tryign this new thing

Generator.ipynb

@@ -2,14 +2,14 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "bbd1b7a1-dbb7-4243-99e0-70a6cd47d573",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fbfb4b038e344f68b344e0998d42112f",
+       "model_id": "bcc2f5482d8342a7915cecf9e7855531",
        "version_major": 2,
        "version_minor": 0
       },
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 13,
    "id": "306958c8-4603-4b9b-b941-6a824777164d",
    "metadata": {},
    "outputs": [],
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 14,
    "id": "4ac69d3b-38c6-49af-aefe-63755bf3f0e9",
    "metadata": {},
    "outputs": [],
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "id": "9192b631-388f-4306-b975-9ba770b9dc4d",
    "metadata": {},
    "outputs": [],
@@ -73,17 +73,84 @@
     "tbl = table.InMemoryTable(\n",
     "    pa.Table.from_pandas(df)\n",
     ")\n",
-    "ds = Dataset(tbl)"
+    "ds = Dataset(tbl, split=[\"test\", \"training\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "bac1a601-a7a1-434e-917d-0e372684f56b",
+   "execution_count": 16,
+   "id": "f37d68ea-cbe7-4dd1-8215-f9449fe047f4",
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds.save_to_disk('.')"
+    "ds.save_to_disk(\"test/\")\n",
+    "ds.save_to_disk(\"training/\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "bac1a601-a7a1-434e-917d-0e372684f56b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Resuming upload of the dataset shards.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61cfa14ccb514ff4961072752bc3d4da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5803c7d37ce1426794af8ad65f618275",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading metadata:   0%|          | 0.00/1.20k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Updating downloaded metadata with the new split.\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Split ['test', 'training'] already present",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [12]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpush_to_hub\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msharpcoder/bjorn_training\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py:4342\u001b[0m, in \u001b[0;36mDataset.push_to_hub\u001b[0;34m(self, repo_id, split, private, token, branch, max_shard_size, shard_size, embed_external_files)\u001b[0m\n\u001b[1;32m   4340\u001b[0m         repo_info\u001b[38;5;241m.\u001b[39mdataset_size \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m dataset_nbytes\n\u001b[1;32m   4341\u001b[0m         repo_info\u001b[38;5;241m.\u001b[39msize_in_bytes \u001b[38;5;241m=\u001b[39m repo_info\u001b[38;5;241m.\u001b[39mdownload_size \u001b[38;5;241m+\u001b[39m repo_info\u001b[38;5;241m.\u001b[39mdataset_size\n\u001b[0;32m-> 4342\u001b[0m         repo_info\u001b[38;5;241m.\u001b[39msplits[split] \u001b[38;5;241m=\u001b[39m SplitInfo(\n\u001b[1;32m   4343\u001b[0m             split, num_bytes\u001b[38;5;241m=\u001b[39mdataset_nbytes, num_examples\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m), dataset_name\u001b[38;5;241m=\u001b[39mdataset_name\n\u001b[1;32m   4344\u001b[0m         )\n\u001b[1;32m   4345\u001b[0m         info_to_dump \u001b[38;5;241m=\u001b[39m repo_info\n\u001b[1;32m   4346\u001b[0m buffer \u001b[38;5;241m=\u001b[39m BytesIO()\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/splits.py:523\u001b[0m, in \u001b[0;36mSplitDict.__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m    521\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot add elem. (key mismatch: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m != \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;241m.\u001b[39mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    522\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 523\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSplit \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m already present\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    524\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__setitem__\u001b[39m(key, value)\n",
+      "\u001b[0;31mValueError\u001b[0m: Split ['test', 'training'] already present"
+     ]
+    }
+   ],
+   "source": [
+    "# ds.push_to_hub(\"sharpcoder/bjorn_training\")"
    ]
   },
   {

test/dataset.arrow

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c4f04ab2df5a7149d88530b5620ac60fc1a7f12e48e51b2abcfe5e8528d808b5
+size 102032

test/dataset_info.json

@@ -0,0 +1,35 @@
+{
+  "builder_name": null,
+  "citation": "",
+  "config_name": null,
+  "dataset_size": null,
+  "description": "",
+  "download_checksums": null,
+  "download_size": null,
+  "features": {
+    "audio": {
+      "feature": {
+        "dtype": "float32",
+        "id": null,
+        "_type": "Value"
+      },
+      "length": -1,
+      "id": null,
+      "_type": "Sequence"
+    },
+    "text": {
+      "dtype": "string",
+      "id": null,
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": "",
+  "post_processed": null,
+  "post_processing_size": null,
+  "size_in_bytes": null,
+  "splits": null,
+  "supervised_keys": null,
+  "task_templates": null,
+  "version": null
+}

test/state.json

@@ -0,0 +1,14 @@
+{
+  "_data_files": [
+    {
+      "filename": "dataset.arrow"
+    }
+  ],
+  "_fingerprint": "34ff31483e0b21bc",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_indexes": {},
+  "_output_all_columns": false,
+  "_split": "['test', 'training']"
+}

training/dataset.arrow

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c4f04ab2df5a7149d88530b5620ac60fc1a7f12e48e51b2abcfe5e8528d808b5
+size 102032

training/dataset_info.json

@@ -0,0 +1,35 @@
+{
+  "builder_name": null,
+  "citation": "",
+  "config_name": null,
+  "dataset_size": null,
+  "description": "",
+  "download_checksums": null,
+  "download_size": null,
+  "features": {
+    "audio": {
+      "feature": {
+        "dtype": "float32",
+        "id": null,
+        "_type": "Value"
+      },
+      "length": -1,
+      "id": null,
+      "_type": "Sequence"
+    },
+    "text": {
+      "dtype": "string",
+      "id": null,
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": "",
+  "post_processed": null,
+  "post_processing_size": null,
+  "size_in_bytes": null,
+  "splits": null,
+  "supervised_keys": null,
+  "task_templates": null,
+  "version": null
+}

training/state.json

@@ -0,0 +1,14 @@
+{
+  "_data_files": [
+    {
+      "filename": "dataset.arrow"
+    }
+  ],
+  "_fingerprint": "34ff31483e0b21bc",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_indexes": {},
+  "_output_all_columns": false,
+  "_split": "['test', 'training']"
+}