datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
flan-t5-small-embed-refinedwebAll of the data together is around 41GB. It's the last hidden states of 131,072 samples from refinedweb padded/truncated to 512 tokens on the left, fed through google/flan-t5-small.
Structure:
{
"encoding": List, shaped (512, 512) aka (tokens, d_model),
"text": String, the original text that was encoded,
"attention_mask": List, binary mask to pass to your model with encoding to not attend to pad tokens
}
just a tip, you cannot load this with the RAM in the free ver of google colab, not… See the full description on the dataset page: https://huggingface.co/datasets/crumb/flan-t5-small-embed-refinedweb.gpqa_diamond_deepseek_moe_16b_token_real_and_predicted_patterns_t5-smallaime2024_deepseek_moe_16b_token_real_and_predicted_patterns_t5-smallmmlu_deepseek_moe_16b_token_real_and_predicted_patterns_t5-smallrts_flan-t5-small_sqlFood101_test_google_flan_t5_small_mode_T_SPECIFIC_A_ns_100
Dataset Card for "Food101_test_google_flan_t5_small_mode_T_SPECIFIC_A_ns_100"
More Information needed
math-500_deepseek_moe_16b_token_real_and_predicted_patterns_t5-smallwmt16_qwen1.5MoEA2.7B_token_real_and_predicted_patterns_t5-smallmath-500_qwen1.5MoEA2.7B_token_real_and_predicted_patterns_t5-smallxsum-watermarked-flan-t5-small-20240828230657alpaca_qwen1.5MoEA2.7B_token_real_and_predicted_patterns_t5-smallgoogle__flan-t5-small-details
Dataset Card for Evaluation run of google/flan-t5-small
Dataset automatically created during the evaluation run of model google/flan-t5-small
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/google__flan-t5-small-details.xsum_qwen1.5MoEA2.7B_token_real_and_predicted_patterns_t5-smallxsum_switch128_token_real_and_predicted_patterns_t5-small_dff2048_dmodel32pattern的维度是 (#layers, #tokens)， 比如假设维度是 (6,32)，则表示 MoE 总共 6 层，输入或输出的 token是 32， pattern[0,:]记录了所有 token 在第一层选择的 expert 的情况。
举个例子
decode_pattern = [ [ 37, 6 ], [ 121, 36 ], [ 56, 76 ], [ 82, 26 ], [ 75, 61 ], [ 100, 60 ] ]
decode_pattern的维度是(6,2)，则表示有 6 层，输出的 token 数是 2。
decode_pattern[0][0]=37，这表示在第0层，第 0 个 token 选择的expert 的index 是 37
类似地， predictor_pattern返回的是 top-k (k=3)的结果，维度是 (#layers, #experts, k)。例如 predictor_pattern[0][0]表示的是在第一层，第一个 token 选中的权重排名前三的 expert 的索引值。
Food101_test_google_flan_t5_small_mode_T_SPECIFIC_A_ns_10000
Dataset Card for "Food101_test_google_flan_t5_small_mode_T_SPECIFIC_A_ns_10000"
More Information needed
semantic-corruption-t5-v1_1-smallcreating real/fake ("chosen"/"rejected") pairs where chosen are true completions and rejected are completions generated by T5 v1.1 small
rts_t5-small_sqlDataset used for training text to sql.
I've pre-tokenized this for faster loading.
Here is the prompt formation for the tokenizer code:
def tokenize_function(example):
start_prompt = "Tables:\n"
middle_prompt = "\n\nQuestion:\n"
end_prompt = "\n\nAnswer:\n"
data_zip = zip(example['context'], example['question'])
prompt = [start_prompt + context + middle_prompt + question + end_prompt for context, question in data_zip]
example['input_ids'] = tokenizer(prompt… See the full description on the dataset page: https://huggingface.co/datasets/howkewlisthat/rts_t5-small_sql.xsum_mixtral8x7bInstructv0.1_token_real_and_predicted_patterns_t5-small_dff2048_dmodel32cnn_dailymail-watermarked-flan-t5-smalltextvqa_mini_validation_google_flan_t5_small_mode_OCR_VQA_Q_rices_ns_10rts_flan-t5-small_sql_512xsum-watermarked-flan-t5-smallxsum-watermarked-t5-smallFood101_test_google_flan_t5_small_mode_T_SPECIFIC_A_ns_25250
Dataset Card for "Food101_test_google_flan_t5_small_mode_T_SPECIFIC_A_ns_25250"
More Information needed
rts_flan-t5-small_sql_1024wmt16_mixtral8x7bInstructv0.1_token_real_and_predicted_patterns_t5-small_dff2048_dmodel32OK-VQA_test_google_flan_t5_small_mode_A_T_D_PNP_FILTER_C_Q_rices_ns_100
Dataset Card for "OK-VQA_test_google_flan_t5_small_mode_A_T_D_PNP_FILTER_C_Q_rices_ns_100"
More Information needed
xsum-watermarked-flan-t5-small-20240828230746Food101_test_google_flan_t5_small_mode_T_SPECIFIC_A_ns_1000
Dataset Card for "Food101_test_google_flan_t5_small_mode_T_SPECIFIC_A_ns_1000"
More Information needed
Hatefulmemes_test_google_flan_t5_small_mode_T_A_C_OCR_rices_ns_1000
Dataset Card for "Hatefulmemes_test_google_flan_t5_small_mode_T_A_C_OCR_rices_ns_1000"
More Information needed