Processed data from AutoTrain data processor ([2023-11-02 19:38 ]

README.md

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+---
+task_categories:
+- text-classification
+
+---
+# AutoTrain Dataset for project: docid
+
+## Dataset Description
+
+This dataset has been automatically processed by AutoTrain for project docid.
+
+### Languages
+
+The BCP-47 code for the dataset's language is unk.
+
+## Dataset Structure
+
+### Data Instances
+
+A sample from this dataset looks as follows:
+
+```json
+[
+  {
+    "text": "MetLife-Walker Information_HI_2023.3.29_14.17_C_B (1).docx.pdf",
+    "feat_url": "datasaur://static/5732/2a298b78-1c2c-4ff8-ad49-357670dd5ea7.pdf",
+    "target": 0,
+    "feat_CarrierName": "Met Life",
+    "feat_ProductTypes": "Hospital Indemnity"
+  },
+  {
+    "text": "Cima Telecom Inc_Prop (002)_ (2).docx.pdf",
+    "feat_url": "datasaur://static/5732/8adee066-55c4-4f8d-8dcd-53d5fdb42732.pdf",
+    "target": 0,
+    "feat_CarrierName": "Met Life",
+    "feat_ProductTypes": "Basic Life;Basic AD&D;Voluntary Life;Voluntary AD&D;Voluntary Dependent AD&D;Short-term Disability;Long-term Disability;Dental;Vision"
+  }
+]
+```
+
+### Dataset Fields
+
+The dataset has the following fields (also called "features"):
+
+```json
+{
+  "text": "Value(dtype='string', id=None)",
+  "feat_url": "Value(dtype='string', id=None)",
+  "target": "ClassLabel(names=['Proposal', 'Summary (including SBC)'], id=None)",
+  "feat_CarrierName": "Value(dtype='string', id=None)",
+  "feat_ProductTypes": "Value(dtype='string', id=None)"
+}
+```
+
+### Dataset Splits
+
+This dataset is split into a train and validation split. The split sizes are as follow:
+
+| Split name   | Num samples         |
+| ------------ | ------------------- |
+| train        | 15 |
+| valid        | 5 |

processed/dataset_dict.json

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+{"splits": ["train", "valid"]}

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processed/train/dataset_info.json

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+{
+  "citation": "",
+  "description": "AutoTrain generated dataset",
+  "features": {
+    "text": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "feat_url": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "target": {
+      "names": [
+        "Proposal",
+        "Summary (including SBC)"
+      ],
+      "_type": "ClassLabel"
+    },
+    "feat_CarrierName": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "feat_ProductTypes": {
+      "dtype": "string",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": "",
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_bytes": 2925,
+      "num_examples": 15,
+      "dataset_name": null
+    }
+  }
+}

processed/train/state.json

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+    "feat_ProductTypes",
+    "feat_url",
+    "target",
+    "text"
+  ],
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+  "_split": null
+}

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processed/valid/dataset_info.json

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+{
+  "citation": "",
+  "description": "AutoTrain generated dataset",
+  "features": {
+    "text": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "feat_url": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "target": {
+      "names": [
+        "Proposal",
+        "Summary (including SBC)"
+      ],
+      "_type": "ClassLabel"
+    },
+    "feat_CarrierName": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "feat_ProductTypes": {
+      "dtype": "string",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": "",
+  "splits": {
+    "valid": {
+      "name": "valid",
+      "num_bytes": 1164,
+      "num_examples": 5,
+      "dataset_name": null
+    }
+  }
+}

processed/valid/state.json

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+{
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+  "_fingerprint": "5b989f28cd55b653",
+  "_format_columns": [
+    "feat_CarrierName",
+    "feat_ProductTypes",
+    "feat_url",
+    "target",
+    "text"
+  ],
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": null
+}