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"_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "source": [ "# Text2Receipt — Part 2 · Exploratory Data Analysis\n", "\n", "**Messy free-text Hebrew income notes → valid, complete Israeli fiscal documents.**\n", "This notebook performs the EDA over the published dataset\n", "[`yonilev/Text2Receipt`](https://huggingface.co/datasets/yonilev/Text2Receipt).\n", "\n", "Because the data is **synthetic**, naive \"stare at the distributions\" EDA would be\n", "circular — we generated the distributions, so finding them tells us nothing. Instead the\n", "analysis is organised around **three pillars**, and every result is tagged as one of two\n", "kinds:\n", "\n", "| Tag | Meaning |\n", "|---|---|\n", "| 🔧 **Verification** | A relationship that is *true by construction* (the generator encodes an Israeli fiscal rule). A near-perfect association / `p ≈ 0` is the **desired** outcome — it proves the rule fired. QA, not discovery. |\n", "| 🔬 **Discovery** | A relationship that is *not* hard-coded: realistic structure that emerged, or an honest **gap** where the synthetic data diverges from real fiscal behaviour. |\n", "\n", "**The three pillars**\n", "1. **Generator validation** — prove the encoded rules fired (VAT calendar, exempt/authorized logic, allocation-number `מספר הקצאה` threshold, ID check digits).\n", "2. **OOD integrity** — prove `ood_test` (pool B) is *lexically disjoint* from training (pool A) yet *distributionally identical*, so it measures pure lexical generalization, not distribution shift.\n", "3. **LLM-noise analysis** — characterise the LLM-rewritten share of notes (the exact percentage is computed live below and written into the dataset card) and confirm the fact-preservation guard held.\n", "\n", "### Contents\n", "1. Load, flatten & integrity gate · 2. Univariate dashboard · 3. Rule verification (🔧)\n", "· 4. Genuine relationships & gaps (🔬) · 5. OOD integrity · 6. LLM-noise / paraphrase\n", "· 7. Summary, limitations & dataset-card export" ], "metadata": { "id": "rt8sEgRre6Yv" } }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "IMSvRbUFe4X9", "outputId": "bdddb138-cd20-468e-c238-c1d3cbfb3c2e" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.9/79.9 MB\u001b[0m \u001b[31m11.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hsetup ready · PUSH_TO_HUB = True · token set: True\n" ] } ], "source": [ "# ── Setup ────────────────────────────────────────────────────────────────────\n", "%pip install -q datasets scipy scikit-learn sentence-transformers plotly \"kaleido==0.2.1\" huggingface_hub\n", "\n", "import os, re, json, math, warnings\n", "warnings.filterwarnings(\"ignore\")\n", "import numpy as np, pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "import seaborn as sns\n", "from scipy.stats import chi2_contingency, ks_2samp, f_oneway, mannwhitneyu\n", "from datasets import load_dataset\n", "from huggingface_hub import HfApi\n", "\n", "# ── Config ───────────────────────────────────────────────────────────────────\n", "DATASET_REPO = \"yonilev/Text2Receipt\"\n", "PUSH_TO_HUB = True # ← flip to True to upload figures + EDA card to the Hub\n", "HF_TOKEN = os.environ.get(\"HF_TOKEN\", \"\")\n", "try:\n", " from google.colab import userdata\n", " HF_TOKEN = userdata.get(\"HF_TOKEN\") or HF_TOKEN\n", "except Exception:\n", " pass\n", "api = HfApi()\n", "\n", "sns.set_theme(style=\"whitegrid\", context=\"notebook\")\n", "mpl.rcParams.update({\"figure.dpi\": 110, \"savefig.facecolor\": \"white\",\n", " \"axes.titleweight\": \"bold\", \"font.size\": 11})\n", "PALETTE = \"viridis\"\n", "\n", "# ── Figure helper: save locally + (optionally) upload to the HF dataset repo ──\n", "os.makedirs(\"assets\", exist_ok=True)\n", "SAVED = []\n", "def save_and_upload_figure(name, caption, fig=None):\n", " path = f\"assets/{name}.png\"\n", " is_plotly = fig is not None and fig.__class__.__module__.startswith(\"plotly\")\n", " if is_plotly:\n", " try:\n", " fig.write_image(path, scale=2, width=1100, height=650)\n", " except Exception as e:\n", " print(f\"[warn] plotly export failed for {name}: {e}\"); return\n", " else:\n", " (fig or plt.gcf()).savefig(path, dpi=150, bbox_inches=\"tight\", facecolor=\"white\")\n", " SAVED.append((name, caption))\n", " if PUSH_TO_HUB and HF_TOKEN:\n", " try:\n", " api.upload_file(path_or_fileobj=path, path_in_repo=f\"assets/{name}.png\",\n", " repo_id=DATASET_REPO, repo_type=\"dataset\", token=HF_TOKEN)\n", " print(f\"[uploaded] assets/{name}.png\")\n", " except Exception as e:\n", " print(f\"[warn] upload failed for {name}: {e}\")\n", " print(f\"[saved] {path} — {caption}\")\n", "\n", "# ── Stats helpers ────────────────────────────────────────────────────────────\n", "def cramers_v(x, y):\n", " \"\"\"Cramer's V and the chi-square p-value for two categorical Series.\"\"\"\n", " ct = pd.crosstab(x, y)\n", " chi2, p, *_ = chi2_contingency(ct)\n", " n = ct.values.sum(); k = min(ct.shape) - 1\n", " return (float(np.sqrt((chi2 / n) / k)) if k > 0 else np.nan), float(p)\n", "\n", "def eta_squared(values, groups):\n", " \"\"\"Eta-squared: fraction of a numeric's variance explained by a categorical.\"\"\"\n", " d = pd.DataFrame({\"v\": np.asarray(values, float), \"g\": np.asarray(groups)})\n", " grand = d.v.mean(); ss_tot = ((d.v - grand) ** 2).sum()\n", " ss_bet = sum(len(s) * (s.v.mean() - grand) ** 2 for _, s in d.groupby(\"g\"))\n", " return float(ss_bet / ss_tot) if ss_tot else np.nan\n", "\n", "print(\"setup ready · PUSH_TO_HUB =\", PUSH_TO_HUB, \"· token set:\", bool(HF_TOKEN))" ] }, { "cell_type": "code", "source": [ "import json, os\n", "from huggingface_hub import HfApi, hf_hub_download\n", "\n", "# Download each split, patch meta, re-upload\n", "api = HfApi()\n", "REPO = \"yonilev/Text2Receipt\"\n", "SPLITS = [\"train\", \"validation\", \"iid_test\", \"ood_test\"]\n", "\n", "for split in SPLITS:\n", " path = hf_hub_download(REPO, f\"data/{split}.jsonl\", repo_type=\"dataset\")\n", " rows = [json.loads(l) for l in open(path, encoding=\"utf-8\")]\n", " patched = []\n", " for r in rows:\n", " if \"human_authored\" not in r[\"meta\"]:\n", " r[\"meta\"][\"human_authored\"] = False\n", " patched.append(r)\n", " out = \"\\n\".join(json.dumps(r, ensure_ascii=False) for r in patched) + \"\\n\"\n", " api.upload_file(\n", " path_or_fileobj=out.encode(\"utf-8\"),\n", " path_in_repo=f\"data/{split}.jsonl\",\n", " repo_id=REPO,\n", " repo_type=\"dataset\",\n", " )\n", " print(f\"patched + pushed {split}: {len(patched)} rows\")\n", "\n", "print(\"done — schema now consistent across all 5 splits\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 391, "referenced_widgets": [ "a3bc653f234d4015b43725c074d2ca08", "f9ed954c091740278585dd2c1e0e5020", "6e10b70c24f84df496b8bc6dbe91b694", "9745407a79404766b9d79c24dd7d9ba3", "0c4776bb2b8f48a893ef49ef5c7fc214", "21b7b5384b0c421e8d762d80bf9be546", "4951bc855b7049dbb826ad5512f99cb1", "7fc8fd2a56dc448dab338aca9910f6ab", "a6e6e237bb534da8a93890c03c527395", "36a0c42f28d443fa967936ffda2f6f6f", "a44fe5f834974b08986a45d5fc2cf6cf", "e690d224a6a048b8b8dcf5dbaeabb66d", "a0398ed82f1646dab94cd899efab11e1", "79d16a72e30640c3b4d149badfc24339", "46c05f3e42be49758fea6973b49cd95f", "e4b589e5901848dbb242ae63ff4bafda", "59f096df44474ad3997c6472596a3d64", "4338ff8f1ac14c19a0cc7812f83dd2da", "33929f780b3f4caa9f9d19fcd22c0904", "9abfd604a0514478866fdc0095968d56", "0ba4196bc9b8439ebd25774d095e1ebf", "349352d4bf3847bba4e1d050592703e4", "a652fe40b3f244ca8bdf88226e6e08d6", "6b8c2426d9884eb18aa56e1cbddc79ff", "1d91f622ae994f5f8e09b7b3b7137dcf", "28f4cff227fc44c2b5c814508a82c64f", "bc87de3c422249dab6a35248cd3551a9", "29cb519babd04b31a8d0f3d08826fa2e", "e6d6071dd25b4599b801e80569e453a7", "1b19df2c06f94074b9a73497514fb48f", "b65dab30670d4c6b88275f073ae683be", "1a7736f9e44041b9ba8f7472e93c9f03", "8d69aad0607543fe926c3372b56c95dd", "33696597b7b2453e8051034bc7c16350", "3042472736c841ae93b7d7d2990bd4f8", "4190200b7fb44789ab048f162b1100b8", "e635993c1c2c477a9d9c52800ab77757", "e6195a346c6845138a0d5161c28b776b", "8b0b6a92227447878ed89cf1c423aa5f", "7086b70f8a664ce5a8d2bd5aff16c90e", "69badbeb007f4405981ebf87a275c272", "227c8d671c0444e3b74bb95ba7159cc8", "7bca9298eb4e48198dbd954c4f163713", "4ed1f44543dc41e890d6a912eb2c4924" ] }, "id": "hMPCrKJo3BrK", "outputId": "8708ecce-3959-4b24-dc3c-0d6c8a596624" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "train.jsonl: 0%| | 0.00/9.94M [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "a3bc653f234d4015b43725c074d2ca08" } }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "patched + pushed train: 7200 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "validation.jsonl: 0%| | 0.00/1.24M [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "e690d224a6a048b8b8dcf5dbaeabb66d" } }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "patched + pushed validation: 900 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "iid_test.jsonl: 0%| | 0.00/1.23M [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "a652fe40b3f244ca8bdf88226e6e08d6" } }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "patched + pushed iid_test: 900 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "ood_test.jsonl: 0%| | 0.00/1.36M [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "33696597b7b2453e8051034bc7c16350" } }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "patched + pushed ood_test: 1000 rows\n", "done — schema now consistent across all 5 splits\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 1 · Load, flatten & integrity gate\n", "\n", "Four top-level columns: `raw_text` (the only model **input**), `parse` (the only field a\n", "model **predicts**), `completed` (the deterministic full document), and `meta`. We flatten\n", "the nested structs into one tidy frame." ], "metadata": { "id": "wkhqwVlOfDWu" } }, { "cell_type": "code", "source": [ "ds = load_dataset(DATASET_REPO)\n", "print(\"splits:\", {k: len(v) for k, v in ds.items()})\n", "\n", "DOC_LABEL = {\"receipt\": \"Receipt\", \"tax_invoice\": \"Tax Invoice\",\n", " \"tax_invoice_receipt\": \"Tax Invoice-Receipt\"}\n", "ST_LABEL = {\"exempt_dealer\": \"Exempt dealer\", \"authorized_dealer\": \"Authorized dealer\"}\n", "\n", "def il_id_valid(idnum):\n", " \"\"\"Israeli 9-digit check-digit algorithm (ID / company number).\"\"\"\n", " s = re.sub(r\"\\D\", \"\", str(idnum))\n", " if not s:\n", " return None\n", " s = s.zfill(9)\n", " if len(s) != 9:\n", " return False\n", " tot = 0\n", " for i, ch in enumerate(s):\n", " d = int(ch) * (1 if i % 2 == 0 else 2)\n", " tot += d if d < 10 else d - 9\n", " return tot % 10 == 0\n", "\n", "FOREIGN = re.compile(r\"[\\u0400-\\u04FF\\u4E00-\\u9FFF\\u3040-\\u30FF\\u0600-\\u06FF]\")\n", "LATIN = re.compile(r\"[A-Za-z]{3,}\")\n", "\n", "def num_variants(x):\n", " iv = int(round(x))\n", " return {s for s in (str(iv), f\"{iv:,}\", f\"{x:.2f}\".rstrip(\"0\").rstrip(\".\")) if s}\n", "\n", "rows = []\n", "for split, d in ds.items():\n", " for r in d:\n", " m, c, p = r[\"meta\"], r[\"completed\"], r[\"parse\"]\n", " iss, cli = (c.get(\"issuer\") or {}), (c.get(\"client\") or {})\n", " items = p.get(\"items\") or []\n", " raw = r[\"raw_text\"]\n", " sub, tot = float(c[\"subtotal\"]), float(c[\"total\"])\n", " client_name = (cli.get(\"name\") or p.get(\"client_name\") or \"\")\n", " amt_present = any(v in raw for v in (num_variants(sub) | num_variants(tot)))\n", " rows.append(dict(\n", " split=split, raw=raw, raw_len=len(raw),\n", " pool=m[\"pool\"], issuer_status=m[\"issuer_status\"],\n", " issuer_is_company=bool(m[\"issuer_is_company\"]),\n", " doc_type=m[\"doc_type\"], doc_label=DOC_LABEL.get(m[\"doc_type\"], m[\"doc_type\"]),\n", " issue_date=str(c.get(\"issue_date\") or \"\")[:10],\n", " vat_rate=float(m[\"vat_rate\"]), category=m[\"category\"],\n", " client_is_business=bool(m[\"client_is_business\"]),\n", " payment_method=m[\"payment_method\"], year=int(m[\"year\"]),\n", " paraphrased=bool(m[\"paraphrased\"]),\n", " subtotal=sub, total=tot, vat_amount=float(c[\"vat_amount\"]),\n", " alloc_req=bool(c[\"allocation_required\"]),\n", " alloc_num=str(c.get(\"allocation_number\") or \"\"),\n", " issuer_name=iss.get(\"name\", \"\"), issuer_tax=str(iss.get(\"tax_id\") or \"\"),\n", " client_name=client_name, client_tax=str(cli.get(\"tax_id\") or p.get(\"client_tax_id\") or \"\"),\n", " item_desc=\" || \".join(str(it.get(\"description\", \"\")) for it in items),\n", " n_items=len(items), amt_present=amt_present,\n", " has_foreign=bool(FOREIGN.search(raw)), has_latin=bool(LATIN.search(raw)),\n", " ))\n", "df = pd.DataFrame(rows)\n", "df[\"is_exempt\"] = df.issuer_status.eq(\"exempt_dealer\")\n", "df[\"alloc_label\"] = np.where(df.alloc_req, \"Allocation required\", \"Not required\")\n", "print(\"flattened rows:\", len(df), \"| columns:\", df.shape[1])\n", "df[[\"split\",\"pool\",\"issuer_status\",\"doc_label\",\"vat_rate\",\"category\",\n", " \"subtotal\",\"total\",\"alloc_req\",\"paraphrased\"]].head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 502, "referenced_widgets": [ "6152f00d40044e8e84db562ebec78a6e", "86c11f4e60ef44c495e0da83ccd4658e", "71521ef941f24d18a754d8df19c2afdd", "6302984c83614f3796d00eb5a9d89109", "ec62d68e880d4233a083ab1fba17ae39", "783c9c9680894fa88ab584c89c98cb43", "ba5628058fdc466ea76c8067143607bc", "1a040999aef647ed9b6f4807d0c3777f", "fbc91a5334ab4362bceaa684a4c0560c", "7c43cea28e5b4a9083293cd73de5e405", "fb909dd9d62348a18482f184d80c1dd1", "c6d853ff0b2548a98d061836c82e67de", "6166f21e1fe84d84bdbce399bb31df16", "9e12234a554c48f7942c6b3be4fc4449", "880c669d53e74e76b8d9be55ef4072c5", "e2af8cd506574718acf6aeb2a3838087", "7f664da467354259a9dfb6671913f073", "6fb3362089ff4d7e9bbe3aa81165b74d", "f80a8395881f4df68e455b372e56930d", "ccbf8740adb847aeb454d66c7e6a6231", "d15790b7101f42968e21ddd88d8463e7", "c63533674e2345afb7d105fde215ecba", "25e9b4b4edc947aba3bdaaebb4e9e907", "a377959109bf439480ff062ad8882f2f", "3ef7565bf3344efa8c4a743caf9bb69d", "df849ee38d684f5181e778e8321e04c6", "2724b1611a544d4fbdc14c7e6ae80361", "fee7da2514094e47be80022f2b65e98f", "28f510b616284d149739d3b76c682b5e", "8f0b50c421c4476f917f89451ff69549", "58e847b9c52e40e088efcaa4eb75fb20", "765a96ec56b3484c89beeb41046b55dd", "a370f012afef4d65a8a4af1c3d3315e4", "db82fbeb5d894094b497869571898daf", "f3cdbc09d7e047419365936b0825e968", "7c55fd060760488dbbe3a6f17d876909", "61345d59807843a088200ea19e825cdf", "9fd9e0499626405cb6afd2f0983cd425", "65ed2232d92c4a3d96183458be31a85a", "e807b67fc56247e68e5a29cc8d7bc593", "e5e99b0c2d0d497ebb20475872aa3416", "9531fe54e5be4436826d1055cfcd8614", "46451cf0ded74767b8e1250b71b52a29", "0dc25c1983454326b2847d292ea3fac0", "26301866d528405abed815950eb4f0a8", "abb7724de6894ad19a04d51f92973c25", "16279e8bce2d497998c80a4d56bd0020", "ec31c35ab6d14647a382cbb84ce63aaa", "2e2bddc6a9654961bd021b8c3d2b279d", "0d44bcd7f233489b96f27876535b1c37", "ff8f1ebf3f8242d288ce68ba4c20b452", "0965f0c7687840cf867b378d5758dee3", "d01715e83c0d4508b136cf7ca558aa5d", "9151e59454134f3ab7a8c40a9793f6a2", "b12029377a8b4140a1cd9db6f182b195", "89a920f3e0504fb099847935349c7961", "9bc7c3db308743939ca2902df9e727d2", "09fd9adb0e93430abea1c93f15099a44", "9e4b0db1e14d4633a233c177ef0a1b34", "7f264b6761b74a0e8f40e7c0fde62077", "bc406b403a8240ac944f1d8bd561f605", "6e98c2728adb4442be3e47d63453b80b", "f98e4712714042af8334b6733fb6d0f1", "f91647303e574a7ab1ef30848921633e", "685d79991b5f4ccf866f5778f3187c57", "519f5ddc8503433088655e436ab20cc0", "3012fb1bedab40079aa5d7ba4c6d3215", "f24883f6618d48f59034aea826ce962e", "29f0dd63ba0b4b669aec514c0ee95b72", "20a0dd6dda1742e7b750fe092cb1bb7f", "b37d06636c5c413abf012dd5223dd8eb", "231b3b04923c43d089e8b776d05a7c09", "d43a383065e14c6eb0447dec5573d22f", "7523df9190d84503ab37b2e8638c1a75", "a02e5872101d4f5bad8278f9c26a4b56", "4ecbab2e455346b3b914392f8c56c172", "5738ca2828024a9dba15ea6d8349de8f" ] }, "id": "v0mSz8_ufDn7", "outputId": "6e9b4f52-e4ca-4b2d-cf2c-e7de697fcf16" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/518 [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "6152f00d40044e8e84db562ebec78a6e" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "human_test.jsonl: 0%| | 0.00/33.5k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "c6d853ff0b2548a98d061836c82e67de" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Generating train split: 0 examples [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "25e9b4b4edc947aba3bdaaebb4e9e907" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Generating validation split: 0 examples [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "db82fbeb5d894094b497869571898daf" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Generating iid_test split: 0 examples [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "26301866d528405abed815950eb4f0a8" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Generating ood_test split: 0 examples [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "89a920f3e0504fb099847935349c7961" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Generating human_test split: 0 examples [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "3012fb1bedab40079aa5d7ba4c6d3215" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "splits: {'train': 7200, 'validation': 900, 'iid_test': 900, 'ood_test': 1000, 'human_test': 25}\n", "flattened rows: 10025 | columns: 31\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " split pool issuer_status doc_label vat_rate category \\\n", "0 train A exempt_dealer Receipt 0.00 trades \n", "1 train A authorized_dealer Tax Invoice-Receipt 0.18 design \n", "2 train A authorized_dealer Tax Invoice 0.18 consulting \n", "3 train A authorized_dealer Receipt 0.18 software \n", "4 train A authorized_dealer Tax Invoice 0.18 photography \n", "\n", " subtotal total alloc_req paraphrased \n", "0 307.00 307.00 False False \n", "1 647.46 764.00 False False \n", "2 3210.00 3787.80 False False \n", "3 1825.00 2153.50 False False \n", "4 3367.00 3973.06 False False " ], "text/html": [ "\n", "
| \n", " | split | \n", "pool | \n", "issuer_status | \n", "doc_label | \n", "vat_rate | \n", "category | \n", "subtotal | \n", "total | \n", "alloc_req | \n", "paraphrased | \n", "
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