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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "070a4097-7a17-409f-af5d-3d0cf43926ca",
"metadata": {},
"outputs": [],
"source": [
"from peft import AutoPeftModelForCausalLM, PeftModelForCausalLM\n",
"from huggingface_hub import list_repo_refs\n",
"from transformers import AutoTokenizer, AutoModelForCausalLM"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "100ec138-f7c1-4d8f-b7e0-eb715f320fdc",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
]
}
],
"source": [
"tokenizer = AutoTokenizer.from_pretrained(\"mnoukhov/pythia410m-tldr-sft\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "dbc9a2db-2c16-4e8f-bd2a-213ddc5d139d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tokenizer.add_special_tokens({\"pad_token\": \"<|padding|>\"}) "
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "03788af8-6733-492f-84e3-fd358bb88ffd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tokenizer.pad_token_id"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "576d3fda-7902-43d7-b4b1-3054f6192b11",
"metadata": {},
"outputs": [],
"source": [
"example_text = \"hello my name is mr hello\""
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "c73ddb0c-1551-4b12-82d8-26d3742d6f57",
"metadata": {},
"outputs": [],
"source": [
"toks = tokenizer(example_text + tokenizer.eos_token, padding=\"max_length\", max_length=7, truncation=True)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "8904af15-4d27-4718-b53a-060ae65173a9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'input_ids': [25521, 619, 1416, 310, 278, 83, 23120], 'attention_mask': [1, 1, 1, 1, 1, 1, 1]}"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"toks"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "8fcf7c83-e8df-457b-9eab-1b1ed2145a76",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"7"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(toks['attention_mask'])"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ef1dddf6-1d26-4950-910a-c40b2cc394c6",
"metadata": {},
"outputs": [],
"source": [
"base_model_name = \"vwxyzjn/EleutherAI_pythia-1b-deduped__sft__tldr\"\n",
"base_model_revision = \"sft__55513__1706646024\""
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "bb0df32c-9d90-4ab0-a87d-0ff6ecab03b6",
"metadata": {},
"outputs": [],
"source": [
"model_path = \"/home/toolkit/trl_results/mnoukhov/EleutherAI_pythia-1b-deduped__sft__tldr_dpo_costa_1b_fp16.yml_3d94f50_b9ff2_merged/main\""
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "3ae77b2a-3132-4dd1-903b-35f28b7e7e5f",
"metadata": {},
"outputs": [],
"source": [
"base_model = AutoModelForCausalLM.from_pretrained(model_path)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "08c1d05d-44a4-4859-9d54-48e7a3cd1da7",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "12749e76749a40469d7732dc23e0f1dc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"model.safetensors: 0%| | 0.00/4.05G [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"CommitInfo(commit_url='https://huggingface.co/mnoukhov/EleutherAI_pythia-1b-deduped__sft__tldr_dpo_costa_1b_fp16.yml_3d94f50_b9ff2_merged/commit/cd8f4bf53ab02881549cb73b6271005b2e8c3be6', commit_message='Upload GPTNeoXForCausalLM', commit_description='', oid='cd8f4bf53ab02881549cb73b6271005b2e8c3be6', pr_url=None, pr_revision=None, pr_num=None)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"base_model.push_to_hub(\"mnoukhov/EleutherAI_pythia-1b-deduped__sft__tldr_dpo_costa_1b_fp16.yml_3d94f50_b9ff2_merged\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9ef8927b-f908-460f-adba-54508b133ae0",
"metadata": {},
"outputs": [],
"source": [
"adapter_repo = \"mnoukhov/EleutherAI_pythia-1b-deduped__sft__tldr_dpo_1b_fp16.yml_24e9f83\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cb7336d2-a4ac-4607-83ae-e7e1e0b1665d",
"metadata": {},
"outputs": [],
"source": [
"refs = list_repo_refs(adapter_repo)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2ab002af-7f3b-41b1-a8ad-f7c2296bd68f",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f4c8e90f4fba4589a00ec3ee75dc7505",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"adapter_config.json: 0%| | 0.00/706 [00:00<?, ?B/s]"
]
},
"metadata": {},
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},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1eb21476d51f44858c32c59e72d70105",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"adapter_model.safetensors: 0%| | 0.00/18.5M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "014e10f937374412aa10524d1a4d7a8f",
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"version_minor": 0
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"text/plain": [
"model.safetensors: 0%| | 0.00/4.05G [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step2324\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7c55865b8a7f4c6795b22c0a68b702a6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"adapter_model.safetensors: 0%| | 0.00/18.5M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "58cb851b46974ae5a3cb066717520f8d",
"version_major": 2,
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"text/plain": [
"model.safetensors: 0%| | 0.00/4.05G [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step1743\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "742410c4a20d4a09b10c0c96c8977df5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"adapter_model.safetensors: 0%| | 0.00/18.5M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "48117e9ab89248038fa8a76ca9a191db",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"model.safetensors: 0%| | 0.00/4.05G [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step1162\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9b9cef19d78c465f900e345ac44acae6",
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"adapter_model.safetensors: 0%| | 0.00/18.5M [00:00<?, ?B/s]"
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{
"data": {
"application/vnd.jupyter.widget-view+json": {
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"version_minor": 0
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"model.safetensors: 0%| | 0.00/4.05G [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step581\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f39fc2322c504c4b9d6b601bbcbbb923",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"adapter_model.safetensors: 0%| | 0.00/18.5M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step1\n"
]
}
],
"source": [
"for branch in refs.branches:\n",
" if branch.name == \"main\":\n",
" continue\n",
"\n",
" model = PeftModelForCausalLM.from_pretrained(base_model, adapter_repo, revision=branch.name)\n",
" merged = model.merge_and_unload()\n",
" merged.push_to_hub(f\"{adapter_repo}_merged\", revision=branch.name)\n",
" print(branch.name)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "24627996-2bc2-4944-a36c-0d86108a82c6",
"metadata": {},
"outputs": [],
"source": [
"from datasets import load_dataset, builder, load_from_disk\n",
"builder.has_sufficient_disk_space = lambda needed_bytes, directory=\".\": True "
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ab8916ed-d39b-4d64-b287-ea4569567005",
"metadata": {},
"outputs": [],
"source": [
"ds = load_from_disk(\"/home/toolkit/trl_results/vwxyzjn_summarize_from_feedback_tldr_3_filtered_oai_preprocessing_1706381144/vwxyzjn_EleutherAI_pythia-1b-deduped__dpo__tldr\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6ee65d83-872d-4d96-9c81-be53f2fc54c1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'?'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds['generations_dpo__55513__1707379566'][0][-1]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "a11a3760-515b-4a02-9053-853aa3b06fd4",
"metadata": {},
"outputs": [],
"source": [
"ppo_ds = load_from_disk(\"vwxyzjn_summarize_from_feedback_tldr_3_filtered_oai_preprocessing_1706381144/vwxyzjn_EleutherAI_pythia-1b-deduped__ppo_left_padding_new_nowhiten_reward__tldr\")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "5d0c3c4f-71b1-46b0-abdb-036e1bd49a26",
"metadata": {},
"outputs": [],
"source": [
"text = ppo_ds[\"generations_ppo_left_padding_new_nowhiten_reward__55513__1709671967\"][0]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8d2ec316-db2b-481b-9e25-82b2dd363772",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
]
}
],
"source": [
"tokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/pythia-6.9b-deduped\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1fedd4e0-a0a5-4499-9561-605e5adc8d88",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tokenizer.encode('<|padding|>')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "42b8260f-19a7-42e1-b809-a24deff3699c",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "032ad7febe1b4eb9899d22e5d44d23a0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading readme: 0%| | 0.00/456 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "954fb6b000ac4b29b0c9033f242aac73",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading data: 0%| | 0.00/122M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3e544b20f15d48f59e901fbaf896a24d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading data: 0%| | 0.00/6.54M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "62d3170267d742ceaf6bdad2a2cef5ae",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating train split: 0%| | 0/160800 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ce7cff29f9c042949acad2dcec3ddd6e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating test split: 0%| | 0/8552 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds = load_dataset(\"sophiex/hh-rlhf\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "df1ccb5e-7206-45e7-a449-76b64fda72ed",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a9abf38ffb184ba4a4995450a4413bf2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map (num_proc=16): 0%| | 0/160800 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8e0af258d31742998176207df5cac540",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map (num_proc=16): 0%| | 0/8552 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tokds = ds.map(lambda x: tokenizer(x['prompt'] + x['chosen']), num_proc=16)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "2e72f7f3-b047-4eab-99a7-cc08d19efeba",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "99c7615c05da46d6be5c68ecfba3e748",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map: 0%| | 0/160800 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c9b61731ac524d8c8ad1a44e47bb12b2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map: 0%| | 0/8552 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tokds = tokds.map(lambda x: {\"length\": len(x['input_ids'])})"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "413e3eb3-ad2f-4f71-9f27-894c4942be4f",
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "a4c42a89-88dd-4f3d-82cb-1fd7ecb60815",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x7f8abec580d0>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 500x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.displot(tokds[\"train\"][\"length\"])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "d11597f9-0441-440c-8214-b9d8b2df6f79",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "46d3909d41c649acb800d4bf00197951",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map (num_proc=16): 0%| | 0/160800 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e886faa17c774740a2058a5dd8e0673d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map (num_proc=16): 0%| | 0/8552 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tokds = ds.map(lambda x: tokenizer(x['prompt']), num_proc=16)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "84290aac-1c4e-4d29-89bd-318cf2c9daf3",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "eb0406bdb9884fcc826630224f2d1a8a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map: 0%| | 0/160800 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "50580c27e575445bb239783adee19f90",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map: 0%| | 0/8552 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tokds = tokds.map(lambda x: {\"prompt_length\": len(x['input_ids'])})"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "44d2f307-118b-493d-b626-97490e2bc4aa",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "588d062fd6c2489da6f57b287c66d6e6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Filter (num_proc=16): 0%| | 0/160800 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bc9327dc6ed2467597a56b4655aca9a9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Filter (num_proc=16): 0%| | 0/8552 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"filttokds = tokds.filter(lambda x: x[\"prompt_length\"] > 1024, num_proc=16)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "2b6d57f7-40b7-4417-88bc-83c63b22f153",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"31"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(filttokds[\"test\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "78d391fa-9a57-446b-9007-fe64ef8fc735",
"metadata": {},
"outputs": [],
"source": [
"tokds = ds.map(lambda x: tokenizer(x['prompt']), num_proc=16)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "176dbd05-67c5-45a6-b891-1237deb7d6c9",
"metadata": {},
"outputs": [],
"source": [
"ds = load_dataset(\"mnoukhov/summarize_from_feedback_tldr_3_filtered_oai_preprocessing_1706381144\")"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "9eab3eaa-55ed-4279-96d6-3c189266ba86",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "911df020ac294d9ca2b360e1a0be3f93",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Filter: 0%| | 0/116722 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds[\"train\"] = ds[\"train\"].filter(lambda x: x[\"has_comparison\"] == True)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "9cc44838-af6d-4e3d-be4e-49436900f469",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4041ceb7527a4e638b138fc12897c35e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Uploading the dataset shards: 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "51b19ac739f14b31a5820a9f077cf177",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Creating parquet from Arrow format: 0%| | 0/10 [00:00<?, ?ba/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d7e0c946845940bb8de920287d982cd6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Uploading the dataset shards: 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "acd8997214f448ca8cc665b4fd3b1af6",
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"text/plain": [
"Creating parquet from Arrow format: 0%| | 0/7 [00:00<?, ?ba/s]"
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"model_id": "cae753400174470f8c97d61fbb557202",
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"text/plain": [
"Uploading the dataset shards: 0%| | 0/1 [00:00<?, ?it/s]"
]
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{
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{
"name": "stderr",
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"text": [
"/home/toolkit/.local/lib/python3.11/site-packages/huggingface_hub/file_download.py:983: UserWarning: Not enough free disk space to download the file. The expected file size is: 0.00 MB. The target location /home/toolkit/huggingface/hub only has 0.00 MB free disk space.\n",
" warnings.warn(\n",
"/home/toolkit/.local/lib/python3.11/site-packages/huggingface_hub/file_download.py:983: UserWarning: Not enough free disk space to download the file. The expected file size is: 0.00 MB. The target location /home/toolkit/huggingface/hub/datasets--mnoukhov--summarize_from_feedback_tldr_3_filtered_oai_preprocessing_1706381144_labelled/blobs only has 0.00 MB free disk space.\n",
" warnings.warn(\n"
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},
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"data": {
"text/plain": [
"CommitInfo(commit_url='https://huggingface.co/datasets/mnoukhov/summarize_from_feedback_tldr_3_filtered_oai_preprocessing_1706381144_labelled/commit/a873a0b902f97283fb440254b724da8257439c33', commit_message='Upload dataset', commit_description='', oid='a873a0b902f97283fb440254b724da8257439c33', pr_url=None, pr_revision=None, pr_num=None)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.push_to_hub(\"mnoukhov/summarize_from_feedback_tldr_3_filtered_oai_preprocessing_1706381144_labelled\")"
]
},
{
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"execution_count": 33,
"id": "557d07cf-781c-4c9a-8499-2bd4d076e98d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['id', 'subreddit', 'title', 'post', 'summary', 'query_token', 'query', 'reference_response', 'reference_response_token', 'reference_response_token_len', 'query_reference_response', 'query_reference_response_token', 'query_reference_response_token_response_label', 'query_reference_response_token_len', 'has_comparison'],\n",
" num_rows: 9504\n",
" })\n",
" validation: Dataset({\n",
" features: ['id', 'subreddit', 'title', 'post', 'summary', 'query_token', 'query', 'reference_response', 'reference_response_token', 'reference_response_token_len', 'query_reference_response', 'query_reference_response_token', 'query_reference_response_token_response_label', 'query_reference_response_token_len', 'has_comparison'],\n",
" num_rows: 6447\n",
" })\n",
" test: Dataset({\n",
" features: ['id', 'subreddit', 'title', 'post', 'summary', 'query_token', 'query', 'reference_response', 'reference_response_token', 'reference_response_token_len', 'query_reference_response', 'query_reference_response_token', 'query_reference_response_token_response_label', 'query_reference_response_token_len', 'has_comparison'],\n",
" num_rows: 6553\n",
" })\n",
"})"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds"
]
},
{
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"id": "a7ad1cea-5ac6-4d57-9aff-6783ea61fb13",
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"outputs": [],
"source": []
}
],
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