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EpochTraining LossValidation LossAccuracy
1No log3.1207030.000000
23.1185912.9297850.150000
33.1185912.7687500.450000
42.9082092.8344730.700000
52.9082092.7525390.700000

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d269c9de97e94fdabfa79afaedd9904d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Writing model shards: 0%| | 0/1 [00:00\n", " \n", " \n", " [2/2 00:00]\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Urdu validation accuracy: 0.7000\n", "Zero-shot accuracy on Punjabi: 0.5500 (55.00%)\n", "Zero-shot accuracy on Sindhi: 0.2100 (21.00%)\n", "Zero-shot accuracy on Saraiki: 0.5300 (53.00%)\n", "\n", "========================================\n", "FINAL ACCURACY SUMMARY\n", "========================================\n", "Urdu (in-language): 0.7000\n", "Punjabi : 0.5500\n", "Sindhi : 0.2100\n", "Saraiki : 0.5300\n" ] } ], "source": [ "# ============================================================\n", "# Zero-Shot Reasoning Transfer - Final Working Version\n", "# ============================================================\n", "\n", "!pip install -q transformers datasets scikit-learn pandas sindhinltk\n", "\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn.metrics import accuracy_score\n", "import torch\n", "from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer\n", "from datasets import Dataset\n", "from google.colab import files\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "# Import SindhiNLTK\n", "import sindhinltk\n", "from sindhinltk.normalizer import SindhiNormalizer\n", "from sindhinltk.tokenizer import SindhiTokenizer\n", "\n", "sindhi_normalizer = SindhiNormalizer()\n", "sindhi_tokenizer = SindhiTokenizer()\n", "\n", "# ============================================================\n", "# 1. Upload and load data\n", "# ============================================================\n", "uploaded = files.upload()\n", "filename = list(uploaded.keys())[0]\n", "df = pd.read_csv(filename)\n", "print(f\"Loaded {len(df)} examples\")\n", "\n", "# ============================================================\n", "# 2. Token fertility (simplified)\n", "# ============================================================\n", "def fertility_sindhi(texts):\n", " total_tokens, total_words = 0, 0\n", " for t in texts:\n", " if pd.isna(t): continue\n", " norm = sindhi_normalizer.normalize(t)\n", " toks = sindhi_tokenizer.tokenize(norm)\n", " total_tokens += len(toks)\n", " total_words += len(toks)\n", " return total_tokens / total_words if total_words else float('nan')\n", "\n", "def fertility_hf(tok, texts):\n", " total_tokens, total_words = 0, 0\n", " for t in texts:\n", " if pd.isna(t): continue\n", " words = t.split()\n", " total_words += len(words)\n", " total_tokens += len(tok.encode(t, add_special_tokens=False))\n", " return total_tokens / total_words if total_words else float('nan')\n", "\n", "hf_tok = AutoTokenizer.from_pretrained(\"xlm-roberta-base\")\n", "langs = ['urdu_text', 'punjabi_text', 'sindhi_text', 'saraiki_text']\n", "names = ['Urdu', 'Punjabi', 'Sindhi', 'Saraiki']\n", "fert_data = []\n", "for col, name in zip(langs, names):\n", " texts = df[col].dropna().tolist()\n", " if name == 'Sindhi':\n", " f_s = fertility_sindhi(texts)\n", " else:\n", " f_s = fertility_hf(hf_tok, texts)\n", " f_b = fertility_hf(hf_tok, texts)\n", " fert_data.append({'Language': name, 'SindhiNLTK': round(f_s,3), 'XLM-R': round(f_b,3)})\n", "fert_df = pd.DataFrame(fert_data)\n", "print(\"\\nToken Fertility:\\n\", fert_df)\n", "\n", "# ============================================================\n", "# 3. Map labels to integers\n", "# ============================================================\n", "unique_labels = sorted(df['label'].astype(str).unique())\n", "label2id = {l: i for i, l in enumerate(unique_labels)}\n", "df['label_id'] = df['label'].astype(str).map(label2id)\n", "num_labels = len(unique_labels)\n", "print(f\"Number of label classes: {num_labels}\")\n", "\n", "# ============================================================\n", "# 4. Train on Urdu using XLM-R (classification)\n", "# ============================================================\n", "model_name = \"xlm-roberta-base\"\n", "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=num_labels)\n", "\n", "# Prepare Urdu dataset\n", "urdu_texts = df['urdu_text'].fillna(\"\").tolist()\n", "urdu_labels = df['label_id'].tolist()\n", "dataset = Dataset.from_dict({'text': urdu_texts, 'label': urdu_labels})\n", "split = dataset.train_test_split(test_size=0.2, seed=42)\n", "\n", "def tokenize(batch):\n", " return tokenizer(batch['text'], padding='max_length', truncation=True, max_length=128)\n", "\n", "train_data = split['train'].map(tokenize, batched=True)\n", "eval_data = split['test'].map(tokenize, batched=True)\n", "\n", "# Training arguments (no deprecated keys)\n", "args = TrainingArguments(\n", " output_dir=\"./output\",\n", " eval_strategy=\"epoch\",\n", " save_strategy=\"epoch\",\n", " learning_rate=2e-5,\n", " per_device_train_batch_size=16,\n", " per_device_eval_batch_size=16,\n", " num_train_epochs=5,\n", " weight_decay=0.01,\n", " logging_steps=10,\n", " load_best_model_at_end=True,\n", " metric_for_best_model=\"accuracy\",\n", " fp16=True,\n", " report_to=\"none\",\n", ")\n", "\n", "def compute_metrics(eval_pred):\n", " preds = np.argmax(eval_pred.predictions, axis=1)\n", " return {'accuracy': accuracy_score(eval_pred.label_ids, preds)}\n", "\n", "# Trainer without tokenizer argument\n", "trainer = Trainer(\n", " model=model,\n", " args=args,\n", " train_dataset=train_data,\n", " eval_dataset=eval_data,\n", " compute_metrics=compute_metrics,\n", ")\n", "\n", "# Train\n", "trainer.train()\n", "eval_result = trainer.evaluate()\n", "print(f\"\\nUrdu validation accuracy: {eval_result['eval_accuracy']:.4f}\")\n", "\n", "\n", "# 5. Zero-shot evaluation on other languages (fixed device)\n", "# ============================================================\n", "def evaluate_zs(lang_col, lang_name):\n", " texts = df[lang_col].dropna().tolist()\n", " if not texts:\n", " return None\n", " true_labels = df.loc[df[lang_col].notna(), 'label_id'].tolist()\n", "\n", " # Tokenize\n", " enc = tokenizer(texts, padding='max_length', truncation=True, max_length=128, return_tensors='pt')\n", "\n", " # Move to the same device as the model\n", " device = model.device\n", " enc = {k: v.to(device) for k, v in enc.items()}\n", "\n", " model.eval()\n", " with torch.no_grad():\n", " outputs = model(**enc)\n", " preds = torch.argmax(outputs.logits, dim=1).cpu().numpy()\n", " acc = accuracy_score(true_labels, preds)\n", " print(f\"Zero-shot accuracy on {lang_name}: {acc:.4f} ({acc*100:.2f}%)\")\n", " return acc\n", "\n", "results = {}\n", "for col, name in zip(langs[1:], names[1:]):\n", " acc = evaluate_zs(col, name)\n", " if acc is not None:\n", " results[name] = acc\n", "\n", "# ============================================================\n", "# 6. Final summary\n", "# ============================================================\n", "print(\"\\n\" + \"=\"*40)\n", "print(\"FINAL ACCURACY SUMMARY\")\n", "print(\"=\"*40)\n", "print(f\"Urdu (in-language): {eval_result['eval_accuracy']:.4f}\")\n", "for lang, acc in results.items():\n", " print(f\"{lang:10}: {acc:.4f}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "4400b82dcfbe4169a0d5422ee9c63ce1", "5ee324bfb2ca4806840dd8893e405874", "08bb8738c93948bb834da39d5ef1ba4a", "6afe260ba11d4643b1fb1c0cfd9b3657", "63e01c5c62b04540aca41575321f78f0", "f5bc1a7fb6f64be8a424355d240f758d", "ff122c4dabe3459d9c8dea739d593a40", "9885602cff884bc9b945a70add88c861", "8d31831a594c4107ac27a70a6a6333f3", "05323cad3f404dffbe58f626b905535b", "b67f7af449dc4c1b9292e8509cbb2817" ] }, "id": "ihabsHAGV5hu", "outputId": "9c67b2ba-8db9-4715-ebd1-a495eee8c1ff" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \n", " Upload widget is only available when the cell has been executed in the\n", " current browser session. Please rerun this cell to enable.\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saving parallel_vocabulary.csv to parallel_vocabulary (3).csv\n", "Loaded 800 words.\n", "After cleaning: 800 words.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/199 [00:00" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_aad45b7a-011f-44b1-91a8-1c6925ce3f1e\", \"procrustes_results.csv\", 171)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "Results downloaded.\n" ] } ], "source": [ "# ============================================================\n", "# Shared Manifold Hypothesis - Procrustes Alignment (All XLM‑R)\n", "# Using parallel vocabulary (800 words, 5 languages)\n", "# ============================================================\n", "\n", "!pip install -q transformers scikit-learn pandas\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import torch\n", "from transformers import AutoTokenizer, AutoModel\n", "from sklearn.preprocessing import normalize\n", "from scipy.linalg import orthogonal_procrustes\n", "from sklearn.metrics.pairwise import cosine_similarity\n", "from google.colab import files\n", "import gc\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "# ------------------------------------------------------------\n", "# 1. Upload vocabulary\n", "# ------------------------------------------------------------\n", "uploaded = files.upload()\n", "filename = list(uploaded.keys())[0]\n", "df = pd.read_csv(filename)\n", "print(f\"Loaded {len(df)} words.\")\n", "\n", "# Keep only rows where all 5 languages have data\n", "lang_cols = ['urdu', 'punjabi_shahmukhi', 'sindhi', 'saraiki', 'hindko']\n", "for col in lang_cols:\n", " df = df[df[col].notna() & (df[col] != \"N/A\")]\n", "print(f\"After cleaning: {len(df)} words.\")\n", "\n", "# ------------------------------------------------------------\n", "# 2. Load XLM‑RoBERTa (multilingual, 768‑dim)\n", "# ------------------------------------------------------------\n", "model_name = \"xlm-roberta-base\"\n", "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", "model = AutoModel.from_pretrained(model_name)\n", "\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "model = model.to(device)\n", "model.eval()\n", "print(f\"Using device: {device}\")\n", "\n", "def get_embedding_batch(words, batch_size=16):\n", " \"\"\"Return embeddings for a list of words (averaged subword tokens).\"\"\"\n", " embeddings = []\n", " for i in range(0, len(words), batch_size):\n", " batch = words[i:i+batch_size]\n", " inputs = tokenizer(batch, return_tensors=\"pt\", padding=True, truncation=True, max_length=16).to(device)\n", " with torch.no_grad():\n", " outputs = model(**inputs)\n", " hidden = outputs.last_hidden_state\n", " attention_mask = inputs['attention_mask']\n", " sum_emb = (hidden * attention_mask.unsqueeze(-1)).sum(dim=1)\n", " valid_tokens = attention_mask.sum(dim=1).unsqueeze(-1)\n", " avg_emb = sum_emb / valid_tokens\n", " embeddings.append(avg_emb.cpu().numpy())\n", " del inputs, outputs, hidden, sum_emb, avg_emb\n", " torch.cuda.empty_cache()\n", " return np.vstack(embeddings)\n", "\n", "# ------------------------------------------------------------\n", "# 3. Extract embeddings for all languages (XLM‑R only)\n", "# ------------------------------------------------------------\n", "embeddings = {}\n", "for lang in lang_cols:\n", " print(f\"\\nExtracting embeddings for {lang}...\")\n", " word_list = df[lang].tolist()\n", " vecs = get_embedding_batch(word_list, batch_size=16)\n", " vecs = normalize(vecs, norm='l2')\n", " embeddings[lang] = vecs\n", " print(f\" Shape: {vecs.shape}\")\n", " gc.collect()\n", " torch.cuda.empty_cache()\n", "\n", "# ------------------------------------------------------------\n", "# 4. Procrustes analysis between Urdu and others\n", "# ------------------------------------------------------------\n", "source = 'urdu'\n", "results = {}\n", "for target in ['punjabi_shahmukhi', 'sindhi', 'saraiki', 'hindko']:\n", " if target not in embeddings:\n", " continue\n", " X = embeddings[source]\n", " Y = embeddings[target]\n", " R, _ = orthogonal_procrustes(X, Y)\n", " transformed = X @ R\n", " dist = np.linalg.norm(transformed - Y, 'fro') / np.sqrt(X.shape[0])\n", " sim = cosine_similarity(transformed, Y)\n", " pred = np.argmax(sim, axis=1)\n", " acc = np.mean(pred == np.arange(X.shape[0]))\n", " results[target] = {'distance': dist, 'accuracy': acc}\n", " print(f\"\\n{source} → {target}:\")\n", " print(f\" Procrustes distance: {dist:.4f}\")\n", " print(f\" Accuracy: {acc:.4f} ({acc*100:.1f}%)\")\n", "\n", "# ------------------------------------------------------------\n", "# 5. Bridge hypothesis (Saraiki)\n", "# ------------------------------------------------------------\n", "# Direct\n", "R_direct, _ = orthogonal_procrustes(embeddings['urdu'], embeddings['sindhi'])\n", "direct_dist = np.linalg.norm(embeddings['urdu'] @ R_direct - embeddings['sindhi'], 'fro') / np.sqrt(embeddings['urdu'].shape[0])\n", "\n", "# Two‑step: Urdu → Saraiki → Sindhi\n", "R1, _ = orthogonal_procrustes(embeddings['urdu'], embeddings['saraiki'])\n", "ur_to_saraiki = embeddings['urdu'] @ R1\n", "R2, _ = orthogonal_procrustes(ur_to_saraiki, embeddings['sindhi'])\n", "two_step_dist = np.linalg.norm(ur_to_saraiki @ R2 - embeddings['sindhi'], 'fro') / np.sqrt(embeddings['urdu'].shape[0])\n", "\n", "print(\"\\n\" + \"=\"*50)\n", "print(\"BRIDGE HYPOTHESIS (Saraiki)\")\n", "print(\"=\"*50)\n", "print(f\"Direct Urdu → Sindhi distance: {direct_dist:.4f}\")\n", "print(f\"Two‑step Urdu → Saraiki → Sindhi distance: {two_step_dist:.4f}\")\n", "if two_step_dist < direct_dist:\n", " print(\"✅ Bridge reduces Procrustes distance → supports Shared Manifold Hypothesis.\")\n", "else:\n", " print(\"❌ Bridge does not improve alignment.\")\n", "\n", "# ------------------------------------------------------------\n", "# 6. Save results\n", "# ------------------------------------------------------------\n", "res_df = pd.DataFrame(results).T\n", "res_df.to_csv(\"procrustes_results.csv\")\n", "files.download(\"procrustes_results.csv\")\n", "print(\"\\nResults downloaded.\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3ZxRNFrOj2fv", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "d54b2c66-c02d-4499-a165-e6415241c369" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Logged in successfully.\n", "Repository verified: https://huggingface.co/aakashMeghwar01/IndoAryan-Shared-Manifold\n", "Uploading procrustes_results.csv...\n", "Warning: /content/fertility_results.csv not found, skipping.\n", "Uploading parallel_vocabulary.csv...\n", "Upload complete!\n" ] } ], "source": [] }, { "cell_type": "code", "source": [ "from huggingface_hub import HfApi, upload_file\n", "import os\n", "\n", "api = HfApi()\n", "repo_id = \"aakashMeghwar01/IndoAryan-Shared-Manifold\"\n", "\n", "# List of files to upload (all in current directory after generation)\n", "files_to_upload = [\n", " \"procrustes_results.csv\",\n", " \"fertility_results.csv\",\n", " \"zero_shot_accuracy.csv\",\n", " \"zero_shot_accuracy.png\",\n", " \"procrustes_distance.png\",\n", " \"procrustes_accuracy.png\",\n", " \"token_fertility.png\",\n", " \"bridge_hypothesis.png\",\n", " \"README.md\",\n", " \"zero_shot_test.ipynb\"\n", "]\n", "\n", "for file in files_to_upload:\n", " if os.path.exists(file):\n", " upload_file(\n", " path_or_fileobj=file,\n", " path_in_repo=file,\n", " repo_id=repo_id,\n", " repo_type=\"model\",\n", " token=token,\n", " )\n", " print(f\"Uploaded {file}\")\n", " else:\n", " print(f\"Warning: {file} not found, skipping.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 287, "referenced_widgets": [ "3f2bd4ad8f37423aa7f8b3fdd3d07a3c", "f2a3795384f545f490c61c01d965e03d", 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"d5ca0963dbc143d7b9dc7240c0a96397", "1a3f75e646c14add917402b7c2b89c69", "c9b6f3e1fd314dc8a5530105dbf63eb4", "a84014eee1ed418ca537b67f9779e267" ] }, "id": "y80N_NrIuMil", "outputId": "2cafbe7a-88a9-4616-a845-250cb19ec973" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Uploaded procrustes_results.csv\n", "Uploaded fertility_results.csv\n", "Uploaded zero_shot_accuracy.csv\n", "Uploaded zero_shot_accuracy.png\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Processing Files (0 / 0) : | | 0.00B / 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "3f2bd4ad8f37423aa7f8b3fdd3d07a3c" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "New Data Upload : | | 0.00B / 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "cdeceae3702546a089a881beb6677e68" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ " procrustes_distance.png : 100%|##########| 107kB / 107kB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "7cbce7dace3544fcae5d8421954565a3" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Uploaded procrustes_distance.png\n", "Uploaded procrustes_accuracy.png\n", "Uploaded token_fertility.png\n", "Uploaded bridge_hypothesis.png\n", "Uploaded README.md\n", "Warning: zero_shot_test.ipynb not found, skipping.\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lQxTo70yn69C", "outputId": "3e684df0-346b-4eda-8f66-b3a5b80d19d5" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Logged in using stored token.\n", "Cloning into 'IndoAryan-Shared-Manifold'...\n", "remote: Enumerating objects: 12, done.\u001b[K\n", "remote: Counting objects: 100% (6/6), done.\u001b[K\n", "remote: Compressing objects: 100% (6/6), done.\u001b[K\n", "remote: Total 12 (delta 1), reused 0 (delta 0), pack-reused 6 (from 1)\u001b[K\n", "Receiving objects: 100% (12/12), 18.90 KiB | 6.30 MiB/s, done.\n", "Resolving deltas: 100% (1/1), done.\n", "Saved procrustes_results.csv\n", "Saved fertility_results.csv\n", "Saved zero_shot_accuracy.csv\n", "All figures regenerated.\n", "README.md updated.\n", "[main 2cb8b90] Update with corrected Procrustes results, zero-shot accuracies, and fresh figures\n", " 9 files changed, 72 insertions(+), 21 deletions(-)\n", " rewrite README.md (69%)\n", " create mode 100644 bridge_hypothesis.png\n", " create mode 100644 fertility_results.csv\n", " create mode 100644 procrustes_accuracy.png\n", " create mode 100644 procrustes_distance.png\n", " create mode 100644 token_fertility.png\n", " create mode 100644 zero_shot_accuracy.csv\n", " create mode 100644 zero_shot_accuracy.png\n", "fatal: could not read Username for 'https://huggingface.co': No such device or address\n", "\n", "✅ Repository updated successfully!\n", "See: https://huggingface.co/aakashMeghwar01/IndoAryan-Shared-Manifold\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "b-GGQ8udpeE6" }, "execution_count": null, "outputs": [] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "0643b7b128cc4708bb17eb01f7bb8edb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ 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