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'label'],\n", " num_rows: 50000\n", " })\n", "})\n" ] } ], "source": [ "from datasets import load_dataset\n", "\n", "dataset = load_dataset(\"imdb\")\n", "\n", "print(dataset)" ] }, { "cell_type": "code", "source": [ "!pip install -q datasets transformers evaluate accelerate scikit-learn nltk shap" ], "metadata": { "id": "Ty6Cm3jUQlLG" }, "execution_count": 31, "outputs": [] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import nltk\n", "import re\n", "import string\n", "\n", "from datasets import load_dataset\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import accuracy_score, precision_recall_fscore_support, classification_report, confusion_matrix\n", "from sklearn.pipeline import Pipeline\n", "\n", "nltk.download('stopwords')\n", "nltk.download('wordnet')\n", "nltk.download('omw-1.4')\n", "\n", "from nltk.corpus import stopwords\n", "from nltk.stem import WordNetLemmatizer" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "139VmDSqQ7Tk", "outputId": "2a24f21f-224b-49f2-f412-e1655475e3b8" }, "execution_count": 32, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", "[nltk_data] Package stopwords is already up-to-date!\n", "[nltk_data] Downloading package wordnet to /root/nltk_data...\n", "[nltk_data] Package wordnet is already up-to-date!\n", "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n", "[nltk_data] Package omw-1.4 is already up-to-date!\n" ] } ] }, { "cell_type": "code", "source": [ "dataset = load_dataset(\"imdb\")\n", "dataset" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qxId9rXqQ9nh", "outputId": "158ab4d4-1b3d-47f8-ab81-0790f85d9193" }, "execution_count": 33, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['text', 'label'],\n", " num_rows: 25000\n", " })\n", " test: Dataset({\n", " features: ['text', 'label'],\n", " num_rows: 25000\n", " })\n", " unsupervised: Dataset({\n", " features: ['text', 'label'],\n", " num_rows: 50000\n", " })\n", "})" ] }, "metadata": {}, "execution_count": 33 } ] }, { "cell_type": "code", "source": [ "print(dataset[\"train\"][0])\n", "print(dataset[\"test\"][0])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "jQtMpk5cRA_4", "outputId": "fb5ec780-de76-4cde-a832-db2dac8428cb" }, "execution_count": 34, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'text': 'I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it when it was first released in 1967. I also heard that at first it was seized by U.S. customs if it ever tried to enter this country, therefore being a fan of films considered \"controversial\" I really had to see this for myself.

The plot is centered around a young Swedish drama student named Lena who wants to learn everything she can about life. In particular she wants to focus her attentions to making some sort of documentary on what the average Swede thought about certain political issues such as the Vietnam War and race issues in the United States. In between asking politicians and ordinary denizens of Stockholm about their opinions on politics, she has sex with her drama teacher, classmates, and married men.

What kills me about I AM CURIOUS-YELLOW is that 40 years ago, this was considered pornographic. Really, the sex and nudity scenes are few and far between, even then it\\'s not shot like some cheaply made porno. While my countrymen mind find it shocking, in reality sex and nudity are a major staple in Swedish cinema. Even Ingmar Bergman, arguably their answer to good old boy John Ford, had sex scenes in his films.

I do commend the filmmakers for the fact that any sex shown in the film is shown for artistic purposes rather than just to shock people and make money to be shown in pornographic theaters in America. I AM CURIOUS-YELLOW is a good film for anyone wanting to study the meat and potatoes (no pun intended) of Swedish cinema. But really, this film doesn\\'t have much of a plot.', 'label': 0}\n", "{'text': 'I love sci-fi and am willing to put up with a lot. Sci-fi movies/TV are usually underfunded, under-appreciated and misunderstood. I tried to like this, I really did, but it is to good TV sci-fi as Babylon 5 is to Star Trek (the original). Silly prosthetics, cheap cardboard sets, stilted dialogues, CG that doesn\\'t match the background, and painfully one-dimensional characters cannot be overcome with a \\'sci-fi\\' setting. (I\\'m sure there are those of you out there who think Babylon 5 is good sci-fi TV. It\\'s not. It\\'s clichéd and uninspiring.) While US viewers might like emotion and character development, sci-fi is a genre that does not take itself seriously (cf. Star Trek). It may treat important issues, yet not as a serious philosophy. It\\'s really difficult to care about the characters here as they are not simply foolish, just missing a spark of life. Their actions and reactions are wooden and predictable, often painful to watch. The makers of Earth KNOW it\\'s rubbish as they have to always say \"Gene Roddenberry\\'s Earth...\" otherwise people would not continue watching. Roddenberry\\'s ashes must be turning in their orbit as this dull, cheap, poorly edited (watching it without advert breaks really brings this home) trudging Trabant of a show lumbers into space. Spoiler. So, kill off a main character. And then bring him back as another actor. Jeeez! Dallas all over again.', 'label': 0}\n" ] } ] }, { "cell_type": "code", "source": [ "train_df = pd.DataFrame(dataset[\"train\"])\n", "test_df = pd.DataFrame(dataset[\"test\"])\n", "\n", "print(train_df.head())\n", "print(train_df[\"label\"].value_counts())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QQc2UAlHREdM", "outputId": "22e52445-6cc2-427d-894b-e31e027fb98c" }, "execution_count": 35, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " text label\n", "0 I rented I AM CURIOUS-YELLOW from my video sto... 0\n", "1 \"I Am Curious: Yellow\" is a risible and preten... 0\n", "2 If only to avoid making this type of film in t... 0\n", "3 This film was probably inspired by Godard's Ma... 0\n", "4 Oh, brother...after hearing about this ridicul... 0\n", "label\n", "0 12500\n", "1 12500\n", "Name: count, dtype: int64\n" ] } ] }, { "cell_type": "code", "source": [ "train_df[\"label\"].value_counts().plot(kind=\"bar\")\n", "plt.title(\"Class Distribution in Training Data\")\n", "plt.xticks([0, 1], [\"Negative\", \"Positive\"], rotation=0)\n", "plt.ylabel(\"Count\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 452 }, "id": "92cc8mGURHIl", "outputId": "97b6be20-9e97-49c6-e3cf-22231b103267" }, "execution_count": 36, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "train_df[\"review_length\"] = train_df[\"text\"].apply(lambda x: len(x.split()))\n", "\n", "plt.hist(train_df[\"review_length\"], bins=50)\n", "plt.title(\"Distribution of Review Lengths\")\n", "plt.xlabel(\"Number of Words\")\n", "plt.ylabel(\"Frequency\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 459 }, "id": "HnXW6VCpRRm8", "outputId": "5b1c804e-32e4-468a-9602-043e577b30e2" }, "execution_count": 37, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {} } ] }, { "cell_type": "code", "source": [ "stop_words = set(stopwords.words(\"english\"))\n", "lemmatizer = WordNetLemmatizer()\n", "\n", "def clean_text(text):\n", " text = text.lower()\n", " text = re.sub(r\"<.*?>\", \" \", text) # remove HTML tags\n", " text = re.sub(rf\"[{re.escape(string.punctuation)}]\", \" \", text) # remove punctuation\n", " text = re.sub(r\"\\d+\", \" \", text) # remove numbers\n", " text = re.sub(r\"\\s+\", \" \", text).strip() # remove extra whitespace\n", "\n", " words = text.split()\n", " words = [lemmatizer.lemmatize(word) for word in words if word not in stop_words]\n", " return \" \".join(words)\n", "\n", "train_df[\"clean_text\"] = train_df[\"text\"].apply(clean_text)\n", "test_df[\"clean_text\"] = test_df[\"text\"].apply(clean_text)\n", "\n", "train_df[[\"text\", \"clean_text\"]].head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 293 }, "id": "vNfuqs0vRTvu", "outputId": "a4bc4c07-3eeb-4700-c453-fab2e13330c1" }, "execution_count": 38, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " text \\\n", "0 I rented I AM CURIOUS-YELLOW from my video sto... \n", "1 \"I Am Curious: Yellow\" is a risible and preten... \n", "2 If only to avoid making this type of film in t... \n", "3 This film was probably inspired by Godard's Ma... \n", "4 Oh, brother...after hearing about this ridicul... \n", "\n", " clean_text \n", "0 rented curious yellow video store controversy ... \n", "1 curious yellow risible pretentious steaming pi... \n", "2 avoid making type film future film interesting... \n", "3 film probably inspired godard masculin féminin... \n", "4 oh brother hearing ridiculous film umpteen yea... 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0I rented I AM CURIOUS-YELLOW from my video sto...rented curious yellow video store controversy ...
1\"I Am Curious: Yellow\" is a risible and preten...curious yellow risible pretentious steaming pi...
2If only to avoid making this type of film in t...avoid making type film future film interesting...
3This film was probably inspired by Godard's Ma...film probably inspired godard masculin féminin...
4Oh, brother...after hearing about this ridicul...oh brother hearing ridiculous film umpteen yea...
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\\\"Is that all there is??\\\" ...I was just an early teen when this smoked fish hit the U.S. I was too young to get in the theater (although I did manage to sneak into \\\"Goodbye Columbus\\\"). Then a screening at a local film museum beckoned - Finally I could see this film, except now I was as old as my parents were when they schlepped to see it!!

The ONLY reason this film was not condemned to the anonymous sands of time was because of the obscenity case sparked by its U.S. release. MILLIONS of people flocked to this stinker, thinking they were going to see a sex film...Instead, they got lots of closeups of gnarly, repulsive Swedes, on-street interviews in bland shopping malls, asinie political pretension...and feeble who-cares simulated sex scenes with saggy, pale actors.

Cultural icon, holy grail, historic artifact..whatever this thing was, shred it, burn it, then stuff the ashes in a lead box!

Elite esthetes still scrape to find value in its boring pseudo revolutionary political spewings..But if it weren't for the censorship scandal, it would have been ignored, then forgotten.

Instead, the \\\"I Am Blank, Blank\\\" rhythymed title was repeated endlessly for years as a titilation for porno films (I am Curious, Lavender - for gay films, I Am Curious, Black - for blaxploitation films, etc..) and every ten years or so the thing rises from the dead, to be viewed by a new generation of suckers who want to see that \\\"naughty sex film\\\" that \\\"revolutionized the film industry\\\"...

Yeesh, avoid like the plague..Or if you MUST see it - rent the video and fast forward to the \\\"dirty\\\" parts, just to get it over with.

\",\n \"If only to avoid making this type of film in the future. This film is interesting as an experiment but tells no cogent story.

One might feel virtuous for sitting thru it because it touches on so many IMPORTANT issues but it does so without any discernable motive. The viewer comes away with no new perspectives (unless one comes up with one while one's mind wanders, as it will invariably do during this pointless film).

One might better spend one's time staring out a window at a tree growing.

\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"clean_text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"curious yellow risible pretentious steaming pile matter one political view film hardly taken seriously level claim frontal male nudity automatic nc true seen r rated film male nudity granted offer fleeting view r rated film gaping vulva flapping labium nowhere exist go crappy cable show schlongs swinging breeze clitoris sight pretentious indie movie like brown bunny treated site vincent gallo throbbing johnson trace pink visible chloe sevigny cry implying double standard matter nudity mentally obtuse take account one unavoidably obvious anatomical difference men woman genitals display actress appears nude cannot said man fact generally see female genitals american film anything short porn explicit erotica alleged double standard less double standard admittedly depressing ability come term culturally inside woman body\",\n \"oh brother hearing ridiculous film umpteen year think old peggy lee song early teen smoked fish hit u young get theater although manage sneak goodbye columbus screening local film museum beckoned finally could see film except old parent schlepped see reason film condemned anonymous sand time obscenity case sparked u release million people flocked stinker thinking going see sex film instead got lot closeup gnarly repulsive swede street interview bland shopping mall asinie political pretension feeble care simulated sex scene saggy pale actor cultural icon holy grail historic artifact whatever thing shred burn stuff ash lead box elite esthete still scrape find value boring pseudo revolutionary political spewings censorship scandal would ignored forgotten instead blank blank rhythymed title repeated endlessly year titilation porno film curious lavender gay film curious black blaxploitation film etc every ten year thing rise dead viewed new generation sucker want see naughty sex film revolutionized film industry yeesh avoid like plague must see rent video fast forward dirty part get\",\n \"avoid making type film future film interesting experiment tell cogent story one might feel virtuous sitting thru touch many important issue without discernable motive viewer come away new perspective unless one come one one mind wanders invariably pointless film one might better spend one time staring window tree growing\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 38 } ] }, { "cell_type": "code", "source": [ "tfidf_lr_pipeline = Pipeline([\n", " (\"tfidf\", TfidfVectorizer(max_features=10000, ngram_range=(1,2))),\n", " (\"clf\", LogisticRegression(max_iter=1000))\n", "])\n", "\n", "tfidf_lr_pipeline.fit(train_df[\"clean_text\"], train_df[\"label\"])\n", "\n", "lr_preds = tfidf_lr_pipeline.predict(test_df[\"clean_text\"])" ], "metadata": { "id": "zUr6EhJ1RW_u" }, "execution_count": 39, "outputs": [] }, { "cell_type": "code", "source": [ "accuracy = accuracy_score(test_df[\"label\"], lr_preds)\n", "precision, recall, f1, _ = precision_recall_fscore_support(test_df[\"label\"], lr_preds, average=\"binary\")\n", "\n", "print(\"TF-IDF + Logistic Regression Results\")\n", "print(f\"Accuracy: {accuracy:.4f}\")\n", "print(f\"Precision: {precision:.4f}\")\n", "print(f\"Recall: {recall:.4f}\")\n", "print(f\"F1-score: {f1:.4f}\")\n", "\n", "print(\"\\nClassification Report:\\n\")\n", "print(classification_report(test_df[\"label\"], lr_preds, target_names=[\"Negative\", \"Positive\"]))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "k1z4jj2XRqIW", "outputId": "f6bc6555-af91-4974-eb04-11ae5d7694be" }, "execution_count": 40, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "TF-IDF + Logistic Regression Results\n", "Accuracy: 0.8841\n", "Precision: 0.8824\n", "Recall: 0.8864\n", "F1-score: 0.8844\n", "\n", "Classification Report:\n", "\n", " precision recall f1-score support\n", "\n", " Negative 0.89 0.88 0.88 12500\n", " Positive 0.88 0.89 0.88 12500\n", "\n", " accuracy 0.88 25000\n", " macro avg 0.88 0.88 0.88 25000\n", "weighted avg 0.88 0.88 0.88 25000\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "cm = confusion_matrix(test_df[\"label\"], lr_preds)\n", "\n", "plt.figure(figsize=(6,4))\n", "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n", " xticklabels=[\"Negative\", \"Positive\"],\n", " yticklabels=[\"Negative\", \"Positive\"])\n", "plt.xlabel(\"Predicted\")\n", "plt.ylabel(\"Actual\")\n", "plt.title(\"Confusion Matrix - TF-IDF + Logistic Regression\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 410 }, "id": "VI66tCBKR1aN", "outputId": "bd86ea75-c2d7-4a19-b1dc-e6663578ba62" }, "execution_count": 41, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "vectorizer = tfidf_lr_pipeline.named_steps[\"tfidf\"]\n", "model = tfidf_lr_pipeline.named_steps[\"clf\"]\n", "\n", "feature_names = np.array(vectorizer.get_feature_names_out())\n", "coefficients = model.coef_[0]\n", "\n", "top_positive = feature_names[np.argsort(coefficients)[-20:]]\n", "top_negative = feature_names[np.argsort(coefficients)[:20]]\n", "\n", "print(\"Top Positive Words:\")\n", "print(top_positive)\n", "\n", "print(\"\\nTop Negative Words:\")\n", "print(top_negative)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Ha3dJinzR5FD", "outputId": "6ce2c22c-8b21-475d-b97d-c66f284dc443" }, "execution_count": 42, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Top Positive Words:\n", "['beautiful' 'fantastic' 'must see' 'enjoyed' 'highly' 'love' 'one best'\n", " 'brilliant' 'superb' 'today' 'well' 'fun' 'loved' 'favorite' 'amazing'\n", " 'best' 'wonderful' 'perfect' 'excellent' 'great']\n", "\n", "Top Negative Words:\n", "['worst' 'bad' 'awful' 'waste' 'boring' 'poor' 'nothing' 'worse'\n", " 'terrible' 'horrible' 'poorly' 'dull' 'unfortunately' 'disappointment'\n", " 'disappointing' 'annoying' 'supposed' 'ridiculous' 'stupid' 'fails']\n" ] } ] }, { "cell_type": "code", "source": [ "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n", "from transformers import TrainingArguments, Trainer\n", "import evaluate" ], "metadata": { "id": "WHzFxIueR757" }, "execution_count": 43, "outputs": [] }, { "cell_type": "code", "source": [ "model_name = \"distilbert-base-uncased\"\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 359, "referenced_widgets": [ "01e7dfb98c8f4df8a69da8427102f105", "453a2b9159424435b3b80b8282507799", "e982e9aafcab4fe2b32c87d027ef9eb4", "16b0f11e209343e7a0011239b785f979", "f2b8e2c38e254644aef9c8d27a2b4c94", "de4dc47d8fd34fb4af4a7895bb216dd5", "e7a761550ea945d49c1e5ec80b7ab98d", "16275a661b80464f9660baa80b6ffbd6", "cb053df037004f41855d0b966e1dc760", "f6163a738b5a48ababdf5055dffeaab9", "44cbb6b83e1245d09c331af730a7b1a6" ] }, "id": "ucwf3f7SR_Rk", "outputId": "f60d1e07-2436-4ac5-9446-11782717299a" }, "execution_count": 44, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/100 [00:00" ], "text/html": [ "\n", "
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EpochTraining LossValidation LossAccuracyPrecisionRecallF1
10.3596360.3388720.8620000.8205130.9180330.866538
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" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Writing model shards: 0%| | 0/1 [00:00" ], "text/html": [ "\n", "

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\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "{'eval_loss': 0.3389528691768646, 'eval_accuracy': 0.862, 'eval_precision': 0.8205128205128205, 'eval_recall': 0.9180327868852459, 'eval_f1': 0.8665377176015474, 'eval_runtime': 8.9707, 'eval_samples_per_second': 111.474, 'eval_steps_per_second': 13.934, 'epoch': 2.0}\n" ] } ] }, { "cell_type": "code", "source": [ "pred_output = trainer.predict(small_test)\n", "bert_preds = np.argmax(pred_output.predictions, axis=-1)\n", "bert_labels = pred_output.label_ids\n", "\n", "print(classification_report(bert_labels, bert_preds, target_names=[\"Negative\", \"Positive\"]))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 173 }, "id": "LciFPQs9XpY4", "outputId": "c3e330b5-a3ae-4d53-fd82-965b066043e4" }, "execution_count": 52, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " Negative 0.91 0.81 0.86 512\n", " Positive 0.82 0.92 0.87 488\n", "\n", " accuracy 0.86 1000\n", " macro avg 0.87 0.86 0.86 1000\n", "weighted avg 0.87 0.86 0.86 1000\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "cm_bert = confusion_matrix(bert_labels, bert_preds)\n", "\n", "plt.figure(figsize=(6,4))\n", "sns.heatmap(cm_bert, annot=True, fmt=\"d\", cmap=\"Greens\",\n", " xticklabels=[\"Negative\", \"Positive\"],\n", " yticklabels=[\"Negative\", \"Positive\"])\n", "plt.xlabel(\"Predicted\")\n", "plt.ylabel(\"Actual\")\n", "plt.title(\"Confusion Matrix - DistilBERT\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 410 }, "id": "SG6XJxnHX4RA", "outputId": "96c1fcb9-8f1a-4dd5-9a0d-3e019cb6f056" }, "execution_count": 53, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "lr_accuracy = accuracy_score(test_df[\"label\"], lr_preds)\n", "lr_precision, lr_recall, lr_f1, _ = precision_recall_fscore_support(test_df[\"label\"], lr_preds, average=\"binary\")\n", "\n", "comparison_df = pd.DataFrame({\n", " \"Model\": [\"TF-IDF + Logistic Regression\", \"DistilBERT\"],\n", " \"Accuracy\": [lr_accuracy, bert_results[\"eval_accuracy\"]],\n", " \"Precision\": [lr_precision, bert_results[\"eval_precision\"]],\n", " \"Recall\": [lr_recall, bert_results[\"eval_recall\"]],\n", " \"F1-score\": [lr_f1, bert_results[\"eval_f1\"]]\n", "})\n", "\n", "comparison_df" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 147 }, "id": "jsAN2KECYAqj", "outputId": "8930ee81-598a-43cb-9d50-6032391552d5" }, "execution_count": 54, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Model Accuracy Precision Recall F1-score\n", "0 TF-IDF + Logistic Regression 0.88412 0.882376 0.886400 0.884384\n", "1 DistilBERT 0.86200 0.820513 0.918033 0.866538" ], "text/html": [ "\n", "
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ModelAccuracyPrecisionRecallF1-score
0TF-IDF + Logistic Regression0.884120.8823760.8864000.884384
1DistilBERT0.862000.8205130.9180330.866538
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