "{'aspect_embedding_loss': 0.0002, 'learning_rate': 6.185567010309279e-06, 'epoch': 0.72}\n",
            "{'aspect_embedding_loss': 0.0001, 'learning_rate': 5.040091638029782e-06, 'epoch': 0.77}\n",
            "{'aspect_embedding_loss': 0.0001, 'learning_rate': 3.8946162657502865e-06, 'epoch': 0.82}\n",
            "{'aspect_embedding_loss': 0.0002, 'learning_rate': 2.7491408934707907e-06, 'epoch': 0.88}\n",
            "{'aspect_embedding_loss': 0.0001, 'learning_rate': 1.6036655211912947e-06, 'epoch': 0.93}\n",
            "{'aspect_embedding_loss': 0.0001, 'learning_rate': 4.581901489117984e-07, 'epoch': 0.98}\n"
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            "***** Running training *****\n",
            "  Num examples = 240\n",
            "  Num epochs = 1\n",
            "  Total optimization steps = 240\n",
            "  Total train batch size = 4\n"
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            "{'train_runtime': 138.2197, 'train_samples_per_second': 28.071, 'train_steps_per_second': 7.018, 'epoch': 1.0}\n"
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          "text": [
            "{'polarity_embedding_loss': 0.4415, 'learning_rate': 8.333333333333333e-07, 'epoch': 0.0}\n",
            "{'polarity_embedding_loss': 0.1503, 'learning_rate': 1.7592592592592595e-05, 'epoch': 0.21}\n",
            "{'polarity_embedding_loss': 0.0051, 'learning_rate': 1.2962962962962964e-05, 'epoch': 0.42}\n",
            "{'polarity_embedding_loss': 0.0012, 'learning_rate': 8.333333333333334e-06, 'epoch': 0.62}\n",
            "{'polarity_embedding_loss': 0.0004, 'learning_rate': 3.7037037037037037e-06, 'epoch': 0.83}\n",
            "{'train_runtime': 34.0637, 'train_samples_per_second': 28.182, 'train_steps_per_second': 7.046, 'epoch': 1.0}\n"
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      "source": [
        "trainer = AbsaTrainer(\n",
        "    model,\n",
        "    args=args,\n",
        "    train_dataset=experiment_ds,  # if you want to train over the entire train set change experiment_ds to train_ds\n",
        "    column_mapping={\n",
        "        \"sentence\": \"text\",\n",
        "        \"aspect\": \"span\",\n",
        "        \"polarity\": \"label\",\n",
        "        \"ordinal\": \"ordinal\",\n",
        "    },\n",
        ")\n",
        "\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wcdDcme_s0Yb"
      },
      "source": [
        "Now, when training has completed,  `model` is a trained ABSA model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rjiT-S2V54vQ"
      },
      "source": [
        "### **Evaluation**\n",
        "As we mentioned above, the evaluated ABSA task is SB2 - aspects are given and the model just needs to predict their corresponding polarity.\n",
        "\n",
        "SetFitABSA supports this by providing the `predict` method with a dataset in a similar format as required for training, minus the `label` column. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fZ9U2z3X1R5U",
        "outputId": "d8b4d7c7-1ae3-49c9-c8e0-fdadb4cee6c3"
      },
      "outputs": [],
      "source": [
        "# rename columns according to predict() requirements\n",
        "test_ds = test_ds.rename_columns({\"sentence\": \"text\",\"aspect\": \"span\"})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "zSHvsv6v2rBF"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text', 'span', 'polarity', 'ordinal', 'pred_polarity'],\n",
              "    num_rows: 235\n",
              "})"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
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      ],
      "source": [
        "output  = model.predict(test_ds) # a new column which holds the predicted polarity, \"pred_polarity\", is added to the dataset\n",
        "output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Then, we can compute the weighted F1 score by taking both the gold polarity and the predicted polarity."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W1woeTA_3NZy",
        "outputId": "8118befb-113a-4920-9fe9-46889548b076"
      },
      "outputs": [
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "weighted_f1_score: 0.7819641265152422\n"
          ]
        }
      ],
      "source": [
        "weighted_f1_score = f1_score(output[\"polarity\"], output[\"pred_polarity\"], average=\"weighted\")\n",
        "print(f\"weighted_f1_score: {weighted_f1_score}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### **Inference**\n",
        "Now that we've trained and evaluated the model, let's also try it out with some examples to get an intuitive feel of the model as well."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "nPkvzkht55oX"
      },
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        {
          "data": {
            "text/plain": [
              "[[{'span': 'Tesla', 'polarity': 'Negative'}],\n",
              " [{'span': 'CIEN', 'polarity': 'Positive'}],\n",
              " [{'span': 'AAPL', 'polarity': 'Negative'}]]"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
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        "sentences = [\n",
        "    \"#Tesla: Model X Recall Adds To Reliability Issues $TSLA https://t.co/jVXQ4DoXnP\",\n",
        "    \"$CIEN seems to have broken out of a major horizontal resistance. Targets $14.35.\",\n",
        "    \"$AAPL I am big OUT from this. seems its falling towards 530.. :(\",\n",
        "]\n",
        "\n",
        "model.predict(sentences)"
      ]
    }
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