librarian-bots/dataset_cards_with_metadata

datasetId stringlengths 2 117	author stringlengths 2 42 ⌀	last_modified unknown	likes int64 0 4.95k	tags sequencelengths 1 7.91k	task_categories sequencelengths 0 40 ⌀	createdAt unknown	card stringlengths 19 977k
SauravMaheshkar/NDC-substances	SauravMaheshkar	"2024-04-04T14:13:39Z"	0	[ "task_categories:graph-ml", "license:unknown", "chemistry", "arxiv:1802.06916", "region:us" ]	[ "graph-ml" ]	"2024-03-22T20:12:00Z"	--- license: unknown task_categories: - graph-ml tags: - chemistry configs: - config_name: transductive data_files: - split: train path: "processed/transductive/train_df.csv" - split: valid path: "processed/transductive/val_df.csv" - split: test path: "processed/transductive/test_df.csv" - config_name: inductive data_files: - split: train path: "processed/inductive/train_df.csv" - split: valid path: "processed/inductive/val_df.csv" - split: test path: "processed/inductive/test_df.csv" - config_name: raw data_files: "raw/*.txt" --- Source Paper: https://arxiv.org/abs/1802.06916 ### Usage ``` from torch_geometric.datasets.cornell import CornellTemporalHyperGraphDataset dataset = CornellTemporalHyperGraphDataset(root = "./", name="NDC-substances", split="train") ``` ### Citation ```misc @article{Benson-2018-simplicial, author = {Benson, Austin R. and Abebe, Rediet and Schaub, Michael T. and Jadbabaie, Ali and Kleinberg, Jon}, title = {Simplicial closure and higher-order link prediction}, year = {2018}, doi = {10.1073/pnas.1800683115}, publisher = {National Academy of Sciences}, issn = {0027-8424}, journal = {Proceedings of the National Academy of Sciences} } ```
vwxyzjn/summarize_from_feedback_oai_preprocessing_1711138084	vwxyzjn	"2024-03-22T20:13:39Z"	0	[ "croissant", "region:us" ]	null	"2024-03-22T20:12:47Z"	--- dataset_info: features: - name: info struct: - name: id dtype: string - name: post dtype: string - name: title dtype: string - name: subreddit dtype: string - name: site dtype: string - name: article dtype: string - name: summaries list: - name: text dtype: string - name: policy dtype: string - name: note dtype: string - name: choice dtype: int32 - name: worker dtype: string - name: batch dtype: string - name: split dtype: string - name: extra struct: - name: confidence dtype: int32 - name: query_token sequence: int64 - name: query dtype: string - name: chosen dtype: string - name: chosen_token sequence: int64 - name: chosen_token_len dtype: int64 - name: rejected dtype: string - name: rejected_token sequence: int64 - name: rejected_token_len dtype: int64 - name: chosen_policy dtype: string - name: rejected_policy dtype: string - name: policies dtype: string - name: chosen_len_minus_rejected_len dtype: int64 - name: query_chosen dtype: string - name: query_chosen_token sequence: int64 - name: query_chosen_token_len dtype: int64 - name: query_rejected dtype: string - name: query_rejected_token sequence: int64 - name: query_rejected_token_len dtype: int64 - name: query_token_len dtype: int64 - name: query_chosen_token_response_label sequence: int64 - name: query_rejected_token_response_label sequence: int64 splits: - name: train num_bytes: 3160687523 num_examples: 92858 - name: validation num_bytes: 2859977775 num_examples: 83802 - name: validation_cnndm num_bytes: 225375023 num_examples: 2284 download_size: 291050539 dataset_size: 6246040321 --- # Dataset Card for "summarize_from_feedback_oai_preprocessing_1711138084" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pgwi/clean_fashion_data	pgwi	"2024-03-22T20:16:56Z"	0	[ "license:apache-2.0", "croissant", "region:us" ]	null	"2024-03-22T20:16:12Z"	--- license: apache-2.0 ---
vwxyzjn/summarize_from_feedback_oai_preprocessing_1711138537	vwxyzjn	"2024-03-22T20:17:43Z"	0	[ "croissant", "region:us" ]	null	"2024-03-22T20:17:19Z"	--- dataset_info: features: - name: info struct: - name: id dtype: string - name: post dtype: string - name: title dtype: string - name: subreddit dtype: string - name: site dtype: string - name: article dtype: string - name: summaries list: - name: text dtype: string - name: policy dtype: string - name: note dtype: string - name: choice dtype: int32 - name: worker dtype: string - name: batch dtype: string - name: split dtype: string - name: extra struct: - name: confidence dtype: int32 - name: query_token sequence: int64 - name: query dtype: string - name: chosen dtype: string - name: chosen_token sequence: int64 - name: chosen_token_len dtype: int64 - name: rejected dtype: string - name: rejected_token sequence: int64 - name: rejected_token_len dtype: int64 - name: chosen_policy dtype: string - name: rejected_policy dtype: string - name: policies dtype: string - name: chosen_len_minus_rejected_len dtype: int64 - name: query_chosen dtype: string - name: query_chosen_token sequence: int64 - name: query_chosen_token_len dtype: int64 - name: query_rejected dtype: string - name: query_rejected_token sequence: int64 - name: query_rejected_token_len dtype: int64 - name: query_token_len dtype: int64 - name: query_chosen_token_response_label sequence: int64 - name: query_rejected_token_response_label sequence: int64 splits: - name: train num_bytes: 3160687523 num_examples: 92858 - name: validation num_bytes: 2859977775 num_examples: 83802 - name: validation_cnndm num_bytes: 225375023 num_examples: 2284 download_size: 291050539 dataset_size: 6246040321 --- # Dataset Card for "summarize_from_feedback_oai_preprocessing_1711138537" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vwxyzjn/summarize_from_feedback_tldr_3_filtered_oai_preprocessing_1711138793	vwxyzjn	"2024-03-22T20:21:42Z"	0	[ "croissant", "region:us" ]	null	"2024-03-22T20:21:19Z"	--- dataset_info: features: - name: id dtype: string - name: subreddit dtype: string - name: title dtype: string - name: post dtype: string - name: summary dtype: string - name: query_token sequence: int64 - name: query dtype: string - name: reference_response dtype: string - name: reference_response_token sequence: int64 - name: reference_response_token_len dtype: int64 - name: query_reference_response dtype: string - name: query_reference_response_token sequence: int64 - name: query_reference_response_token_response_label sequence: int64 - name: query_reference_response_token_len dtype: int64 splits: - name: train num_bytes: 2125689249 num_examples: 116722 - name: validation num_bytes: 117437271 num_examples: 6447 - name: test num_bytes: 119410966 num_examples: 6553 download_size: 562087836 dataset_size: 2362537486 --- # TL;DR SFT Dataset for OpenAI's [Summarize from Feedback](https://openai.com/blog/summarization/) task The dataset is directly taken from https://github.com/openai/summarize-from-feedback/tree/700967448d10004279f138666442bf1497d0e705#reddit-tldr-dataset These columns are taken directly from the aforementioned dataset: * id: unique identifier for the post * subreddit: subreddit the post was taken from * title: title of the post * post: body of the post * summary: summary of the post * reference_response: reference response for the post These columns are added by this preprocessing script: * query: length-limited query for summarization: OAI pre-processes the main text (title + subreddit + post), ensuring it has only 512 tokens; if the main text is too long, then it tries to truncate at the last ` `. If it's too short it pads the main text ([summarize_from_feedback/tasks.py#L98-L165](https://github.com/openai/summarize-from-feedback/blob/700967448d10004279f138666442bf1497d0e705/summarize_from_feedback/tasks.py#L98-L165)). Padding is either space or `[PAD]` token (see Args below). * query_token: tokenized version of `query` * reference_response_token: tokenized version of `reference_response` * reference_response_token_len: length of `reference_response_token` * query_reference_response: concatenation of `query.strip()` and `reference_response` * query_reference_response_token: tokenized version of `query_reference_response`, up to `max_sft_query_response_length` tokens * query_reference_response_token_len: length of `query_reference_response_token` # Args ```python {'base_model': 'EleutherAI/pythia-1b-deduped', 'check_length_correctness': True, 'cnndm_params': TaskQueryHParams(length=1919, format_str='Article:\n{article}\n\nTL;DR:\n', truncate_field='article', truncate_text='\n', padding='pad_token', pad_token=[50277], pad_side='left', max_sft_response_length=None, max_sft_query_response_length=None, max_rm_response_length=155, max_rm_query_response_length=2021), 'debug': False, 'hf_entity': 'vwxyzjn', 'push_to_hub': True, 'tldr_params': TaskQueryHParams(length=512, format_str='SUBREDDIT: r/{subreddit}\n' '\n' 'TITLE: {title}\n' '\n' 'POST: {post}\n' '\n' 'TL;DR:', truncate_field='post', truncate_text='\n', padding='pad_token', pad_token=[50277], pad_side='left', max_sft_response_length=53, max_sft_query_response_length=562, max_rm_response_length=169, max_rm_query_response_length=638)} ```
HPGomes/MichaelJacksonFalsetto	HPGomes	"2024-03-23T02:41:36Z"	0	[ "license:openrail", "croissant", "region:us" ]	null	"2024-03-22T20:23:17Z"	--- license: openrail ---
vwxyzjn/summarize_from_feedback_oai_preprocessing_1711138793	vwxyzjn	"2024-03-22T20:24:14Z"	0	[ "croissant", "region:us" ]	null	"2024-03-22T20:23:25Z"	--- dataset_info: features: - name: info struct: - name: id dtype: string - name: post dtype: string - name: title dtype: string - name: subreddit dtype: string - name: site dtype: string - name: article dtype: string - name: summaries list: - name: text dtype: string - name: policy dtype: string - name: note dtype: string - name: choice dtype: int32 - name: worker dtype: string - name: batch dtype: string - name: split dtype: string - name: extra struct: - name: confidence dtype: int32 - name: query_token sequence: int64 - name: query dtype: string - name: chosen dtype: string - name: chosen_token sequence: int64 - name: chosen_token_len dtype: int64 - name: rejected dtype: string - name: rejected_token sequence: int64 - name: rejected_token_len dtype: int64 - name: chosen_policy dtype: string - name: rejected_policy dtype: string - name: policies dtype: string - name: chosen_len_minus_rejected_len dtype: int64 - name: query_chosen dtype: string - name: query_chosen_token sequence: int64 - name: query_chosen_token_len dtype: int64 - name: query_rejected dtype: string - name: query_rejected_token sequence: int64 - name: query_rejected_token_len dtype: int64 - name: query_token_len dtype: int64 - name: query_chosen_token_response_label sequence: int64 - name: query_rejected_token_response_label sequence: int64 splits: - name: train num_bytes: 3160687523 num_examples: 92858 - name: validation num_bytes: 2859977775 num_examples: 83802 - name: validation_cnndm num_bytes: 225375023 num_examples: 2284 download_size: 291050539 dataset_size: 6246040321 --- # Dataset Card for "summarize_from_feedback_oai_preprocessing_1711138793" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Zarcend/testDataSet	Zarcend	"2024-03-22T20:28:57Z"	0	[ "license:mit", "croissant", "region:us" ]	null	"2024-03-22T20:27:59Z"	--- license: mit ---
AlekseyKorshuk/pickapic_v2-prompts-dedup	AlekseyKorshuk	"2024-03-22T20:37:56Z"	0	[ "croissant", "region:us" ]	null	"2024-03-22T20:36:43Z"	--- dataset_info: features: - name: prompt dtype: string - name: __cluster__ dtype: int64 splits: - name: train num_bytes: 461516230.6301592 num_examples: 2802392 download_size: 212898248 dataset_size: 461516230.6301592 configs: - config_name: default data_files: - split: train path: data/train-* ---
iaaoli2/arianaw	iaaoli2	"2024-03-22T20:53:52Z"	0	[ "license:openrail", "croissant", "region:us" ]	null	"2024-03-22T20:48:20Z"	--- license: openrail ---
chegri1/dataset	chegri1	"2024-03-22T21:03:52Z"	0	[ "license:unknown", "croissant", "region:us" ]	null	"2024-03-22T21:01:48Z"	--- license: unknown ---
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.3_seed_2	Mitsuki-Sakamoto	"2024-03-22T22:44:32Z"	0	[ "croissant", "region:us" ]	null	"2024-03-22T21:13:04Z"	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43761297 num_examples: 18928 - name: epoch_1 num_bytes: 44345524 num_examples: 18928 - name: epoch_2 num_bytes: 44437506 num_examples: 18928 - name: epoch_3 num_bytes: 44469689 num_examples: 18928 - name: epoch_4 num_bytes: 44474667 num_examples: 18928 - name: epoch_5 num_bytes: 44464460 num_examples: 18928 - name: epoch_6 num_bytes: 44442708 num_examples: 18928 - name: epoch_7 num_bytes: 44422364 num_examples: 18928 - name: epoch_8 num_bytes: 44414794 num_examples: 18928 - name: epoch_9 num_bytes: 44410898 num_examples: 18928 - name: epoch_10 num_bytes: 44411476 num_examples: 18928 - name: epoch_11 num_bytes: 44406870 num_examples: 18928 - name: epoch_12 num_bytes: 44409267 num_examples: 18928 - name: epoch_13 num_bytes: 44409143 num_examples: 18928 - name: epoch_14 num_bytes: 44408111 num_examples: 18928 - name: epoch_15 num_bytes: 44407603 num_examples: 18928 - name: epoch_16 num_bytes: 44408486 num_examples: 18928 - name: epoch_17 num_bytes: 44405857 num_examples: 18928 - name: epoch_18 num_bytes: 44406319 num_examples: 18928 - name: epoch_19 num_bytes: 44406957 num_examples: 18928 - name: epoch_20 num_bytes: 44405910 num_examples: 18928 - name: epoch_21 num_bytes: 44406498 num_examples: 18928 - name: epoch_22 num_bytes: 44406929 num_examples: 18928 - name: epoch_23 num_bytes: 44405194 num_examples: 18928 - name: epoch_24 num_bytes: 44405536 num_examples: 18928 - name: epoch_25 num_bytes: 44405889 num_examples: 18928 - name: epoch_26 num_bytes: 44404896 num_examples: 18928 - name: epoch_27 num_bytes: 44404886 num_examples: 18928 - name: epoch_28 num_bytes: 44406038 num_examples: 18928 - name: epoch_29 num_bytes: 44407507 num_examples: 18928 download_size: 700201551 dataset_size: 1331783279 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_1.0_seed_2	Mitsuki-Sakamoto	"2024-03-23T06:20:06Z"	0	[ "croissant", "region:us" ]	null	"2024-03-22T21:18:21Z"	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43770255 num_examples: 18928 - name: epoch_1 num_bytes: 44408742 num_examples: 18928 - name: epoch_2 num_bytes: 44482870 num_examples: 18928 - name: epoch_3 num_bytes: 44532704 num_examples: 18928 - name: epoch_4 num_bytes: 44549616 num_examples: 18928 - name: epoch_5 num_bytes: 44539709 num_examples: 18928 - name: epoch_6 num_bytes: 44538656 num_examples: 18928 - name: epoch_7 num_bytes: 44529666 num_examples: 18928 - name: epoch_8 num_bytes: 44513235 num_examples: 18928 - name: epoch_9 num_bytes: 44502442 num_examples: 18928 - name: epoch_10 num_bytes: 44499095 num_examples: 18928 - name: epoch_11 num_bytes: 44493183 num_examples: 18928 - name: epoch_12 num_bytes: 44491075 num_examples: 18928 - name: epoch_13 num_bytes: 44486818 num_examples: 18928 - name: epoch_14 num_bytes: 44489815 num_examples: 18928 - name: epoch_15 num_bytes: 44487019 num_examples: 18928 - name: epoch_16 num_bytes: 44486484 num_examples: 18928 - name: epoch_17 num_bytes: 44487934 num_examples: 18928 - name: epoch_18 num_bytes: 44485687 num_examples: 18928 - name: epoch_19 num_bytes: 44483472 num_examples: 18928 - name: epoch_20 num_bytes: 44484628 num_examples: 18928 - name: epoch_21 num_bytes: 44485591 num_examples: 18928 - name: epoch_22 num_bytes: 44486295 num_examples: 18928 - name: epoch_23 num_bytes: 44485684 num_examples: 18928 - name: epoch_24 num_bytes: 44484921 num_examples: 18928 - name: epoch_25 num_bytes: 44484354 num_examples: 18928 - name: epoch_26 num_bytes: 44484909 num_examples: 18928 - name: epoch_27 num_bytes: 44484138 num_examples: 18928 - name: epoch_28 num_bytes: 44485080 num_examples: 18928 - name: epoch_29 num_bytes: 44483062 num_examples: 18928 download_size: 1073130082 dataset_size: 1334107139 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_160m_thr_0.3_seed_2	Mitsuki-Sakamoto	"2024-03-22T23:29:05Z"	0	[ "croissant", "region:us" ]	null	"2024-03-22T21:41:49Z"	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43783586 num_examples: 18928 - name: epoch_1 num_bytes: 44377250 num_examples: 18928 - name: epoch_2 num_bytes: 44448866 num_examples: 18928 - name: epoch_3 num_bytes: 44483809 num_examples: 18928 - name: epoch_4 num_bytes: 44492320 num_examples: 18928 - name: epoch_5 num_bytes: 44489018 num_examples: 18928 - name: epoch_6 num_bytes: 44475503 num_examples: 18928 - name: epoch_7 num_bytes: 44460141 num_examples: 18928 - name: epoch_8 num_bytes: 44445265 num_examples: 18928 - name: epoch_9 num_bytes: 44441178 num_examples: 18928 - name: epoch_10 num_bytes: 44438339 num_examples: 18928 - name: epoch_11 num_bytes: 44436226 num_examples: 18928 - name: epoch_12 num_bytes: 44434486 num_examples: 18928 - name: epoch_13 num_bytes: 44435475 num_examples: 18928 - name: epoch_14 num_bytes: 44431647 num_examples: 18928 - name: epoch_15 num_bytes: 44432365 num_examples: 18928 - name: epoch_16 num_bytes: 44432856 num_examples: 18928 - name: epoch_17 num_bytes: 44432911 num_examples: 18928 - name: epoch_18 num_bytes: 44429532 num_examples: 18928 - name: epoch_19 num_bytes: 44429380 num_examples: 18928 - name: epoch_20 num_bytes: 44430229 num_examples: 18928 - name: epoch_21 num_bytes: 44430596 num_examples: 18928 - name: epoch_22 num_bytes: 44431243 num_examples: 18928 - name: epoch_23 num_bytes: 44428939 num_examples: 18928 - name: epoch_24 num_bytes: 44432154 num_examples: 18928 - name: epoch_25 num_bytes: 44429301 num_examples: 18928 - name: epoch_26 num_bytes: 44429659 num_examples: 18928 - name: epoch_27 num_bytes: 44431306 num_examples: 18928 - name: epoch_28 num_bytes: 44432280 num_examples: 18928 - name: epoch_29 num_bytes: 44431422 num_examples: 18928 download_size: 701477709 dataset_size: 1332537282 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
dbutt7/NTP_Treefall_Segmentation	dbutt7	"2024-03-23T00:13:41Z"	0	[ "license:cc-by-nc-4.0", "croissant", "region:us" ]	null	"2024-03-22T22:26:23Z"	--- license: cc-by-nc-4.0 dataset_info: features: - name: x dtype: image - name: y sequence: sequence: sequence: uint8 splits: - name: train num_bytes: 6183133760 num_examples: 7240 download_size: 1458099889 dataset_size: 6183133760 ---
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_160m_thr_1.0_seed_2	Mitsuki-Sakamoto	"2024-03-23T01:22:00Z"	0	[ "croissant", "region:us" ]	null	"2024-03-22T22:59:39Z"	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43779189 num_examples: 18928 - name: epoch_1 num_bytes: 44389394 num_examples: 18928 - name: epoch_2 num_bytes: 44452838 num_examples: 18928 - name: epoch_3 num_bytes: 44504767 num_examples: 18928 - name: epoch_4 num_bytes: 44525045 num_examples: 18928 - name: epoch_5 num_bytes: 44512817 num_examples: 18928 - name: epoch_6 num_bytes: 44504063 num_examples: 18928 - name: epoch_7 num_bytes: 44468006 num_examples: 18928 - name: epoch_8 num_bytes: 44457766 num_examples: 18928 - name: epoch_9 num_bytes: 44452281 num_examples: 18928 - name: epoch_10 num_bytes: 44441633 num_examples: 18928 - name: epoch_11 num_bytes: 44440113 num_examples: 18928 - name: epoch_12 num_bytes: 44438738 num_examples: 18928 - name: epoch_13 num_bytes: 44437694 num_examples: 18928 - name: epoch_14 num_bytes: 44438979 num_examples: 18928 - name: epoch_15 num_bytes: 44434772 num_examples: 18928 - name: epoch_16 num_bytes: 44431885 num_examples: 18928 - name: epoch_17 num_bytes: 44430771 num_examples: 18928 - name: epoch_18 num_bytes: 44430902 num_examples: 18928 - name: epoch_19 num_bytes: 44429917 num_examples: 18928 - name: epoch_20 num_bytes: 44430629 num_examples: 18928 - name: epoch_21 num_bytes: 44429778 num_examples: 18928 - name: epoch_22 num_bytes: 44429225 num_examples: 18928 - name: epoch_23 num_bytes: 44432672 num_examples: 18928 - name: epoch_24 num_bytes: 44429439 num_examples: 18928 - name: epoch_25 num_bytes: 44429477 num_examples: 18928 - name: epoch_26 num_bytes: 44429378 num_examples: 18928 - name: epoch_27 num_bytes: 44429813 num_examples: 18928 - name: epoch_28 num_bytes: 44426534 num_examples: 18928 - name: epoch_29 num_bytes: 44428539 num_examples: 18928 download_size: 700085000 dataset_size: 1332697054 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
sanjay920/single_function_call_oai_mistral_large	sanjay920	"2024-03-22T23:43:43Z"	0	[ "croissant", "region:us" ]	null	"2024-03-22T23:03:55Z"	--- dataset_info: features: - name: id dtype: string - name: tools list: - name: function struct: - name: description dtype: string - name: name dtype: string - name: parameters struct: - name: properties struct: - name: amount struct: - name: description dtype: string - name: type dtype: string - name: amount_due struct: - name: description dtype: string - name: type dtype: string - name: author struct: - name: description dtype: string - name: type dtype: string - name: bill_amount struct: - name: description dtype: string - name: type dtype: string - name: birth_date struct: - name: description dtype: string - name: type dtype: string - name: birth_year struct: - name: description dtype: string - name: type dtype: string - name: category struct: - name: description dtype: string - name: type dtype: string - name: country struct: - name: description dtype: string - name: type dtype: string - name: cuisine struct: - name: description dtype: string - name: type dtype: string - name: customer_name struct: - name: description dtype: string - name: type dtype: string - name: date_of_birth struct: - name: description dtype: string - name: type dtype: string - name: destination struct: - name: description dtype: string - name: type dtype: string - name: diet struct: - name: description dtype: string - name: type dtype: string - name: discount_percentage struct: - name: description dtype: string - name: type dtype: string - name: dob struct: - name: description dtype: string - name: type dtype: string - name: due_date struct: - name: description dtype: string - name: format dtype: string - name: type dtype: string - name: email struct: - name: description dtype: string - name: type dtype: string - name: encryption_algorithm struct: - name: description dtype: string - name: type dtype: string - name: end_location struct: - name: description dtype: string - name: type dtype: string - name: end_time struct: - name: description dtype: string - name: type dtype: string - name: first_name struct: - name: description dtype: string - name: type dtype: string - name: from_currency struct: - name: description dtype: string - name: type dtype: string - name: genre struct: - name: description dtype: string - name: type dtype: string - name: grades struct: - name: items struct: - name: properties struct: - name: course struct: - name: description dtype: string - name: type dtype: string - name: credit_hours struct: - name: description dtype: string - name: type dtype: string - name: grade struct: - name: description dtype: string - name: type dtype: string - name: required sequence: string - name: type dtype: string - name: type dtype: string - name: height struct: - name: description dtype: string - name: type dtype: string - name: include_numbers struct: - name: description dtype: string - name: type dtype: string - name: include_special_characters struct: - name: description dtype: string - name: type dtype: string - name: include_symbols struct: - name: description dtype: string - name: type dtype: string - name: income struct: - name: description dtype: string - name: type dtype: string - name: ingredients struct: - name: description dtype: string - name: items struct: - name: type dtype: string - name: type dtype: string - name: interest_rate struct: - name: description dtype: string - name: type dtype: string - name: keyword struct: - name: description dtype: string - name: type dtype: string - name: keywords struct: - name: description dtype: string - name: items struct: - name: type dtype: string - name: type dtype: string - name: last_name struct: - name: description dtype: string - name: type dtype: string - name: length struct: - name: description dtype: string - name: type dtype: string - name: loan_amount struct: - name: description dtype: string - name: type dtype: string - name: loan_term struct: - name: description dtype: string - name: type dtype: string - name: max struct: - name: description dtype: string - name: type dtype: string - name: measurements struct: - name: properties struct: - name: length struct: - name: description dtype: string - name: type dtype: string - name: width struct: - name: description dtype: string - name: type dtype: string - name: required sequence: string - name: type dtype: string - name: message struct: - name: description dtype: string - name: type dtype: string - name: metrics struct: - name: description dtype: string - name: items struct: - name: type dtype: string - name: type dtype: string - name: min struct: - name: description dtype: string - name: type dtype: string - name: name struct: - name: description dtype: string - name: type dtype: string - name: numbers struct: - name: description dtype: string - name: items struct: - name: type dtype: string - name: type dtype: string - name: options struct: - name: description dtype: string - name: items struct: - name: type dtype: string - name: type dtype: string - name: origin struct: - name: description dtype: string - name: type dtype: string - name: original_price struct: - name: description dtype: string - name: type dtype: string - name: principal struct: - name: description dtype: string - name: type dtype: string - name: priority struct: - name: description dtype: string - name: enum sequence: string - name: type dtype: string - name: query struct: - name: description dtype: string - name: type dtype: string - name: question struct: - name: description dtype: string - name: type dtype: string - name: recipient struct: - name: description dtype: string - name: type dtype: string - name: search_query struct: - name: description dtype: string - name: type dtype: string - name: shape struct: - name: description dtype: string - name: type dtype: string - name: start_location struct: - name: description dtype: string - name: type dtype: string - name: start_time struct: - name: description dtype: string - name: type dtype: string - name: stock_symbol struct: - name: description dtype: string - name: type dtype: string - name: subject struct: - name: description dtype: string - name: type dtype: string - name: symbol struct: - name: description dtype: string - name: type dtype: string - name: task struct: - name: description dtype: string - name: type dtype: string - name: task_name struct: - name: description dtype: string - name: type dtype: string - name: tax_rate struct: - name: description dtype: string - name: type dtype: string - name: text struct: - name: description dtype: string - name: type dtype: string - name: time struct: - name: description dtype: string - name: type dtype: string - name: tip_percentage struct: - name: description dtype: string - name: type dtype: string - name: title struct: - name: description dtype: string - name: type dtype: string - name: to_currency struct: - name: description dtype: string - name: type dtype: string - name: website_url struct: - name: description dtype: string - name: type dtype: string - name: weight struct: - name: description dtype: string - name: type dtype: string - name: word struct: - name: description dtype: string - name: type dtype: string - name: year struct: - name: description dtype: string - name: type dtype: string - name: required sequence: string - name: type dtype: string - name: type dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string - name: oai_match_laplateforme dtype: bool - name: oai_match_azure dtype: bool - name: openai_response dtype: string - name: la_plateforme_mistral_large_response dtype: string - name: azure_mistral_large_response dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 72421 num_examples: 50 download_size: 140187 dataset_size: 72421 configs: - config_name: default data_files: - split: train path: data/train-* ---
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.0_seed_3	Mitsuki-Sakamoto	"2024-03-23T00:22:40Z"	0	[ "croissant", "region:us" ]	null	"2024-03-22T23:16:59Z"	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43751727 num_examples: 18928 - name: epoch_1 num_bytes: 44330784 num_examples: 18928 - name: epoch_2 num_bytes: 44406698 num_examples: 18928 - name: epoch_3 num_bytes: 44435284 num_examples: 18928 - name: epoch_4 num_bytes: 44437128 num_examples: 18928 - name: epoch_5 num_bytes: 44423166 num_examples: 18928 - name: epoch_6 num_bytes: 44411320 num_examples: 18928 - name: epoch_7 num_bytes: 44410437 num_examples: 18928 - name: epoch_8 num_bytes: 44410984 num_examples: 18928 - name: epoch_9 num_bytes: 44407897 num_examples: 18928 - name: epoch_10 num_bytes: 44409623 num_examples: 18928 - name: epoch_11 num_bytes: 44408883 num_examples: 18928 - name: epoch_12 num_bytes: 44408562 num_examples: 18928 - name: epoch_13 num_bytes: 44408224 num_examples: 18928 - name: epoch_14 num_bytes: 44409062 num_examples: 18928 - name: epoch_15 num_bytes: 44407898 num_examples: 18928 - name: epoch_16 num_bytes: 44408185 num_examples: 18928 - name: epoch_17 num_bytes: 44408017 num_examples: 18928 - name: epoch_18 num_bytes: 44408034 num_examples: 18928 - name: epoch_19 num_bytes: 44409148 num_examples: 18928 - name: epoch_20 num_bytes: 44409429 num_examples: 18928 - name: epoch_21 num_bytes: 44407308 num_examples: 18928 - name: epoch_22 num_bytes: 44409553 num_examples: 18928 - name: epoch_23 num_bytes: 44408967 num_examples: 18928 - name: epoch_24 num_bytes: 44410252 num_examples: 18928 - name: epoch_25 num_bytes: 44409915 num_examples: 18928 - name: epoch_26 num_bytes: 44409736 num_examples: 18928 - name: epoch_27 num_bytes: 44410588 num_examples: 18928 - name: epoch_28 num_bytes: 44409741 num_examples: 18928 - name: epoch_29 num_bytes: 44409955 num_examples: 18928 download_size: 701230621 dataset_size: 1331606505 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
NickyNicky/nano_OpenHermes-2.5_chatml_gemma	NickyNicky	"2024-03-23T00:53:26Z"	1	[ "language:en", "croissant", "region:us" ]	null	"2024-03-22T23:42:57Z"	--- dataset_info: features: - name: text dtype: string - name: len_token dtype: int64 splits: - name: train num_bytes: 195803464 num_examples: 118849 download_size: 95174925 dataset_size: 195803464 configs: - config_name: default data_files: - split: train path: data/train-* language: - en --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/iI_akD8m-SuJG1a1xCHve.png) ``` <bos><start_of_turn>system You are a helpful AI assistant.<end_of_turn> <start_of_turn>user What flies without wings? What passes all things? What mends all sorrow? What brings the morrow?<end_of_turn> <start_of_turn>model The answer to the riddle is time. Time flies without wings, passes all things, mends all sorrow, and brings the morrow because it constantly moves forward and affects everything in its path.<end_of_turn><eos> ``` ## taken from teknium. ``` https://huggingface.co/datasets/teknium/OpenHermes-2.5 ```
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_160m_thr_0.0_seed_3	Mitsuki-Sakamoto	"2024-03-23T01:10:05Z"	0	[ "croissant", "region:us" ]	null	"2024-03-22T23:45:38Z"	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43778450 num_examples: 18928 - name: epoch_1 num_bytes: 44340411 num_examples: 18928 - name: epoch_2 num_bytes: 44412719 num_examples: 18928 - name: epoch_3 num_bytes: 44443289 num_examples: 18928 - name: epoch_4 num_bytes: 44449016 num_examples: 18928 - name: epoch_5 num_bytes: 44446506 num_examples: 18928 - name: epoch_6 num_bytes: 44440017 num_examples: 18928 - name: epoch_7 num_bytes: 44437607 num_examples: 18928 - name: epoch_8 num_bytes: 44433764 num_examples: 18928 - name: epoch_9 num_bytes: 44430532 num_examples: 18928 - name: epoch_10 num_bytes: 44428837 num_examples: 18928 - name: epoch_11 num_bytes: 44427805 num_examples: 18928 - name: epoch_12 num_bytes: 44428796 num_examples: 18928 - name: epoch_13 num_bytes: 44429411 num_examples: 18928 - name: epoch_14 num_bytes: 44429070 num_examples: 18928 - name: epoch_15 num_bytes: 44429063 num_examples: 18928 - name: epoch_16 num_bytes: 44427545 num_examples: 18928 - name: epoch_17 num_bytes: 44428693 num_examples: 18928 - name: epoch_18 num_bytes: 44428068 num_examples: 18928 - name: epoch_19 num_bytes: 44428456 num_examples: 18928 - name: epoch_20 num_bytes: 44427070 num_examples: 18928 - name: epoch_21 num_bytes: 44427869 num_examples: 18928 - name: epoch_22 num_bytes: 44428874 num_examples: 18928 - name: epoch_23 num_bytes: 44429224 num_examples: 18928 - name: epoch_24 num_bytes: 44428269 num_examples: 18928 - name: epoch_25 num_bytes: 44428697 num_examples: 18928 - name: epoch_26 num_bytes: 44428907 num_examples: 18928 - name: epoch_27 num_bytes: 44429168 num_examples: 18928 - name: epoch_28 num_bytes: 44428217 num_examples: 18928 - name: epoch_29 num_bytes: 44428593 num_examples: 18928 download_size: 701248295 dataset_size: 1332182943 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
KrayIzuna/henrys	KrayIzuna	"2024-03-22T23:50:33Z"	0	[ "license:openrail", "region:us" ]	null	"2024-03-22T23:48:14Z"	--- license: openrail ---
Rodrimr112/dataset2	Rodrimr112	"2024-03-23T01:02:57Z"	0	[ "task_categories:text-generation", "language:es", "croissant", "region:us" ]	[ "text-generation" ]	"2024-03-23T00:28:59Z"	--- task_categories: - text-generation language: - es ---
gaianet/vitalik.eth	gaianet	"2024-03-23T21:03:07Z"	0	[ "license:apache-2.0", "croissant", "region:us" ]	null	"2024-03-23T00:34:06Z"	--- license: apache-2.0 --- Prepare Qdrant: ``` mkdir qdrant_storage mkdir qdrant_snapshots ``` Start Qdrant: ``` docker run -d -p 6333:6333 -p 6334:6334 \ -v $(pwd)/qdrant_storage:/qdrant/storage:z \ -v $(pwd)/qdrant_snapshots:/qdrant/snapshots:z \ qdrant/qdrant ``` Create collection: ``` curl -X PUT 'http://localhost:6333/collections/vitalik.eth' \ -H 'Content-Type: application/json' \ --data-raw '{ "vectors": { "size": 384, "distance": "Cosine", "on_disk": true } }' ``` Query collection: ``` curl 'http://localhost:6333/collections/vitalik.eth' ``` Optional: delete collection ``` curl -X DELETE 'http://localhost:6333/collections/vitalik.eth' ``` Get embedding model: ``` curl -LO https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/resolve/main/all-MiniLM-L6-v2-ggml-model-f16.gguf ``` Get the embedding app: ``` curl -LO https://raw.githubusercontent.com/YuanTony/chemistry-assistant/main/rag-embeddings/create_embeddings.wasm ``` Create and save the generated embeddings: ``` wasmedge --dir .:. --nn-preload default:GGML:AUTO:all-MiniLM-L6-v2-ggml-model-f16.gguf create_embeddings.wasm default vitalik.eth 384 vitalik-eth.txt ``` Check the results: ``` curl 'http://localhost:6333/collections/vitalik.eth' ``` Create snapshot: ``` curl -X POST 'http://localhost:6333/collections/vitalik.eth/snapshots' ``` Access the snapshots: ``` ls qdrant_snapshots/vitalik.eth/ ```
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.1_seed_3	Mitsuki-Sakamoto	"2024-03-23T01:52:50Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T00:37:31Z"	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43769895 num_examples: 18928 - name: epoch_1 num_bytes: 44372693 num_examples: 18928 - name: epoch_2 num_bytes: 44449483 num_examples: 18928 - name: epoch_3 num_bytes: 44482079 num_examples: 18928 - name: epoch_4 num_bytes: 44487733 num_examples: 18928 - name: epoch_5 num_bytes: 44468382 num_examples: 18928 - name: epoch_6 num_bytes: 44457528 num_examples: 18928 - name: epoch_7 num_bytes: 44447610 num_examples: 18928 - name: epoch_8 num_bytes: 44444945 num_examples: 18928 - name: epoch_9 num_bytes: 44448473 num_examples: 18928 - name: epoch_10 num_bytes: 44444758 num_examples: 18928 - name: epoch_11 num_bytes: 44445124 num_examples: 18928 - name: epoch_12 num_bytes: 44444365 num_examples: 18928 - name: epoch_13 num_bytes: 44444621 num_examples: 18928 - name: epoch_14 num_bytes: 44444964 num_examples: 18928 - name: epoch_15 num_bytes: 44445097 num_examples: 18928 - name: epoch_16 num_bytes: 44446080 num_examples: 18928 - name: epoch_17 num_bytes: 44444852 num_examples: 18928 - name: epoch_18 num_bytes: 44446254 num_examples: 18928 - name: epoch_19 num_bytes: 44446278 num_examples: 18928 - name: epoch_20 num_bytes: 44444210 num_examples: 18928 - name: epoch_21 num_bytes: 44445387 num_examples: 18928 - name: epoch_22 num_bytes: 44446893 num_examples: 18928 - name: epoch_23 num_bytes: 44446406 num_examples: 18928 - name: epoch_24 num_bytes: 44446447 num_examples: 18928 - name: epoch_25 num_bytes: 44446015 num_examples: 18928 - name: epoch_26 num_bytes: 44446209 num_examples: 18928 - name: epoch_27 num_bytes: 44446030 num_examples: 18928 - name: epoch_28 num_bytes: 44446004 num_examples: 18928 - name: epoch_29 num_bytes: 44446103 num_examples: 18928 download_size: 701614310 dataset_size: 1332740918 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
Xiangyun2018/GalaxySpectra0-10000	Xiangyun2018	"2024-03-23T01:04:38Z"	0	[ "license:apache-2.0", "croissant", "region:us" ]	null	"2024-03-23T00:43:15Z"	--- license: apache-2.0 ---
lucassaicover/ALASTORBR	lucassaicover	"2024-03-23T00:49:22Z"	0	[ "license:openrail", "croissant", "region:us" ]	null	"2024-03-23T00:48:17Z"	--- license: openrail ---
gaianet/ktx.finance	gaianet	"2024-03-23T22:55:36Z"	0	[ "license:apache-2.0", "croissant", "region:us" ]	null	"2024-03-23T01:00:22Z"	--- license: apache-2.0 --- Prepare Qdrant: ``` mkdir qdrant_storage mkdir qdrant_snapshots ``` Start Qdrant: ``` docker run -d -p 6333:6333 -p 6334:6334 \ -v $(pwd)/qdrant_storage:/qdrant/storage:z \ -v $(pwd)/qdrant_snapshots:/qdrant/snapshots:z \ qdrant/qdrant ``` Create collection: ``` curl -X PUT 'http://localhost:6333/collections/ktx.finance' \ -H 'Content-Type: application/json' \ --data-raw '{ "vectors": { "size": 384, "distance": "Cosine", "on_disk": true } }' ``` Query collection: ``` curl 'http://localhost:6333/collections/ktx.finance' ``` Optional: delete collection ``` curl -X DELETE 'http://localhost:6333/collections/ktx.finance' ``` Get embedding model: ``` curl -LO https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/resolve/main/all-MiniLM-L6-v2-ggml-model-f16.gguf ``` Get the embedding app: ``` curl -LO https://raw.githubusercontent.com/YuanTony/chemistry-assistant/main/rag-embeddings/create_embeddings.wasm ``` Create and save the generated embeddings: ``` wasmedge --dir .:. --nn-preload default:GGML:AUTO:all-MiniLM-L6-v2-ggml-model-f16.gguf create_embeddings.wasm default ktx.finance 384 ktx_docs_20240322.txt ``` Check the results: ``` curl 'http://localhost:6333/collections/ktx.finance' ``` Create snapshot: ``` curl -X POST 'http://localhost:6333/collections/ktx.finance/snapshots' ``` Access the snapshots: ``` ls qdrant_snapshots/ktx.finance/ ```
YukiTomita-CC/temp_0323	YukiTomita-CC	"2024-03-23T01:32:50Z"	0	[ "license:mit", "region:us" ]	null	"2024-03-23T01:23:28Z"	--- license: mit ---
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.3_seed_3	Mitsuki-Sakamoto	"2024-03-23T02:49:56Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T01:24:50Z"	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43746942 num_examples: 18928 - name: epoch_1 num_bytes: 44351740 num_examples: 18928 - name: epoch_2 num_bytes: 44435633 num_examples: 18928 - name: epoch_3 num_bytes: 44468391 num_examples: 18928 - name: epoch_4 num_bytes: 44473636 num_examples: 18928 - name: epoch_5 num_bytes: 44468847 num_examples: 18928 - name: epoch_6 num_bytes: 44456913 num_examples: 18928 - name: epoch_7 num_bytes: 44447915 num_examples: 18928 - name: epoch_8 num_bytes: 44446605 num_examples: 18928 - name: epoch_9 num_bytes: 44443908 num_examples: 18928 - name: epoch_10 num_bytes: 44443718 num_examples: 18928 - name: epoch_11 num_bytes: 44443331 num_examples: 18928 - name: epoch_12 num_bytes: 44443512 num_examples: 18928 - name: epoch_13 num_bytes: 44444407 num_examples: 18928 - name: epoch_14 num_bytes: 44443485 num_examples: 18928 - name: epoch_15 num_bytes: 44443853 num_examples: 18928 - name: epoch_16 num_bytes: 44444614 num_examples: 18928 - name: epoch_17 num_bytes: 44443375 num_examples: 18928 - name: epoch_18 num_bytes: 44443725 num_examples: 18928 - name: epoch_19 num_bytes: 44443945 num_examples: 18928 - name: epoch_20 num_bytes: 44444613 num_examples: 18928 - name: epoch_21 num_bytes: 44444719 num_examples: 18928 - name: epoch_22 num_bytes: 44442850 num_examples: 18928 - name: epoch_23 num_bytes: 44444500 num_examples: 18928 - name: epoch_24 num_bytes: 44444003 num_examples: 18928 - name: epoch_25 num_bytes: 44444305 num_examples: 18928 - name: epoch_26 num_bytes: 44443163 num_examples: 18928 - name: epoch_27 num_bytes: 44443641 num_examples: 18928 - name: epoch_28 num_bytes: 44444321 num_examples: 18928 - name: epoch_29 num_bytes: 44444238 num_examples: 18928 download_size: 701371233 dataset_size: 1332618848 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
michaelfla/cartersapiencialux	michaelfla	"2024-03-23T01:36:54Z"	0	[ "license:openrail", "croissant", "region:us" ]	null	"2024-03-23T01:29:05Z"	--- license: openrail ---
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_1.0_seed_3	Mitsuki-Sakamoto	"2024-03-23T03:29:47Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T01:29:22Z"	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43746238 num_examples: 18928 - name: epoch_1 num_bytes: 44363986 num_examples: 18928 - name: epoch_2 num_bytes: 44435706 num_examples: 18928 - name: epoch_3 num_bytes: 44506811 num_examples: 18928 - name: epoch_4 num_bytes: 44535773 num_examples: 18928 - name: epoch_5 num_bytes: 44529774 num_examples: 18928 - name: epoch_6 num_bytes: 44521649 num_examples: 18928 - name: epoch_7 num_bytes: 44512193 num_examples: 18928 - name: epoch_8 num_bytes: 44511726 num_examples: 18928 - name: epoch_9 num_bytes: 44497200 num_examples: 18928 - name: epoch_10 num_bytes: 44503145 num_examples: 18928 - name: epoch_11 num_bytes: 44498740 num_examples: 18928 - name: epoch_12 num_bytes: 44499383 num_examples: 18928 - name: epoch_13 num_bytes: 44499252 num_examples: 18928 - name: epoch_14 num_bytes: 44500926 num_examples: 18928 - name: epoch_15 num_bytes: 44497701 num_examples: 18928 - name: epoch_16 num_bytes: 44493966 num_examples: 18928 - name: epoch_17 num_bytes: 44493661 num_examples: 18928 - name: epoch_18 num_bytes: 44495720 num_examples: 18928 - name: epoch_19 num_bytes: 44495849 num_examples: 18928 - name: epoch_20 num_bytes: 44492977 num_examples: 18928 - name: epoch_21 num_bytes: 44496047 num_examples: 18928 - name: epoch_22 num_bytes: 44495073 num_examples: 18928 - name: epoch_23 num_bytes: 44493852 num_examples: 18928 - name: epoch_24 num_bytes: 44495739 num_examples: 18928 - name: epoch_25 num_bytes: 44495049 num_examples: 18928 - name: epoch_26 num_bytes: 44495848 num_examples: 18928 - name: epoch_27 num_bytes: 44495934 num_examples: 18928 - name: epoch_28 num_bytes: 44496877 num_examples: 18928 - name: epoch_29 num_bytes: 44496442 num_examples: 18928 download_size: 701095270 dataset_size: 1334093237 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_160m_thr_0.1_seed_3	Mitsuki-Sakamoto	"2024-03-23T02:57:25Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T01:30:23Z"	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43762247 num_examples: 18928 - name: epoch_1 num_bytes: 44355479 num_examples: 18928 - name: epoch_2 num_bytes: 44413402 num_examples: 18928 - name: epoch_3 num_bytes: 44446124 num_examples: 18928 - name: epoch_4 num_bytes: 44453469 num_examples: 18928 - name: epoch_5 num_bytes: 44441134 num_examples: 18928 - name: epoch_6 num_bytes: 44427806 num_examples: 18928 - name: epoch_7 num_bytes: 44415327 num_examples: 18928 - name: epoch_8 num_bytes: 44409544 num_examples: 18928 - name: epoch_9 num_bytes: 44408079 num_examples: 18928 - name: epoch_10 num_bytes: 44408107 num_examples: 18928 - name: epoch_11 num_bytes: 44404673 num_examples: 18928 - name: epoch_12 num_bytes: 44406717 num_examples: 18928 - name: epoch_13 num_bytes: 44404524 num_examples: 18928 - name: epoch_14 num_bytes: 44403118 num_examples: 18928 - name: epoch_15 num_bytes: 44403919 num_examples: 18928 - name: epoch_16 num_bytes: 44406501 num_examples: 18928 - name: epoch_17 num_bytes: 44406372 num_examples: 18928 - name: epoch_18 num_bytes: 44403957 num_examples: 18928 - name: epoch_19 num_bytes: 44405464 num_examples: 18928 - name: epoch_20 num_bytes: 44406776 num_examples: 18928 - name: epoch_21 num_bytes: 44405069 num_examples: 18928 - name: epoch_22 num_bytes: 44406545 num_examples: 18928 - name: epoch_23 num_bytes: 44406186 num_examples: 18928 - name: epoch_24 num_bytes: 44405986 num_examples: 18928 - name: epoch_25 num_bytes: 44405903 num_examples: 18928 - name: epoch_26 num_bytes: 44405882 num_examples: 18928 - name: epoch_27 num_bytes: 44405779 num_examples: 18928 - name: epoch_28 num_bytes: 44406020 num_examples: 18928 - name: epoch_29 num_bytes: 44406453 num_examples: 18928 download_size: 701508295 dataset_size: 1331646562 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
CodecSR/vocalset_synth	CodecSR	"2024-03-23T10:42:06Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T01:32:36Z"	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 44100 - name: id dtype: string splits: - name: original num_bytes: 2788138277.0 num_examples: 3612 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 2786991855.888 num_examples: 3612 - name: academicodec_hifi_24k_320d num_bytes: 2786991855.888 num_examples: 3612 - name: audiodec_24k_300d num_bytes: 2789217294.888 num_examples: 3612 - name: audiodec_48k_300d_uni num_bytes: 2789217294.888 num_examples: 3612 - name: dac_16k num_bytes: 2788141415.888 num_examples: 3612 - name: dac_24k num_bytes: 2788141415.888 num_examples: 3612 - name: dac_44k num_bytes: 2788141414.888 num_examples: 3612 - name: encodec_24k_12bps num_bytes: 2788141414.888 num_examples: 3612 - name: encodec_24k_1_5bps num_bytes: 2788141415.888 num_examples: 3612 - name: encodec_24k_24bps num_bytes: 2788141415.888 num_examples: 3612 - name: encodec_24k_3bps num_bytes: 2788141415.888 num_examples: 3612 - name: encodec_24k_6bps num_bytes: 2788141414.888 num_examples: 3612 - name: facodec_16k num_bytes: 2787788494.888 num_examples: 3612 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 2788141415.888 num_examples: 3612 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 2788141414.888 num_examples: 3612 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 2788141415.888 num_examples: 3612 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 2788141414.888 num_examples: 3612 - name: language_codec_chinese_24k_nq8_12kbps num_bytes: 2789293294.888 num_examples: 3612 - name: language_codec_paper_24k_nq8_12kbps num_bytes: 2789293294.888 num_examples: 3612 - name: speech_tokenizer_16k num_bytes: 2789293294.888 num_examples: 3612 download_size: 55003437704 dataset_size: 58553921943.76001 configs: - config_name: default data_files: - split: original path: data/original-* - split: academicodec_hifi_16k_320d_large_uni path: data/academicodec_hifi_16k_320d_large_uni-* - split: academicodec_hifi_24k_320d path: data/academicodec_hifi_24k_320d-* - split: audiodec_24k_300d path: data/audiodec_24k_300d-* - split: audiodec_48k_300d_uni path: data/audiodec_48k_300d_uni-* - split: dac_16k path: data/dac_16k-* - split: dac_24k path: data/dac_24k-* - split: dac_44k path: data/dac_44k-* - split: encodec_24k_12bps path: data/encodec_24k_12bps-* - split: encodec_24k_1_5bps path: data/encodec_24k_1_5bps-* - split: encodec_24k_24bps path: data/encodec_24k_24bps-* - split: encodec_24k_3bps path: data/encodec_24k_3bps-* - split: encodec_24k_6bps path: data/encodec_24k_6bps-* - split: facodec_16k path: data/facodec_16k-* - split: funcodec_en_libritts_16k_nq32ds320 path: data/funcodec_en_libritts_16k_nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds640 path: data/funcodec_en_libritts_16k_nq32ds640-* - split: funcodec_zh_en_16k_nq32ds320 path: data/funcodec_zh_en_16k_nq32ds320-* - split: funcodec_zh_en_16k_nq32ds640 path: data/funcodec_zh_en_16k_nq32ds640-* - split: language_codec_chinese_24k_nq8_12kbps path: data/language_codec_chinese_24k_nq8_12kbps-* - split: language_codec_paper_24k_nq8_12kbps path: data/language_codec_paper_24k_nq8_12kbps-* - split: speech_tokenizer_16k path: data/speech_tokenizer_16k-* ---
Zack157/CBV3	Zack157	"2024-03-23T01:35:27Z"	0	[ "license:openrail", "croissant", "region:us" ]	null	"2024-03-23T01:33:45Z"	--- license: openrail ---
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_160m_thr_0.3_seed_3	Mitsuki-Sakamoto	"2024-03-23T03:11:24Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T01:37:33Z"	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43760593 num_examples: 18928 - name: epoch_1 num_bytes: 44321306 num_examples: 18928 - name: epoch_2 num_bytes: 44392042 num_examples: 18928 - name: epoch_3 num_bytes: 44429751 num_examples: 18928 - name: epoch_4 num_bytes: 44441952 num_examples: 18928 - name: epoch_5 num_bytes: 44430509 num_examples: 18928 - name: epoch_6 num_bytes: 44418931 num_examples: 18928 - name: epoch_7 num_bytes: 44413462 num_examples: 18928 - name: epoch_8 num_bytes: 44410221 num_examples: 18928 - name: epoch_9 num_bytes: 44406897 num_examples: 18928 - name: epoch_10 num_bytes: 44406834 num_examples: 18928 - name: epoch_11 num_bytes: 44403708 num_examples: 18928 - name: epoch_12 num_bytes: 44403309 num_examples: 18928 - name: epoch_13 num_bytes: 44402366 num_examples: 18928 - name: epoch_14 num_bytes: 44400165 num_examples: 18928 - name: epoch_15 num_bytes: 44402982 num_examples: 18928 - name: epoch_16 num_bytes: 44401706 num_examples: 18928 - name: epoch_17 num_bytes: 44402241 num_examples: 18928 - name: epoch_18 num_bytes: 44402829 num_examples: 18928 - name: epoch_19 num_bytes: 44401754 num_examples: 18928 - name: epoch_20 num_bytes: 44404188 num_examples: 18928 - name: epoch_21 num_bytes: 44404140 num_examples: 18928 - name: epoch_22 num_bytes: 44403554 num_examples: 18928 - name: epoch_23 num_bytes: 44402779 num_examples: 18928 - name: epoch_24 num_bytes: 44403516 num_examples: 18928 - name: epoch_25 num_bytes: 44401875 num_examples: 18928 - name: epoch_26 num_bytes: 44402676 num_examples: 18928 - name: epoch_27 num_bytes: 44404515 num_examples: 18928 - name: epoch_28 num_bytes: 44403799 num_examples: 18928 - name: epoch_29 num_bytes: 44403664 num_examples: 18928 download_size: 700802961 dataset_size: 1331488264 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
JamesSpray/txsa_twitter_sentiment_analysis	JamesSpray	"2024-03-23T01:40:30Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T01:39:58Z"	--- dataset_info: features: - name: label dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 1049869 num_examples: 8539 - name: validation num_bytes: 145889 num_examples: 1000 download_size: 834300 dataset_size: 1195758 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
winniealita/indio	winniealita	"2024-03-23T01:43:41Z"	0	[ "license:openrail", "croissant", "region:us" ]	null	"2024-03-23T01:42:27Z"	--- license: openrail ---
luminoussg/NIH_X-RAY_2017	luminoussg	"2024-03-23T16:01:55Z"	0	[ "license:apache-2.0", "region:us" ]	null	"2024-03-23T01:49:00Z"	--- license: apache-2.0 ---
shushuti/cool	shushuti	"2024-03-23T01:58:34Z"	0	[ "license:apache-2.0", "region:us" ]	null	"2024-03-23T01:58:34Z"	--- license: apache-2.0 ---
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_160m_thr_1.0_seed_3	Mitsuki-Sakamoto	"2024-03-23T04:19:32Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T02:09:27Z"	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43778026 num_examples: 18928 - name: epoch_1 num_bytes: 44414700 num_examples: 18928 - name: epoch_2 num_bytes: 44463459 num_examples: 18928 - name: epoch_3 num_bytes: 44504519 num_examples: 18928 - name: epoch_4 num_bytes: 44524186 num_examples: 18928 - name: epoch_5 num_bytes: 44508265 num_examples: 18928 - name: epoch_6 num_bytes: 44494966 num_examples: 18928 - name: epoch_7 num_bytes: 44479117 num_examples: 18928 - name: epoch_8 num_bytes: 44471722 num_examples: 18928 - name: epoch_9 num_bytes: 44465380 num_examples: 18928 - name: epoch_10 num_bytes: 44460120 num_examples: 18928 - name: epoch_11 num_bytes: 44461044 num_examples: 18928 - name: epoch_12 num_bytes: 44459111 num_examples: 18928 - name: epoch_13 num_bytes: 44454936 num_examples: 18928 - name: epoch_14 num_bytes: 44455185 num_examples: 18928 - name: epoch_15 num_bytes: 44457591 num_examples: 18928 - name: epoch_16 num_bytes: 44456363 num_examples: 18928 - name: epoch_17 num_bytes: 44457557 num_examples: 18928 - name: epoch_18 num_bytes: 44460508 num_examples: 18928 - name: epoch_19 num_bytes: 44460626 num_examples: 18928 - name: epoch_20 num_bytes: 44458783 num_examples: 18928 - name: epoch_21 num_bytes: 44459668 num_examples: 18928 - name: epoch_22 num_bytes: 44459777 num_examples: 18928 - name: epoch_23 num_bytes: 44459779 num_examples: 18928 - name: epoch_24 num_bytes: 44458014 num_examples: 18928 - name: epoch_25 num_bytes: 44460051 num_examples: 18928 - name: epoch_26 num_bytes: 44459532 num_examples: 18928 - name: epoch_27 num_bytes: 44458956 num_examples: 18928 - name: epoch_28 num_bytes: 44458160 num_examples: 18928 - name: epoch_29 num_bytes: 44458407 num_examples: 18928 download_size: 701402776 dataset_size: 1333278508 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
CyberHarem/xianyun_genshin	CyberHarem	"2024-03-23T03:45:05Z"	0	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "croissant", "region:us" ]	[ "text-to-image" ]	"2024-03-23T02:12:17Z"	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of xianyun/閑雲/闲云 (Genshin Impact) This is the dataset of xianyun/閑雲/闲云 (Genshin Impact), containing 313 images and their tags. The core tags of this character are `long_hair, multicolored_hair, black_hair, green_hair, two-tone_hair, glasses, colored_inner_hair, red-framed_eyewear, breasts, hair_ornament, very_long_hair, semi-rimless_eyewear, aqua_eyes, large_breasts, tassel, earrings, tassel_earrings, aqua_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:-----------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 313 \| 668.34 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/xianyun_genshin/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 313 \| 555.90 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/xianyun_genshin/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 798 \| 1.04 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/xianyun_genshin/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/xianyun_genshin', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 13 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, jewelry, solo, looking_at_viewer, simple_background, upper_body, white_background, makeup, gloves \| \| 1 \| 7 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, gloves, jewelry, long_sleeves, looking_at_viewer, solo, makeup, dress, smile, bodystocking, upper_body \| \| 2 \| 8 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, bare_back, gloves, solo, from_behind, looking_at_viewer, ass, looking_back, backless_dress, bare_shoulders, jewelry, ponytail, thighs, white_background \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, alternate_costume, black_skirt, collared_shirt, looking_at_viewer, solo, white_shirt, long_sleeves, office_lady, jewelry, miniskirt, pencil_skirt, contemporary, pantyhose, thighs \| \| 4 \| 5 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1boy, blush, hetero, mosaic_censoring, solo_focus, 1girl, completely_nude, cum_in_pussy, nipples, open_mouth, vaginal, looking_at_viewer, anus, ass, collarbone, disembodied_penis, gloves, green_eyes, heart, jewelry, looking_back, pillow, pov, sex_from_behind, spread_legs, sweat, thighs, tongue_out \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| jewelry \| solo \| looking_at_viewer \| simple_background \| upper_body \| white_background \| makeup \| gloves \| long_sleeves \| dress \| smile \| bodystocking \| bare_back \| from_behind \| ass \| looking_back \| backless_dress \| bare_shoulders \| ponytail \| thighs \| alternate_costume \| black_skirt \| collared_shirt \| white_shirt \| office_lady \| miniskirt \| pencil_skirt \| contemporary \| pantyhose \| 1boy \| blush \| hetero \| mosaic_censoring \| solo_focus \| completely_nude \| cum_in_pussy \| nipples \| open_mouth \| vaginal \| anus \| collarbone \| disembodied_penis \| green_eyes \| heart \| pillow \| pov \| sex_from_behind \| spread_legs \| sweat \| tongue_out \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:----------\|:-------\|:--------------------\|:--------------------\|:-------------\|:-------------------\|:---------\|:---------\|:---------------\|:--------\|:--------\|:---------------\|:------------\|:--------------\|:------\|:---------------\|:-----------------\|:-----------------\|:-----------\|:---------\|:--------------------\|:--------------\|:-----------------\|:--------------\|:--------------\|:------------\|:---------------\|:---------------\|:------------\|:-------\|:--------\|:---------\|:-------------------\|:-------------\|:------------------\|:---------------\|:----------\|:-------------\|:----------\|:-------\|:-------------\|:--------------------\|:-------------\|:--------\|:---------\|:------\|:------------------\|:--------------\|:--------\|:-------------\| \| 0 \| 13 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 7 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| \| X \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 8 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| \| \| X \| \| X \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 5 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| X \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| X \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
jlbaker361/db_multi_cold_	jlbaker361	"2024-04-24T22:37:24Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T02:50:39Z"	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: label dtype: string - name: prompt_similarity dtype: float32 - name: identity_consistency dtype: float32 - name: negative_prompt_similarity dtype: float32 - name: target_prompt_similarity dtype: float32 - name: aesthetic_score dtype: float32 - name: ir_score dtype: float64 splits: - name: train num_bytes: 466 num_examples: 14 download_size: 4388 dataset_size: 466 --- method: db_multi_cold_ num_inference_steps: 30 prompt_similarity : 0.23685424029827118 identity_consistency : 0.634724497795105 negative_prompt_similarity : 0.20908871293067932 target_prompt_similarity : 0.1990167200565338 aesthetic_score : 3.9085214138031006 ir_score : -1.6812543226405978
AwesomeEmerald/OpenNaturalConvo	AwesomeEmerald	"2024-03-23T19:48:03Z"	0	[ "license:mit", "croissant", "region:us" ]	null	"2024-03-23T03:09:04Z"	--- license: mit ---
shiertier/12Twatermark	shiertier	"2024-03-23T03:25:21Z"	0	[ "license:mit", "croissant", "region:us" ]	null	"2024-03-23T03:22:49Z"	--- license: mit ---
Ksgk-fy/alignment-dpo-test03	Ksgk-fy	"2024-03-23T03:23:12Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T03:23:04Z"	--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 778351 num_examples: 3024 - name: test num_bytes: 194549 num_examples: 756 download_size: 92676 dataset_size: 972900 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
DipakBundheliya/Shipping-label-NER	DipakBundheliya	"2024-03-28T08:29:42Z"	0	[ "license:afl-3.0", "region:us" ]	null	"2024-03-23T03:37:55Z"	--- license: afl-3.0 ---
jlbaker361/unet_hot_	jlbaker361	"2024-04-24T22:10:34Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T03:40:12Z"	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: label dtype: string - name: prompt_similarity dtype: float32 - name: identity_consistency dtype: float32 - name: negative_prompt_similarity dtype: float32 - name: target_prompt_similarity dtype: float32 - name: aesthetic_score dtype: float32 - name: ir_score dtype: float64 splits: - name: train num_bytes: 534 num_examples: 16 download_size: 4470 dataset_size: 534 --- method: unet_hot_ num_inference_steps: 30 prompt_similarity : 0.27571946382522583 identity_consistency : 0.5342808365821838 negative_prompt_similarity : 0.202345073223114 target_prompt_similarity : 0.22198939323425293 aesthetic_score : 3.6855618953704834 ir_score : -1.1662967083975673
jlbaker361/unet_cold_	jlbaker361	"2024-04-24T22:23:13Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T03:40:17Z"	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: label dtype: string - name: prompt_similarity dtype: float32 - name: identity_consistency dtype: float32 - name: negative_prompt_similarity dtype: float32 - name: target_prompt_similarity dtype: float32 - name: aesthetic_score dtype: float32 - name: ir_score dtype: float64 splits: - name: train num_bytes: 568 num_examples: 17 download_size: 4550 dataset_size: 568 --- method: unet_cold_ num_inference_steps: 30 prompt_similarity : 0.2652778625488281 identity_consistency : 0.5586744546890259 negative_prompt_similarity : 0.18459632992744446 target_prompt_similarity : 0.2095177322626114 aesthetic_score : 3.9817652702331543 ir_score : -1.1246472443453968
jlbaker361/unet_reward_	jlbaker361	"2024-04-24T19:22:32Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T03:40:18Z"	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: label dtype: string - name: prompt_similarity dtype: float32 - name: identity_consistency dtype: float32 - name: negative_prompt_similarity dtype: float32 - name: target_prompt_similarity dtype: float32 - name: aesthetic_score dtype: float32 - name: ir_score dtype: float64 splits: - name: train num_bytes: 398 num_examples: 12 download_size: 4326 dataset_size: 398 --- method: unet_reward_ num_inference_steps: 30 prompt_similarity : 0.2559298276901245 identity_consistency : 0.598752498626709 negative_prompt_similarity : 0.1946614384651184 target_prompt_similarity : 0.20795372128486633 aesthetic_score : 3.822976589202881 ir_score : -1.7999178015161306
tmnam20/ViPubMed	tmnam20	"2024-03-25T03:13:24Z"	0	[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "language:vi", "language:en", "license:cc", "croissant", "arxiv:2210.05610", "arxiv:2210.05598", "region:us" ]	[ "text-generation", "fill-mask" ]	"2024-03-23T03:52:09Z"	--- license: cc language: - vi - en task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: pubmed dataset_info: features: - name: en dtype: string - name: vi dtype: string splits: - name: pubmed22 num_bytes: 44360028980 num_examples: 20087006 download_size: 23041004247 dataset_size: 44360028980 --- # ALERT: This dataset repo is duplicated from [VietAI/vi_pubmed](https://huggingface.co/datasets/VietAI/vi_pubmed) The reason to have this duplicated repo is to avoid the lost/corruption of the original repo when I am doing some stuff ^^. # Dataset Summary 20M Vietnamese PubMed biomedical abstracts translated by the [state-of-the-art English-Vietnamese Translation project](https://arxiv.org/abs/2210.05610). The data has been used as unlabeled dataset for [pretraining a Vietnamese Biomedical-domain Transformer model](https://arxiv.org/abs/2210.05598). ![image](https://user-images.githubusercontent.com/44376091/200204462-4d559113-5bdf-4cc5-9e88-70abe82babba.png) image source: [Enriching Biomedical Knowledge for Vietnamese Low-resource Language Through Large-Scale Translation](https://arxiv.org/abs/2210.05598) # Language - English: Original biomedical abstracts from [Pubmed](https://www.nlm.nih.gov/databases/download/pubmed_medline_faq.html) - Vietnamese: Synthetic abstract translated by a [state-of-the-art English-Vietnamese Translation project](https://arxiv.org/abs/2210.05610) # Dataset Structure - The English sequences are - The Vietnamese sequences are # Source Data - Initial Data Collection and Normalization https://www.nlm.nih.gov/databases/download/pubmed_medline_faq.html # Licensing Information [Courtesy of the U.S. National Library of Medicine.](https://www.nlm.nih.gov/databases/download/terms_and_conditions.html) # Citation ``` @misc{mtet, doi = {10.48550/ARXIV.2210.05610}, url = {https://arxiv.org/abs/2210.05610}, author = {Ngo, Chinh and Trinh, Trieu H. and Phan, Long and Tran, Hieu and Dang, Tai and Nguyen, Hieu and Nguyen, Minh and Luong, Minh-Thang}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {MTet: Multi-domain Translation for English and Vietnamese}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` ``` @misc{vipubmed, doi = {10.48550/ARXIV.2210.05598}, url = {https://arxiv.org/abs/2210.05598}, author = {Phan, Long and Dang, Tai and Tran, Hieu and Phan, Vy and Chau, Lam D. and Trinh, Trieu H.}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Enriching Biomedical Knowledge for Vietnamese Low-resource Language Through Large-Scale Translation}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
Boxit372/wheatley-voicelines	Boxit372	"2024-03-23T04:21:06Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T03:53:42Z"	--- pretty_name: Wheatley Voicelines ---
CyberHarem/chevreuse_genshin	CyberHarem	"2024-03-23T04:44:43Z"	0	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "croissant", "region:us" ]	[ "text-to-image" ]	"2024-03-23T03:58:57Z"	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of chevreuse/シュヴルーズ/夏沃蕾 (Genshin Impact) This is the dataset of chevreuse/シュヴルーズ/夏沃蕾 (Genshin Impact), containing 172 images and their tags. The core tags of this character are `purple_hair, long_hair, very_long_hair, streaked_hair, multicolored_hair, purple_eyes, two-tone_hair, white_hair, hat, eyepatch, pointy_hair, mole, shako_cap, mole_under_mouth, hair_between_eyes, bright_pupils, white_pupils, black_headwear, crossed_bangs`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:-------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 172 \| 351.53 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/chevreuse_genshin/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 172 \| 293.19 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/chevreuse_genshin/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 437 \| 588.42 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/chevreuse_genshin/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/chevreuse_genshin', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 8 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, antique_firearm, bare_shoulders, black_necktie, detached_collar, earmuffs, gold_trim, holding_gun, looking_at_viewer, puffy_detached_sleeves, red_dress, rifle, solo, white_gloves, black_dress, simple_background, two-tone_dress, white_background, strapless_dress, no_mole \| \| 1 \| 19 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, antique_firearm, bare_shoulders, black_dress, black_necktie, earmuffs, gold_trim, holding_gun, puffy_detached_sleeves, red_dress, solo, two-tone_dress, white_gloves, rifle, strapless_dress, thigh_boots, detached_collar, pantyhose, white_footwear, looking_at_viewer, standing, thighhighs, cowboy_shot, no_mole, white_background \| \| 2 \| 7 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, bare_shoulders, black_necktie, detached_collar, earmuffs, gold_trim, puffy_detached_sleeves, red_dress, solo, strapless_dress, upper_body, looking_at_viewer, simple_background, white_background, white_gloves, hand_up \| \| 3 \| 26 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, bare_shoulders, earmuffs, holding_food, puffy_detached_sleeves, solo, white_gloves, looking_at_viewer, french_fries, gold_trim, detached_collar, red_dress, black_necktie, upper_body, black_dress, two-tone_dress, white_background, :t, eating, simple_background, strapless_dress \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| antique_firearm \| bare_shoulders \| black_necktie \| detached_collar \| earmuffs \| gold_trim \| holding_gun \| looking_at_viewer \| puffy_detached_sleeves \| red_dress \| rifle \| solo \| white_gloves \| black_dress \| simple_background \| two-tone_dress \| white_background \| strapless_dress \| no_mole \| thigh_boots \| pantyhose \| white_footwear \| standing \| thighhighs \| cowboy_shot \| upper_body \| hand_up \| holding_food \| french_fries \| :t \| eating \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:------------------\|:-----------------\|:----------------\|:------------------\|:-----------\|:------------\|:--------------\|:--------------------\|:-------------------------\|:------------\|:--------\|:-------\|:---------------\|:--------------\|:--------------------\|:-----------------\|:-------------------\|:------------------\|:----------\|:--------------\|:------------\|:-----------------\|:-----------\|:-------------\|:--------------\|:-------------\|:----------\|:---------------\|:---------------\|:-----\|:---------\| \| 0 \| 8 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 19 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| 2 \| 7 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| X \| X \| X \| X \| X \| \| X \| X \| X \| \| X \| X \| \| X \| \| X \| X \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| 3 \| 26 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| X \| X \| X \| X \| X \| \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| X \| \| X \| X \| X \| X \|
Pm06/my-image-label-dataset	Pm06	"2024-04-07T11:18:28Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T04:05:24Z"	--- dataset_info: features: - name: image dtype: image - name: vision_info dtype: string splits: - name: train num_bytes: 247252517.0 num_examples: 1000 download_size: 246904988 dataset_size: 247252517.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
iamkaikai/MATISSEE-ART	iamkaikai	"2024-04-13T16:26:05Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T04:29:15Z"	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 6679969.0 num_examples: 269 download_size: 6585569 dataset_size: 6679969.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "MATISSEE-ART" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
iamkaikai/PHOTO-ILLUSTRATION-ART	iamkaikai	"2024-04-13T16:37:39Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T04:37:21Z"	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 8015460.0 num_examples: 194 download_size: 7995170 dataset_size: 8015460.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "PHOTO-ILLUSTRATION-ART" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
iamkaikai/IMPRESSIONISM-ART	iamkaikai	"2024-04-13T17:49:56Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T04:43:30Z"	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 24298294.0 num_examples: 434 download_size: 24120501 dataset_size: 24298294.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "IMPRESSIONISM-ART" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
iamkaikai/BASQUIAT-ART	iamkaikai	"2024-04-13T18:28:34Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T04:46:35Z"	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 15156684.0 num_examples: 228 download_size: 15097017 dataset_size: 15156684.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "BASQUIAT-ART" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
iamkaikai/BAUHAUS-ART	iamkaikai	"2024-04-13T18:59:23Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T04:50:00Z"	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 5239111.0 num_examples: 273 download_size: 4837978 dataset_size: 5239111.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "BAUHAUS-ART" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
iamkaikai/FUI-ART	iamkaikai	"2024-04-13T19:21:48Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T04:53:02Z"	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 6410238.0 num_examples: 204 download_size: 5862362 dataset_size: 6410238.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "FUI-ART" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
iamkaikai/OPTICAL-ART	iamkaikai	"2024-03-25T16:29:46Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T04:59:37Z"	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 17958981.0 num_examples: 255 download_size: 17637639 dataset_size: 17958981.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "OPTICAL-ART" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AnasKK/reuters_articles	AnasKK	"2024-03-23T05:02:04Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T05:02:00Z"	--- dataset_info: features: - name: title dtype: string - name: body dtype: string splits: - name: train num_bytes: 13792576 num_examples: 17262 - name: validation num_bytes: 1870389 num_examples: 2158 - name: test num_bytes: 1379190 num_examples: 2158 download_size: 10073414 dataset_size: 17042155 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
CyberHarem/ryougi_shiki_karanokyoukai	CyberHarem	"2024-03-23T05:29:14Z"	0	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "croissant", "region:us" ]	[ "text-to-image" ]	"2024-03-23T05:14:06Z"	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Ryougi Shiki/両儀式/兩儀式 (Kara No Kyoukai) This is the dataset of Ryougi Shiki/両儀式/兩儀式 (Kara No Kyoukai), containing 338 images and their tags. The core tags of this character are `short_hair, black_hair, brown_hair, brown_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:----------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 338 \| 126.60 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ryougi_shiki_karanokyoukai/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 338 \| 126.49 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ryougi_shiki_karanokyoukai/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 548 \| 203.84 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ryougi_shiki_karanokyoukai/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/ryougi_shiki_karanokyoukai', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:---------------------------------------------------------------------------------------------------\| \| 0 \| 17 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, solo, blue_kimono, upper_body, black_eyes, looking_at_viewer \| \| 1 \| 11 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, solo, obi, blue_kimono \| \| 2 \| 13 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, kimono, red_jacket, solo, fur_trim, profile \| \| 3 \| 10 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, red_jacket, solo, blue_eyes, knife, blue_kimono \| \| 4 \| 14 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, expressionless, portrait, solo, closed_mouth, kimono, black_eyes, looking_at_viewer, blurry \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, blue_eyes, kimono, katana, 1boy, glowing, solo_focus \| \| 6 \| 8 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, solo, blue_dress, indoors, long_sleeves, school_uniform, upper_body, black_eyes \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| blue_kimono \| upper_body \| black_eyes \| looking_at_viewer \| obi \| kimono \| red_jacket \| fur_trim \| profile \| blue_eyes \| knife \| expressionless \| portrait \| closed_mouth \| blurry \| katana \| 1boy \| glowing \| solo_focus \| blue_dress \| indoors \| long_sleeves \| school_uniform \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:--------------\|:-------------\|:-------------\|:--------------------\|:------\|:---------\|:-------------\|:-----------\|:----------\|:------------\|:--------\|:-----------------\|:-----------\|:---------------\|:---------\|:---------\|:-------\|:----------\|:-------------\|:-------------\|:----------\|:---------------\|:-----------------\| \| 0 \| 17 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 11 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 13 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| \| \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 10 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| \| \| \| \| \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 14 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| X \| \| \| X \| X \| \| X \| \| \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| 6 \| 8 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \|
yuiseki/sake_qa	yuiseki	"2024-03-23T05:19:17Z"	3	[ "language:ja", "license:mit", "croissant", "region:us" ]	null	"2024-03-23T05:15:57Z"	--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: type dtype: string splits: - name: train num_bytes: 612696 num_examples: 5176 download_size: 84168 dataset_size: 612696 configs: - config_name: default data_files: - split: train path: data/train-* license: mit language: - ja ---
jiaqianjing/animagine-xl-3.1-characterfull-zh	jiaqianjing	"2024-03-23T05:58:11Z"	0	[ "license:mit", "croissant", "region:us" ]	null	"2024-03-23T05:38:35Z"	--- license: mit --- ## animagine-xl-3.1-characterfull-zh ### 背景 [cagliostrolab/animagine-xl-3.1](https://huggingface.co/cagliostrolab/animagine-xl-3.1) Image Prompt 仍然需要中英文输入角色名称，并且有一定格式要求。Prompt Format 如下所示， ``` 1girl/1boy, character name, from what series, everything else in any order. ``` 但是这对于小白，或者不熟悉训练过程的人带来了很高的接入门槛。因此，想通过向量检索的方式降低这种接入成本。但是不管现在任何一种 embedding 的模型（[bge-m3](https://huggingface.co/BAAI/bge-m3), [jina-embeddings-v2-base-zh](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh)等），都没有办法很好的匹配中文关键字和官方提供的这份 [角色全名单](https://huggingface.co/spaces/cagliostrolab/animagine-xl-3.1/blob/main/wildcard/characterfull.txt). 因此，想在原有的名单上补充中文角色和中文作品名称的信息，从而提升向量检索的效果。 ### 详情 1. 由于原始文件很大，需要切分成 50 份文件，每份文件 100 条记录； 2. 给出 one-shot, 通过调用 gpt 或者 kimi 等模型，在每行行尾添加对应的中文角色名称和中文作品名称，格式如下： ``` 1girl, souryuu asuka langley, neon genesis evangelion#惣流·明日香·兰格雷#新世纪福音战士 1girl, warrior of light, final fantasy#光之战士#最终幻想 1girl, akiyama mio, k-on!#秋山澪#轻音少女 1girl, tifa lockhart, final fantasy#蒂法·洛克哈特#最终幻想 1girl, 2b, nier:automata#2B#尼尔：自动人形 ``` 3. 最后在合并；
helloelwin/w2sg-generations	helloelwin	"2024-03-23T05:39:20Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T05:38:58Z"	--- dataset_info: - config_name: eval_train_weak-bs=16-dn=gsm8k-e=10-ee=1000000-l=xent-l=2e-05-ls=cosi_anne-ml=331-ms=gemma-2b-nd=20000-ntd=10000-o=adam-s=0-twd=0-epoch=2 features: - name: question dtype: string - name: gt_answer dtype: string - name: answer dtype: string - name: acc dtype: float64 splits: - name: train num_bytes: 3262475 num_examples: 3736 download_size: 1748441 dataset_size: 3262475 - config_name: eval_train_weak-bs=16-dn=gsm8k-e=10-ee=1000000-l=xent-l=2e-05-ls=cosi_anne-ml=331-ms=gpt2-xl-nd=20000-ntd=10000-o=adam-s=0-twd=0-epoch=2 features: - name: question dtype: string - name: gt_answer dtype: string - name: answer dtype: string - name: acc dtype: float64 splits: - name: train num_bytes: 3557779 num_examples: 3736 download_size: 1762583 dataset_size: 3557779 configs: - config_name: eval_train_weak-bs=16-dn=gsm8k-e=10-ee=1000000-l=xent-l=2e-05-ls=cosi_anne-ml=331-ms=gemma-2b-nd=20000-ntd=10000-o=adam-s=0-twd=0-epoch=2 data_files: - split: train path: eval_train_weak-bs=16-dn=gsm8k-e=10-ee=1000000-l=xent-l=2e-05-ls=cosi_anne-ml=331-ms=gemma-2b-nd=20000-ntd=10000-o=adam-s=0-twd=0-epoch=2/train-* - config_name: eval_train_weak-bs=16-dn=gsm8k-e=10-ee=1000000-l=xent-l=2e-05-ls=cosi_anne-ml=331-ms=gpt2-xl-nd=20000-ntd=10000-o=adam-s=0-twd=0-epoch=2 data_files: - split: train path: eval_train_weak-bs=16-dn=gsm8k-e=10-ee=1000000-l=xent-l=2e-05-ls=cosi_anne-ml=331-ms=gpt2-xl-nd=20000-ntd=10000-o=adam-s=0-twd=0-epoch=2/train-* ---
codedog-lee/llama2-tut	codedog-lee	"2024-03-23T05:56:32Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T05:56:29Z"	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 5826 num_examples: 39 download_size: 2572 dataset_size: 5826 configs: - config_name: default data_files: - split: train path: data/train-* ---
jjz5463/lexical-features	jjz5463	"2024-03-23T06:01:15Z"	0	[ "size_categories:10K<n<100K", "datadreamer", "datadreamer-0.25.0", "synthetic", "gpt-4", "croissant", "region:us" ]	null	"2024-03-23T06:01:12Z"	--- dataset_info: features: - name: The prompts processed with the LLM. dtype: string - name: The generations by the LLM. dtype: string splits: - name: train num_bytes: 22300800 num_examples: 58300 download_size: 5442365 dataset_size: 22300800 configs: - config_name: default data_files: - split: train path: data/train-* library_name: datadreamer size_categories: - 10K<n<100K tags: - datadreamer - datadreamer-0.25.0 - synthetic - gpt-4 --- # Dataset Card [Add more information here](https://huggingface.co/datasets/templates/dataset-card-example) --- This dataset was produced with [DataDreamer 🤖💤](https://datadreamer.dev). The synthetic dataset card can be found [here](datadreamer.json).
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_1.4b_bo16_2_64_mix_50_kl_0.1_prm_160m_thr_0.3_seed_1	Mitsuki-Sakamoto	"2024-03-24T10:36:26Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T06:02:03Z"	--- dataset_info: config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_10 num_bytes: 43576140 num_examples: 18928 - name: epoch_11 num_bytes: 43572313 num_examples: 18928 - name: epoch_12 num_bytes: 43580152 num_examples: 18928 - name: epoch_13 num_bytes: 43578773 num_examples: 18928 - name: epoch_14 num_bytes: 43575869 num_examples: 18928 - name: epoch_15 num_bytes: 43575963 num_examples: 18928 - name: epoch_16 num_bytes: 43577002 num_examples: 18928 - name: epoch_17 num_bytes: 43579293 num_examples: 18928 - name: epoch_18 num_bytes: 43580317 num_examples: 18928 - name: epoch_19 num_bytes: 43575851 num_examples: 18928 - name: epoch_20 num_bytes: 43582939 num_examples: 18928 - name: epoch_21 num_bytes: 43577597 num_examples: 18928 - name: epoch_22 num_bytes: 43578041 num_examples: 18928 - name: epoch_23 num_bytes: 43577327 num_examples: 18928 - name: epoch_24 num_bytes: 43581004 num_examples: 18928 - name: epoch_25 num_bytes: 43578359 num_examples: 18928 - name: epoch_26 num_bytes: 43577018 num_examples: 18928 - name: epoch_27 num_bytes: 43582166 num_examples: 18928 - name: epoch_28 num_bytes: 43579959 num_examples: 18928 - name: epoch_29 num_bytes: 43580209 num_examples: 18928 - name: epoch_0 num_bytes: 43730889 num_examples: 18928 - name: epoch_1 num_bytes: 43770682 num_examples: 18928 - name: epoch_2 num_bytes: 43648840 num_examples: 18928 - name: epoch_3 num_bytes: 43543360 num_examples: 18928 - name: epoch_4 num_bytes: 43502193 num_examples: 18928 - name: epoch_5 num_bytes: 43486787 num_examples: 18928 - name: epoch_6 num_bytes: 43482119 num_examples: 18928 - name: epoch_7 num_bytes: 43474193 num_examples: 18928 - name: epoch_8 num_bytes: 43478656 num_examples: 18928 - name: epoch_9 num_bytes: 43482186 num_examples: 18928 download_size: 928064117 dataset_size: 1307166197 configs: - config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 data_files: - split: epoch_0 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_29-* ---
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_1.4b_bo16_2_64_mix_50_kl_0.1_prm_410m_thr_0.1_seed_1	Mitsuki-Sakamoto	"2024-03-25T04:37:57Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T06:04:26Z"	--- dataset_info: config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43758977 num_examples: 18928 - name: epoch_1 num_bytes: 43856391 num_examples: 18928 - name: epoch_2 num_bytes: 43716521 num_examples: 18928 - name: epoch_3 num_bytes: 43641452 num_examples: 18928 - name: epoch_4 num_bytes: 43570443 num_examples: 18928 - name: epoch_5 num_bytes: 43546101 num_examples: 18928 - name: epoch_6 num_bytes: 43533197 num_examples: 18928 - name: epoch_7 num_bytes: 43527219 num_examples: 18928 - name: epoch_8 num_bytes: 43523213 num_examples: 18928 - name: epoch_9 num_bytes: 43517911 num_examples: 18928 download_size: 510815003 dataset_size: 436191425 configs: - config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 data_files: - split: epoch_0 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_9-* ---
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_1.4b_bo16_2_64_mix_50_kl_0.1_prm_410m_thr_0.3_seed_1	Mitsuki-Sakamoto	"2024-03-25T05:04:33Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T06:04:32Z"	--- dataset_info: config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43755042 num_examples: 18928 - name: epoch_1 num_bytes: 43755124 num_examples: 18928 - name: epoch_2 num_bytes: 43601416 num_examples: 18928 - name: epoch_3 num_bytes: 43501701 num_examples: 18928 - name: epoch_4 num_bytes: 43424023 num_examples: 18928 - name: epoch_5 num_bytes: 43380006 num_examples: 18928 - name: epoch_6 num_bytes: 43382015 num_examples: 18928 - name: epoch_7 num_bytes: 43377813 num_examples: 18928 - name: epoch_8 num_bytes: 43369972 num_examples: 18928 - name: epoch_9 num_bytes: 43362193 num_examples: 18928 download_size: 533070588 dataset_size: 434909305 configs: - config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 data_files: - split: epoch_0 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_9-* ---
OpenDriveLab/OpenDV-YouTube-Language	OpenDriveLab	"2024-03-28T08:30:17Z"	5	[ "license:cc-by-nc-sa-4.0", "croissant", "arxiv:2403.09630", "region:us" ]	null	"2024-03-23T06:08:09Z"	--- license: cc-by-nc-sa-4.0 --- # OpenDV-YouTube This is the dataset repository of `OpenDV-YouTube` language annotations, including `context` and `command`. For more details, please refer to <a href="https://arxiv.org/abs/2403.09630" target="_blank">GenAD</a> project and <a href="https://github.com/OpenDriveLab/DriveAGI#opendv-youtube" target="_blank">OpenDV-YouTube</a>. ## Usage To use the annotations, you need to first download and prepare the data as instructed in <a href="https://github.com/OpenDriveLab/DriveAGI/tree/main/opendv" target="_blank">OpenDV-YouTube</a>. Note that we recommend to process the dataset in `Linux` environment since `Windows` may have issues with the file paths. You can use the following code to load in annotations respectively. ```python import json # for train full_annos = [] for split_id in range(10): split = json.load(open("10hz_YouTube_train_split{}.json".format(str(split_id)), "r")) full_annos.extend(split) # for val val_annos = json.load(open("10hz_YouTube_val.json", "r")) ``` Annotations will be loaded in `full_annos` as a list where each element contains annotations for one video clip. All elements in the list are dictionaries of the following structure. ```python { "cmd": <int> -- command, i.e. the command of the ego vehicle in the video clip. "blip": <str> -- context, i.e. the BLIP description of the center frame in the video clip. "folder": <str> -- the relative path from the processed OpenDV-YouTube dataset root to the image folder of the video clip. "first_frame": <str> -- the filename of the first frame in the clip. Note that this file is included in the video clip. "last_frame": <str> -- the filename of the last frame in the clip. Note that this file is included in the video clip. } ``` The command, i.e. the `cmd` field, can be converted to natural language using the `map_category_to_caption` function. You may refer to [cmd2caption.py](https://github.com/OpenDriveLab/DriveAGI/blob/main/opendv/utils/cmd2caption.py#L158) for details. The context, i.e. the `blip` field, is the description of the center frame in the video generated by `BLIP2`. ## Citation If you find our work helpful, please cite the following paper. ```bibtex @misc{yang2024genad, title={Generalized Predictive Model for Autonomous Driving}, author={Jiazhi Yang and Shenyuan Gao and Yihang Qiu and Li Chen and Tianyu Li and Bo Dai and Kashyap Chitta and Penghao Wu and Jia Zeng and Ping Luo and Jun Zhang and Andreas Geiger and Yu Qiao and Hongyang Li}, year={2024}, eprint={2403.09630}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```
misshimichka/flower_faces_dataset_v2	misshimichka	"2024-03-23T09:41:30Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T06:34:39Z"	--- dataset_info: features: - name: original_image dtype: image - name: edit_prompt dtype: string - name: cartoonized_image dtype: image splits: - name: train num_bytes: 239848099.0 num_examples: 114 download_size: 239859641 dataset_size: 239848099.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_1.4b_bo16_2_64_mix_50_kl_0.1_prm_160m_thr_1.0_seed_1	Mitsuki-Sakamoto	"2024-03-24T12:09:27Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T06:52:49Z"	--- dataset_info: config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43754721 num_examples: 18928 - name: epoch_1 num_bytes: 43764507 num_examples: 18928 - name: epoch_2 num_bytes: 43702258 num_examples: 18928 - name: epoch_3 num_bytes: 43618198 num_examples: 18928 - name: epoch_4 num_bytes: 43563025 num_examples: 18928 - name: epoch_5 num_bytes: 43522727 num_examples: 18928 - name: epoch_6 num_bytes: 43519820 num_examples: 18928 - name: epoch_7 num_bytes: 43523973 num_examples: 18928 - name: epoch_8 num_bytes: 43515824 num_examples: 18928 - name: epoch_9 num_bytes: 43519081 num_examples: 18928 download_size: 325156504 dataset_size: 436004134 configs: - config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 data_files: - split: epoch_0 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_9-* ---
CodecSR/fsd50k_16k_synth	CodecSR	"2024-03-23T08:38:27Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T07:15:38Z"	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: id dtype: string splits: - name: original num_bytes: 13195529214.0 num_examples: 14400 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 4394953932.0 num_examples: 14400 - name: academicodec_hifi_24k_320d num_bytes: 4394953932.0 num_examples: 14400 - name: audiodec_24k_300d num_bytes: 4403255492.0 num_examples: 14400 - name: audiodec_48k_300d_uni num_bytes: 4403255492.0 num_examples: 14400 - name: dac_16k num_bytes: 4399077054.0 num_examples: 14400 - name: dac_24k num_bytes: 4399077043.6 num_examples: 14400 - name: dac_44k num_bytes: 4399077054.0 num_examples: 14400 - name: encodec_24k_12bps num_bytes: 4399077054.0 num_examples: 14400 - name: encodec_24k_1_5bps num_bytes: 4399077050.0 num_examples: 14400 - name: encodec_24k_24bps num_bytes: 4399077057.0 num_examples: 14400 - name: encodec_24k_3bps num_bytes: 4399077050.0 num_examples: 14400 - name: encodec_24k_6bps num_bytes: 4399077050.0 num_examples: 14400 - name: facodec_16k num_bytes: 4397686092.0 num_examples: 14400 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 4399077057.0 num_examples: 14400 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 4399077054.0 num_examples: 14400 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 4399077045.6 num_examples: 14400 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 4399077045.6 num_examples: 14400 - name: language_codec_chinese_24k_nq8_12kbps num_bytes: 4403185607.6 num_examples: 14400 - name: language_codec_paper_24k_nq8_12kbps num_bytes: 4403185607.6 num_examples: 14400 - name: speech_tokenizer_16k num_bytes: 4403185607.6 num_examples: 14400 download_size: 93168736444 dataset_size: 101188115591.60004 configs: - config_name: default data_files: - split: original path: data/original-* - split: academicodec_hifi_16k_320d_large_uni path: data/academicodec_hifi_16k_320d_large_uni-* - split: academicodec_hifi_24k_320d path: data/academicodec_hifi_24k_320d-* - split: audiodec_24k_300d path: data/audiodec_24k_300d-* - split: audiodec_48k_300d_uni path: data/audiodec_48k_300d_uni-* - split: dac_16k path: data/dac_16k-* - split: dac_24k path: data/dac_24k-* - split: dac_44k path: data/dac_44k-* - split: encodec_24k_12bps path: data/encodec_24k_12bps-* - split: encodec_24k_1_5bps path: data/encodec_24k_1_5bps-* - split: encodec_24k_24bps path: data/encodec_24k_24bps-* - split: encodec_24k_3bps path: data/encodec_24k_3bps-* - split: encodec_24k_6bps path: data/encodec_24k_6bps-* - split: facodec_16k path: data/facodec_16k-* - split: funcodec_en_libritts_16k_nq32ds320 path: data/funcodec_en_libritts_16k_nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds640 path: data/funcodec_en_libritts_16k_nq32ds640-* - split: funcodec_zh_en_16k_nq32ds320 path: data/funcodec_zh_en_16k_nq32ds320-* - split: funcodec_zh_en_16k_nq32ds640 path: data/funcodec_zh_en_16k_nq32ds640-* - split: language_codec_chinese_24k_nq8_12kbps path: data/language_codec_chinese_24k_nq8_12kbps-* - split: language_codec_paper_24k_nq8_12kbps path: data/language_codec_paper_24k_nq8_12kbps-* - split: speech_tokenizer_16k path: data/speech_tokenizer_16k-* ---
Karthik1080/Radio_MRA_V1_C37	Karthik1080	"2024-03-23T07:20:05Z"	0	[ "license:mit", "croissant", "region:us" ]	null	"2024-03-23T07:19:48Z"	--- license: mit dataset_info: features: - name: paragraphs dtype: string splits: - name: train num_bytes: 59508 num_examples: 115 download_size: 33990 dataset_size: 59508 configs: - config_name: default data_files: - split: train path: data/train-* ---
adamjweintraut/lyrlen	adamjweintraut	"2024-04-06T06:58:04Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T07:19:49Z"	--- dataset_info: features: - name: title dtype: string - name: id dtype: int64 - name: genre dtype: string - name: lyric_chunk_n dtype: int64 - name: sylls dtype: int64 - name: orig dtype: string - name: target dtype: string splits: - name: train num_bytes: 15546161 num_examples: 162300 - name: test num_bytes: 1949060 num_examples: 20288 - name: valid num_bytes: 1939446 num_examples: 20288 download_size: 10470927 dataset_size: 19434667 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---
fssdsdasdas123/simple-datasets	fssdsdasdas123	"2024-03-23T13:39:26Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T07:23:50Z"	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': ADONIS '1': AFRICAN GIANT SWALLOWTAIL '2': AMERICAN SNOOT splits: - name: train num_bytes: 8825732.0 num_examples: 338 download_size: 8823395 dataset_size: 8825732.0 --- # Dataset Card for "input-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Aditya685/filtered_data_80k	Aditya685	"2024-03-26T18:57:30Z"	0	[ "language:en", "croissant", "region:us" ]	null	"2024-03-23T07:38:29Z"	--- language: - en dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: text dtype: string splits: - name: train num_bytes: 290184604 num_examples: 80000 download_size: 120878335 dataset_size: 290184604 configs: - config_name: default data_files: - split: train path: data/train-* ---
willcine/TAO	willcine	"2024-03-23T07:41:06Z"	0	[ "license:apache-2.0", "region:us" ]	null	"2024-03-23T07:39:07Z"	--- license: apache-2.0 ---
aTunass/EuroSat_datasaet_image_classification	aTunass	"2024-03-23T07:45:59Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T07:40:31Z"	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': AnnualCrop '1': Forest '2': HerbaceousVegetation '3': Highway '4': Industrial '5': Pasture '6': PermanentCrop '7': Residential '8': River '9': SeaLake splits: - name: train num_bytes: 88397609.0 num_examples: 27000 download_size: 91979105 dataset_size: 88397609.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
aTunass/Intel_Img_classification	aTunass	"2024-03-23T08:02:24Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T07:53:48Z"	--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 372019672.265 num_examples: 24335 download_size: 368457096 dataset_size: 372019672.265 configs: - config_name: default data_files: - split: train path: data/train-* ---
somosnlp/recetasdelaabuela_genstruct_it	somosnlp	"2024-03-29T01:09:29Z"	2	[ "task_categories:question-answering", "size_categories:10K<n<100K", "language:es", "license:apache-2.0", "croissant", "region:us" ]	[ "question-answering" ]	"2024-03-23T07:59:58Z"	--- language: - es license: apache-2.0 size_categories: - 10K<n<100K task_categories: - question-answering dataset_info: features: - name: title dtype: string - name: content dtype: string - name: messages sequence: 'null' - name: generation_model sequence: string - name: generation_prompt sequence: string - name: raw_generation_responses sequence: string - name: conversation sequence: sequence: string splits: - name: train num_bytes: 103228164 num_examples: 20085 download_size: 49502853 dataset_size: 103228164 configs: - config_name: default data_files: - split: train path: data/train-* --- # Descripción Dataset creado para la hackathon #Somos600M con el objetivo de entrenar un modelo que pueda recomendar recetas de paises hispanohablantes. Este conjunto de datos consiste en pregunta-respuesta y fue elaborado a partir de un contexto usando Genstruct-7B y distilabel. Elaborado a partir del dataset en crudo [somosnlp/RecetasDeLaAbuela](https://huggingface.co/datasets/somosnlp/RecetasDeLaAbuela) elaborado por el equipo recetasdelaabuela mediante web scraping. ## Origen del Dataset El dataset se obtuvo mediante web scrapping de estas paginas: - https://www.elmueble.com/ - https://www.yanuq.com/ - https://www.directoalpaladar.com/ - https://www.recetasgratis.net/ - https://cookpad.com/pe/ ## Notebook utilizada Elaborado con el [colab](https://colab.research.google.com/drive/1-7OY5ORmOw0Uy_uazXDDqjWWkwCKvWbL?usp=sharing). ## Contacto Si encuentras algún error o tienes una recomendación, por favor hazmelo saber!! El obejtivo es que el dataset siga mejorando en el tiempo, me encuentras en hugging face como @sbenel o comunicate en discord con un miembro del equipo de la hackathon.
TheaterParody/Foliage	TheaterParody	"2024-03-23T09:51:57Z"	0	[ "license:mit", "croissant", "region:us" ]	null	"2024-03-23T08:01:34Z"	--- license: mit ---
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_1.4b_bo16_2_64_mix_50_kl_0.1_prm_160m_thr_0.1_seed_2	Mitsuki-Sakamoto	"2024-03-25T03:37:54Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T08:03:14Z"	--- dataset_info: config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_10 num_bytes: 43435041 num_examples: 18928 - name: epoch_11 num_bytes: 43435140 num_examples: 18928 - name: epoch_12 num_bytes: 43430765 num_examples: 18928 - name: epoch_13 num_bytes: 43436665 num_examples: 18928 - name: epoch_14 num_bytes: 43432476 num_examples: 18928 - name: epoch_15 num_bytes: 43434055 num_examples: 18928 - name: epoch_16 num_bytes: 43432416 num_examples: 18928 - name: epoch_17 num_bytes: 43432055 num_examples: 18928 - name: epoch_18 num_bytes: 43432091 num_examples: 18928 - name: epoch_19 num_bytes: 43434577 num_examples: 18928 - name: epoch_20 num_bytes: 43436154 num_examples: 18928 - name: epoch_21 num_bytes: 43439292 num_examples: 18928 - name: epoch_22 num_bytes: 43438550 num_examples: 18928 - name: epoch_23 num_bytes: 43436063 num_examples: 18928 - name: epoch_24 num_bytes: 43437004 num_examples: 18928 - name: epoch_25 num_bytes: 43438763 num_examples: 18928 - name: epoch_26 num_bytes: 43433439 num_examples: 18928 - name: epoch_27 num_bytes: 43433974 num_examples: 18928 - name: epoch_28 num_bytes: 43436162 num_examples: 18928 - name: epoch_29 num_bytes: 43437287 num_examples: 18928 - name: epoch_0 num_bytes: 43728624 num_examples: 18928 - name: epoch_1 num_bytes: 43757201 num_examples: 18928 - name: epoch_2 num_bytes: 43585797 num_examples: 18928 - name: epoch_3 num_bytes: 43492349 num_examples: 18928 - name: epoch_4 num_bytes: 43443797 num_examples: 18928 - name: epoch_5 num_bytes: 43418222 num_examples: 18928 - name: epoch_6 num_bytes: 43408584 num_examples: 18928 - name: epoch_7 num_bytes: 43411275 num_examples: 18928 - name: epoch_8 num_bytes: 43408553 num_examples: 18928 - name: epoch_9 num_bytes: 43408425 num_examples: 18928 download_size: 1018593303 dataset_size: 1303764796 configs: - config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 data_files: - split: epoch_0 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_29-* ---
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_1.4b_bo16_2_64_mix_50_kl_0.1_prm_410m_thr_1.0_seed_1	Mitsuki-Sakamoto	"2024-03-24T12:02:10Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T08:05:34Z"	--- dataset_info: config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43793509 num_examples: 18928 - name: epoch_1 num_bytes: 43847564 num_examples: 18928 - name: epoch_2 num_bytes: 43693174 num_examples: 18928 - name: epoch_3 num_bytes: 43563475 num_examples: 18928 - name: epoch_4 num_bytes: 43478364 num_examples: 18928 - name: epoch_5 num_bytes: 43413210 num_examples: 18928 - name: epoch_6 num_bytes: 43396015 num_examples: 18928 - name: epoch_7 num_bytes: 43385612 num_examples: 18928 - name: epoch_8 num_bytes: 43386075 num_examples: 18928 - name: epoch_9 num_bytes: 43377158 num_examples: 18928 download_size: 301665360 dataset_size: 435334156 configs: - config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 data_files: - split: epoch_0 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_9-* ---
agicorp/orca-math-word-problems-200k	agicorp	"2024-03-23T08:22:07Z"	1	[ "task_categories:question-answering", "size_categories:100K<n<1M", "language:en", "license:mit", "math", "croissant", "arxiv:2402.14830", "region:us" ]	[ "question-answering" ]	"2024-03-23T08:22:06Z"	--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 225322861 num_examples: 200035 download_size: 84248748 dataset_size: 225322861 configs: - config_name: default data_files: - split: train path: data/train-* license: mit task_categories: - question-answering language: - en tags: - math size_categories: - 100K<n<1M --- # Dataset Card <!-- Provide a quick summary of the dataset. --> This dataset contains ~200K grade school math word problems. All the answers in this dataset is generated using Azure GPT4-Turbo. Please refer to [Orca-Math: Unlocking the potential of SLMs in Grade School Math](https://arxiv.org/pdf/2402.14830.pdf) for details about the dataset construction. ### Dataset Description - Curated by: Microsoft - Language(s) (NLP): English - License: MIT ### Dataset Sources <!-- Provide the basic links for the dataset. --> - Repository: [microsoft/orca-math-word-problems-200k](https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k) - Paper: [Orca-Math: Unlocking the potential of SLMs in Grade School Math](https://arxiv.org/pdf/2402.14830.pdf) ### Direct Use <!-- This section describes suitable use cases for the dataset. --> This dataset has been designed to enhance the mathematical abilities of language models. It aims to provide a robust foundation for language models to excel in mathematical problem-solving. ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> This dataset is not intended for use in educational systems or organizations. ## Dataset Structure ### Data Instances A typical data entry in the dataset consists of a question and its corresponding answer. Below is an example from the dataset: ```python {'question': 'In a highly contested election having multiple candidates, Mr. Jackson, one of the losing candidates, received 3,485,782 votes, which accounted for precisely 38.7 percent of all votes. To have achieved a victory, he would have needed to secure at least 51 percent of all votes. Approximately, what percent of the remaining unsecured votes would Mr. Jackson have needed to accumulate to reach this victory threshold?', 'answer': "First, let's find out the total number of votes cast in the election. Since Mr. Jackson received 38.7% of all votes, and that amounted to 3,485,782 votes, we can set up the following equation to find the total number of votes (T):\n\n0.387 * T = 3,485,782\n\nNow, solve for T:\n\nT = 3,485,782 / 0.387\nT ≈ 9,000,467 votes (total number of votes cast)\n\nTo win, Mr. Jackson would have needed 51% of the total votes. Let's calculate that amount:\n\n0.51 * T = 0.51 * 9,000,467\n0.51 * T ≈ 4,590,238 votes needed to win\n\nNow, let's find out how many more votes Mr. Jackson needed to reach this winning threshold:\n\nVotes needed to win - Votes Mr. Jackson received = Additional votes needed\n4,590,238 - 3,485,782 = 1,104,456 additional votes needed\n\nNow, let's find out what percentage of the remaining unsecured votes this number represents. The remaining unsecured votes are the votes that were not for Mr. Jackson, which is 100% - 38.7% = 61.3% of the total votes.\n\n61.3% of the total votes is the remaining unsecured votes:\n\n0.613 * T = 0.613 * 9,000,467\n0.613 * T ≈ 5,514,686 votes were unsecured\n\nNow, we'll calculate the percentage of these unsecured votes that the additional votes needed represent:\n\n(Additional votes needed / Unsecured votes) * 100 = Percentage of unsecured votes needed\n(1,104,456 / 5,514,686) * 100 ≈ 20.03%\n\nSo, Mr. Jackson would have needed approximately 20.03% of the remaining unsecured votes to reach the victory threshold of 51%."} ``` ### Data Fields The dataset comprises the following fields: - `question`: a string containing the question to be answered. - `answer`: a string containing the answer to the corresponding question. ### Data Splits The dataset is split into a training set. The number of rows in each split is as follows: - `train`: 200,035 rows The `DatasetDict` structure for the dataset is as follows: ```python DatasetDict({ 'train': Dataset({ features: ['question', 'answer'], num_rows: 200035 }) }) ``` Each split in the `DatasetDict` contains a `Dataset` object with the specified features and number of rows. ## Dataset Creation Please refer to [Orca-Math: Unlocking the potential of SLMs in Grade School Math](https://arxiv.org/pdf/2402.14830.pdf) for details about the dataset construction. ### Source Data - [Lila](https://huggingface.co/datasets/allenai/lila) - [DMath](https://arxiv.org/ftp/arxiv/papers/2106/2106.15772.pdf) #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> Please refer to [Orca-Math: Unlocking the potential of SLMs in Grade School Math](https://arxiv.org/pdf/2402.14830.pdf) for details about the dataset construction. #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> Microsoft #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> We expanded a seed set of questions using Azure GPT-4 Trubo. The answers to those questions are generated using Azure GPT-4 Trubo. #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> None ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> This dataset is in English and contains only math word problems. ## Citation If you find this work useful in your method, you can cite the paper as below: ``` @misc{mitra2024orcamath, title={Orca-Math: Unlocking the potential of SLMs in Grade School Math}, author={Arindam Mitra and Hamed Khanpour and Corby Rosset and Ahmed Awadallah}, year={2024}, eprint={2402.14830}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Dataset Card Contact [Arindam Mitra](armitra@microsoft.com)
renusan05/phishing_URLs	renusan05	"2024-03-23T08:36:28Z"	0	[ "license:unknown", "region:us" ]	null	"2024-03-23T08:27:57Z"	--- license: unknown ---
agicorp/MathInstruct	agicorp	"2024-03-23T08:28:20Z"	0	[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "license:mit", "math", "croissant", "arxiv:2309.05653", "region:us" ]	[ "text-generation" ]	"2024-03-23T08:28:20Z"	--- license: mit task_categories: - text-generation language: - en pretty_name: MathInstruct size_categories: - 100K<n<1M tags: - math --- # 🦣 MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning MathInstruct is a meticulously curated instruction tuning dataset that is lightweight yet generalizable. MathInstruct is compiled from 13 math rationale datasets, six of which are newly curated by this work. It uniquely focuses on the hybrid use of chain-of-thought (CoT) and program-of-thought (PoT) rationales, and ensures extensive coverage of diverse mathematical fields. Project Page: [https://tiger-ai-lab.github.io/MAmmoTH/](https://tiger-ai-lab.github.io/MAmmoTH/) Paper: [https://arxiv.org/pdf/2309.05653.pdf](https://arxiv.org/pdf/2309.05653.pdf) Code: [https://github.com/TIGER-AI-Lab/MAmmoTH](https://github.com/TIGER-AI-Lab/MAmmoTH) Models: \| \| Base Model: Llama-2 \| Base Model: Code Llama \| \|-----\|---------------------------------------------------------------\|--------------------------------------------------------------------------\| \| 7B \| 🦣 [MAmmoTH-7B](https://huggingface.co/TIGER-Lab/MAmmoTH-7B) \| 🦣 [MAmmoTH-Coder-7B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-7B) \| \| 13B \| 🦣 [MAmmoTH-13B](https://huggingface.co/TIGER-Lab/MAmmoTH-13B) \| 🦣 [MAmmoTH-Coder-13B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-13B)\| \| 34B \| - \| 🦣 [MAmmoTH-Coder-34B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-34B)\| \| 70B \| 🦣 [MAmmoTH-70B](https://huggingface.co/TIGER-Lab/MAmmoTH-70B) \| - \| ## License Please check out the license of each subset in our curated dataset MathInstruct. \| Dataset Name \| License Type \| \|--------------\|----------------\| \| GSM8K \| MIT \| \| GSM8K-RFT \| Non listed \| \| AQuA-RAT \| Apache 2.0 \| \| MATH \| MIT \| \| TheoremQA \| MIT \| \| Camel-Math \| Attribution-NonCommercial 4.0 International \| \| NumGLUE \| Apache-2.0 \| \| MathQA \| Apache-2.0 \| \| Our Curated \| MIT \| ## Citation Please cite our paper if you use our data, model or code. Please also kindly cite the original dataset papers. ``` @article{yue2023mammoth, title={MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning}, author={Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wenhao Huang, Huan Sun, Yu Su, Wenhu Chen}, journal={arXiv preprint arXiv:2309.05653}, year={2023} } ```
agicorp/Nectar	agicorp	"2024-03-23T08:30:15Z"	0	[ "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "RLHF", "RLAIF", "reward model", "croissant", "region:us" ]	null	"2024-03-23T08:30:15Z"	--- license: apache-2.0 language: - en size_categories: - 100K<n<1M configs: - config_name: default data_files: - split: train path: data/rlaif.parquet tags: - RLHF - RLAIF - reward model --- # Dataset Card for Nectar - Developed by: Banghua Zhu * , Evan Frick * , Tianhao Wu * , Hanlin Zhu and Jiantao Jiao. - License: Apache-2.0 license under the condition that the dataset is not used to compete with OpenAI Nectar is the first high-quality 7-wise comparison dataset, generated through GPT-4-based ranking. Nectar contains diverse chat prompts, high-quality and diverse responses, and accurate ranking labels. Nectar's prompts are an amalgamation of diverse sources, including [lmsys-chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m), [ShareGPT](https://sharegpt.com/), [Antropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf), [UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback), [Evol-Instruct](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k), and [Flan](https://huggingface.co/datasets/SirNeural/flan_v2). Nectar's 7 responses per prompt are primarily derived from a variety of models, namely GPT-4, GPT-3.5-turbo, GPT-3.5-turbo-instruct, [LLama-2-7B-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf), and [Mistral-7B-Instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1), alongside other existing datasets and models. Each prompt's responses are sorted into a 7-wise ranking labeled by GPT-4, resulting in a total of 3.8M pairwise comparisons. Nectar was used to train the reward model [Starling-RM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-RM-7B-alpha) which propelled [Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha) to an MT-Bench score of 8.09, the current highest for any 7B model. #### Disclaimer: This dataset contains conversations and responses that are possibly unsafe, offensive, and/or disturbing. These are included only for the purpose of training safer models. Viewer discretion is advised. ## Schema: ``` { prompt: str, // in format "\n\nHuman: ... \n\nAssistant: " answers: [ { answer: str, // the model's response model: str, // name of the model that generated the response rank: int // the rank this response recieved }, ... { answer: str, // the model's response model: str, // name of the model that generated the response rank: int // the rank this response recieved }, ] turns: int, // number of turns in the conversation num_response: int, // number of responses for the prompt source: list[str], // list of the dataset sources for the prompt and answers good_natured: bool // if the prompt is good natured } ``` Note: The ```good_natured``` label is derived as a by-product of generating GPT-4 rankings. Since we always first ask GPT-4 if the prompt is good natured before ranking, we were able to parse GPT-4's classification of the prompt's nature to create this label. It is important to note that this label is an approximation generated by GPT-4, and not a representation of the authors' personal beliefs or views. ## Process ### Collecting Prompts 1. For each dataset, generate prompt and answer pairs. 2. For each dataset, group by prompt. 3. Concatenate datasets from (2), down sample according to the following. a. Take all ShareGPT prompts. b. Randomly sample without replacement 75,000 Anthropic HH prompts. c. Take all Ultrafeedback prompts. d. Randomly sample without replacement 45,000 lmsys prompts with more than 1 answer 4. Group the concatenated dataset by prompt, concatenate the answer lists. 5. Take all prompts that are longer than 40 characters and have more than 1 answer Notes: 1. When we process the raw datasets, we identify the top 3 languages, if English is not present, we do not take the datapoint. 2. When we group prompts, we use a sanitized version where all non-alphanumeric characters are removed. ### Collecting Responses To achieve 7 responses for every prompt, we distilled responses from Llama-2-7B-chat, Mistral-7B-instruct, GPT-4, GPT-4-0613, GPT-3.5-turbo, and GPT-3.5-turbo-instruct. GPT-4-0613 was given a system prompt to help set the chatting context yielding better responses. From the k responses for each prompt, we pick 7 with the following priority from highest to lowest: 1. GPT-4-0613, GPT-4, Anthropic HH, GPT-3.5-Turbo-Instruct 2. Other models we did not distill and Mistral-7B-Instruct 3. GPT-3.5-Turbo 4. Llama-2-7B-chat 5. Any repeat model (not Llama-2-7B-chat or GPT-3.5-Turbo) 6. Repeated GPT-3.5-Turbo 7. Repeated Llama-2-7B-chat response 8. Default* Note that the default response is: "I apologize, but I can’t assist with that request." (regardless of the user prompt) As result, we get a dataset with 182,954 prompts and 7 responses per prompt. ### Collecting Rankings To ranking the 7 responses for each prompt, we ask GPT-4 to rank the responses from best to worse in accordance to our rubric. We implemented several strategies to mitigate positional bias, which is convered in detailed in our paper (coming soon). Nectar's response rankings are based off both helpfulness and harmlessess. The rubric expects, first and foremost, that all responses should be harmless. Helpfulness should be maximized when responding to good natured prompts. An excerpt from the GPT-4 judging prompt defines the ranking rubric as follow: >We have 2 rubrics, one is the helpfulness rubric, one is the harmlessness rubric. > > >The helpfulness rubric is as follows: > 1. Is the completion relevant? The completion should be the best possible assistant answer. > 2. Is the completion accurate? The completion should accurately fulfill the prompt’s request. > 3. If a creative answer is expected, is the completion creative? If an analytical answer is expected, is the completion factual/objectively correct? > 4. Is the completion detailed? The completion should at minimum satisfy the full level of detail required by the prompt. > >The harmlessness rubric is as follows: > 1. Does the completion adhere to moral and ethical standards? > 2. Is the completion truthful? > 3. Is the response polite and respectful? > 4. If the request given is not good natured, does the completion politely decline to reply while giving a short explanation why they cannot comply with the request? > >Completions should always maintain harmlessness in the face of adversarial requests. We may see conversations that are good natured and some that are not good natured. All conversations should be completed in a manner that aligns with the harmlessness rubric. Good natured conversations additionally should align with the helpfulness rubric. <!-- ## Stats <img src="images/sources.png" alt="alt text" width="600"/> <img src="images/model_responses.png" alt="alt text" width="600"/> --> ### Dataset Sources <!-- Provide the basic links for the model. --> - Blog:* https://starling.cs.berkeley.edu/ - Paper: Coming soon! - Code: Coming soon! ## License The dataset, model and online demo is a research preview intended for non-commercial use only, subject to the data distillation [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation. ## Acknowledgment We would like to thank Wei-Lin Chiang from Berkeley for detailed feedback of the blog and the projects. We would like to thank the [LMSYS Organization](https://lmsys.org/) for their support of [lmsys-chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) dataset, evaluation and online demo. We would like to thank the open source community for their efforts in providing the datasets and base models we used to develope the project, including but not limited to Anthropic, Llama, Mistral, Hugging Face H4, LMSYS, OpenChat, OpenBMB, Flan and ShareGPT. ✉ Correspondence to: Banghua Zhu (banghua@berkeley.edu). ## Citation ``` @misc{starling2023, title = {Starling-7B: Improving LLM Helpfulness & Harmlessness with RLAIF}, url = {}, author = {Zhu, Banghua and Frick, Evan and Wu, Tianhao and Zhu, Hanlin and Jiao, Jiantao}, month = {November}, year = {2023} } ```
agicorp/Agentinstruct	agicorp	"2024-03-23T08:37:10Z"	1	[ "language:en", "croissant", "arxiv:2310.12823", "region:us" ]	null	"2024-03-23T08:37:06Z"	--- configs: - config_name: default data_files: - split: os path: data/os-* - split: db path: data/db-* - split: alfworld path: data/alfworld-* - split: webshop path: data/webshop-* - split: kg path: data/kg-* - split: mind2web path: data/mind2web-* dataset_info: features: - name: conversations list: - name: from dtype: string - name: loss dtype: bool - name: value dtype: string - name: id dtype: string splits: - name: os num_bytes: 660245 num_examples: 195 - name: db num_bytes: 1436655 num_examples: 538 - name: alfworld num_bytes: 1223363 num_examples: 336 - name: webshop num_bytes: 1602648 num_examples: 351 - name: kg num_bytes: 2960010 num_examples: 324 - name: mind2web num_bytes: 159590 num_examples: 122 download_size: 1255385 dataset_size: 8042511 language: - en pretty_name: AgentInstruct --- # AgentInstruct Dataset <p align="center"> 🤗 <a href="https://huggingface.co/THUDM/agentlm-70b" target="_blank">[Models]</a> • 💻 <a href="https://github.com/THUDM/AgentTuning" target="_blank">[Github Repo]</a> • 📌 <a href="https://THUDM.github.io/AgentTuning/" target="_blank">[Project Page]</a> • 📃 <a href="https://arxiv.org/abs/2310.12823" target="_blank">[Paper]</a> </p> AgentInstruct is a meticulously curated dataset featuring 1,866 high-quality interactions, designed to enhance AI agents across six diverse real-world tasks, leveraging innovative methods like Task Derivation and Self-Instruct. - 🔍 CoT - Harness the power of [ReAct](https://react-lm.github.io/), offering detailed thought explanations for each action, ensuring an intricate understanding of the model's decision-making journey. - 🌍 Diversity - Spanning 6 real-world scenarios, from Daily Household Routines to Database Operations, and their average turns range from 5 to 35. - 🎯 Precision - Not all trajectories of GPT-4 are effective! Ours are rigorously filtered using strict rewards to ensure top-notch quality. - ✅ Assurance - Rigorous checks to avoid data leakage, ensuring pristine dataset quality. ## Task Overview \| Task \| # Filt. Traj. \| Avg # Filt. Traj. Turns \| \|---\|---\|---\| \|ALFWorld\|336\|13.52\| \|WebShop\|351\|3.68\| \|Mind2Web\|122\|1.00\| \|Knowledge Graph\|324\|6.04\| \|Operating System\|195\|3.85\| \|Database\|538\|2.06\| \|AgentInstruct\|1866\|5.24\| AgentInstruct includes 1,866 trajectories from 6 agents tasks. "Traj." stands for interaction trajectory. "Filt. Traj." stands for filtered trajectories. ## Models AgentLM models are produced by mixed training on AgentInstruct dataset and ShareGPT dataset from Llama-2-chat models. The models follow the conversation format of [Llama-2-chat](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), with system prompt fixed as ``` You are a helpful, respectful and honest assistant. ``` 7B, 13B, and 70B models are available on Huggingface model hub. \|Model\|Huggingface Repo\| \|---\|---\| \|AgentLM-7B\| [🤗Huggingface Repo](https://huggingface.co/THUDM/agentlm-7b) \| \|AgentLM-13B\| [🤗Huggingface Repo](https://huggingface.co/THUDM/agentlm-13b) \| \|AgentLM-70B\| [🤗Huggingface Repo](https://huggingface.co/THUDM/agentlm-70b) \| Check our [[Github Repo]](https://github.com/THUDM/AgentTuning) for details about AgentTuning. ## Citation If you find our work useful, please consider citing AgentTuning: ``` @misc{zeng2023agenttuning, title={AgentTuning: Enabling Generalized Agent Abilities for LLMs}, author={Aohan Zeng and Mingdao Liu and Rui Lu and Bowen Wang and Xiao Liu and Yuxiao Dong and Jie Tang}, year={2023}, eprint={2310.12823}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
ovieyra21/recording-delete	ovieyra21	"2024-03-23T08:44:44Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T08:40:41Z"	--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string splits: - name: train num_bytes: 5892712.0 num_examples: 1 download_size: 3846099 dataset_size: 5892712.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
MatrixStudio/Codeforces-Python-Submissions-PPO	MatrixStudio	"2024-03-24T02:19:30Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T08:52:12Z"	--- dataset_info: features: - name: contestId dtype: int64 - name: index dtype: string - name: name dtype: string - name: type dtype: string - name: rating dtype: int64 - name: tags sequence: string - name: title dtype: string - name: time-limit dtype: string - name: memory-limit dtype: string - name: problem-description dtype: string - name: input-specification dtype: string - name: output-specification dtype: string - name: demo-input sequence: string - name: demo-output sequence: string - name: note dtype: string - name: points dtype: float64 - name: test_cases list: - name: input dtype: string - name: output dtype: string - name: creationTimeSeconds dtype: int64 - name: relativeTimeSeconds dtype: int64 - name: programmingLanguage dtype: string - name: verdict dtype: string - name: testset dtype: string - name: passedTestCount dtype: int64 - name: timeConsumedMillis dtype: int64 - name: memoryConsumedBytes dtype: int64 - name: code dtype: string - name: prompt dtype: string - name: response dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 326352255.9446646 num_examples: 49021 - name: test num_bytes: 41407414 num_examples: 6115 download_size: 49192265 dataset_size: 367759669.9446646 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
driesverachtert/basic_shapes_object_detection	driesverachtert	"2024-03-27T13:00:51Z"	0	[ "task_categories:object-detection", "annotations_creators:machine-generated", "language:en", "license:apache-2.0", "object-detection", "simple", "example", "basic-geometric-shapes", "croissant", "region:us" ]	[ "object-detection" ]	"2024-03-23T08:54:30Z"	--- language: - en license: apache-2.0 pretty_name: Basic Shapes Object Detection tags: - object-detection - simple - example - basic-geometric-shapes annotations_creators: - machine-generated task_categories: - object-detection dataset_info: features: - name: image_id dtype: int64 - name: image dtype: image - name: width dtype: int32 - name: height dtype: int32 - name: objects sequence: - name: id dtype: int64 - name: area dtype: int64 - name: bbox sequence: float32 length: 4 - name: category dtype: class_label: names: '0': Square '1': Circle '2': Triangle configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Basic Shapes Object Detection ## Description This Basic Shapes Object Detection dataset has been created to test fine-tuning of object detection models. Fine-tuning some model to detect the basic shapes should be rather easy: just a bit of training should be enough to get the model to do correct object detection quite fast. Each entry in the dataset has a RGB PNG image with a white background and 3 basic geometric shapes: * A blue square * A red circle * A green triangle All images have the same size. Each image has exactly 1 square, 1 circle and 1 triangle, with their fixed colors. Each entry in the dataset has consequently 3 bounding boxes. The shapes do not overlap.The category IDs are 0, 1 and 2, corresponding to the labels Square, Circle and Triangle. The dataset has exactly the same structure as the https://huggingface.co/datasets/cppe-5 dataset, but fine-tuning some model to this dataset with basic geometric shapes should require considerable less training compared to the cppe-5 dataset. Once you have tested your fine-tuning code on this dataset, it should also work on more complicated datasets such as the cppe-5 dataset. ![](https://github.com/DriesVerachtert/basic_shapes_object_detection_dataset/blob/main/examples.png) ## Links The Python code to generate the images can be found at https://github.com/DriesVerachtert/basic_shapes_object_detection_dataset The dataset can be downloaded from https://huggingface.co/datasets/driesverachtert/basic_shapes_object_detection ## Structure The bounding boxes are in COCO format (x_min, y_min, width, height). ## License This dataset is released under Apache 2.0. ## Usage ```python from datasets import load_dataset dataset = load_dataset("driesverachtert/basic_shapes_object_detection") ```
alvations/c4p0-v1-en-es	alvations	"2024-03-23T09:30:19Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T09:30:18Z"	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 23053968 num_examples: 18476 download_size: 10003956 dataset_size: 23053968 configs: - config_name: default data_files: - split: train path: data/train-* ---
alvations/c4p0-v1-en-zh	alvations	"2024-03-23T09:30:23Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T09:30:20Z"	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 20548462 num_examples: 16072 download_size: 8673734 dataset_size: 20548462 configs: - config_name: default data_files: - split: train path: data/train-* ---
alvations/c4p0-v1-en-ja	alvations	"2024-03-23T09:30:27Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T09:30:26Z"	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 22109670 num_examples: 17956 download_size: 8614674 dataset_size: 22109670 configs: - config_name: default data_files: - split: train path: data/train-* ---
alvations/c4p0-v1-ja-en	alvations	"2024-03-23T09:30:29Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T09:30:28Z"	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 24906424 num_examples: 20164 download_size: 10803890 dataset_size: 24906424 configs: - config_name: default data_files: - split: train path: data/train-* ---
alvations/c4p0-v1-en-it	alvations	"2024-03-23T09:30:31Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T09:30:30Z"	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 20371694 num_examples: 15744 download_size: 8748727 dataset_size: 20371694 configs: - config_name: default data_files: - split: train path: data/train-* ---
alvations/c4p0-v1-en-de	alvations	"2024-03-23T09:30:33Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T09:30:32Z"	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 14282882 num_examples: 11920 download_size: 6534015 dataset_size: 14282882 configs: - config_name: default data_files: - split: train path: data/train-* ---
alvations/c4p0-v1-en-fr	alvations	"2024-03-23T09:30:34Z"	0	[ "croissant", "region:us" ]	null	"2024-03-23T09:30:33Z"	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 9739130 num_examples: 7510 download_size: 4040042 dataset_size: 9739130 configs: - config_name: default data_files: - split: train path: data/train-* ---

SauravMaheshkar/NDC-substances

SauravMaheshkar

"2024-04-04T14:13:39Z"

0

[ "task_categories:graph-ml", "license:unknown", "chemistry", "arxiv:1802.06916", "region:us" ]

[ "graph-ml" ]

"2024-03-22T20:12:00Z"

--- license: unknown task_categories: - graph-ml tags: - chemistry configs: - config_name: transductive data_files: - split: train path: "processed/transductive/train_df.csv" - split: valid path: "processed/transductive/val_df.csv" - split: test path: "processed/transductive/test_df.csv" - config_name: inductive data_files: - split: train path: "processed/inductive/train_df.csv" - split: valid path: "processed/inductive/val_df.csv" - split: test path: "processed/inductive/test_df.csv" - config_name: raw data_files: "raw/*.txt" --- Source Paper: https://arxiv.org/abs/1802.06916 ### Usage ``` from torch_geometric.datasets.cornell import CornellTemporalHyperGraphDataset dataset = CornellTemporalHyperGraphDataset(root = "./", name="NDC-substances", split="train") ``` ### Citation ```misc @article{Benson-2018-simplicial, author = {Benson, Austin R. and Abebe, Rediet and Schaub, Michael T. and Jadbabaie, Ali and Kleinberg, Jon}, title = {Simplicial closure and higher-order link prediction}, year = {2018}, doi = {10.1073/pnas.1800683115}, publisher = {National Academy of Sciences}, issn = {0027-8424}, journal = {Proceedings of the National Academy of Sciences} } ```

vwxyzjn/summarize_from_feedback_oai_preprocessing_1711138084

vwxyzjn

"2024-03-22T20:13:39Z"

0

[ "croissant", "region:us" ]

null

"2024-03-22T20:12:47Z"

--- dataset_info: features: - name: info struct: - name: id dtype: string - name: post dtype: string - name: title dtype: string - name: subreddit dtype: string - name: site dtype: string - name: article dtype: string - name: summaries list: - name: text dtype: string - name: policy dtype: string - name: note dtype: string - name: choice dtype: int32 - name: worker dtype: string - name: batch dtype: string - name: split dtype: string - name: extra struct: - name: confidence dtype: int32 - name: query_token sequence: int64 - name: query dtype: string - name: chosen dtype: string - name: chosen_token sequence: int64 - name: chosen_token_len dtype: int64 - name: rejected dtype: string - name: rejected_token sequence: int64 - name: rejected_token_len dtype: int64 - name: chosen_policy dtype: string - name: rejected_policy dtype: string - name: policies dtype: string - name: chosen_len_minus_rejected_len dtype: int64 - name: query_chosen dtype: string - name: query_chosen_token sequence: int64 - name: query_chosen_token_len dtype: int64 - name: query_rejected dtype: string - name: query_rejected_token sequence: int64 - name: query_rejected_token_len dtype: int64 - name: query_token_len dtype: int64 - name: query_chosen_token_response_label sequence: int64 - name: query_rejected_token_response_label sequence: int64 splits: - name: train num_bytes: 3160687523 num_examples: 92858 - name: validation num_bytes: 2859977775 num_examples: 83802 - name: validation_cnndm num_bytes: 225375023 num_examples: 2284 download_size: 291050539 dataset_size: 6246040321 --- # Dataset Card for "summarize_from_feedback_oai_preprocessing_1711138084" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

pgwi/clean_fashion_data

pgwi

"2024-03-22T20:16:56Z"

0

[ "license:apache-2.0", "croissant", "region:us" ]

null

"2024-03-22T20:16:12Z"

--- license: apache-2.0 ---

vwxyzjn/summarize_from_feedback_oai_preprocessing_1711138537

vwxyzjn

"2024-03-22T20:17:43Z"

0

[ "croissant", "region:us" ]

null

"2024-03-22T20:17:19Z"

--- dataset_info: features: - name: info struct: - name: id dtype: string - name: post dtype: string - name: title dtype: string - name: subreddit dtype: string - name: site dtype: string - name: article dtype: string - name: summaries list: - name: text dtype: string - name: policy dtype: string - name: note dtype: string - name: choice dtype: int32 - name: worker dtype: string - name: batch dtype: string - name: split dtype: string - name: extra struct: - name: confidence dtype: int32 - name: query_token sequence: int64 - name: query dtype: string - name: chosen dtype: string - name: chosen_token sequence: int64 - name: chosen_token_len dtype: int64 - name: rejected dtype: string - name: rejected_token sequence: int64 - name: rejected_token_len dtype: int64 - name: chosen_policy dtype: string - name: rejected_policy dtype: string - name: policies dtype: string - name: chosen_len_minus_rejected_len dtype: int64 - name: query_chosen dtype: string - name: query_chosen_token sequence: int64 - name: query_chosen_token_len dtype: int64 - name: query_rejected dtype: string - name: query_rejected_token sequence: int64 - name: query_rejected_token_len dtype: int64 - name: query_token_len dtype: int64 - name: query_chosen_token_response_label sequence: int64 - name: query_rejected_token_response_label sequence: int64 splits: - name: train num_bytes: 3160687523 num_examples: 92858 - name: validation num_bytes: 2859977775 num_examples: 83802 - name: validation_cnndm num_bytes: 225375023 num_examples: 2284 download_size: 291050539 dataset_size: 6246040321 --- # Dataset Card for "summarize_from_feedback_oai_preprocessing_1711138537" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

vwxyzjn/summarize_from_feedback_tldr_3_filtered_oai_preprocessing_1711138793

vwxyzjn

"2024-03-22T20:21:42Z"

0

[ "croissant", "region:us" ]

null

"2024-03-22T20:21:19Z"

--- dataset_info: features: - name: id dtype: string - name: subreddit dtype: string - name: title dtype: string - name: post dtype: string - name: summary dtype: string - name: query_token sequence: int64 - name: query dtype: string - name: reference_response dtype: string - name: reference_response_token sequence: int64 - name: reference_response_token_len dtype: int64 - name: query_reference_response dtype: string - name: query_reference_response_token sequence: int64 - name: query_reference_response_token_response_label sequence: int64 - name: query_reference_response_token_len dtype: int64 splits: - name: train num_bytes: 2125689249 num_examples: 116722 - name: validation num_bytes: 117437271 num_examples: 6447 - name: test num_bytes: 119410966 num_examples: 6553 download_size: 562087836 dataset_size: 2362537486 --- # TL;DR SFT Dataset for OpenAI's [Summarize from Feedback](https://openai.com/blog/summarization/) task The dataset is directly taken from https://github.com/openai/summarize-from-feedback/tree/700967448d10004279f138666442bf1497d0e705#reddit-tldr-dataset These columns are taken directly from the aforementioned dataset: * **id**: unique identifier for the post * **subreddit**: subreddit the post was taken from * **title**: title of the post * **post**: body of the post * **summary**: summary of the post * **reference_response**: reference response for the post These columns are added by this preprocessing script: * **query**: length-limited query for summarization: OAI pre-processes the main text (title + subreddit + post), ensuring it has only 512 tokens; if the main text is too long, then it tries to truncate at the last ` `. If it's too short it pads the main text ([summarize_from_feedback/tasks.py#L98-L165](https://github.com/openai/summarize-from-feedback/blob/700967448d10004279f138666442bf1497d0e705/summarize_from_feedback/tasks.py#L98-L165)). Padding is either space or `[PAD]` token (see Args below). * **query_token**: tokenized version of `query` * **reference_response_token**: tokenized version of `reference_response` * **reference_response_token_len**: length of `reference_response_token` * **query_reference_response**: concatenation of `query.strip()` and `reference_response` * **query_reference_response_token**: tokenized version of `query_reference_response`, up to `max_sft_query_response_length` tokens * **query_reference_response_token_len**: length of `query_reference_response_token` # Args ```python {'base_model': 'EleutherAI/pythia-1b-deduped', 'check_length_correctness': True, 'cnndm_params': TaskQueryHParams(length=1919, format_str='Article:\n{article}\n\nTL;DR:\n', truncate_field='article', truncate_text='\n', padding='pad_token', pad_token=[50277], pad_side='left', max_sft_response_length=None, max_sft_query_response_length=None, max_rm_response_length=155, max_rm_query_response_length=2021), 'debug': False, 'hf_entity': 'vwxyzjn', 'push_to_hub': True, 'tldr_params': TaskQueryHParams(length=512, format_str='SUBREDDIT: r/{subreddit}\n' '\n' 'TITLE: {title}\n' '\n' 'POST: {post}\n' '\n' 'TL;DR:', truncate_field='post', truncate_text='\n', padding='pad_token', pad_token=[50277], pad_side='left', max_sft_response_length=53, max_sft_query_response_length=562, max_rm_response_length=169, max_rm_query_response_length=638)} ```

HPGomes/MichaelJacksonFalsetto

HPGomes

"2024-03-23T02:41:36Z"

0

[ "license:openrail", "croissant", "region:us" ]

null

"2024-03-22T20:23:17Z"

--- license: openrail ---

vwxyzjn/summarize_from_feedback_oai_preprocessing_1711138793

vwxyzjn

"2024-03-22T20:24:14Z"

0

[ "croissant", "region:us" ]

null

"2024-03-22T20:23:25Z"

--- dataset_info: features: - name: info struct: - name: id dtype: string - name: post dtype: string - name: title dtype: string - name: subreddit dtype: string - name: site dtype: string - name: article dtype: string - name: summaries list: - name: text dtype: string - name: policy dtype: string - name: note dtype: string - name: choice dtype: int32 - name: worker dtype: string - name: batch dtype: string - name: split dtype: string - name: extra struct: - name: confidence dtype: int32 - name: query_token sequence: int64 - name: query dtype: string - name: chosen dtype: string - name: chosen_token sequence: int64 - name: chosen_token_len dtype: int64 - name: rejected dtype: string - name: rejected_token sequence: int64 - name: rejected_token_len dtype: int64 - name: chosen_policy dtype: string - name: rejected_policy dtype: string - name: policies dtype: string - name: chosen_len_minus_rejected_len dtype: int64 - name: query_chosen dtype: string - name: query_chosen_token sequence: int64 - name: query_chosen_token_len dtype: int64 - name: query_rejected dtype: string - name: query_rejected_token sequence: int64 - name: query_rejected_token_len dtype: int64 - name: query_token_len dtype: int64 - name: query_chosen_token_response_label sequence: int64 - name: query_rejected_token_response_label sequence: int64 splits: - name: train num_bytes: 3160687523 num_examples: 92858 - name: validation num_bytes: 2859977775 num_examples: 83802 - name: validation_cnndm num_bytes: 225375023 num_examples: 2284 download_size: 291050539 dataset_size: 6246040321 --- # Dataset Card for "summarize_from_feedback_oai_preprocessing_1711138793" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Zarcend/testDataSet

Zarcend

"2024-03-22T20:28:57Z"

0

[ "license:mit", "croissant", "region:us" ]

null

"2024-03-22T20:27:59Z"

--- license: mit ---

AlekseyKorshuk/pickapic_v2-prompts-dedup

AlekseyKorshuk

"2024-03-22T20:37:56Z"

0

[ "croissant", "region:us" ]

null

"2024-03-22T20:36:43Z"

--- dataset_info: features: - name: prompt dtype: string - name: __cluster__ dtype: int64 splits: - name: train num_bytes: 461516230.6301592 num_examples: 2802392 download_size: 212898248 dataset_size: 461516230.6301592 configs: - config_name: default data_files: - split: train path: data/train-* ---

iaaoli2/arianaw

iaaoli2

"2024-03-22T20:53:52Z"

0

[ "license:openrail", "croissant", "region:us" ]

null

"2024-03-22T20:48:20Z"

--- license: openrail ---

chegri1/dataset

chegri1

"2024-03-22T21:03:52Z"

0

[ "license:unknown", "croissant", "region:us" ]

null

"2024-03-22T21:01:48Z"

--- license: unknown ---

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.3_seed_2

Mitsuki-Sakamoto

"2024-03-22T22:44:32Z"

0

[ "croissant", "region:us" ]

null

"2024-03-22T21:13:04Z"

--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43761297 num_examples: 18928 - name: epoch_1 num_bytes: 44345524 num_examples: 18928 - name: epoch_2 num_bytes: 44437506 num_examples: 18928 - name: epoch_3 num_bytes: 44469689 num_examples: 18928 - name: epoch_4 num_bytes: 44474667 num_examples: 18928 - name: epoch_5 num_bytes: 44464460 num_examples: 18928 - name: epoch_6 num_bytes: 44442708 num_examples: 18928 - name: epoch_7 num_bytes: 44422364 num_examples: 18928 - name: epoch_8 num_bytes: 44414794 num_examples: 18928 - name: epoch_9 num_bytes: 44410898 num_examples: 18928 - name: epoch_10 num_bytes: 44411476 num_examples: 18928 - name: epoch_11 num_bytes: 44406870 num_examples: 18928 - name: epoch_12 num_bytes: 44409267 num_examples: 18928 - name: epoch_13 num_bytes: 44409143 num_examples: 18928 - name: epoch_14 num_bytes: 44408111 num_examples: 18928 - name: epoch_15 num_bytes: 44407603 num_examples: 18928 - name: epoch_16 num_bytes: 44408486 num_examples: 18928 - name: epoch_17 num_bytes: 44405857 num_examples: 18928 - name: epoch_18 num_bytes: 44406319 num_examples: 18928 - name: epoch_19 num_bytes: 44406957 num_examples: 18928 - name: epoch_20 num_bytes: 44405910 num_examples: 18928 - name: epoch_21 num_bytes: 44406498 num_examples: 18928 - name: epoch_22 num_bytes: 44406929 num_examples: 18928 - name: epoch_23 num_bytes: 44405194 num_examples: 18928 - name: epoch_24 num_bytes: 44405536 num_examples: 18928 - name: epoch_25 num_bytes: 44405889 num_examples: 18928 - name: epoch_26 num_bytes: 44404896 num_examples: 18928 - name: epoch_27 num_bytes: 44404886 num_examples: 18928 - name: epoch_28 num_bytes: 44406038 num_examples: 18928 - name: epoch_29 num_bytes: 44407507 num_examples: 18928 download_size: 700201551 dataset_size: 1331783279 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_1.0_seed_2

Mitsuki-Sakamoto

"2024-03-23T06:20:06Z"

0

[ "croissant", "region:us" ]

null

"2024-03-22T21:18:21Z"

--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43770255 num_examples: 18928 - name: epoch_1 num_bytes: 44408742 num_examples: 18928 - name: epoch_2 num_bytes: 44482870 num_examples: 18928 - name: epoch_3 num_bytes: 44532704 num_examples: 18928 - name: epoch_4 num_bytes: 44549616 num_examples: 18928 - name: epoch_5 num_bytes: 44539709 num_examples: 18928 - name: epoch_6 num_bytes: 44538656 num_examples: 18928 - name: epoch_7 num_bytes: 44529666 num_examples: 18928 - name: epoch_8 num_bytes: 44513235 num_examples: 18928 - name: epoch_9 num_bytes: 44502442 num_examples: 18928 - name: epoch_10 num_bytes: 44499095 num_examples: 18928 - name: epoch_11 num_bytes: 44493183 num_examples: 18928 - name: epoch_12 num_bytes: 44491075 num_examples: 18928 - name: epoch_13 num_bytes: 44486818 num_examples: 18928 - name: epoch_14 num_bytes: 44489815 num_examples: 18928 - name: epoch_15 num_bytes: 44487019 num_examples: 18928 - name: epoch_16 num_bytes: 44486484 num_examples: 18928 - name: epoch_17 num_bytes: 44487934 num_examples: 18928 - name: epoch_18 num_bytes: 44485687 num_examples: 18928 - name: epoch_19 num_bytes: 44483472 num_examples: 18928 - name: epoch_20 num_bytes: 44484628 num_examples: 18928 - name: epoch_21 num_bytes: 44485591 num_examples: 18928 - name: epoch_22 num_bytes: 44486295 num_examples: 18928 - name: epoch_23 num_bytes: 44485684 num_examples: 18928 - name: epoch_24 num_bytes: 44484921 num_examples: 18928 - name: epoch_25 num_bytes: 44484354 num_examples: 18928 - name: epoch_26 num_bytes: 44484909 num_examples: 18928 - name: epoch_27 num_bytes: 44484138 num_examples: 18928 - name: epoch_28 num_bytes: 44485080 num_examples: 18928 - name: epoch_29 num_bytes: 44483062 num_examples: 18928 download_size: 1073130082 dataset_size: 1334107139 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_160m_thr_0.3_seed_2

Mitsuki-Sakamoto

"2024-03-22T23:29:05Z"

0

[ "croissant", "region:us" ]

null

"2024-03-22T21:41:49Z"

--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43783586 num_examples: 18928 - name: epoch_1 num_bytes: 44377250 num_examples: 18928 - name: epoch_2 num_bytes: 44448866 num_examples: 18928 - name: epoch_3 num_bytes: 44483809 num_examples: 18928 - name: epoch_4 num_bytes: 44492320 num_examples: 18928 - name: epoch_5 num_bytes: 44489018 num_examples: 18928 - name: epoch_6 num_bytes: 44475503 num_examples: 18928 - name: epoch_7 num_bytes: 44460141 num_examples: 18928 - name: epoch_8 num_bytes: 44445265 num_examples: 18928 - name: epoch_9 num_bytes: 44441178 num_examples: 18928 - name: epoch_10 num_bytes: 44438339 num_examples: 18928 - name: epoch_11 num_bytes: 44436226 num_examples: 18928 - name: epoch_12 num_bytes: 44434486 num_examples: 18928 - name: epoch_13 num_bytes: 44435475 num_examples: 18928 - name: epoch_14 num_bytes: 44431647 num_examples: 18928 - name: epoch_15 num_bytes: 44432365 num_examples: 18928 - name: epoch_16 num_bytes: 44432856 num_examples: 18928 - name: epoch_17 num_bytes: 44432911 num_examples: 18928 - name: epoch_18 num_bytes: 44429532 num_examples: 18928 - name: epoch_19 num_bytes: 44429380 num_examples: 18928 - name: epoch_20 num_bytes: 44430229 num_examples: 18928 - name: epoch_21 num_bytes: 44430596 num_examples: 18928 - name: epoch_22 num_bytes: 44431243 num_examples: 18928 - name: epoch_23 num_bytes: 44428939 num_examples: 18928 - name: epoch_24 num_bytes: 44432154 num_examples: 18928 - name: epoch_25 num_bytes: 44429301 num_examples: 18928 - name: epoch_26 num_bytes: 44429659 num_examples: 18928 - name: epoch_27 num_bytes: 44431306 num_examples: 18928 - name: epoch_28 num_bytes: 44432280 num_examples: 18928 - name: epoch_29 num_bytes: 44431422 num_examples: 18928 download_size: 701477709 dataset_size: 1332537282 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---

dbutt7/NTP_Treefall_Segmentation

dbutt7

"2024-03-23T00:13:41Z"

0

[ "license:cc-by-nc-4.0", "croissant", "region:us" ]

null

"2024-03-22T22:26:23Z"

--- license: cc-by-nc-4.0 dataset_info: features: - name: x dtype: image - name: y sequence: sequence: sequence: uint8 splits: - name: train num_bytes: 6183133760 num_examples: 7240 download_size: 1458099889 dataset_size: 6183133760 ---

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_160m_thr_1.0_seed_2

Mitsuki-Sakamoto

"2024-03-23T01:22:00Z"

0

[ "croissant", "region:us" ]

null

"2024-03-22T22:59:39Z"

--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43779189 num_examples: 18928 - name: epoch_1 num_bytes: 44389394 num_examples: 18928 - name: epoch_2 num_bytes: 44452838 num_examples: 18928 - name: epoch_3 num_bytes: 44504767 num_examples: 18928 - name: epoch_4 num_bytes: 44525045 num_examples: 18928 - name: epoch_5 num_bytes: 44512817 num_examples: 18928 - name: epoch_6 num_bytes: 44504063 num_examples: 18928 - name: epoch_7 num_bytes: 44468006 num_examples: 18928 - name: epoch_8 num_bytes: 44457766 num_examples: 18928 - name: epoch_9 num_bytes: 44452281 num_examples: 18928 - name: epoch_10 num_bytes: 44441633 num_examples: 18928 - name: epoch_11 num_bytes: 44440113 num_examples: 18928 - name: epoch_12 num_bytes: 44438738 num_examples: 18928 - name: epoch_13 num_bytes: 44437694 num_examples: 18928 - name: epoch_14 num_bytes: 44438979 num_examples: 18928 - name: epoch_15 num_bytes: 44434772 num_examples: 18928 - name: epoch_16 num_bytes: 44431885 num_examples: 18928 - name: epoch_17 num_bytes: 44430771 num_examples: 18928 - name: epoch_18 num_bytes: 44430902 num_examples: 18928 - name: epoch_19 num_bytes: 44429917 num_examples: 18928 - name: epoch_20 num_bytes: 44430629 num_examples: 18928 - name: epoch_21 num_bytes: 44429778 num_examples: 18928 - name: epoch_22 num_bytes: 44429225 num_examples: 18928 - name: epoch_23 num_bytes: 44432672 num_examples: 18928 - name: epoch_24 num_bytes: 44429439 num_examples: 18928 - name: epoch_25 num_bytes: 44429477 num_examples: 18928 - name: epoch_26 num_bytes: 44429378 num_examples: 18928 - name: epoch_27 num_bytes: 44429813 num_examples: 18928 - name: epoch_28 num_bytes: 44426534 num_examples: 18928 - name: epoch_29 num_bytes: 44428539 num_examples: 18928 download_size: 700085000 dataset_size: 1332697054 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---

sanjay920/single_function_call_oai_mistral_large

sanjay920

"2024-03-22T23:43:43Z"

0

[ "croissant", "region:us" ]

null

"2024-03-22T23:03:55Z"

--- dataset_info: features: - name: id dtype: string - name: tools list: - name: function struct: - name: description dtype: string - name: name dtype: string - name: parameters struct: - name: properties struct: - name: amount struct: - name: description dtype: string - name: type dtype: string - name: amount_due struct: - name: description dtype: string - name: type dtype: string - name: author struct: - name: description dtype: string - name: type dtype: string - name: bill_amount struct: - name: description dtype: string - name: type dtype: string - name: birth_date struct: - name: description dtype: string - name: type dtype: string - name: birth_year struct: - name: description dtype: string - name: type dtype: string - name: category struct: - name: description dtype: string - name: type dtype: string - name: country struct: - name: description dtype: string - name: type dtype: string - name: cuisine struct: - name: description dtype: string - name: type dtype: string - name: customer_name struct: - name: description dtype: string - name: type dtype: string - name: date_of_birth struct: - name: description dtype: string - name: type dtype: string - name: destination struct: - name: description dtype: string - name: type dtype: string - name: diet struct: - name: description dtype: string - name: type dtype: string - name: discount_percentage struct: - name: description dtype: string - name: type dtype: string - name: dob struct: - name: description dtype: string - name: type dtype: string - name: due_date struct: - name: description dtype: string - name: format dtype: string - name: type dtype: string - name: email struct: - name: description dtype: string - name: type dtype: string - name: encryption_algorithm struct: - name: description dtype: string - name: type dtype: string - name: end_location struct: - name: description dtype: string - name: type dtype: string - name: end_time struct: - name: description dtype: string - name: type dtype: string - name: first_name struct: - name: description dtype: string - name: type dtype: string - name: from_currency struct: - name: description dtype: string - name: type dtype: string - name: genre struct: - name: description dtype: string - name: type dtype: string - name: grades struct: - name: items struct: - name: properties struct: - name: course struct: - name: description dtype: string - name: type dtype: string - name: credit_hours struct: - name: description dtype: string - name: type dtype: string - name: grade struct: - name: description dtype: string - name: type dtype: string - name: required sequence: string - name: type dtype: string - name: type dtype: string - name: height struct: - name: description dtype: string - name: type dtype: string - name: include_numbers struct: - name: description dtype: string - name: type dtype: string - name: include_special_characters struct: - name: description dtype: string - name: type dtype: string - name: include_symbols struct: - name: description dtype: string - name: type dtype: string - name: income struct: - name: description dtype: string - name: type dtype: string - name: ingredients struct: - name: description dtype: string - name: items struct: - name: type dtype: string - name: type dtype: string - name: interest_rate struct: - name: description dtype: string - name: type dtype: string - name: keyword struct: - name: description dtype: string - name: type dtype: string - name: keywords struct: - name: description dtype: string - name: items struct: - name: type dtype: string - name: type dtype: string - name: last_name struct: - name: description dtype: string - name: type dtype: string - name: length struct: - name: description dtype: string - name: type dtype: string - name: loan_amount struct: - name: description dtype: string - name: type dtype: string - name: loan_term struct: - name: description dtype: string - name: type dtype: string - name: max struct: - name: description dtype: string - name: type dtype: string - name: measurements struct: - name: properties struct: - name: length struct: - name: description dtype: string - name: type dtype: string - name: width struct: - name: description dtype: string - name: type dtype: string - name: required sequence: string - name: type dtype: string - name: message struct: - name: description dtype: string - name: type dtype: string - name: metrics struct: - name: description dtype: string - name: items struct: - name: type dtype: string - name: type dtype: string - name: min struct: - name: description dtype: string - name: type dtype: string - name: name struct: - name: description dtype: string - name: type dtype: string - name: numbers struct: - name: description dtype: string - name: items struct: - name: type dtype: string - name: type dtype: string - name: options struct: - name: description dtype: string - name: items struct: - name: type dtype: string - name: type dtype: string - name: origin struct: - name: description dtype: string - name: type dtype: string - name: original_price struct: - name: description dtype: string - name: type dtype: string - name: principal struct: - name: description dtype: string - name: type dtype: string - name: priority struct: - name: description dtype: string - name: enum sequence: string - name: type dtype: string - name: query struct: - name: description dtype: string - name: type dtype: string - name: question struct: - name: description dtype: string - name: type dtype: string - name: recipient struct: - name: description dtype: string - name: type dtype: string - name: search_query struct: - name: description dtype: string - name: type dtype: string - name: shape struct: - name: description dtype: string - name: type dtype: string - name: start_location struct: - name: description dtype: string - name: type dtype: string - name: start_time struct: - name: description dtype: string - name: type dtype: string - name: stock_symbol struct: - name: description dtype: string - name: type dtype: string - name: subject struct: - name: description dtype: string - name: type dtype: string - name: symbol struct: - name: description dtype: string - name: type dtype: string - name: task struct: - name: description dtype: string - name: type dtype: string - name: task_name struct: - name: description dtype: string - name: type dtype: string - name: tax_rate struct: - name: description dtype: string - name: type dtype: string - name: text struct: - name: description dtype: string - name: type dtype: string - name: time struct: - name: description dtype: string - name: type dtype: string - name: tip_percentage struct: - name: description dtype: string - name: type dtype: string - name: title struct: - name: description dtype: string - name: type dtype: string - name: to_currency struct: - name: description dtype: string - name: type dtype: string - name: website_url struct: - name: description dtype: string - name: type dtype: string - name: weight struct: - name: description dtype: string - name: type dtype: string - name: word struct: - name: description dtype: string - name: type dtype: string - name: year struct: - name: description dtype: string - name: type dtype: string - name: required sequence: string - name: type dtype: string - name: type dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string - name: oai_match_laplateforme dtype: bool - name: oai_match_azure dtype: bool - name: openai_response dtype: string - name: la_plateforme_mistral_large_response dtype: string - name: azure_mistral_large_response dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 72421 num_examples: 50 download_size: 140187 dataset_size: 72421 configs: - config_name: default data_files: - split: train path: data/train-* ---

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.0_seed_3

Mitsuki-Sakamoto

"2024-03-23T00:22:40Z"

0

[ "croissant", "region:us" ]

null

"2024-03-22T23:16:59Z"

--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43751727 num_examples: 18928 - name: epoch_1 num_bytes: 44330784 num_examples: 18928 - name: epoch_2 num_bytes: 44406698 num_examples: 18928 - name: epoch_3 num_bytes: 44435284 num_examples: 18928 - name: epoch_4 num_bytes: 44437128 num_examples: 18928 - name: epoch_5 num_bytes: 44423166 num_examples: 18928 - name: epoch_6 num_bytes: 44411320 num_examples: 18928 - name: epoch_7 num_bytes: 44410437 num_examples: 18928 - name: epoch_8 num_bytes: 44410984 num_examples: 18928 - name: epoch_9 num_bytes: 44407897 num_examples: 18928 - name: epoch_10 num_bytes: 44409623 num_examples: 18928 - name: epoch_11 num_bytes: 44408883 num_examples: 18928 - name: epoch_12 num_bytes: 44408562 num_examples: 18928 - name: epoch_13 num_bytes: 44408224 num_examples: 18928 - name: epoch_14 num_bytes: 44409062 num_examples: 18928 - name: epoch_15 num_bytes: 44407898 num_examples: 18928 - name: epoch_16 num_bytes: 44408185 num_examples: 18928 - name: epoch_17 num_bytes: 44408017 num_examples: 18928 - name: epoch_18 num_bytes: 44408034 num_examples: 18928 - name: epoch_19 num_bytes: 44409148 num_examples: 18928 - name: epoch_20 num_bytes: 44409429 num_examples: 18928 - name: epoch_21 num_bytes: 44407308 num_examples: 18928 - name: epoch_22 num_bytes: 44409553 num_examples: 18928 - name: epoch_23 num_bytes: 44408967 num_examples: 18928 - name: epoch_24 num_bytes: 44410252 num_examples: 18928 - name: epoch_25 num_bytes: 44409915 num_examples: 18928 - name: epoch_26 num_bytes: 44409736 num_examples: 18928 - name: epoch_27 num_bytes: 44410588 num_examples: 18928 - name: epoch_28 num_bytes: 44409741 num_examples: 18928 - name: epoch_29 num_bytes: 44409955 num_examples: 18928 download_size: 701230621 dataset_size: 1331606505 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---

NickyNicky/nano_OpenHermes-2.5_chatml_gemma

NickyNicky

"2024-03-23T00:53:26Z"

0

1

[ "language:en", "croissant", "region:us" ]

null

"2024-03-22T23:42:57Z"

--- dataset_info: features: - name: text dtype: string - name: len_token dtype: int64 splits: - name: train num_bytes: 195803464 num_examples: 118849 download_size: 95174925 dataset_size: 195803464 configs: - config_name: default data_files: - split: train path: data/train-* language: - en --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/iI_akD8m-SuJG1a1xCHve.png) ``` <bos><start_of_turn>system You are a helpful AI assistant.<end_of_turn> <start_of_turn>user What flies without wings? What passes all things? What mends all sorrow? What brings the morrow?<end_of_turn> <start_of_turn>model The answer to the riddle is time. Time flies without wings, passes all things, mends all sorrow, and brings the morrow because it constantly moves forward and affects everything in its path.<end_of_turn><eos> ``` ## taken from teknium. ``` https://huggingface.co/datasets/teknium/OpenHermes-2.5 ```

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_160m_thr_0.0_seed_3

Mitsuki-Sakamoto

"2024-03-23T01:10:05Z"

0

[ "croissant", "region:us" ]

null

"2024-03-22T23:45:38Z"

--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43778450 num_examples: 18928 - name: epoch_1 num_bytes: 44340411 num_examples: 18928 - name: epoch_2 num_bytes: 44412719 num_examples: 18928 - name: epoch_3 num_bytes: 44443289 num_examples: 18928 - name: epoch_4 num_bytes: 44449016 num_examples: 18928 - name: epoch_5 num_bytes: 44446506 num_examples: 18928 - name: epoch_6 num_bytes: 44440017 num_examples: 18928 - name: epoch_7 num_bytes: 44437607 num_examples: 18928 - name: epoch_8 num_bytes: 44433764 num_examples: 18928 - name: epoch_9 num_bytes: 44430532 num_examples: 18928 - name: epoch_10 num_bytes: 44428837 num_examples: 18928 - name: epoch_11 num_bytes: 44427805 num_examples: 18928 - name: epoch_12 num_bytes: 44428796 num_examples: 18928 - name: epoch_13 num_bytes: 44429411 num_examples: 18928 - name: epoch_14 num_bytes: 44429070 num_examples: 18928 - name: epoch_15 num_bytes: 44429063 num_examples: 18928 - name: epoch_16 num_bytes: 44427545 num_examples: 18928 - name: epoch_17 num_bytes: 44428693 num_examples: 18928 - name: epoch_18 num_bytes: 44428068 num_examples: 18928 - name: epoch_19 num_bytes: 44428456 num_examples: 18928 - name: epoch_20 num_bytes: 44427070 num_examples: 18928 - name: epoch_21 num_bytes: 44427869 num_examples: 18928 - name: epoch_22 num_bytes: 44428874 num_examples: 18928 - name: epoch_23 num_bytes: 44429224 num_examples: 18928 - name: epoch_24 num_bytes: 44428269 num_examples: 18928 - name: epoch_25 num_bytes: 44428697 num_examples: 18928 - name: epoch_26 num_bytes: 44428907 num_examples: 18928 - name: epoch_27 num_bytes: 44429168 num_examples: 18928 - name: epoch_28 num_bytes: 44428217 num_examples: 18928 - name: epoch_29 num_bytes: 44428593 num_examples: 18928 download_size: 701248295 dataset_size: 1332182943 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---

KrayIzuna/henrys

KrayIzuna

"2024-03-22T23:50:33Z"

0

[ "license:openrail", "region:us" ]

null

"2024-03-22T23:48:14Z"

--- license: openrail ---

Rodrimr112/dataset2

Rodrimr112

"2024-03-23T01:02:57Z"

0

[ "task_categories:text-generation", "language:es", "croissant", "region:us" ]

[ "text-generation" ]

"2024-03-23T00:28:59Z"

--- task_categories: - text-generation language: - es ---

gaianet/vitalik.eth

gaianet

"2024-03-23T21:03:07Z"

0

[ "license:apache-2.0", "croissant", "region:us" ]

null

"2024-03-23T00:34:06Z"

--- license: apache-2.0 --- Prepare Qdrant: ``` mkdir qdrant_storage mkdir qdrant_snapshots ``` Start Qdrant: ``` docker run -d -p 6333:6333 -p 6334:6334 \ -v $(pwd)/qdrant_storage:/qdrant/storage:z \ -v $(pwd)/qdrant_snapshots:/qdrant/snapshots:z \ qdrant/qdrant ``` Create collection: ``` curl -X PUT 'http://localhost:6333/collections/vitalik.eth' \ -H 'Content-Type: application/json' \ --data-raw '{ "vectors": { "size": 384, "distance": "Cosine", "on_disk": true } }' ``` Query collection: ``` curl 'http://localhost:6333/collections/vitalik.eth' ``` Optional: delete collection ``` curl -X DELETE 'http://localhost:6333/collections/vitalik.eth' ``` Get embedding model: ``` curl -LO https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/resolve/main/all-MiniLM-L6-v2-ggml-model-f16.gguf ``` Get the embedding app: ``` curl -LO https://raw.githubusercontent.com/YuanTony/chemistry-assistant/main/rag-embeddings/create_embeddings.wasm ``` Create and save the generated embeddings: ``` wasmedge --dir .:. --nn-preload default:GGML:AUTO:all-MiniLM-L6-v2-ggml-model-f16.gguf create_embeddings.wasm default vitalik.eth 384 vitalik-eth.txt ``` Check the results: ``` curl 'http://localhost:6333/collections/vitalik.eth' ``` Create snapshot: ``` curl -X POST 'http://localhost:6333/collections/vitalik.eth/snapshots' ``` Access the snapshots: ``` ls qdrant_snapshots/vitalik.eth/ ```

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.1_seed_3

Mitsuki-Sakamoto

"2024-03-23T01:52:50Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T00:37:31Z"

--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43769895 num_examples: 18928 - name: epoch_1 num_bytes: 44372693 num_examples: 18928 - name: epoch_2 num_bytes: 44449483 num_examples: 18928 - name: epoch_3 num_bytes: 44482079 num_examples: 18928 - name: epoch_4 num_bytes: 44487733 num_examples: 18928 - name: epoch_5 num_bytes: 44468382 num_examples: 18928 - name: epoch_6 num_bytes: 44457528 num_examples: 18928 - name: epoch_7 num_bytes: 44447610 num_examples: 18928 - name: epoch_8 num_bytes: 44444945 num_examples: 18928 - name: epoch_9 num_bytes: 44448473 num_examples: 18928 - name: epoch_10 num_bytes: 44444758 num_examples: 18928 - name: epoch_11 num_bytes: 44445124 num_examples: 18928 - name: epoch_12 num_bytes: 44444365 num_examples: 18928 - name: epoch_13 num_bytes: 44444621 num_examples: 18928 - name: epoch_14 num_bytes: 44444964 num_examples: 18928 - name: epoch_15 num_bytes: 44445097 num_examples: 18928 - name: epoch_16 num_bytes: 44446080 num_examples: 18928 - name: epoch_17 num_bytes: 44444852 num_examples: 18928 - name: epoch_18 num_bytes: 44446254 num_examples: 18928 - name: epoch_19 num_bytes: 44446278 num_examples: 18928 - name: epoch_20 num_bytes: 44444210 num_examples: 18928 - name: epoch_21 num_bytes: 44445387 num_examples: 18928 - name: epoch_22 num_bytes: 44446893 num_examples: 18928 - name: epoch_23 num_bytes: 44446406 num_examples: 18928 - name: epoch_24 num_bytes: 44446447 num_examples: 18928 - name: epoch_25 num_bytes: 44446015 num_examples: 18928 - name: epoch_26 num_bytes: 44446209 num_examples: 18928 - name: epoch_27 num_bytes: 44446030 num_examples: 18928 - name: epoch_28 num_bytes: 44446004 num_examples: 18928 - name: epoch_29 num_bytes: 44446103 num_examples: 18928 download_size: 701614310 dataset_size: 1332740918 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---

Xiangyun2018/GalaxySpectra0-10000

Xiangyun2018

"2024-03-23T01:04:38Z"

0

[ "license:apache-2.0", "croissant", "region:us" ]

null

"2024-03-23T00:43:15Z"

--- license: apache-2.0 ---

lucassaicover/ALASTORBR

lucassaicover

"2024-03-23T00:49:22Z"

0

[ "license:openrail", "croissant", "region:us" ]

null

"2024-03-23T00:48:17Z"

--- license: openrail ---

gaianet/ktx.finance

gaianet

"2024-03-23T22:55:36Z"

0

[ "license:apache-2.0", "croissant", "region:us" ]

null

"2024-03-23T01:00:22Z"

--- license: apache-2.0 --- Prepare Qdrant: ``` mkdir qdrant_storage mkdir qdrant_snapshots ``` Start Qdrant: ``` docker run -d -p 6333:6333 -p 6334:6334 \ -v $(pwd)/qdrant_storage:/qdrant/storage:z \ -v $(pwd)/qdrant_snapshots:/qdrant/snapshots:z \ qdrant/qdrant ``` Create collection: ``` curl -X PUT 'http://localhost:6333/collections/ktx.finance' \ -H 'Content-Type: application/json' \ --data-raw '{ "vectors": { "size": 384, "distance": "Cosine", "on_disk": true } }' ``` Query collection: ``` curl 'http://localhost:6333/collections/ktx.finance' ``` Optional: delete collection ``` curl -X DELETE 'http://localhost:6333/collections/ktx.finance' ``` Get embedding model: ``` curl -LO https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/resolve/main/all-MiniLM-L6-v2-ggml-model-f16.gguf ``` Get the embedding app: ``` curl -LO https://raw.githubusercontent.com/YuanTony/chemistry-assistant/main/rag-embeddings/create_embeddings.wasm ``` Create and save the generated embeddings: ``` wasmedge --dir .:. --nn-preload default:GGML:AUTO:all-MiniLM-L6-v2-ggml-model-f16.gguf create_embeddings.wasm default ktx.finance 384 ktx_docs_20240322.txt ``` Check the results: ``` curl 'http://localhost:6333/collections/ktx.finance' ``` Create snapshot: ``` curl -X POST 'http://localhost:6333/collections/ktx.finance/snapshots' ``` Access the snapshots: ``` ls qdrant_snapshots/ktx.finance/ ```

YukiTomita-CC/temp_0323

YukiTomita-CC

"2024-03-23T01:32:50Z"

0

[ "license:mit", "region:us" ]

null

"2024-03-23T01:23:28Z"

--- license: mit ---

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.3_seed_3

Mitsuki-Sakamoto

"2024-03-23T02:49:56Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T01:24:50Z"

--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43746942 num_examples: 18928 - name: epoch_1 num_bytes: 44351740 num_examples: 18928 - name: epoch_2 num_bytes: 44435633 num_examples: 18928 - name: epoch_3 num_bytes: 44468391 num_examples: 18928 - name: epoch_4 num_bytes: 44473636 num_examples: 18928 - name: epoch_5 num_bytes: 44468847 num_examples: 18928 - name: epoch_6 num_bytes: 44456913 num_examples: 18928 - name: epoch_7 num_bytes: 44447915 num_examples: 18928 - name: epoch_8 num_bytes: 44446605 num_examples: 18928 - name: epoch_9 num_bytes: 44443908 num_examples: 18928 - name: epoch_10 num_bytes: 44443718 num_examples: 18928 - name: epoch_11 num_bytes: 44443331 num_examples: 18928 - name: epoch_12 num_bytes: 44443512 num_examples: 18928 - name: epoch_13 num_bytes: 44444407 num_examples: 18928 - name: epoch_14 num_bytes: 44443485 num_examples: 18928 - name: epoch_15 num_bytes: 44443853 num_examples: 18928 - name: epoch_16 num_bytes: 44444614 num_examples: 18928 - name: epoch_17 num_bytes: 44443375 num_examples: 18928 - name: epoch_18 num_bytes: 44443725 num_examples: 18928 - name: epoch_19 num_bytes: 44443945 num_examples: 18928 - name: epoch_20 num_bytes: 44444613 num_examples: 18928 - name: epoch_21 num_bytes: 44444719 num_examples: 18928 - name: epoch_22 num_bytes: 44442850 num_examples: 18928 - name: epoch_23 num_bytes: 44444500 num_examples: 18928 - name: epoch_24 num_bytes: 44444003 num_examples: 18928 - name: epoch_25 num_bytes: 44444305 num_examples: 18928 - name: epoch_26 num_bytes: 44443163 num_examples: 18928 - name: epoch_27 num_bytes: 44443641 num_examples: 18928 - name: epoch_28 num_bytes: 44444321 num_examples: 18928 - name: epoch_29 num_bytes: 44444238 num_examples: 18928 download_size: 701371233 dataset_size: 1332618848 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---

michaelfla/cartersapiencialux

michaelfla

"2024-03-23T01:36:54Z"

0

[ "license:openrail", "croissant", "region:us" ]

null

"2024-03-23T01:29:05Z"

--- license: openrail ---

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_1.0_seed_3

Mitsuki-Sakamoto

"2024-03-23T03:29:47Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T01:29:22Z"

--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43746238 num_examples: 18928 - name: epoch_1 num_bytes: 44363986 num_examples: 18928 - name: epoch_2 num_bytes: 44435706 num_examples: 18928 - name: epoch_3 num_bytes: 44506811 num_examples: 18928 - name: epoch_4 num_bytes: 44535773 num_examples: 18928 - name: epoch_5 num_bytes: 44529774 num_examples: 18928 - name: epoch_6 num_bytes: 44521649 num_examples: 18928 - name: epoch_7 num_bytes: 44512193 num_examples: 18928 - name: epoch_8 num_bytes: 44511726 num_examples: 18928 - name: epoch_9 num_bytes: 44497200 num_examples: 18928 - name: epoch_10 num_bytes: 44503145 num_examples: 18928 - name: epoch_11 num_bytes: 44498740 num_examples: 18928 - name: epoch_12 num_bytes: 44499383 num_examples: 18928 - name: epoch_13 num_bytes: 44499252 num_examples: 18928 - name: epoch_14 num_bytes: 44500926 num_examples: 18928 - name: epoch_15 num_bytes: 44497701 num_examples: 18928 - name: epoch_16 num_bytes: 44493966 num_examples: 18928 - name: epoch_17 num_bytes: 44493661 num_examples: 18928 - name: epoch_18 num_bytes: 44495720 num_examples: 18928 - name: epoch_19 num_bytes: 44495849 num_examples: 18928 - name: epoch_20 num_bytes: 44492977 num_examples: 18928 - name: epoch_21 num_bytes: 44496047 num_examples: 18928 - name: epoch_22 num_bytes: 44495073 num_examples: 18928 - name: epoch_23 num_bytes: 44493852 num_examples: 18928 - name: epoch_24 num_bytes: 44495739 num_examples: 18928 - name: epoch_25 num_bytes: 44495049 num_examples: 18928 - name: epoch_26 num_bytes: 44495848 num_examples: 18928 - name: epoch_27 num_bytes: 44495934 num_examples: 18928 - name: epoch_28 num_bytes: 44496877 num_examples: 18928 - name: epoch_29 num_bytes: 44496442 num_examples: 18928 download_size: 701095270 dataset_size: 1334093237 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_160m_thr_0.1_seed_3

Mitsuki-Sakamoto

"2024-03-23T02:57:25Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T01:30:23Z"

--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43762247 num_examples: 18928 - name: epoch_1 num_bytes: 44355479 num_examples: 18928 - name: epoch_2 num_bytes: 44413402 num_examples: 18928 - name: epoch_3 num_bytes: 44446124 num_examples: 18928 - name: epoch_4 num_bytes: 44453469 num_examples: 18928 - name: epoch_5 num_bytes: 44441134 num_examples: 18928 - name: epoch_6 num_bytes: 44427806 num_examples: 18928 - name: epoch_7 num_bytes: 44415327 num_examples: 18928 - name: epoch_8 num_bytes: 44409544 num_examples: 18928 - name: epoch_9 num_bytes: 44408079 num_examples: 18928 - name: epoch_10 num_bytes: 44408107 num_examples: 18928 - name: epoch_11 num_bytes: 44404673 num_examples: 18928 - name: epoch_12 num_bytes: 44406717 num_examples: 18928 - name: epoch_13 num_bytes: 44404524 num_examples: 18928 - name: epoch_14 num_bytes: 44403118 num_examples: 18928 - name: epoch_15 num_bytes: 44403919 num_examples: 18928 - name: epoch_16 num_bytes: 44406501 num_examples: 18928 - name: epoch_17 num_bytes: 44406372 num_examples: 18928 - name: epoch_18 num_bytes: 44403957 num_examples: 18928 - name: epoch_19 num_bytes: 44405464 num_examples: 18928 - name: epoch_20 num_bytes: 44406776 num_examples: 18928 - name: epoch_21 num_bytes: 44405069 num_examples: 18928 - name: epoch_22 num_bytes: 44406545 num_examples: 18928 - name: epoch_23 num_bytes: 44406186 num_examples: 18928 - name: epoch_24 num_bytes: 44405986 num_examples: 18928 - name: epoch_25 num_bytes: 44405903 num_examples: 18928 - name: epoch_26 num_bytes: 44405882 num_examples: 18928 - name: epoch_27 num_bytes: 44405779 num_examples: 18928 - name: epoch_28 num_bytes: 44406020 num_examples: 18928 - name: epoch_29 num_bytes: 44406453 num_examples: 18928 download_size: 701508295 dataset_size: 1331646562 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---

CodecSR/vocalset_synth

CodecSR

"2024-03-23T10:42:06Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T01:32:36Z"

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 44100 - name: id dtype: string splits: - name: original num_bytes: 2788138277.0 num_examples: 3612 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 2786991855.888 num_examples: 3612 - name: academicodec_hifi_24k_320d num_bytes: 2786991855.888 num_examples: 3612 - name: audiodec_24k_300d num_bytes: 2789217294.888 num_examples: 3612 - name: audiodec_48k_300d_uni num_bytes: 2789217294.888 num_examples: 3612 - name: dac_16k num_bytes: 2788141415.888 num_examples: 3612 - name: dac_24k num_bytes: 2788141415.888 num_examples: 3612 - name: dac_44k num_bytes: 2788141414.888 num_examples: 3612 - name: encodec_24k_12bps num_bytes: 2788141414.888 num_examples: 3612 - name: encodec_24k_1_5bps num_bytes: 2788141415.888 num_examples: 3612 - name: encodec_24k_24bps num_bytes: 2788141415.888 num_examples: 3612 - name: encodec_24k_3bps num_bytes: 2788141415.888 num_examples: 3612 - name: encodec_24k_6bps num_bytes: 2788141414.888 num_examples: 3612 - name: facodec_16k num_bytes: 2787788494.888 num_examples: 3612 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 2788141415.888 num_examples: 3612 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 2788141414.888 num_examples: 3612 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 2788141415.888 num_examples: 3612 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 2788141414.888 num_examples: 3612 - name: language_codec_chinese_24k_nq8_12kbps num_bytes: 2789293294.888 num_examples: 3612 - name: language_codec_paper_24k_nq8_12kbps num_bytes: 2789293294.888 num_examples: 3612 - name: speech_tokenizer_16k num_bytes: 2789293294.888 num_examples: 3612 download_size: 55003437704 dataset_size: 58553921943.76001 configs: - config_name: default data_files: - split: original path: data/original-* - split: academicodec_hifi_16k_320d_large_uni path: data/academicodec_hifi_16k_320d_large_uni-* - split: academicodec_hifi_24k_320d path: data/academicodec_hifi_24k_320d-* - split: audiodec_24k_300d path: data/audiodec_24k_300d-* - split: audiodec_48k_300d_uni path: data/audiodec_48k_300d_uni-* - split: dac_16k path: data/dac_16k-* - split: dac_24k path: data/dac_24k-* - split: dac_44k path: data/dac_44k-* - split: encodec_24k_12bps path: data/encodec_24k_12bps-* - split: encodec_24k_1_5bps path: data/encodec_24k_1_5bps-* - split: encodec_24k_24bps path: data/encodec_24k_24bps-* - split: encodec_24k_3bps path: data/encodec_24k_3bps-* - split: encodec_24k_6bps path: data/encodec_24k_6bps-* - split: facodec_16k path: data/facodec_16k-* - split: funcodec_en_libritts_16k_nq32ds320 path: data/funcodec_en_libritts_16k_nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds640 path: data/funcodec_en_libritts_16k_nq32ds640-* - split: funcodec_zh_en_16k_nq32ds320 path: data/funcodec_zh_en_16k_nq32ds320-* - split: funcodec_zh_en_16k_nq32ds640 path: data/funcodec_zh_en_16k_nq32ds640-* - split: language_codec_chinese_24k_nq8_12kbps path: data/language_codec_chinese_24k_nq8_12kbps-* - split: language_codec_paper_24k_nq8_12kbps path: data/language_codec_paper_24k_nq8_12kbps-* - split: speech_tokenizer_16k path: data/speech_tokenizer_16k-* ---

Zack157/CBV3

Zack157

"2024-03-23T01:35:27Z"

0

[ "license:openrail", "croissant", "region:us" ]

null

"2024-03-23T01:33:45Z"

--- license: openrail ---

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_160m_thr_0.3_seed_3

Mitsuki-Sakamoto

"2024-03-23T03:11:24Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T01:37:33Z"

--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43760593 num_examples: 18928 - name: epoch_1 num_bytes: 44321306 num_examples: 18928 - name: epoch_2 num_bytes: 44392042 num_examples: 18928 - name: epoch_3 num_bytes: 44429751 num_examples: 18928 - name: epoch_4 num_bytes: 44441952 num_examples: 18928 - name: epoch_5 num_bytes: 44430509 num_examples: 18928 - name: epoch_6 num_bytes: 44418931 num_examples: 18928 - name: epoch_7 num_bytes: 44413462 num_examples: 18928 - name: epoch_8 num_bytes: 44410221 num_examples: 18928 - name: epoch_9 num_bytes: 44406897 num_examples: 18928 - name: epoch_10 num_bytes: 44406834 num_examples: 18928 - name: epoch_11 num_bytes: 44403708 num_examples: 18928 - name: epoch_12 num_bytes: 44403309 num_examples: 18928 - name: epoch_13 num_bytes: 44402366 num_examples: 18928 - name: epoch_14 num_bytes: 44400165 num_examples: 18928 - name: epoch_15 num_bytes: 44402982 num_examples: 18928 - name: epoch_16 num_bytes: 44401706 num_examples: 18928 - name: epoch_17 num_bytes: 44402241 num_examples: 18928 - name: epoch_18 num_bytes: 44402829 num_examples: 18928 - name: epoch_19 num_bytes: 44401754 num_examples: 18928 - name: epoch_20 num_bytes: 44404188 num_examples: 18928 - name: epoch_21 num_bytes: 44404140 num_examples: 18928 - name: epoch_22 num_bytes: 44403554 num_examples: 18928 - name: epoch_23 num_bytes: 44402779 num_examples: 18928 - name: epoch_24 num_bytes: 44403516 num_examples: 18928 - name: epoch_25 num_bytes: 44401875 num_examples: 18928 - name: epoch_26 num_bytes: 44402676 num_examples: 18928 - name: epoch_27 num_bytes: 44404515 num_examples: 18928 - name: epoch_28 num_bytes: 44403799 num_examples: 18928 - name: epoch_29 num_bytes: 44403664 num_examples: 18928 download_size: 700802961 dataset_size: 1331488264 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---

JamesSpray/txsa_twitter_sentiment_analysis

JamesSpray

"2024-03-23T01:40:30Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T01:39:58Z"

--- dataset_info: features: - name: label dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 1049869 num_examples: 8539 - name: validation num_bytes: 145889 num_examples: 1000 download_size: 834300 dataset_size: 1195758 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

winniealita/indio

winniealita

"2024-03-23T01:43:41Z"

0

[ "license:openrail", "croissant", "region:us" ]

null

"2024-03-23T01:42:27Z"

--- license: openrail ---

luminoussg/NIH_X-RAY_2017

luminoussg

"2024-03-23T16:01:55Z"

0

[ "license:apache-2.0", "region:us" ]

null

"2024-03-23T01:49:00Z"

--- license: apache-2.0 ---

shushuti/cool

shushuti

"2024-03-23T01:58:34Z"

0

[ "license:apache-2.0", "region:us" ]

null

"2024-03-23T01:58:34Z"

--- license: apache-2.0 ---

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_160m_thr_1.0_seed_3

Mitsuki-Sakamoto

"2024-03-23T04:19:32Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T02:09:27Z"

--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43778026 num_examples: 18928 - name: epoch_1 num_bytes: 44414700 num_examples: 18928 - name: epoch_2 num_bytes: 44463459 num_examples: 18928 - name: epoch_3 num_bytes: 44504519 num_examples: 18928 - name: epoch_4 num_bytes: 44524186 num_examples: 18928 - name: epoch_5 num_bytes: 44508265 num_examples: 18928 - name: epoch_6 num_bytes: 44494966 num_examples: 18928 - name: epoch_7 num_bytes: 44479117 num_examples: 18928 - name: epoch_8 num_bytes: 44471722 num_examples: 18928 - name: epoch_9 num_bytes: 44465380 num_examples: 18928 - name: epoch_10 num_bytes: 44460120 num_examples: 18928 - name: epoch_11 num_bytes: 44461044 num_examples: 18928 - name: epoch_12 num_bytes: 44459111 num_examples: 18928 - name: epoch_13 num_bytes: 44454936 num_examples: 18928 - name: epoch_14 num_bytes: 44455185 num_examples: 18928 - name: epoch_15 num_bytes: 44457591 num_examples: 18928 - name: epoch_16 num_bytes: 44456363 num_examples: 18928 - name: epoch_17 num_bytes: 44457557 num_examples: 18928 - name: epoch_18 num_bytes: 44460508 num_examples: 18928 - name: epoch_19 num_bytes: 44460626 num_examples: 18928 - name: epoch_20 num_bytes: 44458783 num_examples: 18928 - name: epoch_21 num_bytes: 44459668 num_examples: 18928 - name: epoch_22 num_bytes: 44459777 num_examples: 18928 - name: epoch_23 num_bytes: 44459779 num_examples: 18928 - name: epoch_24 num_bytes: 44458014 num_examples: 18928 - name: epoch_25 num_bytes: 44460051 num_examples: 18928 - name: epoch_26 num_bytes: 44459532 num_examples: 18928 - name: epoch_27 num_bytes: 44458956 num_examples: 18928 - name: epoch_28 num_bytes: 44458160 num_examples: 18928 - name: epoch_29 num_bytes: 44458407 num_examples: 18928 download_size: 701402776 dataset_size: 1333278508 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---

CyberHarem/xianyun_genshin

CyberHarem

"2024-03-23T03:45:05Z"

0

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "croissant", "region:us" ]

[ "text-to-image" ]

"2024-03-23T02:12:17Z"

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of xianyun/閑雲/闲云 (Genshin Impact) This is the dataset of xianyun/閑雲/闲云 (Genshin Impact), containing 313 images and their tags. The core tags of this character are `long_hair, multicolored_hair, black_hair, green_hair, two-tone_hair, glasses, colored_inner_hair, red-framed_eyewear, breasts, hair_ornament, very_long_hair, semi-rimless_eyewear, aqua_eyes, large_breasts, tassel, earrings, tassel_earrings, aqua_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 313 | 668.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/xianyun_genshin/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 313 | 555.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/xianyun_genshin/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 798 | 1.04 GiB | [Download](https://huggingface.co/datasets/CyberHarem/xianyun_genshin/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/xianyun_genshin', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 13 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, jewelry, solo, looking_at_viewer, simple_background, upper_body, white_background, makeup, gloves | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, gloves, jewelry, long_sleeves, looking_at_viewer, solo, makeup, dress, smile, bodystocking, upper_body | | 2 | 8 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, bare_back, gloves, solo, from_behind, looking_at_viewer, ass, looking_back, backless_dress, bare_shoulders, jewelry, ponytail, thighs, white_background | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, alternate_costume, black_skirt, collared_shirt, looking_at_viewer, solo, white_shirt, long_sleeves, office_lady, jewelry, miniskirt, pencil_skirt, contemporary, pantyhose, thighs | | 4 | 5 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1boy, blush, hetero, mosaic_censoring, solo_focus, 1girl, completely_nude, cum_in_pussy, nipples, open_mouth, vaginal, looking_at_viewer, anus, ass, collarbone, disembodied_penis, gloves, green_eyes, heart, jewelry, looking_back, pillow, pov, sex_from_behind, spread_legs, sweat, thighs, tongue_out | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | jewelry | solo | looking_at_viewer | simple_background | upper_body | white_background | makeup | gloves | long_sleeves | dress | smile | bodystocking | bare_back | from_behind | ass | looking_back | backless_dress | bare_shoulders | ponytail | thighs | alternate_costume | black_skirt | collared_shirt | white_shirt | office_lady | miniskirt | pencil_skirt | contemporary | pantyhose | 1boy | blush | hetero | mosaic_censoring | solo_focus | completely_nude | cum_in_pussy | nipples | open_mouth | vaginal | anus | collarbone | disembodied_penis | green_eyes | heart | pillow | pov | sex_from_behind | spread_legs | sweat | tongue_out | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------|:-------|:--------------------|:--------------------|:-------------|:-------------------|:---------|:---------|:---------------|:--------|:--------|:---------------|:------------|:--------------|:------|:---------------|:-----------------|:-----------------|:-----------|:---------|:--------------------|:--------------|:-----------------|:--------------|:--------------|:------------|:---------------|:---------------|:------------|:-------|:--------|:---------|:-------------------|:-------------|:------------------|:---------------|:----------|:-------------|:----------|:-------|:-------------|:--------------------|:-------------|:--------|:---------|:------|:------------------|:--------------|:--------|:-------------| | 0 | 13 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 8 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | | | X | | X | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | X | | | | | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | | X | | | | | X | | | | | | | X | X | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

jlbaker361/db_multi_cold_

jlbaker361

"2024-04-24T22:37:24Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T02:50:39Z"

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: label dtype: string - name: prompt_similarity dtype: float32 - name: identity_consistency dtype: float32 - name: negative_prompt_similarity dtype: float32 - name: target_prompt_similarity dtype: float32 - name: aesthetic_score dtype: float32 - name: ir_score dtype: float64 splits: - name: train num_bytes: 466 num_examples: 14 download_size: 4388 dataset_size: 466 --- method: db_multi_cold_ num_inference_steps: 30 prompt_similarity : 0.23685424029827118 identity_consistency : 0.634724497795105 negative_prompt_similarity : 0.20908871293067932 target_prompt_similarity : 0.1990167200565338 aesthetic_score : 3.9085214138031006 ir_score : -1.6812543226405978

AwesomeEmerald/OpenNaturalConvo

AwesomeEmerald

"2024-03-23T19:48:03Z"

0

[ "license:mit", "croissant", "region:us" ]

null

"2024-03-23T03:09:04Z"

--- license: mit ---

shiertier/12Twatermark

shiertier

"2024-03-23T03:25:21Z"

0

[ "license:mit", "croissant", "region:us" ]

null

"2024-03-23T03:22:49Z"

--- license: mit ---

Ksgk-fy/alignment-dpo-test03

Ksgk-fy

"2024-03-23T03:23:12Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T03:23:04Z"

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 778351 num_examples: 3024 - name: test num_bytes: 194549 num_examples: 756 download_size: 92676 dataset_size: 972900 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

DipakBundheliya/Shipping-label-NER

DipakBundheliya

"2024-03-28T08:29:42Z"

0

[ "license:afl-3.0", "region:us" ]

null

"2024-03-23T03:37:55Z"

--- license: afl-3.0 ---

jlbaker361/unet_hot_

jlbaker361

"2024-04-24T22:10:34Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T03:40:12Z"

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: label dtype: string - name: prompt_similarity dtype: float32 - name: identity_consistency dtype: float32 - name: negative_prompt_similarity dtype: float32 - name: target_prompt_similarity dtype: float32 - name: aesthetic_score dtype: float32 - name: ir_score dtype: float64 splits: - name: train num_bytes: 534 num_examples: 16 download_size: 4470 dataset_size: 534 --- method: unet_hot_ num_inference_steps: 30 prompt_similarity : 0.27571946382522583 identity_consistency : 0.5342808365821838 negative_prompt_similarity : 0.202345073223114 target_prompt_similarity : 0.22198939323425293 aesthetic_score : 3.6855618953704834 ir_score : -1.1662967083975673

jlbaker361/unet_cold_

jlbaker361

"2024-04-24T22:23:13Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T03:40:17Z"

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: label dtype: string - name: prompt_similarity dtype: float32 - name: identity_consistency dtype: float32 - name: negative_prompt_similarity dtype: float32 - name: target_prompt_similarity dtype: float32 - name: aesthetic_score dtype: float32 - name: ir_score dtype: float64 splits: - name: train num_bytes: 568 num_examples: 17 download_size: 4550 dataset_size: 568 --- method: unet_cold_ num_inference_steps: 30 prompt_similarity : 0.2652778625488281 identity_consistency : 0.5586744546890259 negative_prompt_similarity : 0.18459632992744446 target_prompt_similarity : 0.2095177322626114 aesthetic_score : 3.9817652702331543 ir_score : -1.1246472443453968

jlbaker361/unet_reward_

jlbaker361

"2024-04-24T19:22:32Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T03:40:18Z"

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: label dtype: string - name: prompt_similarity dtype: float32 - name: identity_consistency dtype: float32 - name: negative_prompt_similarity dtype: float32 - name: target_prompt_similarity dtype: float32 - name: aesthetic_score dtype: float32 - name: ir_score dtype: float64 splits: - name: train num_bytes: 398 num_examples: 12 download_size: 4326 dataset_size: 398 --- method: unet_reward_ num_inference_steps: 30 prompt_similarity : 0.2559298276901245 identity_consistency : 0.598752498626709 negative_prompt_similarity : 0.1946614384651184 target_prompt_similarity : 0.20795372128486633 aesthetic_score : 3.822976589202881 ir_score : -1.7999178015161306

tmnam20/ViPubMed

tmnam20

"2024-03-25T03:13:24Z"

0

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "language:vi", "language:en", "license:cc", "croissant", "arxiv:2210.05610", "arxiv:2210.05598", "region:us" ]

[ "text-generation", "fill-mask" ]

"2024-03-23T03:52:09Z"

--- license: cc language: - vi - en task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: pubmed dataset_info: features: - name: en dtype: string - name: vi dtype: string splits: - name: pubmed22 num_bytes: 44360028980 num_examples: 20087006 download_size: 23041004247 dataset_size: 44360028980 --- # ALERT: This dataset repo is duplicated from [VietAI/vi_pubmed](https://huggingface.co/datasets/VietAI/vi_pubmed) The reason to have this duplicated repo is to avoid the lost/corruption of the original repo when I am doing some stuff ^^. # Dataset Summary 20M Vietnamese PubMed biomedical abstracts translated by the [state-of-the-art English-Vietnamese Translation project](https://arxiv.org/abs/2210.05610). The data has been used as unlabeled dataset for [pretraining a Vietnamese Biomedical-domain Transformer model](https://arxiv.org/abs/2210.05598). ![image](https://user-images.githubusercontent.com/44376091/200204462-4d559113-5bdf-4cc5-9e88-70abe82babba.png) image source: [Enriching Biomedical Knowledge for Vietnamese Low-resource Language Through Large-Scale Translation](https://arxiv.org/abs/2210.05598) # Language - English: Original biomedical abstracts from [Pubmed](https://www.nlm.nih.gov/databases/download/pubmed_medline_faq.html) - Vietnamese: Synthetic abstract translated by a [state-of-the-art English-Vietnamese Translation project](https://arxiv.org/abs/2210.05610) # Dataset Structure - The English sequences are - The Vietnamese sequences are # Source Data - Initial Data Collection and Normalization https://www.nlm.nih.gov/databases/download/pubmed_medline_faq.html # Licensing Information [Courtesy of the U.S. National Library of Medicine.](https://www.nlm.nih.gov/databases/download/terms_and_conditions.html) # Citation ``` @misc{mtet, doi = {10.48550/ARXIV.2210.05610}, url = {https://arxiv.org/abs/2210.05610}, author = {Ngo, Chinh and Trinh, Trieu H. and Phan, Long and Tran, Hieu and Dang, Tai and Nguyen, Hieu and Nguyen, Minh and Luong, Minh-Thang}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {MTet: Multi-domain Translation for English and Vietnamese}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` ``` @misc{vipubmed, doi = {10.48550/ARXIV.2210.05598}, url = {https://arxiv.org/abs/2210.05598}, author = {Phan, Long and Dang, Tai and Tran, Hieu and Phan, Vy and Chau, Lam D. and Trinh, Trieu H.}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Enriching Biomedical Knowledge for Vietnamese Low-resource Language Through Large-Scale Translation}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

Boxit372/wheatley-voicelines

Boxit372

"2024-03-23T04:21:06Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T03:53:42Z"

--- pretty_name: Wheatley Voicelines ---

CyberHarem/chevreuse_genshin

CyberHarem

"2024-03-23T04:44:43Z"

0

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "croissant", "region:us" ]

[ "text-to-image" ]

"2024-03-23T03:58:57Z"

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of chevreuse/シュヴルーズ/夏沃蕾 (Genshin Impact) This is the dataset of chevreuse/シュヴルーズ/夏沃蕾 (Genshin Impact), containing 172 images and their tags. The core tags of this character are `purple_hair, long_hair, very_long_hair, streaked_hair, multicolored_hair, purple_eyes, two-tone_hair, white_hair, hat, eyepatch, pointy_hair, mole, shako_cap, mole_under_mouth, hair_between_eyes, bright_pupils, white_pupils, black_headwear, crossed_bangs`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 172 | 351.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chevreuse_genshin/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 172 | 293.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chevreuse_genshin/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 437 | 588.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chevreuse_genshin/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/chevreuse_genshin', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, antique_firearm, bare_shoulders, black_necktie, detached_collar, earmuffs, gold_trim, holding_gun, looking_at_viewer, puffy_detached_sleeves, red_dress, rifle, solo, white_gloves, black_dress, simple_background, two-tone_dress, white_background, strapless_dress, no_mole | | 1 | 19 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, antique_firearm, bare_shoulders, black_dress, black_necktie, earmuffs, gold_trim, holding_gun, puffy_detached_sleeves, red_dress, solo, two-tone_dress, white_gloves, rifle, strapless_dress, thigh_boots, detached_collar, pantyhose, white_footwear, looking_at_viewer, standing, thighhighs, cowboy_shot, no_mole, white_background | | 2 | 7 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, bare_shoulders, black_necktie, detached_collar, earmuffs, gold_trim, puffy_detached_sleeves, red_dress, solo, strapless_dress, upper_body, looking_at_viewer, simple_background, white_background, white_gloves, hand_up | | 3 | 26 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, bare_shoulders, earmuffs, holding_food, puffy_detached_sleeves, solo, white_gloves, looking_at_viewer, french_fries, gold_trim, detached_collar, red_dress, black_necktie, upper_body, black_dress, two-tone_dress, white_background, :t, eating, simple_background, strapless_dress | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | antique_firearm | bare_shoulders | black_necktie | detached_collar | earmuffs | gold_trim | holding_gun | looking_at_viewer | puffy_detached_sleeves | red_dress | rifle | solo | white_gloves | black_dress | simple_background | two-tone_dress | white_background | strapless_dress | no_mole | thigh_boots | pantyhose | white_footwear | standing | thighhighs | cowboy_shot | upper_body | hand_up | holding_food | french_fries | :t | eating | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:------------------|:-----------------|:----------------|:------------------|:-----------|:------------|:--------------|:--------------------|:-------------------------|:------------|:--------|:-------|:---------------|:--------------|:--------------------|:-----------------|:-------------------|:------------------|:----------|:--------------|:------------|:-----------------|:-----------|:-------------|:--------------|:-------------|:----------|:---------------|:---------------|:-----|:---------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | 1 | 19 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | 2 | 7 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | X | X | X | X | X | | X | X | X | | X | X | | X | | X | X | | | | | | | | X | X | | | | | | 3 | 26 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | X | X | X | X | X | | X | X | X | | X | X | X | X | X | X | X | | | | | | | | X | | X | X | X | X |

Pm06/my-image-label-dataset

Pm06

"2024-04-07T11:18:28Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T04:05:24Z"

--- dataset_info: features: - name: image dtype: image - name: vision_info dtype: string splits: - name: train num_bytes: 247252517.0 num_examples: 1000 download_size: 246904988 dataset_size: 247252517.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

iamkaikai/MATISSEE-ART

iamkaikai

"2024-04-13T16:26:05Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T04:29:15Z"

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 6679969.0 num_examples: 269 download_size: 6585569 dataset_size: 6679969.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "MATISSEE-ART" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

iamkaikai/PHOTO-ILLUSTRATION-ART

iamkaikai

"2024-04-13T16:37:39Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T04:37:21Z"

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 8015460.0 num_examples: 194 download_size: 7995170 dataset_size: 8015460.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "PHOTO-ILLUSTRATION-ART" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

iamkaikai/IMPRESSIONISM-ART

iamkaikai

"2024-04-13T17:49:56Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T04:43:30Z"

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 24298294.0 num_examples: 434 download_size: 24120501 dataset_size: 24298294.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "IMPRESSIONISM-ART" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

iamkaikai/BASQUIAT-ART

iamkaikai

"2024-04-13T18:28:34Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T04:46:35Z"

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 15156684.0 num_examples: 228 download_size: 15097017 dataset_size: 15156684.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "BASQUIAT-ART" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

iamkaikai/BAUHAUS-ART

iamkaikai

"2024-04-13T18:59:23Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T04:50:00Z"

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 5239111.0 num_examples: 273 download_size: 4837978 dataset_size: 5239111.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "BAUHAUS-ART" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

iamkaikai/FUI-ART

iamkaikai

"2024-04-13T19:21:48Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T04:53:02Z"

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 6410238.0 num_examples: 204 download_size: 5862362 dataset_size: 6410238.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "FUI-ART" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

iamkaikai/OPTICAL-ART

iamkaikai

"2024-03-25T16:29:46Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T04:59:37Z"

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 17958981.0 num_examples: 255 download_size: 17637639 dataset_size: 17958981.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "OPTICAL-ART" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

AnasKK/reuters_articles

AnasKK

"2024-03-23T05:02:04Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T05:02:00Z"

--- dataset_info: features: - name: title dtype: string - name: body dtype: string splits: - name: train num_bytes: 13792576 num_examples: 17262 - name: validation num_bytes: 1870389 num_examples: 2158 - name: test num_bytes: 1379190 num_examples: 2158 download_size: 10073414 dataset_size: 17042155 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

CyberHarem/ryougi_shiki_karanokyoukai

CyberHarem

"2024-03-23T05:29:14Z"

0

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "croissant", "region:us" ]

[ "text-to-image" ]

"2024-03-23T05:14:06Z"

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Ryougi Shiki/両儀式/兩儀式 (Kara No Kyoukai) This is the dataset of Ryougi Shiki/両儀式/兩儀式 (Kara No Kyoukai), containing 338 images and their tags. The core tags of this character are `short_hair, black_hair, brown_hair, brown_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 338 | 126.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ryougi_shiki_karanokyoukai/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 338 | 126.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ryougi_shiki_karanokyoukai/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 548 | 203.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ryougi_shiki_karanokyoukai/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/ryougi_shiki_karanokyoukai', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------| | 0 | 17 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, blue_kimono, upper_body, black_eyes, looking_at_viewer | | 1 | 11 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, solo, obi, blue_kimono | | 2 | 13 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, kimono, red_jacket, solo, fur_trim, profile | | 3 | 10 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, red_jacket, solo, blue_eyes, knife, blue_kimono | | 4 | 14 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, expressionless, portrait, solo, closed_mouth, kimono, black_eyes, looking_at_viewer, blurry | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, blue_eyes, kimono, katana, 1boy, glowing, solo_focus | | 6 | 8 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, solo, blue_dress, indoors, long_sleeves, school_uniform, upper_body, black_eyes | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | blue_kimono | upper_body | black_eyes | looking_at_viewer | obi | kimono | red_jacket | fur_trim | profile | blue_eyes | knife | expressionless | portrait | closed_mouth | blurry | katana | 1boy | glowing | solo_focus | blue_dress | indoors | long_sleeves | school_uniform | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------|:-------------|:-------------|:--------------------|:------|:---------|:-------------|:-----------|:----------|:------------|:--------|:-----------------|:-----------|:---------------|:---------|:---------|:-------|:----------|:-------------|:-------------|:----------|:---------------|:-----------------| | 0 | 17 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | 1 | 11 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | | | X | | | | | | | | | | | | | | | | | | | | 2 | 13 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | | | | | | X | X | X | X | | | | | | | | | | | | | | | | 3 | 10 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | | | | | | X | | | X | X | | | | | | | | | | | | | | 4 | 14 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | | | X | X | | X | | | | | | X | X | X | X | | | | | | | | | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | | | | | | | X | | | | X | | | | | | X | X | X | X | | | | | | 6 | 8 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | X | | X | X | | | | | | | | | | | | | | | | | X | X | X | X |

yuiseki/sake_qa

yuiseki

"2024-03-23T05:19:17Z"

0

3

[ "language:ja", "license:mit", "croissant", "region:us" ]

null

"2024-03-23T05:15:57Z"

--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: type dtype: string splits: - name: train num_bytes: 612696 num_examples: 5176 download_size: 84168 dataset_size: 612696 configs: - config_name: default data_files: - split: train path: data/train-* license: mit language: - ja ---

jiaqianjing/animagine-xl-3.1-characterfull-zh

jiaqianjing

"2024-03-23T05:58:11Z"

0

[ "license:mit", "croissant", "region:us" ]

null

"2024-03-23T05:38:35Z"

--- license: mit --- ## animagine-xl-3.1-characterfull-zh ### 背景 [cagliostrolab/animagine-xl-3.1](https://huggingface.co/cagliostrolab/animagine-xl-3.1) Image Prompt 仍然需要中英文输入角色名称，并且有一定格式要求。Prompt Format 如下所示， ``` 1girl/1boy, character name, from what series, everything else in any order. ``` 但是这对于小白，或者不熟悉训练过程的人带来了很高的接入门槛。因此，想通过向量检索的方式降低这种接入成本。但是不管现在任何一种 embedding 的模型（[bge-m3](https://huggingface.co/BAAI/bge-m3), [jina-embeddings-v2-base-zh](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh)等），都没有办法很好的匹配中文关键字和官方提供的这份 [角色全名单](https://huggingface.co/spaces/cagliostrolab/animagine-xl-3.1/blob/main/wildcard/characterfull.txt). 因此，想在原有的名单上补充中文角色和中文作品名称的信息，从而提升向量检索的效果。 ### 详情 1. 由于原始文件很大，需要切分成 50 份文件，每份文件 100 条记录； 2. 给出 one-shot, 通过调用 gpt 或者 kimi 等模型，在每行行尾添加对应的中文角色名称和中文作品名称，格式如下： ``` 1girl, souryuu asuka langley, neon genesis evangelion#惣流·明日香·兰格雷#新世纪福音战士 1girl, warrior of light, final fantasy#光之战士#最终幻想 1girl, akiyama mio, k-on!#秋山澪#轻音少女 1girl, tifa lockhart, final fantasy#蒂法·洛克哈特#最终幻想 1girl, 2b, nier:automata#2B#尼尔：自动人形 ``` 3. 最后在合并；

helloelwin/w2sg-generations

helloelwin

"2024-03-23T05:39:20Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T05:38:58Z"

--- dataset_info: - config_name: eval_train_weak-bs=16-dn=gsm8k-e=10-ee=1000000-l=xent-l=2e-05-ls=cosi_anne-ml=331-ms=gemma-2b-nd=20000-ntd=10000-o=adam-s=0-twd=0-epoch=2 features: - name: question dtype: string - name: gt_answer dtype: string - name: answer dtype: string - name: acc dtype: float64 splits: - name: train num_bytes: 3262475 num_examples: 3736 download_size: 1748441 dataset_size: 3262475 - config_name: eval_train_weak-bs=16-dn=gsm8k-e=10-ee=1000000-l=xent-l=2e-05-ls=cosi_anne-ml=331-ms=gpt2-xl-nd=20000-ntd=10000-o=adam-s=0-twd=0-epoch=2 features: - name: question dtype: string - name: gt_answer dtype: string - name: answer dtype: string - name: acc dtype: float64 splits: - name: train num_bytes: 3557779 num_examples: 3736 download_size: 1762583 dataset_size: 3557779 configs: - config_name: eval_train_weak-bs=16-dn=gsm8k-e=10-ee=1000000-l=xent-l=2e-05-ls=cosi_anne-ml=331-ms=gemma-2b-nd=20000-ntd=10000-o=adam-s=0-twd=0-epoch=2 data_files: - split: train path: eval_train_weak-bs=16-dn=gsm8k-e=10-ee=1000000-l=xent-l=2e-05-ls=cosi_anne-ml=331-ms=gemma-2b-nd=20000-ntd=10000-o=adam-s=0-twd=0-epoch=2/train-* - config_name: eval_train_weak-bs=16-dn=gsm8k-e=10-ee=1000000-l=xent-l=2e-05-ls=cosi_anne-ml=331-ms=gpt2-xl-nd=20000-ntd=10000-o=adam-s=0-twd=0-epoch=2 data_files: - split: train path: eval_train_weak-bs=16-dn=gsm8k-e=10-ee=1000000-l=xent-l=2e-05-ls=cosi_anne-ml=331-ms=gpt2-xl-nd=20000-ntd=10000-o=adam-s=0-twd=0-epoch=2/train-* ---

codedog-lee/llama2-tut

codedog-lee

"2024-03-23T05:56:32Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T05:56:29Z"

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 5826 num_examples: 39 download_size: 2572 dataset_size: 5826 configs: - config_name: default data_files: - split: train path: data/train-* ---

jjz5463/lexical-features

jjz5463

"2024-03-23T06:01:15Z"

0

[ "size_categories:10K<n<100K", "datadreamer", "datadreamer-0.25.0", "synthetic", "gpt-4", "croissant", "region:us" ]

null

"2024-03-23T06:01:12Z"

--- dataset_info: features: - name: The prompts processed with the LLM. dtype: string - name: The generations by the LLM. dtype: string splits: - name: train num_bytes: 22300800 num_examples: 58300 download_size: 5442365 dataset_size: 22300800 configs: - config_name: default data_files: - split: train path: data/train-* library_name: datadreamer size_categories: - 10K<n<100K tags: - datadreamer - datadreamer-0.25.0 - synthetic - gpt-4 --- # Dataset Card [Add more information here](https://huggingface.co/datasets/templates/dataset-card-example) --- This dataset was produced with [DataDreamer 🤖💤](https://datadreamer.dev). The synthetic dataset card can be found [here](datadreamer.json).

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_1.4b_bo16_2_64_mix_50_kl_0.1_prm_160m_thr_0.3_seed_1

Mitsuki-Sakamoto

"2024-03-24T10:36:26Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T06:02:03Z"

--- dataset_info: config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_10 num_bytes: 43576140 num_examples: 18928 - name: epoch_11 num_bytes: 43572313 num_examples: 18928 - name: epoch_12 num_bytes: 43580152 num_examples: 18928 - name: epoch_13 num_bytes: 43578773 num_examples: 18928 - name: epoch_14 num_bytes: 43575869 num_examples: 18928 - name: epoch_15 num_bytes: 43575963 num_examples: 18928 - name: epoch_16 num_bytes: 43577002 num_examples: 18928 - name: epoch_17 num_bytes: 43579293 num_examples: 18928 - name: epoch_18 num_bytes: 43580317 num_examples: 18928 - name: epoch_19 num_bytes: 43575851 num_examples: 18928 - name: epoch_20 num_bytes: 43582939 num_examples: 18928 - name: epoch_21 num_bytes: 43577597 num_examples: 18928 - name: epoch_22 num_bytes: 43578041 num_examples: 18928 - name: epoch_23 num_bytes: 43577327 num_examples: 18928 - name: epoch_24 num_bytes: 43581004 num_examples: 18928 - name: epoch_25 num_bytes: 43578359 num_examples: 18928 - name: epoch_26 num_bytes: 43577018 num_examples: 18928 - name: epoch_27 num_bytes: 43582166 num_examples: 18928 - name: epoch_28 num_bytes: 43579959 num_examples: 18928 - name: epoch_29 num_bytes: 43580209 num_examples: 18928 - name: epoch_0 num_bytes: 43730889 num_examples: 18928 - name: epoch_1 num_bytes: 43770682 num_examples: 18928 - name: epoch_2 num_bytes: 43648840 num_examples: 18928 - name: epoch_3 num_bytes: 43543360 num_examples: 18928 - name: epoch_4 num_bytes: 43502193 num_examples: 18928 - name: epoch_5 num_bytes: 43486787 num_examples: 18928 - name: epoch_6 num_bytes: 43482119 num_examples: 18928 - name: epoch_7 num_bytes: 43474193 num_examples: 18928 - name: epoch_8 num_bytes: 43478656 num_examples: 18928 - name: epoch_9 num_bytes: 43482186 num_examples: 18928 download_size: 928064117 dataset_size: 1307166197 configs: - config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 data_files: - split: epoch_0 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_29-* ---

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_1.4b_bo16_2_64_mix_50_kl_0.1_prm_410m_thr_0.1_seed_1

Mitsuki-Sakamoto

"2024-03-25T04:37:57Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T06:04:26Z"

--- dataset_info: config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43758977 num_examples: 18928 - name: epoch_1 num_bytes: 43856391 num_examples: 18928 - name: epoch_2 num_bytes: 43716521 num_examples: 18928 - name: epoch_3 num_bytes: 43641452 num_examples: 18928 - name: epoch_4 num_bytes: 43570443 num_examples: 18928 - name: epoch_5 num_bytes: 43546101 num_examples: 18928 - name: epoch_6 num_bytes: 43533197 num_examples: 18928 - name: epoch_7 num_bytes: 43527219 num_examples: 18928 - name: epoch_8 num_bytes: 43523213 num_examples: 18928 - name: epoch_9 num_bytes: 43517911 num_examples: 18928 download_size: 510815003 dataset_size: 436191425 configs: - config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 data_files: - split: epoch_0 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_9-* ---

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_1.4b_bo16_2_64_mix_50_kl_0.1_prm_410m_thr_0.3_seed_1

Mitsuki-Sakamoto

"2024-03-25T05:04:33Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T06:04:32Z"

--- dataset_info: config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43755042 num_examples: 18928 - name: epoch_1 num_bytes: 43755124 num_examples: 18928 - name: epoch_2 num_bytes: 43601416 num_examples: 18928 - name: epoch_3 num_bytes: 43501701 num_examples: 18928 - name: epoch_4 num_bytes: 43424023 num_examples: 18928 - name: epoch_5 num_bytes: 43380006 num_examples: 18928 - name: epoch_6 num_bytes: 43382015 num_examples: 18928 - name: epoch_7 num_bytes: 43377813 num_examples: 18928 - name: epoch_8 num_bytes: 43369972 num_examples: 18928 - name: epoch_9 num_bytes: 43362193 num_examples: 18928 download_size: 533070588 dataset_size: 434909305 configs: - config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 data_files: - split: epoch_0 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_9-* ---

OpenDriveLab/OpenDV-YouTube-Language

OpenDriveLab

"2024-03-28T08:30:17Z"

0

5

[ "license:cc-by-nc-sa-4.0", "croissant", "arxiv:2403.09630", "region:us" ]

null

"2024-03-23T06:08:09Z"

--- license: cc-by-nc-sa-4.0 --- # OpenDV-YouTube This is the dataset repository of `OpenDV-YouTube` language annotations, including `context` and `command`. For more details, please refer to <a href="https://arxiv.org/abs/2403.09630" target="_blank">GenAD</a> project and <a href="https://github.com/OpenDriveLab/DriveAGI#opendv-youtube" target="_blank">OpenDV-YouTube</a>. ## Usage To use the annotations, you need to first download and prepare the data as instructed in <a href="https://github.com/OpenDriveLab/DriveAGI/tree/main/opendv" target="_blank">OpenDV-YouTube</a>. **Note that we recommend to process the dataset in `Linux` environment since `Windows` may have issues with the file paths.** You can use the following code to load in annotations respectively. ```python import json # for train full_annos = [] for split_id in range(10): split = json.load(open("10hz_YouTube_train_split{}.json".format(str(split_id)), "r")) full_annos.extend(split) # for val val_annos = json.load(open("10hz_YouTube_val.json", "r")) ``` Annotations will be loaded in `full_annos` as a list where each element contains annotations for one video clip. All elements in the list are dictionaries of the following structure. ```python { "cmd": <int> -- command, i.e. the command of the ego vehicle in the video clip. "blip": <str> -- context, i.e. the BLIP description of the center frame in the video clip. "folder": <str> -- the relative path from the processed OpenDV-YouTube dataset root to the image folder of the video clip. "first_frame": <str> -- the filename of the first frame in the clip. Note that this file is included in the video clip. "last_frame": <str> -- the filename of the last frame in the clip. Note that this file is included in the video clip. } ``` The command, *i.e.* the `cmd` field, can be converted to natural language using the `map_category_to_caption` function. You may refer to [cmd2caption.py](https://github.com/OpenDriveLab/DriveAGI/blob/main/opendv/utils/cmd2caption.py#L158) for details. The context, *i.e.* the `blip` field, is the description of the **center frame** in the video generated by `BLIP2`. ## Citation If you find our work helpful, please cite the following paper. ```bibtex @misc{yang2024genad, title={Generalized Predictive Model for Autonomous Driving}, author={Jiazhi Yang and Shenyuan Gao and Yihang Qiu and Li Chen and Tianyu Li and Bo Dai and Kashyap Chitta and Penghao Wu and Jia Zeng and Ping Luo and Jun Zhang and Andreas Geiger and Yu Qiao and Hongyang Li}, year={2024}, eprint={2403.09630}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

misshimichka/flower_faces_dataset_v2

misshimichka

"2024-03-23T09:41:30Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T06:34:39Z"

--- dataset_info: features: - name: original_image dtype: image - name: edit_prompt dtype: string - name: cartoonized_image dtype: image splits: - name: train num_bytes: 239848099.0 num_examples: 114 download_size: 239859641 dataset_size: 239848099.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_1.4b_bo16_2_64_mix_50_kl_0.1_prm_160m_thr_1.0_seed_1

Mitsuki-Sakamoto

"2024-03-24T12:09:27Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T06:52:49Z"

--- dataset_info: config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43754721 num_examples: 18928 - name: epoch_1 num_bytes: 43764507 num_examples: 18928 - name: epoch_2 num_bytes: 43702258 num_examples: 18928 - name: epoch_3 num_bytes: 43618198 num_examples: 18928 - name: epoch_4 num_bytes: 43563025 num_examples: 18928 - name: epoch_5 num_bytes: 43522727 num_examples: 18928 - name: epoch_6 num_bytes: 43519820 num_examples: 18928 - name: epoch_7 num_bytes: 43523973 num_examples: 18928 - name: epoch_8 num_bytes: 43515824 num_examples: 18928 - name: epoch_9 num_bytes: 43519081 num_examples: 18928 download_size: 325156504 dataset_size: 436004134 configs: - config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 data_files: - split: epoch_0 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_9-* ---

CodecSR/fsd50k_16k_synth

CodecSR

"2024-03-23T08:38:27Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T07:15:38Z"

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: id dtype: string splits: - name: original num_bytes: 13195529214.0 num_examples: 14400 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 4394953932.0 num_examples: 14400 - name: academicodec_hifi_24k_320d num_bytes: 4394953932.0 num_examples: 14400 - name: audiodec_24k_300d num_bytes: 4403255492.0 num_examples: 14400 - name: audiodec_48k_300d_uni num_bytes: 4403255492.0 num_examples: 14400 - name: dac_16k num_bytes: 4399077054.0 num_examples: 14400 - name: dac_24k num_bytes: 4399077043.6 num_examples: 14400 - name: dac_44k num_bytes: 4399077054.0 num_examples: 14400 - name: encodec_24k_12bps num_bytes: 4399077054.0 num_examples: 14400 - name: encodec_24k_1_5bps num_bytes: 4399077050.0 num_examples: 14400 - name: encodec_24k_24bps num_bytes: 4399077057.0 num_examples: 14400 - name: encodec_24k_3bps num_bytes: 4399077050.0 num_examples: 14400 - name: encodec_24k_6bps num_bytes: 4399077050.0 num_examples: 14400 - name: facodec_16k num_bytes: 4397686092.0 num_examples: 14400 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 4399077057.0 num_examples: 14400 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 4399077054.0 num_examples: 14400 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 4399077045.6 num_examples: 14400 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 4399077045.6 num_examples: 14400 - name: language_codec_chinese_24k_nq8_12kbps num_bytes: 4403185607.6 num_examples: 14400 - name: language_codec_paper_24k_nq8_12kbps num_bytes: 4403185607.6 num_examples: 14400 - name: speech_tokenizer_16k num_bytes: 4403185607.6 num_examples: 14400 download_size: 93168736444 dataset_size: 101188115591.60004 configs: - config_name: default data_files: - split: original path: data/original-* - split: academicodec_hifi_16k_320d_large_uni path: data/academicodec_hifi_16k_320d_large_uni-* - split: academicodec_hifi_24k_320d path: data/academicodec_hifi_24k_320d-* - split: audiodec_24k_300d path: data/audiodec_24k_300d-* - split: audiodec_48k_300d_uni path: data/audiodec_48k_300d_uni-* - split: dac_16k path: data/dac_16k-* - split: dac_24k path: data/dac_24k-* - split: dac_44k path: data/dac_44k-* - split: encodec_24k_12bps path: data/encodec_24k_12bps-* - split: encodec_24k_1_5bps path: data/encodec_24k_1_5bps-* - split: encodec_24k_24bps path: data/encodec_24k_24bps-* - split: encodec_24k_3bps path: data/encodec_24k_3bps-* - split: encodec_24k_6bps path: data/encodec_24k_6bps-* - split: facodec_16k path: data/facodec_16k-* - split: funcodec_en_libritts_16k_nq32ds320 path: data/funcodec_en_libritts_16k_nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds640 path: data/funcodec_en_libritts_16k_nq32ds640-* - split: funcodec_zh_en_16k_nq32ds320 path: data/funcodec_zh_en_16k_nq32ds320-* - split: funcodec_zh_en_16k_nq32ds640 path: data/funcodec_zh_en_16k_nq32ds640-* - split: language_codec_chinese_24k_nq8_12kbps path: data/language_codec_chinese_24k_nq8_12kbps-* - split: language_codec_paper_24k_nq8_12kbps path: data/language_codec_paper_24k_nq8_12kbps-* - split: speech_tokenizer_16k path: data/speech_tokenizer_16k-* ---

Karthik1080/Radio_MRA_V1_C37

Karthik1080

"2024-03-23T07:20:05Z"

0

[ "license:mit", "croissant", "region:us" ]

null

"2024-03-23T07:19:48Z"

--- license: mit dataset_info: features: - name: paragraphs dtype: string splits: - name: train num_bytes: 59508 num_examples: 115 download_size: 33990 dataset_size: 59508 configs: - config_name: default data_files: - split: train path: data/train-* ---

adamjweintraut/lyrlen

adamjweintraut

"2024-04-06T06:58:04Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T07:19:49Z"

--- dataset_info: features: - name: title dtype: string - name: id dtype: int64 - name: genre dtype: string - name: lyric_chunk_n dtype: int64 - name: sylls dtype: int64 - name: orig dtype: string - name: target dtype: string splits: - name: train num_bytes: 15546161 num_examples: 162300 - name: test num_bytes: 1949060 num_examples: 20288 - name: valid num_bytes: 1939446 num_examples: 20288 download_size: 10470927 dataset_size: 19434667 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---

fssdsdasdas123/simple-datasets

fssdsdasdas123

"2024-03-23T13:39:26Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T07:23:50Z"

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': ADONIS '1': AFRICAN GIANT SWALLOWTAIL '2': AMERICAN SNOOT splits: - name: train num_bytes: 8825732.0 num_examples: 338 download_size: 8823395 dataset_size: 8825732.0 --- # Dataset Card for "input-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Aditya685/filtered_data_80k

Aditya685

"2024-03-26T18:57:30Z"

0

[ "language:en", "croissant", "region:us" ]

null

"2024-03-23T07:38:29Z"

--- language: - en dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: text dtype: string splits: - name: train num_bytes: 290184604 num_examples: 80000 download_size: 120878335 dataset_size: 290184604 configs: - config_name: default data_files: - split: train path: data/train-* ---

willcine/TAO

willcine

"2024-03-23T07:41:06Z"

0

[ "license:apache-2.0", "region:us" ]

null

"2024-03-23T07:39:07Z"

--- license: apache-2.0 ---

aTunass/EuroSat_datasaet_image_classification

aTunass

"2024-03-23T07:45:59Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T07:40:31Z"

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': AnnualCrop '1': Forest '2': HerbaceousVegetation '3': Highway '4': Industrial '5': Pasture '6': PermanentCrop '7': Residential '8': River '9': SeaLake splits: - name: train num_bytes: 88397609.0 num_examples: 27000 download_size: 91979105 dataset_size: 88397609.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

aTunass/Intel_Img_classification

aTunass

"2024-03-23T08:02:24Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T07:53:48Z"

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 372019672.265 num_examples: 24335 download_size: 368457096 dataset_size: 372019672.265 configs: - config_name: default data_files: - split: train path: data/train-* ---

somosnlp/recetasdelaabuela_genstruct_it

somosnlp

"2024-03-29T01:09:29Z"

0

2

[ "task_categories:question-answering", "size_categories:10K<n<100K", "language:es", "license:apache-2.0", "croissant", "region:us" ]

[ "question-answering" ]

"2024-03-23T07:59:58Z"

--- language: - es license: apache-2.0 size_categories: - 10K<n<100K task_categories: - question-answering dataset_info: features: - name: title dtype: string - name: content dtype: string - name: messages sequence: 'null' - name: generation_model sequence: string - name: generation_prompt sequence: string - name: raw_generation_responses sequence: string - name: conversation sequence: sequence: string splits: - name: train num_bytes: 103228164 num_examples: 20085 download_size: 49502853 dataset_size: 103228164 configs: - config_name: default data_files: - split: train path: data/train-* --- # Descripción Dataset creado para la hackathon #Somos600M con el objetivo de entrenar un modelo que pueda recomendar recetas de paises hispanohablantes. Este conjunto de datos consiste en pregunta-respuesta y fue elaborado a partir de un contexto usando Genstruct-7B y distilabel. Elaborado a partir del dataset en crudo [somosnlp/RecetasDeLaAbuela](https://huggingface.co/datasets/somosnlp/RecetasDeLaAbuela) elaborado por el equipo recetasdelaabuela mediante web scraping. ## Origen del Dataset El dataset se obtuvo mediante web scrapping de estas paginas: - https://www.elmueble.com/ - https://www.yanuq.com/ - https://www.directoalpaladar.com/ - https://www.recetasgratis.net/ - https://cookpad.com/pe/ ## Notebook utilizada Elaborado con el [colab](https://colab.research.google.com/drive/1-7OY5ORmOw0Uy_uazXDDqjWWkwCKvWbL?usp=sharing). ## Contacto Si encuentras algún error o tienes una recomendación, por favor hazmelo saber!! El obejtivo es que el dataset siga mejorando en el tiempo, me encuentras en hugging face como @sbenel o comunicate en discord con un miembro del equipo de la hackathon.

TheaterParody/Foliage

TheaterParody

"2024-03-23T09:51:57Z"

0

[ "license:mit", "croissant", "region:us" ]

null

"2024-03-23T08:01:34Z"

--- license: mit ---

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_1.4b_bo16_2_64_mix_50_kl_0.1_prm_160m_thr_0.1_seed_2

Mitsuki-Sakamoto

"2024-03-25T03:37:54Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T08:03:14Z"

--- dataset_info: config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_10 num_bytes: 43435041 num_examples: 18928 - name: epoch_11 num_bytes: 43435140 num_examples: 18928 - name: epoch_12 num_bytes: 43430765 num_examples: 18928 - name: epoch_13 num_bytes: 43436665 num_examples: 18928 - name: epoch_14 num_bytes: 43432476 num_examples: 18928 - name: epoch_15 num_bytes: 43434055 num_examples: 18928 - name: epoch_16 num_bytes: 43432416 num_examples: 18928 - name: epoch_17 num_bytes: 43432055 num_examples: 18928 - name: epoch_18 num_bytes: 43432091 num_examples: 18928 - name: epoch_19 num_bytes: 43434577 num_examples: 18928 - name: epoch_20 num_bytes: 43436154 num_examples: 18928 - name: epoch_21 num_bytes: 43439292 num_examples: 18928 - name: epoch_22 num_bytes: 43438550 num_examples: 18928 - name: epoch_23 num_bytes: 43436063 num_examples: 18928 - name: epoch_24 num_bytes: 43437004 num_examples: 18928 - name: epoch_25 num_bytes: 43438763 num_examples: 18928 - name: epoch_26 num_bytes: 43433439 num_examples: 18928 - name: epoch_27 num_bytes: 43433974 num_examples: 18928 - name: epoch_28 num_bytes: 43436162 num_examples: 18928 - name: epoch_29 num_bytes: 43437287 num_examples: 18928 - name: epoch_0 num_bytes: 43728624 num_examples: 18928 - name: epoch_1 num_bytes: 43757201 num_examples: 18928 - name: epoch_2 num_bytes: 43585797 num_examples: 18928 - name: epoch_3 num_bytes: 43492349 num_examples: 18928 - name: epoch_4 num_bytes: 43443797 num_examples: 18928 - name: epoch_5 num_bytes: 43418222 num_examples: 18928 - name: epoch_6 num_bytes: 43408584 num_examples: 18928 - name: epoch_7 num_bytes: 43411275 num_examples: 18928 - name: epoch_8 num_bytes: 43408553 num_examples: 18928 - name: epoch_9 num_bytes: 43408425 num_examples: 18928 download_size: 1018593303 dataset_size: 1303764796 configs: - config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 data_files: - split: epoch_0 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_29-* ---

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_1.4b_bo16_2_64_mix_50_kl_0.1_prm_410m_thr_1.0_seed_1

Mitsuki-Sakamoto

"2024-03-24T12:02:10Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T08:05:34Z"

--- dataset_info: config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43793509 num_examples: 18928 - name: epoch_1 num_bytes: 43847564 num_examples: 18928 - name: epoch_2 num_bytes: 43693174 num_examples: 18928 - name: epoch_3 num_bytes: 43563475 num_examples: 18928 - name: epoch_4 num_bytes: 43478364 num_examples: 18928 - name: epoch_5 num_bytes: 43413210 num_examples: 18928 - name: epoch_6 num_bytes: 43396015 num_examples: 18928 - name: epoch_7 num_bytes: 43385612 num_examples: 18928 - name: epoch_8 num_bytes: 43386075 num_examples: 18928 - name: epoch_9 num_bytes: 43377158 num_examples: 18928 download_size: 301665360 dataset_size: 435334156 configs: - config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 data_files: - split: epoch_0 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_9-* ---

agicorp/orca-math-word-problems-200k

agicorp

"2024-03-23T08:22:07Z"

0

1

[ "task_categories:question-answering", "size_categories:100K<n<1M", "language:en", "license:mit", "math", "croissant", "arxiv:2402.14830", "region:us" ]

[ "question-answering" ]

"2024-03-23T08:22:06Z"

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 225322861 num_examples: 200035 download_size: 84248748 dataset_size: 225322861 configs: - config_name: default data_files: - split: train path: data/train-* license: mit task_categories: - question-answering language: - en tags: - math size_categories: - 100K<n<1M --- # Dataset Card  This dataset contains ~200K grade school math word problems. All the answers in this dataset is generated using Azure GPT4-Turbo. Please refer to [Orca-Math: Unlocking the potential of SLMs in Grade School Math](https://arxiv.org/pdf/2402.14830.pdf) for details about the dataset construction. ### Dataset Description - **Curated by:** Microsoft - **Language(s) (NLP):** English - **License:** MIT ### Dataset Sources  - **Repository:** [microsoft/orca-math-word-problems-200k](https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k) - **Paper:** [Orca-Math: Unlocking the potential of SLMs in Grade School Math](https://arxiv.org/pdf/2402.14830.pdf) ### Direct Use  This dataset has been designed to enhance the mathematical abilities of language models. It aims to provide a robust foundation for language models to excel in mathematical problem-solving. ### Out-of-Scope Use  This dataset is not intended for use in educational systems or organizations. ## Dataset Structure ### Data Instances A typical data entry in the dataset consists of a question and its corresponding answer. Below is an example from the dataset: ```python {'question': 'In a highly contested election having multiple candidates, Mr. Jackson, one of the losing candidates, received 3,485,782 votes, which accounted for precisely 38.7 percent of all votes. To have achieved a victory, he would have needed to secure at least 51 percent of all votes. Approximately, what percent of the remaining unsecured votes would Mr. Jackson have needed to accumulate to reach this victory threshold?', 'answer': "First, let's find out the total number of votes cast in the election. Since Mr. Jackson received 38.7% of all votes, and that amounted to 3,485,782 votes, we can set up the following equation to find the total number of votes (T):\n\n0.387 * T = 3,485,782\n\nNow, solve for T:\n\nT = 3,485,782 / 0.387\nT ≈ 9,000,467 votes (total number of votes cast)\n\nTo win, Mr. Jackson would have needed 51% of the total votes. Let's calculate that amount:\n\n0.51 * T = 0.51 * 9,000,467\n0.51 * T ≈ 4,590,238 votes needed to win\n\nNow, let's find out how many more votes Mr. Jackson needed to reach this winning threshold:\n\nVotes needed to win - Votes Mr. Jackson received = Additional votes needed\n4,590,238 - 3,485,782 = 1,104,456 additional votes needed\n\nNow, let's find out what percentage of the remaining unsecured votes this number represents. The remaining unsecured votes are the votes that were not for Mr. Jackson, which is 100% - 38.7% = 61.3% of the total votes.\n\n61.3% of the total votes is the remaining unsecured votes:\n\n0.613 * T = 0.613 * 9,000,467\n0.613 * T ≈ 5,514,686 votes were unsecured\n\nNow, we'll calculate the percentage of these unsecured votes that the additional votes needed represent:\n\n(Additional votes needed / Unsecured votes) * 100 = Percentage of unsecured votes needed\n(1,104,456 / 5,514,686) * 100 ≈ 20.03%\n\nSo, Mr. Jackson would have needed approximately 20.03% of the remaining unsecured votes to reach the victory threshold of 51%."} ``` ### Data Fields The dataset comprises the following fields: - `question`: a string containing the question to be answered. - `answer`: a string containing the answer to the corresponding question. ### Data Splits The dataset is split into a training set. The number of rows in each split is as follows: - `train`: 200,035 rows The `DatasetDict` structure for the dataset is as follows: ```python DatasetDict({ 'train': Dataset({ features: ['question', 'answer'], num_rows: 200035 }) }) ``` Each split in the `DatasetDict` contains a `Dataset` object with the specified features and number of rows. ## Dataset Creation Please refer to [Orca-Math: Unlocking the potential of SLMs in Grade School Math](https://arxiv.org/pdf/2402.14830.pdf) for details about the dataset construction. ### Source Data - [Lila](https://huggingface.co/datasets/allenai/lila) - [DMath](https://arxiv.org/ftp/arxiv/papers/2106/2106.15772.pdf) #### Data Collection and Processing  Please refer to [Orca-Math: Unlocking the potential of SLMs in Grade School Math](https://arxiv.org/pdf/2402.14830.pdf) for details about the dataset construction. #### Who are the source data producers?  Microsoft #### Annotation process  We expanded a seed set of questions using Azure GPT-4 Trubo. The answers to those questions are generated using Azure GPT-4 Trubo. #### Personal and Sensitive Information  None ## Bias, Risks, and Limitations  This dataset is in English and contains only math word problems. ## Citation If you find this work useful in your method, you can cite the paper as below: ``` @misc{mitra2024orcamath, title={Orca-Math: Unlocking the potential of SLMs in Grade School Math}, author={Arindam Mitra and Hamed Khanpour and Corby Rosset and Ahmed Awadallah}, year={2024}, eprint={2402.14830}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Dataset Card Contact [Arindam Mitra](armitra@microsoft.com)

renusan05/phishing_URLs

renusan05

"2024-03-23T08:36:28Z"

0

[ "license:unknown", "region:us" ]

null

"2024-03-23T08:27:57Z"

--- license: unknown ---

agicorp/MathInstruct

agicorp

"2024-03-23T08:28:20Z"

0

[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "license:mit", "math", "croissant", "arxiv:2309.05653", "region:us" ]

[ "text-generation" ]

"2024-03-23T08:28:20Z"

--- license: mit task_categories: - text-generation language: - en pretty_name: MathInstruct size_categories: - 100K<n<1M tags: - math --- # 🦣 MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning MathInstruct is a meticulously curated instruction tuning dataset that is lightweight yet generalizable. MathInstruct is compiled from 13 math rationale datasets, six of which are newly curated by this work. It uniquely focuses on the hybrid use of chain-of-thought (CoT) and program-of-thought (PoT) rationales, and ensures extensive coverage of diverse mathematical fields. Project Page: [https://tiger-ai-lab.github.io/MAmmoTH/](https://tiger-ai-lab.github.io/MAmmoTH/) Paper: [https://arxiv.org/pdf/2309.05653.pdf](https://arxiv.org/pdf/2309.05653.pdf) Code: [https://github.com/TIGER-AI-Lab/MAmmoTH](https://github.com/TIGER-AI-Lab/MAmmoTH) Models: | | **Base Model: Llama-2** | **Base Model: Code Llama** | |-----|---------------------------------------------------------------|--------------------------------------------------------------------------| | 7B | 🦣 [MAmmoTH-7B](https://huggingface.co/TIGER-Lab/MAmmoTH-7B) | 🦣 [MAmmoTH-Coder-7B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-7B) | | 13B | 🦣 [MAmmoTH-13B](https://huggingface.co/TIGER-Lab/MAmmoTH-13B) | 🦣 [MAmmoTH-Coder-13B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-13B)| | 34B | - | 🦣 [MAmmoTH-Coder-34B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-34B)| | 70B | 🦣 [MAmmoTH-70B](https://huggingface.co/TIGER-Lab/MAmmoTH-70B) | - | ## **License** Please check out the license of each subset in our curated dataset MathInstruct. | Dataset Name | License Type | |--------------|----------------| | GSM8K | MIT | | GSM8K-RFT | Non listed | | AQuA-RAT | Apache 2.0 | | MATH | MIT | | TheoremQA | MIT | | Camel-Math | Attribution-NonCommercial 4.0 International | | NumGLUE | Apache-2.0 | | MathQA | Apache-2.0 | | Our Curated | MIT | ## **Citation** Please cite our paper if you use our data, model or code. Please also kindly cite the original dataset papers. ``` @article{yue2023mammoth, title={MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning}, author={Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wenhao Huang, Huan Sun, Yu Su, Wenhu Chen}, journal={arXiv preprint arXiv:2309.05653}, year={2023} } ```

agicorp/Nectar

agicorp

"2024-03-23T08:30:15Z"

0

[ "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "RLHF", "RLAIF", "reward model", "croissant", "region:us" ]

null

"2024-03-23T08:30:15Z"

--- license: apache-2.0 language: - en size_categories: - 100K<n<1M configs: - config_name: default data_files: - split: train path: data/rlaif.parquet tags: - RLHF - RLAIF - reward model --- # Dataset Card for Nectar - **Developed by:** Banghua Zhu * , Evan Frick * , Tianhao Wu * , Hanlin Zhu and Jiantao Jiao. - **License:** Apache-2.0 license under the condition that the dataset is not used to compete with OpenAI Nectar is the first high-quality 7-wise comparison dataset, generated through GPT-4-based ranking. Nectar contains diverse chat prompts, high-quality and diverse responses, and accurate ranking labels. Nectar's prompts are an amalgamation of diverse sources, including [lmsys-chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m), [ShareGPT](https://sharegpt.com/), [Antropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf), [UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback), [Evol-Instruct](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k), and [Flan](https://huggingface.co/datasets/SirNeural/flan_v2). Nectar's 7 responses per prompt are primarily derived from a variety of models, namely GPT-4, GPT-3.5-turbo, GPT-3.5-turbo-instruct, [LLama-2-7B-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf), and [Mistral-7B-Instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1), alongside other existing datasets and models. Each prompt's responses are sorted into a 7-wise ranking labeled by GPT-4, resulting in a total of 3.8M pairwise comparisons. Nectar was used to train the reward model [Starling-RM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-RM-7B-alpha) which propelled [Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha) to an MT-Bench score of 8.09, the current highest for any 7B model. #### Disclaimer: This dataset contains conversations and responses that are possibly unsafe, offensive, and/or disturbing. These are included only for the purpose of training safer models. Viewer discretion is advised. ## Schema: ``` { prompt: str, // in format "\n\nHuman: ... \n\nAssistant: " answers: [ { answer: str, // the model's response model: str, // name of the model that generated the response rank: int // the rank this response recieved }, ... { answer: str, // the model's response model: str, // name of the model that generated the response rank: int // the rank this response recieved }, ] turns: int, // number of turns in the conversation num_response: int, // number of responses for the prompt source: list[str], // list of the dataset sources for the prompt and answers good_natured: bool // if the prompt is good natured } ``` Note: The ```good_natured``` label is derived as a by-product of generating GPT-4 rankings. Since we always first ask GPT-4 if the prompt is good natured before ranking, we were able to parse GPT-4's classification of the prompt's nature to create this label. It is important to note that this label is an approximation generated by GPT-4, and not a representation of the authors' personal beliefs or views. ## Process ### Collecting Prompts 1. For each dataset, generate prompt and answer pairs. 2. For each dataset, group by prompt. 3. Concatenate datasets from (2), down sample according to the following. a. Take all ShareGPT prompts. b. Randomly sample without replacement 75,000 Anthropic HH prompts. c. Take all Ultrafeedback prompts. d. Randomly sample without replacement 45,000 lmsys prompts with more than 1 answer 4. Group the concatenated dataset by prompt, concatenate the answer lists. 5. Take all prompts that are longer than 40 characters and have more than 1 answer Notes: 1. When we process the raw datasets, we identify the top 3 languages, if English is not present, we do not take the datapoint. 2. When we group prompts, we use a sanitized version where all non-alphanumeric characters are removed. ### Collecting Responses To achieve 7 responses for every prompt, we distilled responses from Llama-2-7B-chat, Mistral-7B-instruct, GPT-4, GPT-4-0613, GPT-3.5-turbo, and GPT-3.5-turbo-instruct. GPT-4-0613 was given a system prompt to help set the chatting context yielding better responses. From the k responses for each prompt, we pick 7 with the following priority from highest to lowest: 1. GPT-4-0613, GPT-4, Anthropic HH, GPT-3.5-Turbo-Instruct 2. Other models we did not distill and Mistral-7B-Instruct 3. GPT-3.5-Turbo 4. Llama-2-7B-chat 5. Any repeat model (not Llama-2-7B-chat or GPT-3.5-Turbo) 6. Repeated GPT-3.5-Turbo 7. Repeated Llama-2-7B-chat response 8. Default* *Note that the default response is: "I apologize, but I can’t assist with that request." (regardless of the user prompt) As result, we get a dataset with 182,954 prompts and 7 responses per prompt. ### Collecting Rankings To ranking the 7 responses for each prompt, we ask GPT-4 to rank the responses from best to worse in accordance to our rubric. We implemented several strategies to mitigate positional bias, which is convered in detailed in our paper (coming soon). Nectar's response rankings are based off both helpfulness and harmlessess. The rubric expects, first and foremost, that all responses should be harmless. Helpfulness should be maximized when responding to good natured prompts. An excerpt from the GPT-4 judging prompt defines the ranking rubric as follow: >We have 2 rubrics, one is the helpfulness rubric, one is the harmlessness rubric. > > >The helpfulness rubric is as follows: > 1. Is the completion relevant? The completion should be the best possible assistant answer. > 2. Is the completion accurate? The completion should accurately fulfill the prompt’s request. > 3. If a creative answer is expected, is the completion creative? If an analytical answer is expected, is the completion factual/objectively correct? > 4. Is the completion detailed? The completion should at minimum satisfy the full level of detail required by the prompt. > >The harmlessness rubric is as follows: > 1. Does the completion adhere to moral and ethical standards? > 2. Is the completion truthful? > 3. Is the response polite and respectful? > 4. If the request given is not good natured, does the completion politely decline to reply while giving a short explanation why they cannot comply with the request? > >Completions should always maintain harmlessness in the face of adversarial requests. We may see conversations that are good natured and some that are not good natured. All conversations should be completed in a manner that aligns with the harmlessness rubric. Good natured conversations additionally should align with the helpfulness rubric.  ### Dataset Sources  - **Blog:** https://starling.cs.berkeley.edu/ - **Paper:** Coming soon! - **Code:** Coming soon! ## License The dataset, model and online demo is a research preview intended for non-commercial use only, subject to the data distillation [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation. ## Acknowledgment We would like to thank Wei-Lin Chiang from Berkeley for detailed feedback of the blog and the projects. We would like to thank the [LMSYS Organization](https://lmsys.org/) for their support of [lmsys-chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) dataset, evaluation and online demo. We would like to thank the open source community for their efforts in providing the datasets and base models we used to develope the project, including but not limited to Anthropic, Llama, Mistral, Hugging Face H4, LMSYS, OpenChat, OpenBMB, Flan and ShareGPT. **✉ Correspondence to:** Banghua Zhu (banghua@berkeley.edu). ## Citation ``` @misc{starling2023, title = {Starling-7B: Improving LLM Helpfulness & Harmlessness with RLAIF}, url = {}, author = {Zhu, Banghua and Frick, Evan and Wu, Tianhao and Zhu, Hanlin and Jiao, Jiantao}, month = {November}, year = {2023} } ```

agicorp/Agentinstruct

agicorp

"2024-03-23T08:37:10Z"

0

1

[ "language:en", "croissant", "arxiv:2310.12823", "region:us" ]

null

"2024-03-23T08:37:06Z"

--- configs: - config_name: default data_files: - split: os path: data/os-* - split: db path: data/db-* - split: alfworld path: data/alfworld-* - split: webshop path: data/webshop-* - split: kg path: data/kg-* - split: mind2web path: data/mind2web-* dataset_info: features: - name: conversations list: - name: from dtype: string - name: loss dtype: bool - name: value dtype: string - name: id dtype: string splits: - name: os num_bytes: 660245 num_examples: 195 - name: db num_bytes: 1436655 num_examples: 538 - name: alfworld num_bytes: 1223363 num_examples: 336 - name: webshop num_bytes: 1602648 num_examples: 351 - name: kg num_bytes: 2960010 num_examples: 324 - name: mind2web num_bytes: 159590 num_examples: 122 download_size: 1255385 dataset_size: 8042511 language: - en pretty_name: AgentInstruct --- # AgentInstruct Dataset <p align="center"> 🤗 <a href="https://huggingface.co/THUDM/agentlm-70b" target="_blank">[Models]</a> • 💻 <a href="https://github.com/THUDM/AgentTuning" target="_blank">[Github Repo]</a> • 📌 <a href="https://THUDM.github.io/AgentTuning/" target="_blank">[Project Page]</a> • 📃 <a href="https://arxiv.org/abs/2310.12823" target="_blank">[Paper]</a> </p> **AgentInstruct** is a meticulously curated dataset featuring **1,866** high-quality interactions, designed to enhance AI agents across six diverse real-world tasks, leveraging innovative methods like **Task Derivation** and **Self-Instruct**. - 🔍 **CoT** - Harness the power of [ReAct](https://react-lm.github.io/), offering detailed thought explanations for each action, ensuring an intricate understanding of the model's decision-making journey. - 🌍 **Diversity** - Spanning 6 real-world scenarios, from Daily Household Routines to Database Operations, and their average turns range from 5 to 35. - 🎯 **Precision** - Not all trajectories of GPT-4 are effective! Ours are rigorously filtered using strict rewards to ensure top-notch quality. - ✅ **Assurance** - Rigorous checks to avoid data leakage, ensuring pristine dataset quality. ## Task Overview | Task | # Filt. Traj. | Avg # Filt. Traj. Turns | |---|---|---| |ALFWorld|336|13.52| |WebShop|351|3.68| |Mind2Web|122|1.00| |Knowledge Graph|324|6.04| |Operating System|195|3.85| |Database|538|2.06| |**AgentInstruct**|1866|5.24| AgentInstruct includes 1,866 trajectories from 6 agents tasks. "Traj." stands for interaction trajectory. "Filt. Traj." stands for filtered trajectories. ## Models **AgentLM** models are produced by mixed training on AgentInstruct dataset and ShareGPT dataset from Llama-2-chat models. The models follow the conversation format of [Llama-2-chat](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), with system prompt fixed as ``` You are a helpful, respectful and honest assistant. ``` 7B, 13B, and 70B models are available on Huggingface model hub. |Model|Huggingface Repo| |---|---| |AgentLM-7B| [🤗Huggingface Repo](https://huggingface.co/THUDM/agentlm-7b) | |AgentLM-13B| [🤗Huggingface Repo](https://huggingface.co/THUDM/agentlm-13b) | |AgentLM-70B| [🤗Huggingface Repo](https://huggingface.co/THUDM/agentlm-70b) | Check our [[Github Repo]](https://github.com/THUDM/AgentTuning) for details about **AgentTuning**. ## Citation If you find our work useful, please consider citing AgentTuning: ``` @misc{zeng2023agenttuning, title={AgentTuning: Enabling Generalized Agent Abilities for LLMs}, author={Aohan Zeng and Mingdao Liu and Rui Lu and Bowen Wang and Xiao Liu and Yuxiao Dong and Jie Tang}, year={2023}, eprint={2310.12823}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

ovieyra21/recording-delete

ovieyra21

"2024-03-23T08:44:44Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T08:40:41Z"

--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string splits: - name: train num_bytes: 5892712.0 num_examples: 1 download_size: 3846099 dataset_size: 5892712.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

MatrixStudio/Codeforces-Python-Submissions-PPO

MatrixStudio

"2024-03-24T02:19:30Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T08:52:12Z"

--- dataset_info: features: - name: contestId dtype: int64 - name: index dtype: string - name: name dtype: string - name: type dtype: string - name: rating dtype: int64 - name: tags sequence: string - name: title dtype: string - name: time-limit dtype: string - name: memory-limit dtype: string - name: problem-description dtype: string - name: input-specification dtype: string - name: output-specification dtype: string - name: demo-input sequence: string - name: demo-output sequence: string - name: note dtype: string - name: points dtype: float64 - name: test_cases list: - name: input dtype: string - name: output dtype: string - name: creationTimeSeconds dtype: int64 - name: relativeTimeSeconds dtype: int64 - name: programmingLanguage dtype: string - name: verdict dtype: string - name: testset dtype: string - name: passedTestCount dtype: int64 - name: timeConsumedMillis dtype: int64 - name: memoryConsumedBytes dtype: int64 - name: code dtype: string - name: prompt dtype: string - name: response dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 326352255.9446646 num_examples: 49021 - name: test num_bytes: 41407414 num_examples: 6115 download_size: 49192265 dataset_size: 367759669.9446646 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

driesverachtert/basic_shapes_object_detection

driesverachtert

"2024-03-27T13:00:51Z"

0

[ "task_categories:object-detection", "annotations_creators:machine-generated", "language:en", "license:apache-2.0", "object-detection", "simple", "example", "basic-geometric-shapes", "croissant", "region:us" ]

[ "object-detection" ]

"2024-03-23T08:54:30Z"

--- language: - en license: apache-2.0 pretty_name: Basic Shapes Object Detection tags: - object-detection - simple - example - basic-geometric-shapes annotations_creators: - machine-generated task_categories: - object-detection dataset_info: features: - name: image_id dtype: int64 - name: image dtype: image - name: width dtype: int32 - name: height dtype: int32 - name: objects sequence: - name: id dtype: int64 - name: area dtype: int64 - name: bbox sequence: float32 length: 4 - name: category dtype: class_label: names: '0': Square '1': Circle '2': Triangle configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Basic Shapes Object Detection ## Description This Basic Shapes Object Detection dataset has been created to test fine-tuning of object detection models. Fine-tuning some model to detect the basic shapes should be rather easy: just a bit of training should be enough to get the model to do correct object detection quite fast. Each entry in the dataset has a RGB PNG image with a white background and 3 basic geometric shapes: * A blue square * A red circle * A green triangle All images have the same size. Each image has exactly 1 square, 1 circle and 1 triangle, with their fixed colors. Each entry in the dataset has consequently 3 bounding boxes. The shapes do not overlap.The category IDs are 0, 1 and 2, corresponding to the labels Square, Circle and Triangle. The dataset has exactly the same structure as the https://huggingface.co/datasets/cppe-5 dataset, but fine-tuning some model to this dataset with basic geometric shapes should require considerable less training compared to the cppe-5 dataset. Once you have tested your fine-tuning code on this dataset, it should also work on more complicated datasets such as the cppe-5 dataset. ![](https://github.com/DriesVerachtert/basic_shapes_object_detection_dataset/blob/main/examples.png) ## Links The Python code to generate the images can be found at https://github.com/DriesVerachtert/basic_shapes_object_detection_dataset The dataset can be downloaded from https://huggingface.co/datasets/driesverachtert/basic_shapes_object_detection ## Structure The bounding boxes are in COCO format (x_min, y_min, width, height). ## License This dataset is released under Apache 2.0. ## Usage ```python from datasets import load_dataset dataset = load_dataset("driesverachtert/basic_shapes_object_detection") ```

alvations/c4p0-v1-en-es

alvations

"2024-03-23T09:30:19Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T09:30:18Z"

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 23053968 num_examples: 18476 download_size: 10003956 dataset_size: 23053968 configs: - config_name: default data_files: - split: train path: data/train-* ---

alvations/c4p0-v1-en-zh

alvations

"2024-03-23T09:30:23Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T09:30:20Z"

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 20548462 num_examples: 16072 download_size: 8673734 dataset_size: 20548462 configs: - config_name: default data_files: - split: train path: data/train-* ---

alvations/c4p0-v1-en-ja

alvations

"2024-03-23T09:30:27Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T09:30:26Z"

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 22109670 num_examples: 17956 download_size: 8614674 dataset_size: 22109670 configs: - config_name: default data_files: - split: train path: data/train-* ---

alvations/c4p0-v1-ja-en

alvations

"2024-03-23T09:30:29Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T09:30:28Z"

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 24906424 num_examples: 20164 download_size: 10803890 dataset_size: 24906424 configs: - config_name: default data_files: - split: train path: data/train-* ---

alvations/c4p0-v1-en-it

alvations

"2024-03-23T09:30:31Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T09:30:30Z"

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 20371694 num_examples: 15744 download_size: 8748727 dataset_size: 20371694 configs: - config_name: default data_files: - split: train path: data/train-* ---

alvations/c4p0-v1-en-de

alvations

"2024-03-23T09:30:33Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T09:30:32Z"

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 14282882 num_examples: 11920 download_size: 6534015 dataset_size: 14282882 configs: - config_name: default data_files: - split: train path: data/train-* ---

alvations/c4p0-v1-en-fr

alvations

"2024-03-23T09:30:34Z"

0

[ "croissant", "region:us" ]

null

"2024-03-23T09:30:33Z"

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 9739130 num_examples: 7510 download_size: 4040042 dataset_size: 9739130 configs: - config_name: default data_files: - split: train path: data/train-* ---