datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
andreassa/sla | ---
license: openrail
---
|
arnoudbuzing/cubic-equation-training | ---
license: mit
---
|
center-for-humans-and-machines/rlhf-hackathon-data | ---
dataset_info:
features:
- name: prompt_id
dtype: int64
- name: answer_id_1
dtype: int64
- name: answer_id_2
dtype: int64
- name: identity
dtype: string
- name: preference_prompt
dtype: string
- name: preference
dtype: int64
splits:
- name: train
num_bytes: 33114
num_examples: 30
download_size: 14217
dataset_size: 33114
---
# Dataset Card for "rlhf-hackathon-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/VALUE_mnli_drop_aux | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: train
num_bytes: 16847569
num_examples: 78157
- name: dev_matched
num_bytes: 416576
num_examples: 1924
- name: dev_mismatched
num_bytes: 415096
num_examples: 1847
- name: test_matched
num_bytes: 402499
num_examples: 1945
- name: test_mismatched
num_bytes: 417259
num_examples: 1836
download_size: 11952293
dataset_size: 18498999
---
# Dataset Card for "VALUE2_mnli_drop_aux"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dell-research-harvard/AmericanStoriesTraining | ---
license: apache-2.0
---
|
argilla/distilabel-intel-orca-dpo-pairs | ---
language:
- en
license: apache-2.0
dataset_info:
features:
- name: system
dtype: string
- name: input
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: generations
sequence: string
- name: order
sequence: string
- name: labelling_model
dtype: string
- name: labelling_prompt
list:
- name: content
dtype: string
- name: role
dtype: string
- name: raw_labelling_response
dtype: string
- name: rating
sequence: float64
- name: rationale
dtype: string
- name: status
dtype: string
- name: original_chosen
dtype: string
- name: original_rejected
dtype: string
- name: chosen_score
dtype: float64
- name: in_gsm8k_train
dtype: bool
splits:
- name: train
num_bytes: 161845559
num_examples: 12859
download_size: 79210071
dataset_size: 161845559
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- rlaif
- dpo
- rlhf
- distilabel
- synthetic
---
<p align="right">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
# distilabel Orca Pairs for DPO
The dataset is a "distilabeled" version of the widely used dataset: [Intel/orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs). The original dataset has been used by 100s of open-source practitioners and models. We knew from fixing UltraFeedback (and before that, Alpacas and Dollys) that this dataset could be highly improved.
Continuing with our mission to build the best alignment datasets for open-source LLMs and the community, we spent a few hours improving it with [distilabel](https://github.com/argilla-io/distilabel).
This was our main intuition: the original dataset just assumes gpt4/3.5-turbo are always the best response. We know from UltraFeedback that's not always the case. Moreover, DPO fine-tuning benefits from the diversity of preference pairs.
Additionally, we have added a new column indicating whether the question in the dataset is part of the train set of gsm8k (there were no examples from the test set). See the reproduction section for more details.
## Using this dataset
This dataset is useful for preference tuning and we recommend using it instead of the original. It's already prepared in the "standard" chosen, rejected format with additional information for further filtering and experimentation.
The main changes are:
1. ~2K pairs have been swapped: rejected become the chosen response. We have kept the original chosen and rejected on two new columns `original_*` for reproducibility purposes.
2. 4K pairs have been identified as `tie`: equally bad or good.
3. Chosen scores have been added: you can now filter out based on a threshold (see our distilabeled Hermes 2.5 model for an example)
4. We have kept the ratings and rationales generated with gpt-4-turbo and distilabel so you can prepare the data differently if you want.
5. We have added a column to indicate if the input is part of gsm8k train set.
In our experiments, we have got very good results by reducing the size of the dataset by more than 50%. Here's an example of how to achieve that:
```python
from datasets import load_dataset
# Instead of this:
# dataset = load_dataset("Intel/orca_dpo_pairs", split="train")
# use this:
dataset = load_dataset("argilla/distilabel-intel-orca-dpo-pairs", split="train")
dataset = dataset.filter(
lambda r:
r["status"] != "tie" and
r["chosen_score"] >= 8 and
not r["in_gsm8k_train"]
)
```
This results in `5,922` instead of `12,859` samples (54% reduction) and leads to better performance than the same model tuned with 100% of the samples in the original dataset.
> We'd love to hear about your experiments! If you want to try this out, consider joining our [Slack community](https://join.slack.com/t/rubrixworkspace/shared_invite/zt-whigkyjn-a3IUJLD7gDbTZ0rKlvcJ5g) and let's build some open datasets and models together.
## Reproducing the dataset
In this section, we outline the steps to reproduce this dataset.
### Rate original dataset pairs
Build a preference dataset with distilabel using the original dataset:
```python
from distilabel.llm import OpenAILLM
from distilabel.tasks import JudgeLMTask
from distilabel.pipeline import Pipeline
from datasets import load_dataset
# Shuffle 'chosen' and 'rejected' to avoid positional bias and keep track of the order
def shuffle_and_track(chosen, rejected):
pair = [chosen, rejected]
random.shuffle(pair)
order = ["chosen" if x == chosen else "rejected" for x in pair]
return {"generations": pair, "order": order}
dataset = load_dataset("Intel/orca_dpo_pairs", split="train")
# This shuffles the pairs to mitigate positional bias
dataset = dataset.map(lambda x: shuffle_and_track(x["chosen"], x["rejected"]))
# We use our JudgeLM implementation to rate the original pairs
labeler = OpenAILLM(
task=JudgeLMTask(),
model="gpt-4-1106-preview",
num_threads=16,
max_new_tokens=512,
)
dataset = dataset.rename_columns({"question": "input"})
distipipe = Pipeline(
labeller=labeler
)
# This computes ratings and natural language critiques for each pair
ds = distipipe.generate(dataset=dataset, num_generations=2)
```
If you want to further filter and curate the dataset, you can push the dataset to [Argilla](https://github.com/argilla-io/argilla) as follows:
```python
rg_dataset = ds.to_argilla()
rg_dataset.push_to_argilla(name="your_dataset_name", workspace="your_workspace_name")
```
You get a nice UI with a lot of pre-computed metadata to explore and curate the dataset:
The resulting dataset is now much more useful: we know which response is preferred (by gpt-4-turbo), which ones have low scores, and we even have natural language explanations. But what did we find? Was our intuition confirmed?
The above chart shows the following:
* ~4,000 pairs were given the same rating (a tie).
* ~7,000 pairs were correct according to our AI judge (`unchanged`).
* and ~2,000 times the rejected response was preferred (`swapped`).
Now the next question is: can we build better models with this new knowledge? The answer is the "distilabeled Hermes" model, check it out!
### Post-processing to add useful information
Swap rejected and chosen, and add chosen scores and status:
```python
def add_status(r):
status = "unchanged"
highest_rated_idx = np.argmax(r['rating'])
# Compare to the index of the chosen response
if r['rating']== None or r['rating'][0] == r['rating'][1]:
status = "tie"
elif r['order'][highest_rated_idx] != 'chosen':
status = "swapped"
return {"status": status}
def swap(r):
chosen = r["chosen"]
rejected = r["rejected"]
if r['rating'] is not None:
chosen_score = r['rating'][np.argmax(r['rating'])]
else:
chosen_score = None
if r['status'] == "swapped":
chosen = r["rejected"]
rejected = r["chosen"]
return {
"chosen": chosen,
"rejected": rejected,
"original_chosen": r["chosen"],
"original_rejected": r["rejected"],
"chosen_score": chosen_score
}
updated = ds.map(add_status).map(swap)
```
### gsm8k "decontamination"
The basic approach for finding duplicated examples. We didn't find any from the test sets. We experimented with lower thresholds but below 0.8 they introduced false positives:
```python
import pandas as pd
import nltk
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from datasets import load_dataset
nltk.download('punkt')
# Load the datasets
source_dataset = load_dataset("gsm8k", "main", split="train")
source_dataset_socratic = load_dataset("gsm8k", "socratic", split="train")
#target_dataset = load_dataset("Intel/orca_dpo_pairs", split="train")
target_dataset = load_dataset("argilla/distilabel-intel-orca-dpo-pairs", split="train")
# Extract the 'question' column from each dataset
source_questions = source_dataset['question']
source_questions_socratic = source_dataset_socratic['question']
target_questions = target_dataset['input']
# Function to preprocess the text
def preprocess(text):
return nltk.word_tokenize(text.lower())
# Preprocess the questions
source_questions_processed = [preprocess(q) for q in source_questions]
source_questions.extend([preprocess(q) for q in source_questions_socratic])
target_questions_processed = [preprocess(q) for q in target_questions]
# Vectorize the questions
vectorizer = TfidfVectorizer()
source_vec = vectorizer.fit_transform([' '.join(q) for q in source_questions_processed])
target_vec = vectorizer.transform([' '.join(q) for q in target_questions_processed])
# Calculate cosine similarity
similarity_matrix = cosine_similarity(source_vec, target_vec)
# Determine matches based on a threshold:
# checked manually and below 0.8 there are only false positives
threshold = 0.8
matching_pairs = []
for i, row in enumerate(similarity_matrix):
for j, similarity in enumerate(row):
if similarity >= threshold:
matching_pairs.append((source_questions[i], target_questions[j], similarity))
# Create a DataFrame from the matching pairs
df = pd.DataFrame(matching_pairs, columns=['Source Question', 'Target Question', 'Similarity Score'])
# Create a set of matching target questions
matching_target_questions = list(df['Target Question'])
# Add a column to the target dataset indicating whether each question is matched
target_dataset = target_dataset.map(lambda example: {"in_gsm8k_train": example['input'] in matching_target_questions})
```
Result:
```
False 12780
True 79
Name: in_gsm8k_train
``` |
TeamSODA/ae-signal_processing_attacks_whisper_librispeech | ---
dataset_info:
features:
- name: audio_0
dtype: audio
- name: audio_1
dtype: audio
splits:
- name: train
num_bytes: 14183982634
num_examples: 9000
download_size: 432744050
dataset_size: 14183982634
license: openrail
task_categories:
- audio-to-audio
language:
- en
pretty_name: SodaSpPair
size_categories:
- 1K<n<10K
--- |
open-llm-leaderboard/details_mwitiderrick__open_llama_3b_instruct_v_0.2 | ---
pretty_name: Evaluation run of mwitiderrick/open_llama_3b_instruct_v_0.2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [mwitiderrick/open_llama_3b_instruct_v_0.2](https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe 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.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_mwitiderrick__open_llama_3b_instruct_v_0.2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-09T17:00:38.950221](https://huggingface.co/datasets/open-llm-leaderboard/details_mwitiderrick__open_llama_3b_instruct_v_0.2/blob/main/results_2023-12-09T17-00-38.950221.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.26151949284222964,\n\
\ \"acc_stderr\": 0.03090435201991025,\n \"acc_norm\": 0.26267138346311003,\n\
\ \"acc_norm_stderr\": 0.03166222554081637,\n \"mc1\": 0.25703794369645044,\n\
\ \"mc1_stderr\": 0.01529807750948508,\n \"mc2\": 0.3816212066330296,\n\
\ \"mc2_stderr\": 0.013929117644890942\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.36177474402730375,\n \"acc_stderr\": 0.014041957945038059,\n\
\ \"acc_norm\": 0.3848122866894198,\n \"acc_norm_stderr\": 0.014218371065251107\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4953196574387572,\n\
\ \"acc_stderr\": 0.00498956279828052,\n \"acc_norm\": 0.6676956781517626,\n\
\ \"acc_norm_stderr\": 0.004700767741735565\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847415,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847415\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.28888888888888886,\n\
\ \"acc_stderr\": 0.0391545063041425,\n \"acc_norm\": 0.28888888888888886,\n\
\ \"acc_norm_stderr\": 0.0391545063041425\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.18421052631578946,\n \"acc_stderr\": 0.0315469804508223,\n\
\ \"acc_norm\": 0.18421052631578946,\n \"acc_norm_stderr\": 0.0315469804508223\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.27,\n\
\ \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.27,\n \
\ \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.2528301886792453,\n \"acc_stderr\": 0.026749899771241238,\n\
\ \"acc_norm\": 0.2528301886792453,\n \"acc_norm_stderr\": 0.026749899771241238\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2569444444444444,\n\
\ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n\
\ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \
\ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\"\
: 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.24277456647398843,\n\
\ \"acc_stderr\": 0.0326926380614177,\n \"acc_norm\": 0.24277456647398843,\n\
\ \"acc_norm_stderr\": 0.0326926380614177\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179961,\n\
\ \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179961\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.26,\n \"acc_stderr\": 0.04408440022768077,\n \"acc_norm\": 0.26,\n\
\ \"acc_norm_stderr\": 0.04408440022768077\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.30638297872340425,\n \"acc_stderr\": 0.030135906478517563,\n\
\ \"acc_norm\": 0.30638297872340425,\n \"acc_norm_stderr\": 0.030135906478517563\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2631578947368421,\n\
\ \"acc_stderr\": 0.0414243971948936,\n \"acc_norm\": 0.2631578947368421,\n\
\ \"acc_norm_stderr\": 0.0414243971948936\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2482758620689655,\n \"acc_stderr\": 0.03600105692727771,\n\
\ \"acc_norm\": 0.2482758620689655,\n \"acc_norm_stderr\": 0.03600105692727771\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.21428571428571427,\n \"acc_stderr\": 0.021132859182754444,\n \"\
acc_norm\": 0.21428571428571427,\n \"acc_norm_stderr\": 0.021132859182754444\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.29365079365079366,\n\
\ \"acc_stderr\": 0.040735243221471276,\n \"acc_norm\": 0.29365079365079366,\n\
\ \"acc_norm_stderr\": 0.040735243221471276\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.26129032258064516,\n\
\ \"acc_stderr\": 0.024993053397764815,\n \"acc_norm\": 0.26129032258064516,\n\
\ \"acc_norm_stderr\": 0.024993053397764815\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.2660098522167488,\n \"acc_stderr\": 0.03108982600293753,\n\
\ \"acc_norm\": 0.2660098522167488,\n \"acc_norm_stderr\": 0.03108982600293753\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.17,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\"\
: 0.17,\n \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.2727272727272727,\n \"acc_stderr\": 0.03477691162163659,\n\
\ \"acc_norm\": 0.2727272727272727,\n \"acc_norm_stderr\": 0.03477691162163659\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.22727272727272727,\n \"acc_stderr\": 0.02985751567338641,\n \"\
acc_norm\": 0.22727272727272727,\n \"acc_norm_stderr\": 0.02985751567338641\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.21761658031088082,\n \"acc_stderr\": 0.029778663037752954,\n\
\ \"acc_norm\": 0.21761658031088082,\n \"acc_norm_stderr\": 0.029778663037752954\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.23076923076923078,\n \"acc_stderr\": 0.02136202772522271,\n\
\ \"acc_norm\": 0.23076923076923078,\n \"acc_norm_stderr\": 0.02136202772522271\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.24074074074074073,\n \"acc_stderr\": 0.026067159222275805,\n \
\ \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.026067159222275805\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.21428571428571427,\n \"acc_stderr\": 0.026653531596715484,\n\
\ \"acc_norm\": 0.21428571428571427,\n \"acc_norm_stderr\": 0.026653531596715484\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.271523178807947,\n \"acc_stderr\": 0.036313298039696545,\n \"\
acc_norm\": 0.271523178807947,\n \"acc_norm_stderr\": 0.036313298039696545\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.23669724770642203,\n \"acc_stderr\": 0.018224078117299085,\n \"\
acc_norm\": 0.23669724770642203,\n \"acc_norm_stderr\": 0.018224078117299085\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.18055555555555555,\n \"acc_stderr\": 0.026232878971491666,\n \"\
acc_norm\": 0.18055555555555555,\n \"acc_norm_stderr\": 0.026232878971491666\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.2647058823529412,\n \"acc_stderr\": 0.030964517926923393,\n \"\
acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.030964517926923393\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.26582278481012656,\n \"acc_stderr\": 0.02875679962965834,\n \
\ \"acc_norm\": 0.26582278481012656,\n \"acc_norm_stderr\": 0.02875679962965834\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.336322869955157,\n\
\ \"acc_stderr\": 0.031708824268455,\n \"acc_norm\": 0.336322869955157,\n\
\ \"acc_norm_stderr\": 0.031708824268455\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.22137404580152673,\n \"acc_stderr\": 0.036412970813137276,\n\
\ \"acc_norm\": 0.22137404580152673,\n \"acc_norm_stderr\": 0.036412970813137276\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.24793388429752067,\n \"acc_stderr\": 0.039418975265163025,\n \"\
acc_norm\": 0.24793388429752067,\n \"acc_norm_stderr\": 0.039418975265163025\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.2962962962962963,\n\
\ \"acc_stderr\": 0.04414343666854933,\n \"acc_norm\": 0.2962962962962963,\n\
\ \"acc_norm_stderr\": 0.04414343666854933\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.19631901840490798,\n \"acc_stderr\": 0.031207970394709215,\n\
\ \"acc_norm\": 0.19631901840490798,\n \"acc_norm_stderr\": 0.031207970394709215\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.22321428571428573,\n\
\ \"acc_stderr\": 0.039523019677025116,\n \"acc_norm\": 0.22321428571428573,\n\
\ \"acc_norm_stderr\": 0.039523019677025116\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.2524271844660194,\n \"acc_stderr\": 0.04301250399690877,\n\
\ \"acc_norm\": 0.2524271844660194,\n \"acc_norm_stderr\": 0.04301250399690877\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2692307692307692,\n\
\ \"acc_stderr\": 0.02905858830374884,\n \"acc_norm\": 0.2692307692307692,\n\
\ \"acc_norm_stderr\": 0.02905858830374884\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384741,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384741\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2886334610472541,\n\
\ \"acc_stderr\": 0.016203792703197797,\n \"acc_norm\": 0.2886334610472541,\n\
\ \"acc_norm_stderr\": 0.016203792703197797\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.29190751445086704,\n \"acc_stderr\": 0.024476994076247333,\n\
\ \"acc_norm\": 0.29190751445086704,\n \"acc_norm_stderr\": 0.024476994076247333\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24692737430167597,\n\
\ \"acc_stderr\": 0.014422292204808835,\n \"acc_norm\": 0.24692737430167597,\n\
\ \"acc_norm_stderr\": 0.014422292204808835\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.23529411764705882,\n \"acc_stderr\": 0.024288619466046105,\n\
\ \"acc_norm\": 0.23529411764705882,\n \"acc_norm_stderr\": 0.024288619466046105\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2765273311897106,\n\
\ \"acc_stderr\": 0.025403832978179622,\n \"acc_norm\": 0.2765273311897106,\n\
\ \"acc_norm_stderr\": 0.025403832978179622\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.2654320987654321,\n \"acc_stderr\": 0.024569223600460845,\n\
\ \"acc_norm\": 0.2654320987654321,\n \"acc_norm_stderr\": 0.024569223600460845\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.2730496453900709,\n \"acc_stderr\": 0.02657786094330786,\n \
\ \"acc_norm\": 0.2730496453900709,\n \"acc_norm_stderr\": 0.02657786094330786\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.23989569752281617,\n\
\ \"acc_stderr\": 0.010906282617981634,\n \"acc_norm\": 0.23989569752281617,\n\
\ \"acc_norm_stderr\": 0.010906282617981634\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.20220588235294118,\n \"acc_stderr\": 0.02439819298665492,\n\
\ \"acc_norm\": 0.20220588235294118,\n \"acc_norm_stderr\": 0.02439819298665492\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.2581699346405229,\n \"acc_stderr\": 0.017704531653250075,\n \
\ \"acc_norm\": 0.2581699346405229,\n \"acc_norm_stderr\": 0.017704531653250075\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.36363636363636365,\n\
\ \"acc_stderr\": 0.04607582090719976,\n \"acc_norm\": 0.36363636363636365,\n\
\ \"acc_norm_stderr\": 0.04607582090719976\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.2163265306122449,\n \"acc_stderr\": 0.026358916334904035,\n\
\ \"acc_norm\": 0.2163265306122449,\n \"acc_norm_stderr\": 0.026358916334904035\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.27860696517412936,\n\
\ \"acc_stderr\": 0.031700561834973086,\n \"acc_norm\": 0.27860696517412936,\n\
\ \"acc_norm_stderr\": 0.031700561834973086\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \
\ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3313253012048193,\n\
\ \"acc_stderr\": 0.036643147772880864,\n \"acc_norm\": 0.3313253012048193,\n\
\ \"acc_norm_stderr\": 0.036643147772880864\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.28654970760233917,\n \"acc_stderr\": 0.034678266857038266,\n\
\ \"acc_norm\": 0.28654970760233917,\n \"acc_norm_stderr\": 0.034678266857038266\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.25703794369645044,\n\
\ \"mc1_stderr\": 0.01529807750948508,\n \"mc2\": 0.3816212066330296,\n\
\ \"mc2_stderr\": 0.013929117644890942\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6345698500394633,\n \"acc_stderr\": 0.013533965097638795\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.01592115238817286,\n \
\ \"acc_stderr\": 0.0034478192723889946\n }\n}\n```"
repo_url: https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|arc:challenge|25_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|gsm8k|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hellaswag|10_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T17-00-38.950221.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-09T17-00-38.950221.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- '**/details_harness|winogrande|5_2023-12-09T17-00-38.950221.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-09T17-00-38.950221.parquet'
- config_name: results
data_files:
- split: 2023_12_09T17_00_38.950221
path:
- results_2023-12-09T17-00-38.950221.parquet
- split: latest
path:
- results_2023-12-09T17-00-38.950221.parquet
---
# Dataset Card for Evaluation run of mwitiderrick/open_llama_3b_instruct_v_0.2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [mwitiderrick/open_llama_3b_instruct_v_0.2](https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding 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 configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_mwitiderrick__open_llama_3b_instruct_v_0.2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-09T17:00:38.950221](https://huggingface.co/datasets/open-llm-leaderboard/details_mwitiderrick__open_llama_3b_instruct_v_0.2/blob/main/results_2023-12-09T17-00-38.950221.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
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"acc_stderr": 0.03090435201991025,
"acc_norm": 0.26267138346311003,
"acc_norm_stderr": 0.03166222554081637,
"mc1": 0.25703794369645044,
"mc1_stderr": 0.01529807750948508,
"mc2": 0.3816212066330296,
"mc2_stderr": 0.013929117644890942
},
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"acc_stderr": 0.014041957945038059,
"acc_norm": 0.3848122866894198,
"acc_norm_stderr": 0.014218371065251107
},
"harness|hellaswag|10": {
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"acc_norm_stderr": 0.004700767741735565
},
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"harness|hendrycksTest-management|5": {
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"harness|hendrycksTest-marketing|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|truthfulqa:mc|0": {
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"harness|winogrande|5": {
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},
"harness|gsm8k|5": {
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"acc_stderr": 0.0034478192723889946
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
AvishayDev/hebrew-BenIshHai | ---
dataset_info:
features:
- name: year
dtype: string
- name: parasha
dtype: string
- name: number
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2035374
num_examples: 1992
download_size: 948349
dataset_size: 2035374
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- question-answering
- text-generation
language:
- he
---
# Dataset Card for "hebrew-holy-DS-BenIishHay"
## Overview
This dataset was created with [ויקיטקסט](https://he.wikisource.org/wiki/%D7%A2%D7%9E%D7%95%D7%93_%D7%A8%D7%90%D7%A9%D7%99).
It contain all the Halaha's from the book 'Ben Ish Hai' in hebrew.
## Dataset Structure
The dataset is structured with the following columns:
- **Year:** Sign indicating the year of the text, representing a part of the book.
- **Parasha:** Sign marking the parasha of the text, resembling an episode in the book.
- **Number:** Sign marking the number of the text, often representing a part of an episode, typically consisting of 2 paragraphs.
- **Text:** The actual text content from the book.
Plase use it *only* to sanctify the name of G-d in the world! Thanks |
CyberHarem/scw_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of scw/SCW/SCW (Girls' Frontline)
This is the dataset of scw/SCW/SCW (Girls' Frontline), containing 14 images and their tags.
The core tags of this character are `blonde_hair, short_hair, red_eyes, headphones, 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 | 14 | 16.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/scw_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 14 | 11.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/scw_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 30 | 20.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/scw_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 14 | 16.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/scw_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 30 | 25.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/scw_girlsfrontline/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/scw_girlsfrontline',
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 | 14 |  |  |  |  |  | 1girl, solo, jacket, gloves, looking_at_viewer, smile, assault_rifle, armband, boots, holding_gun, single_thighhigh, socks, uneven_legwear, bag, eagle, full_body, headset, red_scarf |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | jacket | gloves | looking_at_viewer | smile | assault_rifle | armband | boots | holding_gun | single_thighhigh | socks | uneven_legwear | bag | eagle | full_body | headset | red_scarf |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------|:---------|:--------------------|:--------|:----------------|:----------|:--------|:--------------|:-------------------|:--------|:-----------------|:------|:--------|:------------|:----------|:------------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
open-llm-leaderboard/details_Ba2han__TinyOpenHermes-1.1B-4k | ---
pretty_name: Evaluation run of Ba2han/TinyOpenHermes-1.1B-4k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Ba2han/TinyOpenHermes-1.1B-4k](https://huggingface.co/Ba2han/TinyOpenHermes-1.1B-4k)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe 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.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Ba2han__TinyOpenHermes-1.1B-4k\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-21T01:30:57.754034](https://huggingface.co/datasets/open-llm-leaderboard/details_Ba2han__TinyOpenHermes-1.1B-4k/blob/main/results_2024-01-21T01-30-57.754034.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.26936710062529556,\n\
\ \"acc_stderr\": 0.031263567264242856,\n \"acc_norm\": 0.2711224560641922,\n\
\ \"acc_norm_stderr\": 0.03208061243582453,\n \"mc1\": 0.23378212974296206,\n\
\ \"mc1_stderr\": 0.01481619599193158,\n \"mc2\": 0.3732859557148343,\n\
\ \"mc2_stderr\": 0.014080021093470342\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.31313993174061433,\n \"acc_stderr\": 0.013552671543623497,\n\
\ \"acc_norm\": 0.3361774744027304,\n \"acc_norm_stderr\": 0.01380485502620576\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.44373630750846443,\n\
\ \"acc_stderr\": 0.004958089432669985,\n \"acc_norm\": 0.5853415654252141,\n\
\ \"acc_norm_stderr\": 0.004916561213591294\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720683,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720683\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.21481481481481482,\n\
\ \"acc_stderr\": 0.035478541985608236,\n \"acc_norm\": 0.21481481481481482,\n\
\ \"acc_norm_stderr\": 0.035478541985608236\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.24342105263157895,\n \"acc_stderr\": 0.034923496688842384,\n\
\ \"acc_norm\": 0.24342105263157895,\n \"acc_norm_stderr\": 0.034923496688842384\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.26,\n\
\ \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.26,\n \
\ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.2528301886792453,\n \"acc_stderr\": 0.026749899771241235,\n\
\ \"acc_norm\": 0.2528301886792453,\n \"acc_norm_stderr\": 0.026749899771241235\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2222222222222222,\n\
\ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.2222222222222222,\n\
\ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \"acc_norm\"\
: 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.19653179190751446,\n\
\ \"acc_stderr\": 0.03029957466478815,\n \"acc_norm\": 0.19653179190751446,\n\
\ \"acc_norm_stderr\": 0.03029957466478815\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.04023382273617748,\n\
\ \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.04023382273617748\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.22,\n \"acc_stderr\": 0.041633319989322674,\n \"acc_norm\": 0.22,\n\
\ \"acc_norm_stderr\": 0.041633319989322674\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.3021276595744681,\n \"acc_stderr\": 0.030017554471880554,\n\
\ \"acc_norm\": 0.3021276595744681,\n \"acc_norm_stderr\": 0.030017554471880554\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.30701754385964913,\n\
\ \"acc_stderr\": 0.04339138322579861,\n \"acc_norm\": 0.30701754385964913,\n\
\ \"acc_norm_stderr\": 0.04339138322579861\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2,\n \"acc_stderr\": 0.0333333333333333,\n \
\ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.0333333333333333\n },\n\
\ \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.2698412698412698,\n\
\ \"acc_stderr\": 0.02286083830923207,\n \"acc_norm\": 0.2698412698412698,\n\
\ \"acc_norm_stderr\": 0.02286083830923207\n },\n \"harness|hendrycksTest-formal_logic|5\"\
: {\n \"acc\": 0.29365079365079366,\n \"acc_stderr\": 0.04073524322147125,\n\
\ \"acc_norm\": 0.29365079365079366,\n \"acc_norm_stderr\": 0.04073524322147125\n\
\ },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.3,\n\
\ \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \
\ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_biology|5\"\
: {\n \"acc\": 0.25161290322580643,\n \"acc_stderr\": 0.024685979286239966,\n\
\ \"acc_norm\": 0.25161290322580643,\n \"acc_norm_stderr\": 0.024685979286239966\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.2660098522167488,\n \"acc_stderr\": 0.031089826002937523,\n \"\
acc_norm\": 0.2660098522167488,\n \"acc_norm_stderr\": 0.031089826002937523\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.26,\n \"acc_stderr\": 0.044084400227680794,\n \"acc_norm\"\
: 0.26,\n \"acc_norm_stderr\": 0.044084400227680794\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.23636363636363636,\n \"acc_stderr\": 0.033175059300091805,\n\
\ \"acc_norm\": 0.23636363636363636,\n \"acc_norm_stderr\": 0.033175059300091805\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.25252525252525254,\n \"acc_stderr\": 0.03095405547036592,\n \"\
acc_norm\": 0.25252525252525254,\n \"acc_norm_stderr\": 0.03095405547036592\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.3626943005181347,\n \"acc_stderr\": 0.034697137917043715,\n\
\ \"acc_norm\": 0.3626943005181347,\n \"acc_norm_stderr\": 0.034697137917043715\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.28974358974358977,\n \"acc_stderr\": 0.023000628243687964,\n\
\ \"acc_norm\": 0.28974358974358977,\n \"acc_norm_stderr\": 0.023000628243687964\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.22962962962962963,\n \"acc_stderr\": 0.025644108639267624,\n \
\ \"acc_norm\": 0.22962962962962963,\n \"acc_norm_stderr\": 0.025644108639267624\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.2605042016806723,\n \"acc_stderr\": 0.028510251512341937,\n\
\ \"acc_norm\": 0.2605042016806723,\n \"acc_norm_stderr\": 0.028510251512341937\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.304635761589404,\n \"acc_stderr\": 0.03757949922943342,\n \"acc_norm\"\
: 0.304635761589404,\n \"acc_norm_stderr\": 0.03757949922943342\n },\n\
\ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.25137614678899084,\n\
\ \"acc_stderr\": 0.018599206360287415,\n \"acc_norm\": 0.25137614678899084,\n\
\ \"acc_norm_stderr\": 0.018599206360287415\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\
: {\n \"acc\": 0.41203703703703703,\n \"acc_stderr\": 0.03356787758160835,\n\
\ \"acc_norm\": 0.41203703703703703,\n \"acc_norm_stderr\": 0.03356787758160835\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.27941176470588236,\n \"acc_stderr\": 0.03149328104507956,\n \"\
acc_norm\": 0.27941176470588236,\n \"acc_norm_stderr\": 0.03149328104507956\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.28270042194092826,\n \"acc_stderr\": 0.029312814153955934,\n \
\ \"acc_norm\": 0.28270042194092826,\n \"acc_norm_stderr\": 0.029312814153955934\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3542600896860987,\n\
\ \"acc_stderr\": 0.032100621541349864,\n \"acc_norm\": 0.3542600896860987,\n\
\ \"acc_norm_stderr\": 0.032100621541349864\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.2366412213740458,\n \"acc_stderr\": 0.0372767357559692,\n\
\ \"acc_norm\": 0.2366412213740458,\n \"acc_norm_stderr\": 0.0372767357559692\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"\
acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.2222222222222222,\n\
\ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.2222222222222222,\n\
\ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.3312883435582822,\n \"acc_stderr\": 0.03697983910025588,\n\
\ \"acc_norm\": 0.3312883435582822,\n \"acc_norm_stderr\": 0.03697983910025588\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.1875,\n\
\ \"acc_stderr\": 0.0370468111477387,\n \"acc_norm\": 0.1875,\n \
\ \"acc_norm_stderr\": 0.0370468111477387\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.27184466019417475,\n \"acc_stderr\": 0.044052680241409216,\n\
\ \"acc_norm\": 0.27184466019417475,\n \"acc_norm_stderr\": 0.044052680241409216\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2564102564102564,\n\
\ \"acc_stderr\": 0.028605953702004243,\n \"acc_norm\": 0.2564102564102564,\n\
\ \"acc_norm_stderr\": 0.028605953702004243\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2707535121328225,\n\
\ \"acc_stderr\": 0.01588988836256049,\n \"acc_norm\": 0.2707535121328225,\n\
\ \"acc_norm_stderr\": 0.01588988836256049\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.23410404624277456,\n \"acc_stderr\": 0.022797110278071145,\n\
\ \"acc_norm\": 0.23410404624277456,\n \"acc_norm_stderr\": 0.022797110278071145\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24134078212290502,\n\
\ \"acc_stderr\": 0.014310999547961452,\n \"acc_norm\": 0.24134078212290502,\n\
\ \"acc_norm_stderr\": 0.014310999547961452\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.023805186524888146,\n\
\ \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.023805186524888146\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.3054662379421222,\n\
\ \"acc_stderr\": 0.026160584450140478,\n \"acc_norm\": 0.3054662379421222,\n\
\ \"acc_norm_stderr\": 0.026160584450140478\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.2654320987654321,\n \"acc_stderr\": 0.024569223600460845,\n\
\ \"acc_norm\": 0.2654320987654321,\n \"acc_norm_stderr\": 0.024569223600460845\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.2695035460992908,\n \"acc_stderr\": 0.026469036818590613,\n \
\ \"acc_norm\": 0.2695035460992908,\n \"acc_norm_stderr\": 0.026469036818590613\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.22294654498044328,\n\
\ \"acc_stderr\": 0.010630525747386077,\n \"acc_norm\": 0.22294654498044328,\n\
\ \"acc_norm_stderr\": 0.010630525747386077\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.22794117647058823,\n \"acc_stderr\": 0.025483081468029804,\n\
\ \"acc_norm\": 0.22794117647058823,\n \"acc_norm_stderr\": 0.025483081468029804\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.2369281045751634,\n \"acc_stderr\": 0.017201662169789775,\n \
\ \"acc_norm\": 0.2369281045751634,\n \"acc_norm_stderr\": 0.017201662169789775\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.3090909090909091,\n\
\ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.3090909090909091,\n\
\ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.19591836734693877,\n \"acc_stderr\": 0.025409301953225678,\n\
\ \"acc_norm\": 0.19591836734693877,\n \"acc_norm_stderr\": 0.025409301953225678\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.2537313432835821,\n\
\ \"acc_stderr\": 0.030769444967296024,\n \"acc_norm\": 0.2537313432835821,\n\
\ \"acc_norm_stderr\": 0.030769444967296024\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.18,\n \"acc_stderr\": 0.03861229196653694,\n \
\ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.03861229196653694\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.30120481927710846,\n\
\ \"acc_stderr\": 0.0357160923005348,\n \"acc_norm\": 0.30120481927710846,\n\
\ \"acc_norm_stderr\": 0.0357160923005348\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.2807017543859649,\n \"acc_stderr\": 0.034462962170884265,\n\
\ \"acc_norm\": 0.2807017543859649,\n \"acc_norm_stderr\": 0.034462962170884265\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.23378212974296206,\n\
\ \"mc1_stderr\": 0.01481619599193158,\n \"mc2\": 0.3732859557148343,\n\
\ \"mc2_stderr\": 0.014080021093470342\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5990528808208366,\n \"acc_stderr\": 0.01377397455494803\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.000758150113722517,\n \
\ \"acc_stderr\": 0.0007581501137225406\n }\n}\n```"
repo_url: https://huggingface.co/Ba2han/TinyOpenHermes-1.1B-4k
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|arc:challenge|25_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|gsm8k|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hellaswag|10_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-21T01-30-57.754034.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-21T01-30-57.754034.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- '**/details_harness|winogrande|5_2024-01-21T01-30-57.754034.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-21T01-30-57.754034.parquet'
- config_name: results
data_files:
- split: 2024_01_21T01_30_57.754034
path:
- results_2024-01-21T01-30-57.754034.parquet
- split: latest
path:
- results_2024-01-21T01-30-57.754034.parquet
---
# Dataset Card for Evaluation run of Ba2han/TinyOpenHermes-1.1B-4k
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Ba2han/TinyOpenHermes-1.1B-4k](https://huggingface.co/Ba2han/TinyOpenHermes-1.1B-4k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding 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 configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Ba2han__TinyOpenHermes-1.1B-4k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-21T01:30:57.754034](https://huggingface.co/datasets/open-llm-leaderboard/details_Ba2han__TinyOpenHermes-1.1B-4k/blob/main/results_2024-01-21T01-30-57.754034.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.26936710062529556,
"acc_stderr": 0.031263567264242856,
"acc_norm": 0.2711224560641922,
"acc_norm_stderr": 0.03208061243582453,
"mc1": 0.23378212974296206,
"mc1_stderr": 0.01481619599193158,
"mc2": 0.3732859557148343,
"mc2_stderr": 0.014080021093470342
},
"harness|arc:challenge|25": {
"acc": 0.31313993174061433,
"acc_stderr": 0.013552671543623497,
"acc_norm": 0.3361774744027304,
"acc_norm_stderr": 0.01380485502620576
},
"harness|hellaswag|10": {
"acc": 0.44373630750846443,
"acc_stderr": 0.004958089432669985,
"acc_norm": 0.5853415654252141,
"acc_norm_stderr": 0.004916561213591294
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720683,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720683
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.21481481481481482,
"acc_stderr": 0.035478541985608236,
"acc_norm": 0.21481481481481482,
"acc_norm_stderr": 0.035478541985608236
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.24342105263157895,
"acc_stderr": 0.034923496688842384,
"acc_norm": 0.24342105263157895,
"acc_norm_stderr": 0.034923496688842384
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.26,
"acc_stderr": 0.04408440022768078,
"acc_norm": 0.26,
"acc_norm_stderr": 0.04408440022768078
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.2528301886792453,
"acc_stderr": 0.026749899771241235,
"acc_norm": 0.2528301886792453,
"acc_norm_stderr": 0.026749899771241235
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2222222222222222,
"acc_stderr": 0.03476590104304134,
"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252605,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252605
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.28,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.28,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.19653179190751446,
"acc_stderr": 0.03029957466478815,
"acc_norm": 0.19653179190751446,
"acc_norm_stderr": 0.03029957466478815
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.20588235294117646,
"acc_stderr": 0.04023382273617748,
"acc_norm": 0.20588235294117646,
"acc_norm_stderr": 0.04023382273617748
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.22,
"acc_stderr": 0.041633319989322674,
"acc_norm": 0.22,
"acc_norm_stderr": 0.041633319989322674
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.3021276595744681,
"acc_stderr": 0.030017554471880554,
"acc_norm": 0.3021276595744681,
"acc_norm_stderr": 0.030017554471880554
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.30701754385964913,
"acc_stderr": 0.04339138322579861,
"acc_norm": 0.30701754385964913,
"acc_norm_stderr": 0.04339138322579861
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2,
"acc_stderr": 0.0333333333333333,
"acc_norm": 0.2,
"acc_norm_stderr": 0.0333333333333333
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2698412698412698,
"acc_stderr": 0.02286083830923207,
"acc_norm": 0.2698412698412698,
"acc_norm_stderr": 0.02286083830923207
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.29365079365079366,
"acc_stderr": 0.04073524322147125,
"acc_norm": 0.29365079365079366,
"acc_norm_stderr": 0.04073524322147125
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.25161290322580643,
"acc_stderr": 0.024685979286239966,
"acc_norm": 0.25161290322580643,
"acc_norm_stderr": 0.024685979286239966
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.2660098522167488,
"acc_stderr": 0.031089826002937523,
"acc_norm": 0.2660098522167488,
"acc_norm_stderr": 0.031089826002937523
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.26,
"acc_stderr": 0.044084400227680794,
"acc_norm": 0.26,
"acc_norm_stderr": 0.044084400227680794
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.23636363636363636,
"acc_stderr": 0.033175059300091805,
"acc_norm": 0.23636363636363636,
"acc_norm_stderr": 0.033175059300091805
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.25252525252525254,
"acc_stderr": 0.03095405547036592,
"acc_norm": 0.25252525252525254,
"acc_norm_stderr": 0.03095405547036592
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.3626943005181347,
"acc_stderr": 0.034697137917043715,
"acc_norm": 0.3626943005181347,
"acc_norm_stderr": 0.034697137917043715
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.28974358974358977,
"acc_stderr": 0.023000628243687964,
"acc_norm": 0.28974358974358977,
"acc_norm_stderr": 0.023000628243687964
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.22962962962962963,
"acc_stderr": 0.025644108639267624,
"acc_norm": 0.22962962962962963,
"acc_norm_stderr": 0.025644108639267624
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.2605042016806723,
"acc_stderr": 0.028510251512341937,
"acc_norm": 0.2605042016806723,
"acc_norm_stderr": 0.028510251512341937
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.304635761589404,
"acc_stderr": 0.03757949922943342,
"acc_norm": 0.304635761589404,
"acc_norm_stderr": 0.03757949922943342
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.25137614678899084,
"acc_stderr": 0.018599206360287415,
"acc_norm": 0.25137614678899084,
"acc_norm_stderr": 0.018599206360287415
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.41203703703703703,
"acc_stderr": 0.03356787758160835,
"acc_norm": 0.41203703703703703,
"acc_norm_stderr": 0.03356787758160835
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.27941176470588236,
"acc_stderr": 0.03149328104507956,
"acc_norm": 0.27941176470588236,
"acc_norm_stderr": 0.03149328104507956
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.28270042194092826,
"acc_stderr": 0.029312814153955934,
"acc_norm": 0.28270042194092826,
"acc_norm_stderr": 0.029312814153955934
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.3542600896860987,
"acc_stderr": 0.032100621541349864,
"acc_norm": 0.3542600896860987,
"acc_norm_stderr": 0.032100621541349864
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.2366412213740458,
"acc_stderr": 0.0372767357559692,
"acc_norm": 0.2366412213740458,
"acc_norm_stderr": 0.0372767357559692
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.2396694214876033,
"acc_stderr": 0.03896878985070417,
"acc_norm": 0.2396694214876033,
"acc_norm_stderr": 0.03896878985070417
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.2222222222222222,
"acc_stderr": 0.0401910747255735,
"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.0401910747255735
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.3312883435582822,
"acc_stderr": 0.03697983910025588,
"acc_norm": 0.3312883435582822,
"acc_norm_stderr": 0.03697983910025588
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.1875,
"acc_stderr": 0.0370468111477387,
"acc_norm": 0.1875,
"acc_norm_stderr": 0.0370468111477387
},
"harness|hendrycksTest-management|5": {
"acc": 0.27184466019417475,
"acc_stderr": 0.044052680241409216,
"acc_norm": 0.27184466019417475,
"acc_norm_stderr": 0.044052680241409216
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.2564102564102564,
"acc_stderr": 0.028605953702004243,
"acc_norm": 0.2564102564102564,
"acc_norm_stderr": 0.028605953702004243
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.2707535121328225,
"acc_stderr": 0.01588988836256049,
"acc_norm": 0.2707535121328225,
"acc_norm_stderr": 0.01588988836256049
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.23410404624277456,
"acc_stderr": 0.022797110278071145,
"acc_norm": 0.23410404624277456,
"acc_norm_stderr": 0.022797110278071145
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.24134078212290502,
"acc_stderr": 0.014310999547961452,
"acc_norm": 0.24134078212290502,
"acc_norm_stderr": 0.014310999547961452
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.2222222222222222,
"acc_stderr": 0.023805186524888146,
"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.023805186524888146
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.3054662379421222,
"acc_stderr": 0.026160584450140478,
"acc_norm": 0.3054662379421222,
"acc_norm_stderr": 0.026160584450140478
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.2654320987654321,
"acc_stderr": 0.024569223600460845,
"acc_norm": 0.2654320987654321,
"acc_norm_stderr": 0.024569223600460845
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.2695035460992908,
"acc_stderr": 0.026469036818590613,
"acc_norm": 0.2695035460992908,
"acc_norm_stderr": 0.026469036818590613
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.22294654498044328,
"acc_stderr": 0.010630525747386077,
"acc_norm": 0.22294654498044328,
"acc_norm_stderr": 0.010630525747386077
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.22794117647058823,
"acc_stderr": 0.025483081468029804,
"acc_norm": 0.22794117647058823,
"acc_norm_stderr": 0.025483081468029804
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.2369281045751634,
"acc_stderr": 0.017201662169789775,
"acc_norm": 0.2369281045751634,
"acc_norm_stderr": 0.017201662169789775
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.3090909090909091,
"acc_stderr": 0.044262946482000985,
"acc_norm": 0.3090909090909091,
"acc_norm_stderr": 0.044262946482000985
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.19591836734693877,
"acc_stderr": 0.025409301953225678,
"acc_norm": 0.19591836734693877,
"acc_norm_stderr": 0.025409301953225678
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.2537313432835821,
"acc_stderr": 0.030769444967296024,
"acc_norm": 0.2537313432835821,
"acc_norm_stderr": 0.030769444967296024
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.18,
"acc_stderr": 0.03861229196653694,
"acc_norm": 0.18,
"acc_norm_stderr": 0.03861229196653694
},
"harness|hendrycksTest-virology|5": {
"acc": 0.30120481927710846,
"acc_stderr": 0.0357160923005348,
"acc_norm": 0.30120481927710846,
"acc_norm_stderr": 0.0357160923005348
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.2807017543859649,
"acc_stderr": 0.034462962170884265,
"acc_norm": 0.2807017543859649,
"acc_norm_stderr": 0.034462962170884265
},
"harness|truthfulqa:mc|0": {
"mc1": 0.23378212974296206,
"mc1_stderr": 0.01481619599193158,
"mc2": 0.3732859557148343,
"mc2_stderr": 0.014080021093470342
},
"harness|winogrande|5": {
"acc": 0.5990528808208366,
"acc_stderr": 0.01377397455494803
},
"harness|gsm8k|5": {
"acc": 0.000758150113722517,
"acc_stderr": 0.0007581501137225406
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### 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. -->
[More Information Needed]
#### 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. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### 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. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### 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. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
[More Information Needed] |
adkhamboy/sentiment-uz | ---
license: mit
---
|
VaibhavGp69/Aarogya_wiki_medical_terms | ---
dataset_info:
features:
- name: __index_level_0__
dtype: int64
- name: Aarogya_prompt
dtype: string
- name: page_title
dtype: string
- name: page_text
dtype: string
splits:
- name: train
num_bytes: 61396949
num_examples: 6861
download_size: 33268455
dataset_size: 61396949
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_Sao10K__Typhon-Mixtral-v1 | ---
pretty_name: Evaluation run of Sao10K/Typhon-Mixtral-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Sao10K/Typhon-Mixtral-v1](https://huggingface.co/Sao10K/Typhon-Mixtral-v1) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe 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.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Sao10K__Typhon-Mixtral-v1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-01T05:18:14.404134](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Typhon-Mixtral-v1/blob/main/results_2024-03-01T05-18-14.404134.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7105708887005399,\n\
\ \"acc_stderr\": 0.030127833585423123,\n \"acc_norm\": 0.7139835293589697,\n\
\ \"acc_norm_stderr\": 0.03070859215116814,\n \"mc1\": 0.5201958384332925,\n\
\ \"mc1_stderr\": 0.017489216849737057,\n \"mc2\": 0.6880606485475675,\n\
\ \"mc2_stderr\": 0.01485204583043956\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.681740614334471,\n \"acc_stderr\": 0.013611993916971453,\n\
\ \"acc_norm\": 0.7184300341296929,\n \"acc_norm_stderr\": 0.013143376735009019\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6948814977096196,\n\
\ \"acc_stderr\": 0.004595165551383617,\n \"acc_norm\": 0.874726150169289,\n\
\ \"acc_norm_stderr\": 0.003303526413123495\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6962962962962963,\n\
\ \"acc_stderr\": 0.03972552884785136,\n \"acc_norm\": 0.6962962962962963,\n\
\ \"acc_norm_stderr\": 0.03972552884785136\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8223684210526315,\n \"acc_stderr\": 0.03110318238312338,\n\
\ \"acc_norm\": 0.8223684210526315,\n \"acc_norm_stderr\": 0.03110318238312338\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.76,\n\
\ \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \
\ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7773584905660378,\n \"acc_stderr\": 0.025604233470899098,\n\
\ \"acc_norm\": 0.7773584905660378,\n \"acc_norm_stderr\": 0.025604233470899098\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.875,\n\
\ \"acc_stderr\": 0.02765610492929436,\n \"acc_norm\": 0.875,\n \
\ \"acc_norm_stderr\": 0.02765610492929436\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n\
\ \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.049888765156985884,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.049888765156985884\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6994219653179191,\n\
\ \"acc_stderr\": 0.034961014811911786,\n \"acc_norm\": 0.6994219653179191,\n\
\ \"acc_norm_stderr\": 0.034961014811911786\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.049512182523962625,\n\
\ \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.049512182523962625\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\
\ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.676595744680851,\n \"acc_stderr\": 0.030579442773610334,\n\
\ \"acc_norm\": 0.676595744680851,\n \"acc_norm_stderr\": 0.030579442773610334\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.6052631578947368,\n\
\ \"acc_stderr\": 0.04598188057816542,\n \"acc_norm\": 0.6052631578947368,\n\
\ \"acc_norm_stderr\": 0.04598188057816542\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6,\n \"acc_stderr\": 0.040824829046386284,\n \
\ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.040824829046386284\n \
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.5079365079365079,\n \"acc_stderr\": 0.02574806587167329,\n \"\
acc_norm\": 0.5079365079365079,\n \"acc_norm_stderr\": 0.02574806587167329\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5238095238095238,\n\
\ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.5238095238095238,\n\
\ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8612903225806452,\n\
\ \"acc_stderr\": 0.019662961321414024,\n \"acc_norm\": 0.8612903225806452,\n\
\ \"acc_norm_stderr\": 0.019662961321414024\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.03465304488406796,\n\
\ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.03465304488406796\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\"\
: 0.77,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.806060606060606,\n \"acc_stderr\": 0.030874145136562097,\n\
\ \"acc_norm\": 0.806060606060606,\n \"acc_norm_stderr\": 0.030874145136562097\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8737373737373737,\n \"acc_stderr\": 0.023664359402880236,\n \"\
acc_norm\": 0.8737373737373737,\n \"acc_norm_stderr\": 0.023664359402880236\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9533678756476683,\n \"acc_stderr\": 0.015216761819262577,\n\
\ \"acc_norm\": 0.9533678756476683,\n \"acc_norm_stderr\": 0.015216761819262577\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6871794871794872,\n \"acc_stderr\": 0.02350757902064535,\n \
\ \"acc_norm\": 0.6871794871794872,\n \"acc_norm_stderr\": 0.02350757902064535\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.35185185185185186,\n \"acc_stderr\": 0.029116617606083025,\n \
\ \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.029116617606083025\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.8277310924369747,\n \"acc_stderr\": 0.024528664971305424,\n\
\ \"acc_norm\": 0.8277310924369747,\n \"acc_norm_stderr\": 0.024528664971305424\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.47019867549668876,\n \"acc_stderr\": 0.040752249922169775,\n \"\
acc_norm\": 0.47019867549668876,\n \"acc_norm_stderr\": 0.040752249922169775\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8752293577981651,\n \"acc_stderr\": 0.01416829835915634,\n \"\
acc_norm\": 0.8752293577981651,\n \"acc_norm_stderr\": 0.01416829835915634\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5833333333333334,\n \"acc_stderr\": 0.033622774366080424,\n \"\
acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.033622774366080424\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8578431372549019,\n \"acc_stderr\": 0.024509803921568624,\n \"\
acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.024509803921568624\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8818565400843882,\n \"acc_stderr\": 0.02101105265987846,\n \
\ \"acc_norm\": 0.8818565400843882,\n \"acc_norm_stderr\": 0.02101105265987846\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7488789237668162,\n\
\ \"acc_stderr\": 0.029105220833224615,\n \"acc_norm\": 0.7488789237668162,\n\
\ \"acc_norm_stderr\": 0.029105220833224615\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8320610687022901,\n \"acc_stderr\": 0.03278548537343138,\n\
\ \"acc_norm\": 0.8320610687022901,\n \"acc_norm_stderr\": 0.03278548537343138\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035206,\n \"\
acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035206\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.036028141763926456,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.036028141763926456\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7975460122699386,\n \"acc_stderr\": 0.031570650789119005,\n\
\ \"acc_norm\": 0.7975460122699386,\n \"acc_norm_stderr\": 0.031570650789119005\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5446428571428571,\n\
\ \"acc_stderr\": 0.04726835553719097,\n \"acc_norm\": 0.5446428571428571,\n\
\ \"acc_norm_stderr\": 0.04726835553719097\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8543689320388349,\n \"acc_stderr\": 0.034926064766237906,\n\
\ \"acc_norm\": 0.8543689320388349,\n \"acc_norm_stderr\": 0.034926064766237906\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9230769230769231,\n\
\ \"acc_stderr\": 0.017456987872436186,\n \"acc_norm\": 0.9230769230769231,\n\
\ \"acc_norm_stderr\": 0.017456987872436186\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8850574712643678,\n\
\ \"acc_stderr\": 0.011405720724593964,\n \"acc_norm\": 0.8850574712643678,\n\
\ \"acc_norm_stderr\": 0.011405720724593964\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7630057803468208,\n \"acc_stderr\": 0.02289408248992599,\n\
\ \"acc_norm\": 0.7630057803468208,\n \"acc_norm_stderr\": 0.02289408248992599\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.46033519553072627,\n\
\ \"acc_stderr\": 0.016669799592112032,\n \"acc_norm\": 0.46033519553072627,\n\
\ \"acc_norm_stderr\": 0.016669799592112032\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.8071895424836601,\n \"acc_stderr\": 0.022589318888176703,\n\
\ \"acc_norm\": 0.8071895424836601,\n \"acc_norm_stderr\": 0.022589318888176703\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7909967845659164,\n\
\ \"acc_stderr\": 0.02309314039837422,\n \"acc_norm\": 0.7909967845659164,\n\
\ \"acc_norm_stderr\": 0.02309314039837422\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.845679012345679,\n \"acc_stderr\": 0.020100830999851004,\n\
\ \"acc_norm\": 0.845679012345679,\n \"acc_norm_stderr\": 0.020100830999851004\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5425531914893617,\n \"acc_stderr\": 0.029719281272236837,\n \
\ \"acc_norm\": 0.5425531914893617,\n \"acc_norm_stderr\": 0.029719281272236837\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.559322033898305,\n\
\ \"acc_stderr\": 0.012680037994097053,\n \"acc_norm\": 0.559322033898305,\n\
\ \"acc_norm_stderr\": 0.012680037994097053\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7830882352941176,\n \"acc_stderr\": 0.025035845227711274,\n\
\ \"acc_norm\": 0.7830882352941176,\n \"acc_norm_stderr\": 0.025035845227711274\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7565359477124183,\n \"acc_stderr\": 0.017362473762146634,\n \
\ \"acc_norm\": 0.7565359477124183,\n \"acc_norm_stderr\": 0.017362473762146634\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\
\ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\
\ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7918367346938775,\n \"acc_stderr\": 0.025991117672813296,\n\
\ \"acc_norm\": 0.7918367346938775,\n \"acc_norm_stderr\": 0.025991117672813296\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8656716417910447,\n\
\ \"acc_stderr\": 0.024112678240900794,\n \"acc_norm\": 0.8656716417910447,\n\
\ \"acc_norm_stderr\": 0.024112678240900794\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \
\ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\
\ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\
\ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8947368421052632,\n \"acc_stderr\": 0.02353755765789255,\n\
\ \"acc_norm\": 0.8947368421052632,\n \"acc_norm_stderr\": 0.02353755765789255\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5201958384332925,\n\
\ \"mc1_stderr\": 0.017489216849737057,\n \"mc2\": 0.6880606485475675,\n\
\ \"mc2_stderr\": 0.01485204583043956\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8176795580110497,\n \"acc_stderr\": 0.010851565594267204\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6186504927975739,\n \
\ \"acc_stderr\": 0.013379089877400725\n }\n}\n```"
repo_url: https://huggingface.co/Sao10K/Typhon-Mixtral-v1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|arc:challenge|25_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|gsm8k|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hellaswag|10_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T05-18-14.404134.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-01T05-18-14.404134.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- '**/details_harness|winogrande|5_2024-03-01T05-18-14.404134.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-01T05-18-14.404134.parquet'
- config_name: results
data_files:
- split: 2024_03_01T05_18_14.404134
path:
- results_2024-03-01T05-18-14.404134.parquet
- split: latest
path:
- results_2024-03-01T05-18-14.404134.parquet
---
# Dataset Card for Evaluation run of Sao10K/Typhon-Mixtral-v1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Sao10K/Typhon-Mixtral-v1](https://huggingface.co/Sao10K/Typhon-Mixtral-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding 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 configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Sao10K__Typhon-Mixtral-v1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-01T05:18:14.404134](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Typhon-Mixtral-v1/blob/main/results_2024-03-01T05-18-14.404134.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.7105708887005399,
"acc_stderr": 0.030127833585423123,
"acc_norm": 0.7139835293589697,
"acc_norm_stderr": 0.03070859215116814,
"mc1": 0.5201958384332925,
"mc1_stderr": 0.017489216849737057,
"mc2": 0.6880606485475675,
"mc2_stderr": 0.01485204583043956
},
"harness|arc:challenge|25": {
"acc": 0.681740614334471,
"acc_stderr": 0.013611993916971453,
"acc_norm": 0.7184300341296929,
"acc_norm_stderr": 0.013143376735009019
},
"harness|hellaswag|10": {
"acc": 0.6948814977096196,
"acc_stderr": 0.004595165551383617,
"acc_norm": 0.874726150169289,
"acc_norm_stderr": 0.003303526413123495
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6962962962962963,
"acc_stderr": 0.03972552884785136,
"acc_norm": 0.6962962962962963,
"acc_norm_stderr": 0.03972552884785136
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8223684210526315,
"acc_stderr": 0.03110318238312338,
"acc_norm": 0.8223684210526315,
"acc_norm_stderr": 0.03110318238312338
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7773584905660378,
"acc_stderr": 0.025604233470899098,
"acc_norm": 0.7773584905660378,
"acc_norm_stderr": 0.025604233470899098
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.875,
"acc_stderr": 0.02765610492929436,
"acc_norm": 0.875,
"acc_norm_stderr": 0.02765610492929436
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.44,
"acc_stderr": 0.049888765156985884,
"acc_norm": 0.44,
"acc_norm_stderr": 0.049888765156985884
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6994219653179191,
"acc_stderr": 0.034961014811911786,
"acc_norm": 0.6994219653179191,
"acc_norm_stderr": 0.034961014811911786
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.45098039215686275,
"acc_stderr": 0.049512182523962625,
"acc_norm": 0.45098039215686275,
"acc_norm_stderr": 0.049512182523962625
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.81,
"acc_stderr": 0.039427724440366234,
"acc_norm": 0.81,
"acc_norm_stderr": 0.039427724440366234
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.676595744680851,
"acc_stderr": 0.030579442773610334,
"acc_norm": 0.676595744680851,
"acc_norm_stderr": 0.030579442773610334
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.6052631578947368,
"acc_stderr": 0.04598188057816542,
"acc_norm": 0.6052631578947368,
"acc_norm_stderr": 0.04598188057816542
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6,
"acc_stderr": 0.040824829046386284,
"acc_norm": 0.6,
"acc_norm_stderr": 0.040824829046386284
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.5079365079365079,
"acc_stderr": 0.02574806587167329,
"acc_norm": 0.5079365079365079,
"acc_norm_stderr": 0.02574806587167329
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5238095238095238,
"acc_stderr": 0.04467062628403273,
"acc_norm": 0.5238095238095238,
"acc_norm_stderr": 0.04467062628403273
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8612903225806452,
"acc_stderr": 0.019662961321414024,
"acc_norm": 0.8612903225806452,
"acc_norm_stderr": 0.019662961321414024
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5862068965517241,
"acc_stderr": 0.03465304488406796,
"acc_norm": 0.5862068965517241,
"acc_norm_stderr": 0.03465304488406796
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816506,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816506
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.806060606060606,
"acc_stderr": 0.030874145136562097,
"acc_norm": 0.806060606060606,
"acc_norm_stderr": 0.030874145136562097
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8737373737373737,
"acc_stderr": 0.023664359402880236,
"acc_norm": 0.8737373737373737,
"acc_norm_stderr": 0.023664359402880236
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9533678756476683,
"acc_stderr": 0.015216761819262577,
"acc_norm": 0.9533678756476683,
"acc_norm_stderr": 0.015216761819262577
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6871794871794872,
"acc_stderr": 0.02350757902064535,
"acc_norm": 0.6871794871794872,
"acc_norm_stderr": 0.02350757902064535
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.35185185185185186,
"acc_stderr": 0.029116617606083025,
"acc_norm": 0.35185185185185186,
"acc_norm_stderr": 0.029116617606083025
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.8277310924369747,
"acc_stderr": 0.024528664971305424,
"acc_norm": 0.8277310924369747,
"acc_norm_stderr": 0.024528664971305424
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.47019867549668876,
"acc_stderr": 0.040752249922169775,
"acc_norm": 0.47019867549668876,
"acc_norm_stderr": 0.040752249922169775
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8752293577981651,
"acc_stderr": 0.01416829835915634,
"acc_norm": 0.8752293577981651,
"acc_norm_stderr": 0.01416829835915634
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5833333333333334,
"acc_stderr": 0.033622774366080424,
"acc_norm": 0.5833333333333334,
"acc_norm_stderr": 0.033622774366080424
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8578431372549019,
"acc_stderr": 0.024509803921568624,
"acc_norm": 0.8578431372549019,
"acc_norm_stderr": 0.024509803921568624
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8818565400843882,
"acc_stderr": 0.02101105265987846,
"acc_norm": 0.8818565400843882,
"acc_norm_stderr": 0.02101105265987846
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7488789237668162,
"acc_stderr": 0.029105220833224615,
"acc_norm": 0.7488789237668162,
"acc_norm_stderr": 0.029105220833224615
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8320610687022901,
"acc_stderr": 0.03278548537343138,
"acc_norm": 0.8320610687022901,
"acc_norm_stderr": 0.03278548537343138
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8760330578512396,
"acc_stderr": 0.030083098716035206,
"acc_norm": 0.8760330578512396,
"acc_norm_stderr": 0.030083098716035206
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.036028141763926456,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.036028141763926456
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7975460122699386,
"acc_stderr": 0.031570650789119005,
"acc_norm": 0.7975460122699386,
"acc_norm_stderr": 0.031570650789119005
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5446428571428571,
"acc_stderr": 0.04726835553719097,
"acc_norm": 0.5446428571428571,
"acc_norm_stderr": 0.04726835553719097
},
"harness|hendrycksTest-management|5": {
"acc": 0.8543689320388349,
"acc_stderr": 0.034926064766237906,
"acc_norm": 0.8543689320388349,
"acc_norm_stderr": 0.034926064766237906
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.9230769230769231,
"acc_stderr": 0.017456987872436186,
"acc_norm": 0.9230769230769231,
"acc_norm_stderr": 0.017456987872436186
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8850574712643678,
"acc_stderr": 0.011405720724593964,
"acc_norm": 0.8850574712643678,
"acc_norm_stderr": 0.011405720724593964
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7630057803468208,
"acc_stderr": 0.02289408248992599,
"acc_norm": 0.7630057803468208,
"acc_norm_stderr": 0.02289408248992599
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.46033519553072627,
"acc_stderr": 0.016669799592112032,
"acc_norm": 0.46033519553072627,
"acc_norm_stderr": 0.016669799592112032
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.8071895424836601,
"acc_stderr": 0.022589318888176703,
"acc_norm": 0.8071895424836601,
"acc_norm_stderr": 0.022589318888176703
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7909967845659164,
"acc_stderr": 0.02309314039837422,
"acc_norm": 0.7909967845659164,
"acc_norm_stderr": 0.02309314039837422
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.845679012345679,
"acc_stderr": 0.020100830999851004,
"acc_norm": 0.845679012345679,
"acc_norm_stderr": 0.020100830999851004
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5425531914893617,
"acc_stderr": 0.029719281272236837,
"acc_norm": 0.5425531914893617,
"acc_norm_stderr": 0.029719281272236837
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.559322033898305,
"acc_stderr": 0.012680037994097053,
"acc_norm": 0.559322033898305,
"acc_norm_stderr": 0.012680037994097053
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7830882352941176,
"acc_stderr": 0.025035845227711274,
"acc_norm": 0.7830882352941176,
"acc_norm_stderr": 0.025035845227711274
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.7565359477124183,
"acc_stderr": 0.017362473762146634,
"acc_norm": 0.7565359477124183,
"acc_norm_stderr": 0.017362473762146634
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6909090909090909,
"acc_stderr": 0.044262946482000985,
"acc_norm": 0.6909090909090909,
"acc_norm_stderr": 0.044262946482000985
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7918367346938775,
"acc_stderr": 0.025991117672813296,
"acc_norm": 0.7918367346938775,
"acc_norm_stderr": 0.025991117672813296
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8656716417910447,
"acc_stderr": 0.024112678240900794,
"acc_norm": 0.8656716417910447,
"acc_norm_stderr": 0.024112678240900794
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.9,
"acc_stderr": 0.030151134457776334,
"acc_norm": 0.9,
"acc_norm_stderr": 0.030151134457776334
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5240963855421686,
"acc_stderr": 0.03887971849597264,
"acc_norm": 0.5240963855421686,
"acc_norm_stderr": 0.03887971849597264
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8947368421052632,
"acc_stderr": 0.02353755765789255,
"acc_norm": 0.8947368421052632,
"acc_norm_stderr": 0.02353755765789255
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5201958384332925,
"mc1_stderr": 0.017489216849737057,
"mc2": 0.6880606485475675,
"mc2_stderr": 0.01485204583043956
},
"harness|winogrande|5": {
"acc": 0.8176795580110497,
"acc_stderr": 0.010851565594267204
},
"harness|gsm8k|5": {
"acc": 0.6186504927975739,
"acc_stderr": 0.013379089877400725
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### 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. -->
[More Information Needed]
#### 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. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### 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. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### 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. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
autoevaluate/autoeval-staging-eval-project-squad_v2-26568076-11755566 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/bert-large-uncased-whole-word-masking-squad2
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/bert-large-uncased-whole-word-masking-squad2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sjrlee](https://huggingface.co/sjrlee) for evaluating this model. |
ThankGod/melanoma | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Actinic keratosis
'1': Benign keratosis
'2': Dermatofibroma
'3': Melanocytic nevus
'4': Vascular lesion
splits:
- name: train
num_bytes: 5807355933.47
num_examples: 14318
- name: validation
num_bytes: 406410771.682
num_examples: 1262
- name: test
num_bytes: 393276175.928
num_examples: 1278
download_size: 5993086085
dataset_size: 6607042881.08
---
# Dataset Card for "melanoma"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_minlik__chinese-alpaca-33b-merged | ---
pretty_name: Evaluation run of minlik/chinese-alpaca-33b-merged
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [minlik/chinese-alpaca-33b-merged](https://huggingface.co/minlik/chinese-alpaca-33b-merged)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 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.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_minlik__chinese-alpaca-33b-merged\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T18:26:45.770833](https://huggingface.co/datasets/open-llm-leaderboard/details_minlik__chinese-alpaca-33b-merged/blob/main/results_2023-09-23T18-26-45.770833.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.34375,\n \
\ \"em_stderr\": 0.004864023482291936,\n \"f1\": 0.39666317114094085,\n\
\ \"f1_stderr\": 0.00476283705283174,\n \"acc\": 0.4206081596579169,\n\
\ \"acc_stderr\": 0.00973840020904149\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.34375,\n \"em_stderr\": 0.004864023482291936,\n \
\ \"f1\": 0.39666317114094085,\n \"f1_stderr\": 0.00476283705283174\n \
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0803639120545868,\n \
\ \"acc_stderr\": 0.007488258573239077\n },\n \"harness|winogrande|5\": {\n\
\ \"acc\": 0.760852407261247,\n \"acc_stderr\": 0.011988541844843902\n\
\ }\n}\n```"
repo_url: https://huggingface.co/minlik/chinese-alpaca-33b-merged
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|arc:challenge|25_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T18_26_45.770833
path:
- '**/details_harness|drop|3_2023-09-23T18-26-45.770833.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T18-26-45.770833.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T18_26_45.770833
path:
- '**/details_harness|gsm8k|5_2023-09-23T18-26-45.770833.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T18-26-45.770833.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hellaswag|10_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T14:44:03.944390.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T14:44:03.944390.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T14:44:03.944390.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T18_26_45.770833
path:
- '**/details_harness|winogrande|5_2023-09-23T18-26-45.770833.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T18-26-45.770833.parquet'
- config_name: results
data_files:
- split: 2023_08_18T14_44_03.944390
path:
- results_2023-08-18T14:44:03.944390.parquet
- split: 2023_09_23T18_26_45.770833
path:
- results_2023-09-23T18-26-45.770833.parquet
- split: latest
path:
- results_2023-09-23T18-26-45.770833.parquet
---
# Dataset Card for Evaluation run of minlik/chinese-alpaca-33b-merged
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/minlik/chinese-alpaca-33b-merged
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [minlik/chinese-alpaca-33b-merged](https://huggingface.co/minlik/chinese-alpaca-33b-merged) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 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 configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_minlik__chinese-alpaca-33b-merged",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T18:26:45.770833](https://huggingface.co/datasets/open-llm-leaderboard/details_minlik__chinese-alpaca-33b-merged/blob/main/results_2023-09-23T18-26-45.770833.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.34375,
"em_stderr": 0.004864023482291936,
"f1": 0.39666317114094085,
"f1_stderr": 0.00476283705283174,
"acc": 0.4206081596579169,
"acc_stderr": 0.00973840020904149
},
"harness|drop|3": {
"em": 0.34375,
"em_stderr": 0.004864023482291936,
"f1": 0.39666317114094085,
"f1_stderr": 0.00476283705283174
},
"harness|gsm8k|5": {
"acc": 0.0803639120545868,
"acc_stderr": 0.007488258573239077
},
"harness|winogrande|5": {
"acc": 0.760852407261247,
"acc_stderr": 0.011988541844843902
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
mesolitica/DPO-malaysian-questions | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 63284259
num_examples: 32459
download_size: 29759496
dataset_size: 63284259
---
# Dataset Card for "DPO-malaysian-questions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Ez4Real/515K_Hotel_Reviews | ---
task_categories:
- text-classification
language:
- en
tags:
- code
pretty_name: 515K Hotel Reviews Data in Europe
size_categories:
- 100K<n<1M
--- |
Dzeniks/fever-nei-no-wiki-based | ---
license: mit
---
|
MichiganNLP/TID-8 | ---
annotations_creators:
- crowdsourced
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<200K
source_datasets:
- extended|other
task_categories:
- text-classification
task_ids:
- natural-language-inference
- sentiment-analysis
- hate-speech-detection
paperswithcode_id: placeholder
pretty_name: TID-8
tags:
- tid8
- annotation disagreement
dataset_info:
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features:
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dtype: string
- name: Verb
dtype: string
- name: Context
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- name: Episode
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configs:
- config_name: commitmentbank-ann
data_files:
- split: train
path: commitmentbank-ann/train-*
- split: test
path: commitmentbank-ann/test-*
- config_name: commitmentbank-atr
data_files:
- split: train
path: commitmentbank-atr/train-*
- split: test
path: commitmentbank-atr/test-*
- config_name: friends_qia-ann
data_files:
- split: validation
path: friends_qia-ann/validation-*
- split: train
path: friends_qia-ann/train-*
- split: test
path: friends_qia-ann/test-*
- config_name: friends_qia-atr
data_files:
- split: train
path: friends_qia-atr/train-*
- split: test
path: friends_qia-atr/test-*
- config_name: goemotions-ann
data_files:
- split: train
path: goemotions-ann/train-*
- split: test
path: goemotions-ann/test-*
- config_name: goemotions-atr
data_files:
- split: train
path: goemotions-atr/train-*
- split: test
path: goemotions-atr/test-*
- config_name: hs_brexit-ann
data_files:
- split: train
path: hs_brexit-ann/train-*
- split: test
path: hs_brexit-ann/test-*
- config_name: hs_brexit-atr
data_files:
- split: train
path: hs_brexit-atr/train-*
- split: test
path: hs_brexit-atr/test-*
- config_name: humor-ann
data_files:
- split: train
path: humor-ann/train-*
- split: test
path: humor-ann/test-*
- config_name: humor-atr
data_files:
- split: train
path: humor-atr/train-*
- split: test
path: humor-atr/test-*
- config_name: md-agreement-ann
data_files:
- split: train
path: md-agreement-ann/train-*
- split: test
path: md-agreement-ann/test-*
- config_name: md-agreement-atr
data_files:
- split: train
path: md-agreement-atr/train-*
- split: test
path: md-agreement-atr/test-*
- config_name: pejorative-ann
data_files:
- split: train
path: pejorative-ann/train-*
- split: test
path: pejorative-ann/test-*
- config_name: pejorative-atr
data_files:
- split: train
path: pejorative-atr/train-*
- split: test
path: pejorative-atr/test-*
- config_name: sentiment-ann
data_files:
- split: train
path: sentiment-ann/train-*
- split: test
path: sentiment-ann/test-*
- config_name: sentiment-atr
data_files:
- split: train
path: sentiment-atr/train-*
- split: test
path: sentiment-atr/test-*
---
# Dataset Card for "TID-8"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** placeholder
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
TID-8 is a new aggregated benchmark focused on the task of letting models learn from data that has inherent disagreement proposed in [link](https://arxiv.org/pdf/2305.14663.pdf) at Findings of EMNLP 2023.
In the paper, we focus on the inherent disagreement and let the model directly learn from data that has such disagreement.
We provide two split for TID-8.
*Annotation Split*
We split the annotations for each annotator into train and test set.
In other words, the same set of annotators appear in both train, (val),
and test sets.
For datasets that have splits originally, we follow the original split and remove
datapoints in test sets that are annotated by an annotator who is not in
the training set.
For datasets that do not have splits originally, we split the data into
train and test set for convenience, you may further split the train set
into a train and val set.
*Annotator Split*
We split annotators into train and test set.
In other words, a different set of annotators would appear in train and test sets.
We split the data into train and test set for convenience, you may consider
further splitting the train set into a train and val set for performance validation.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
### Data Fields
The data fields are the same among all splits.
See aforementioned information.
### Data Splits
See aforementioned information.
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{deng2023tid8,
title={You Are What You Annotate: Towards Better Models through Annotator Representations},
author={Deng, Naihao and Liu, Siyang and Zhang, Frederick Xinliang and Wu, Winston and Wang, Lu and Mihalcea, Rada},
booktitle={Findings of EMNLP 2023},
year={2023}
}
Note that each TID-8 dataset has its own citation. Please see the source to
get the correct citation for each contained dataset.
```
|
cakiki/go_paths | ---
dataset_info:
features:
- name: repository_name
dtype: string
splits:
- name: train
num_bytes: 301556518
num_examples: 12078461
download_size: 219608192
dataset_size: 301556518
---
# Dataset Card for "go_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mstz/electricity | ---
language:
- en
tags:
- electricity
- tabular_classification
- binary_classification
- UCI
pretty_name: Electricity
size_categories:
- 10k<n<100K
task_categories:
- tabular-classification
configs:
- electricity
license: cc
---
# Electricity
The [Electricity dataset](https://www.openml.org/search?type=data&sort=runs&id=151&status=active) from the [OpenML repository](https://www.openml.org/).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-------------------------|
| electricity | Binary classification | Has the electricity cost gone up?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/electricity", "electricity")["train"]
``` |
kooshan/Astrology-B | ---
license: other
license_name: bcj
license_link: LICENSE
---
|
alex-miller/iati-text-enhanced-crs | ---
dataset_info:
features:
- name: text
dtype: string
- name: Year
dtype: int64
- name: ProjectNumber
dtype: string
- name: RecipientName
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dtype: int64
- name: DonorName
dtype: string
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dtype: int64
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dtype: string
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dtype: string
- name: PurposeName
dtype: string
- name: FlowName
dtype: string
- name: ShortDescription
dtype: string
- name: LongDescription
dtype: string
- name: matched_iati_identifier
dtype: string
- name: iati_text
dtype: string
- name: iati_match_type
dtype: string
splits:
- name: train
num_bytes: 770488023
num_examples: 585611
download_size: 333766966
dataset_size: 770488023
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
amd-nicknick/processed_bert_dataset | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: special_tokens_mask
sequence: int8
splits:
- name: train
num_bytes: 24027285600.0
num_examples: 6674246
download_size: 5887301738
dataset_size: 24027285600.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
damand2061/innermore-x | ---
license: cc-by-4.0
dataset_info:
features:
- name: tokens
dtype: string
- name: tags
dtype: string
splits:
- name: train
num_bytes: 203836
num_examples: 438
- name: validation
num_bytes: 26180
num_examples: 55
download_size: 79051
dataset_size: 230016
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
malaysia-ai/wikipedia-malaysian-road-sign-images-multiturn | ---
language:
- ms
---
# wikipedia-malaysian-road-sign-images multiturn
Original dataset at https://huggingface.co/datasets/wanadzhar913/wikipedia-malaysian-road-sign-images, we just prepare multi-turn chat template. |
eaglewatch/Korean_Wikipedia_Dataset_for_GPT2_August_2022 | ---
annotations_creators:
- other
language:
- ko
language_creators:
- other
license:
- apache-2.0
multilinguality:
- multilingual
pretty_name: Korean wikipedia dataset for GPT-2 training
size_categories:
- 100M<n<1B
source_datasets: []
tags:
- gpt2
- korean
- wikipedia
- pertained
task_categories:
- question-answering
- text2text-generation
- translation
- conversational
- visual-question-answering
task_ids:
- open-domain-qa
- closed-domain-qa
- closed-domain-qa
- dialogue-generation
- visual-question-answering
viewer: true
---
# Dataset Card for korean_wikipedia_dataset_for_GPT2
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Contributions](#contributions)
## Dataset Description
Entire Korean language Wikipedia data for GPT-2 training as of August 1st, 2022.
email: oscar.eaglewatch@gmail.com
### Dataset Summary
This is to make a pre-trained GPT-2 Korean model
### Languages
Korean
## Dataset Structure
### Data Instances
Train wikipedia article count: 334420
validation wikipedia article count: 83605
### Data Fields
'text'
### Data Splits
80% vs. 20%, randomly, according to the Pareto Principle.
## Dataset Creation
### Source Data
Wikipedia
https://dumps.wikimedia.org/kowiki/latest/kowiki-latest-pages-articles.xml.bz2
## Considerations for Using the Data
### Social Impact of Dataset
None
### Discussion of Biases
None
### Other Known Limitations
None
## Additional Information
### Dataset Curators
Yongwoo Jeong
|
Paul/hatecheck-dutch | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- nl
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Dutch HateCheck
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card for Multilingual HateCheck
## Dataset Description
Multilingual HateCheck (MHC) is a suite of functional tests for hate speech detection models in 10 different languages: Arabic, Dutch, French, German, Hindi, Italian, Mandarin, Polish, Portuguese and Spanish.
For each language, there are 25+ functional tests that correspond to distinct types of hate and challenging non-hate.
This allows for targeted diagnostic insights into model performance.
For more details, please refer to our paper about MHC, published at the 2022 Workshop on Online Abuse and Harms (WOAH) at NAACL 2022. If you are using MHC, please cite our work!
- **Paper:** Röttger et al. (2022) - Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models. https://arxiv.org/abs/2206.09917
- **Repository:** https://github.com/rewire-online/multilingual-hatecheck
- **Point of Contact:** paul@rewire.online
## Dataset Structure
The csv format mostly matches the original HateCheck data, with some adjustments for specific languages.
**mhc_case_id**
The test case ID that is unique to each test case across languages (e.g., "mandarin-1305")
**functionality**
The shorthand for the functionality tested by the test case (e.g, "target_obj_nh"). The same functionalities are tested in all languages, except for Mandarin and Arabic, where non-Latin script required adapting the tests for spelling variations.
**test_case**
The test case text.
**label_gold**
The gold standard label ("hateful" or "non-hateful") of the test case. All test cases within a given functionality have the same gold standard label.
**target_ident**
Where applicable, the protected group that is targeted or referenced in the test case. All HateChecks cover seven target groups, but their composition varies across languages.
**ref_case_id**
For hateful cases, where applicable, the ID of the hateful case which was perturbed to generate this test case. For non-hateful cases, where applicable, the ID of the hateful case which is contrasted by this test case.
**ref_templ_id**
The equivalent to ref_case_id, but for template IDs.
**templ_id**
The ID of the template from which the test case was generated.
**case_templ**
The template from which the test case was generated (where applicable).
**gender_male** and **gender_female**
For gender-inflected languages (French, Spanish, Portuguese, Hindi, Arabic, Italian, Polish, German), only for cases where gender inflection is relevant, separate entries for gender_male and gender_female replace case_templ.
**label_annotated**
A list of labels given by the three annotators who reviewed the test case (e.g., "['hateful', 'hateful', 'hateful']").
**label_annotated_maj**
The majority vote of the three annotators (e.g., "hateful"). In some cases this differs from the gold label given by our language experts.
**disagreement_in_case**
True if label_annotated_maj does not match label_gold for the entry.
**disagreement_in_template**
True if the test case is generated from an IDENT template and there is at least one case with disagreement_in_case generated from the same template. This can be used to exclude entire templates from MHC. |
kim1111/car | ---
license: other
---
|
result-kand2-sdxl-wuerst-karlo/b776d96a | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 173
num_examples: 10
download_size: 1318
dataset_size: 173
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "b776d96a"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
wanng/laion-high-resolution-chinese | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- zh
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: laion-high-resolution-chinese
task_categories:
- feature-extraction
---
# laion-high-resolution-chinese
## 简介 Brief Introduction
取自Laion5B-high-resolution多语言多模态数据集中的中文部分，一共2.66M个图文对。
A subset from Laion5B-high-resolution (a multimodal dataset), around 2.66M image-text pairs (only Chinese).
## 数据集信息 Dataset Information
大约一共2.66M个中文图文对。大约占用381MB空间（仅仅是url等文本信息，不包含图片）。
- Homepage: [laion-5b](https://laion.ai/blog/laion-5b/)
- Huggingface: [laion/laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution)
## 下载 Download
```bash
mkdir release && cd release
for i in {00000..00015}; do wget https://huggingface.co/datasets/wanng/laion-high-resolution-chinese/resolve/main/data/train-$i-of-00016.parquet; done
cd ..
```
## Lisence
CC-BY-4.0
|
Falah/interior_design_prompts_SDXL | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 150250573
num_examples: 1000000
download_size: 23064289
dataset_size: 150250573
---
# Dataset Card for "interior_design_prompts_SDXL"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rumeysacelik/turkishReviews-ds-commerce | ---
dataset_info:
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num_examples: 376
download_size: 896651
dataset_size: 1392332.0
configs:
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data_files:
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path: data/validation-*
---
|
chrystians/oasst1_pl_2_2 | ---
dataset_info:
features:
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struct:
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sequence: int64
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sequence: string
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struct:
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sequence: int64
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sequence: string
- name: value
sequence: float64
splits:
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num_bytes: 67590476
num_examples: 81037
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num_bytes: 2432688
num_examples: 3001
download_size: 20433061
dataset_size: 70023164
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
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path: data/validation-*
---
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/7eea16f3 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 178
num_examples: 10
download_size: 1332
dataset_size: 178
---
# Dataset Card for "7eea16f3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
seonjeongh/parallel_en.am | ---
dataset_info:
features:
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dtype: string
splits:
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download_size: 21200415
dataset_size: 34707491
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "parallel_en.am"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
allenai/scirepeval | ---
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dtype: uint64
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splits:
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features:
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dtype: string
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dtype: string
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list:
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dtype: string
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dtype: string
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dtype: float64
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sequence: string
- name: n_citations
dtype: int32
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dtype: int32
- name: score
dtype: uint32
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- config_name: tweet_mentions
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dtype: string
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dtype: string
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dtype: int32
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dtype: float32
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dtype: int32
- name: mentions
dtype: float32
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num_bytes: 25895172
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configs:
- config_name: biomimicry
data_files:
- split: evaluation
path: biomimicry/evaluation-*
- config_name: cite_count
data_files:
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path: cite_count/evaluation-*
- split: train
path: cite_count/train-*
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path: cite_count/validation-*
- config_name: cite_prediction
data_files:
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path: cite_prediction/train-*
- split: validation
path: cite_prediction/validation-*
- config_name: cite_prediction_aug2023refresh
data_files:
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path: cite_prediction_aug2023refresh/train-*
- config_name: cite_prediction_new
data_files:
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path: cite_prediction_new/train-*
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path: cite_prediction_new/validation-*
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data_files:
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path: drsm/evaluation-*
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data_files:
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path: fos/evaluation-*
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path: fos/train-*
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path: fos/validation-*
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data_files:
- split: evaluation
path: high_influence_cite/evaluation-*
- split: train
path: high_influence_cite/train-*
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path: high_influence_cite/validation-*
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data_files:
- split: evaluation
path: mesh_descriptors/evaluation-*
- split: train
path: mesh_descriptors/train-*
- split: validation
path: mesh_descriptors/validation-*
- config_name: nfcorpus
data_files:
- split: evaluation
path: nfcorpus/evaluation-*
- config_name: paper_reviewer_matching
data_files:
- split: evaluation
path: paper_reviewer_matching/evaluation-*
- config_name: peer_review_score_hIndex
data_files:
- split: evaluation
path: peer_review_score_hIndex/evaluation-*
- config_name: pub_year
data_files:
- split: evaluation
path: pub_year/evaluation-*
- split: train
path: pub_year/train-*
- split: validation
path: pub_year/validation-*
- config_name: relish
data_files:
- split: evaluation
path: relish/evaluation-*
- config_name: same_author
data_files:
- split: evaluation
path: same_author/evaluation-*
- split: train
path: same_author/train-*
- split: validation
path: same_author/validation-*
- config_name: scidocs_mag_mesh
data_files:
- split: evaluation
path: scidocs_mag_mesh/evaluation-*
- config_name: scidocs_view_cite_read
data_files:
- split: evaluation
path: scidocs_view_cite_read/evaluation-*
- config_name: search
data_files:
- split: evaluation
path: search/evaluation-*
- split: train
path: search/train-*
- split: validation
path: search/validation-*
- config_name: trec_covid
data_files:
- split: evaluation
path: trec_covid/evaluation-*
- config_name: tweet_mentions
data_files:
- split: evaluation
path: tweet_mentions/evaluation-*
---
|
Asimok/KGLQA-LangChain-NCR | ---
license: apache-2.0
---
|
CyberHarem/durin_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of durin/ドゥリン/杜林 (Arknights)
This is the dataset of durin/ドゥリン/杜林 (Arknights), containing 81 images and their tags.
The core tags of this character are `blonde_hair, hat, long_hair, blue_eyes, pointy_ears, black_headwear, hair_between_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 | 81 | 95.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/durin_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 81 | 83.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/durin_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 183 | 165.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/durin_arknights/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/durin_arknights',
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 | 5 |  |  |  |  |  | 1girl, black_jacket, id_card, long_sleeves, open_jacket, solo, upper_body, blue_background, looking_at_viewer, simple_background, white_shirt, closed_mouth, sleeves_past_fingers, blush, scarf |
| 1 | 23 |  |  |  |  |  | 1girl, looking_at_viewer, long_sleeves, solo, white_shirt, black_skirt, black_thighhighs, pleated_skirt, black_jacket, sleeves_past_fingers, open_jacket, simple_background, miniskirt, white_background, zettai_ryouiki, blush, closed_mouth, scarf |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_jacket | id_card | long_sleeves | open_jacket | solo | upper_body | blue_background | looking_at_viewer | simple_background | white_shirt | closed_mouth | sleeves_past_fingers | blush | scarf | black_skirt | black_thighhighs | pleated_skirt | miniskirt | white_background | zettai_ryouiki |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:----------|:---------------|:--------------|:-------|:-------------|:------------------|:--------------------|:--------------------|:--------------|:---------------|:-----------------------|:--------|:--------|:--------------|:-------------------|:----------------|:------------|:-------------------|:-----------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | |
| 1 | 23 |  |  |  |  |  | X | X | | X | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
joey234/mmlu-miscellaneous-rule-neg | ---
dataset_info:
features:
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: question
dtype: string
splits:
- name: test
num_bytes: 149912
num_examples: 783
download_size: 97982
dataset_size: 149912
---
# Dataset Card for "mmlu-miscellaneous-rule-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rathi2023/binima | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
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'2810': B000AFL70W
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'2813': B000ALLMI8
'2814': B000AM7VXM
'2815': B000AMAUSA
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'2870': B000BOAFM8
'2871': B000BOCBBG
'2872': B000BOH91S
'2873': B000BOPE56
'2874': B000BOPK00
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'2876': B000BP7MJG
'2877': B000BPBV20
'2878': B000BPC1J2
'2879': B000BPECSK
'2880': B000BPEDZC
'2881': B000BPEE3S
'2882': B000BPGP6M
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'2884': B000BPIKHY
'2885': B000BPLGZ2
'2886': B000BPTJ4M
'2887': B000BPW8PY
'2888': B000BQ8DRA
'2889': B000BQK0CQ
'2890': B000BQK2JW
'2891': B000BQL82C
'2892': B000BQM1LE
'2893': B000BQN6MM
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'2895': B000BQQ2KA
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'2897': B000BQUPZ8
'2898': B000BQW5LK
'2899': B000BQXDMU
'2900': B000BQY9XW
'2901': B000BQYBC6
'2902': B000BR4KOE
'2903': B000BR9D1O
'2904': B000BRP9US
'2905': B000BS27H0
'2906': B000BSJCLY
'2907': B000BT0M9Y
'2908': B000BTH46I
'2909': B000BU7TZS
'2910': B000BU7U9S
'2911': B000BU8ZU6
'2912': B000BUIP6K
'2913': B000BVM1V4
'2914': B000BVZBSE
'2915': B000BW8VJO
'2916': B000BWCDES
'2917': B000BWE418
'2918': B000BWQ7J0
'2919': B000BX1ZSM
'2920': B000BX47X2
'2921': B000BX4QW4
'2922': B000BXHPEU
'2923': B000BXMF7C
'2924': B000BYGD2O
'2925': B000BYOK6U
'2926': B000BYPZIM
'2927': B000BZ4YZ6
'2928': B000BZ6ZX0
'2929': B000BZ95NW
'2930': B000BZG5QC
'2931': B000BZG6F2
'2932': B000BZJ762
'2933': B000BZZT4Q
'2934': B000C01TQM
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'2936': B000C16XPI
'2937': B000C1JX5K
'2938': B000C2GI4S
'2939': B000C2S7U6
'2940': B000C2SBAM
'2941': B000C2SHC4
'2942': B000C2W9TQ
'2943': B000C2WAL8
'2944': B000C2Y4RQ
'2945': B000C2YWJ6
'2946': B000C2ZKG0
'2947': B000C31K0E
'2948': B000C31K2M
'2949': B000C40TGE
'2950': B000C46J6I
'2951': B000C55WME
'2952': B000C5FLSY
'2953': B000C5GASY
'2954': B000C5GB24
'2955': B000C6KKCU
'2956': B000C6MLIQ
'2957': B000C6MLT0
'2958': B000C7L654
'2959': B000C7QFIW
'2960': B000C8224M
'2961': B000C8T89Y
'2962': B000C94HVW
'2963': B000C984SE
'2964': B000C9H8AY
'2965': B000C9H9HG
'2966': B000C9HBZQ
'2967': B000C9HF9I
'2968': B000C9HJOE
'2969': B000C9IYIO
'2970': B000C9J8T8
'2971': B000C9JBMM
'2972': B000C9JC1M
'2973': B000C9MHIW
'2974': B000C9O0G4
'2975': B000C9O1KE
'2976': B000C9OSJ8
'2977': B000C9PXEW
'2978': B000C9RPIO
'2979': B000C9RTH6
'2980': B000C9S2J0
'2981': B000C9S3L2
'2982': B000C9S5C4
'2983': B000C9SHMM
'2984': B000C9TB0Y
'2985': B000C9UKB8
'2986': B000C9ULGM
'2987': B000C9VXL4
'2988': B000C9Y0KK
'2989': B000CAU238
'2990': B000CAXIK2
'2991': B000CB0KJ8
'2992': B000CB7DG6
'2993': B000CBVAQ0
'2994': B000CBWVGS
'2995': B000CC6686
'2996': B000CCBEKG
'2997': B000CCFK1U
'2998': B000CCIFHG
'2999': B000CCXB2U
'3000': B000CD9XGC
'3001': B000CDUEK6
'3002': B000CEVUE4
'3003': B000CFME2A
'3004': B000CFNCKS
'3005': B000CFO0L8
'3006': B000CFPYR2
'3007': B000CGVT7A
'3008': B000CIIE12
'3009': B000CIOC90
'3010': B000CIR1QG
'3011': B000CIUB7C
'3012': B000CJ05WW
'3013': B000CJ24VM
'3014': B000CL7BUE
'3015': B000CMD602
'3016': B000CMDA12
'3017': B000CMDATY
'3018': B000CMF4OI
'3019': B000CMFK5Q
'3020': B000CMH4RI
'3021': B000CMHVEY
'3022': B000CMIG66
'3023': B000CMKC5Y
'3024': B000CMKGRI
'3025': B000CN5JS8
'3026': B000CN9BK0
'3027': B000CNAEEW
'3028': B000CNB34M
'3029': B000CNKFSW
'3030': B000CO71W4
'3031': B000CO72H8
'3032': B000COA2ZW
'3033': B000COBVXE
'3034': B000COC5MK
'3035': B000COCRUA
'3036': B000CODL8W
'3037': B000CON2K4
'3038': B000CONU0G
'3039': B000COOLMC
'3040': B000COS0DI
'3041': B000COS0HE
'3042': B000COU4LY
'3043': B000COY4RO
'3044': B000COY5PK
'3045': B000CP0R1U
'3046': B000CP86ZE
'3047': B000CPAS1E
'3048': B000CPATK4
'3049': B000CPIMNA
'3050': B000CPINSY
'3051': B000CQ254Q
'3052': B000CQ6JIE
'3053': B000CRBPWS
'3054': B000CRFJOS
'3055': B000CRIBC0
'3056': B000CSD2LO
'3057': B000CSD2S2
'3058': B000CSD4C6
'3059': B000CSGTEG
'3060': B000CSVV80
'3061': B000DILOOK
'3062': B000DLBX4I
'3063': B000DN85LA
'3064': B000DZ6W40
'3065': B000DZFVB0
'3066': B000DZKW9G
'3067': B000E0KMLI
'3068': B000E0RARC
'3069': B000E1385Y
'3070': B000E1AK50
'3071': B000E1MXSW
'3072': B000E20HWA
'3073': B000E25W98
'3074': B000E267M4
'3075': B000E28N9Y
'3076': B000E2TFXC
'3077': B000E2ZOYQ
'3078': B000E300Q2
'3079': B000E3CYJ8
'3080': B000E3ZN2I
'3081': B000E46IXU
'3082': B000E47294
'3083': B000E49RUG
'3084': B000E4ALGU
'3085': B000E5C234
'3086': B000E5TDYA
'3087': B000E60U6Y
'3088': B000E62TCC
'3089': B000E6BRUW
'3090': B000E6EJAW
'3091': B000E6LBXK
'3092': B000E78WR2
'3093': B000E7DLY6
'3094': B000E8T80Q
'3095': B000E903XQ
'3096': B000E964W0
'3097': B000E9DQFI
'3098': B000EBD9S0
'3099': B000EBIDIQ
'3100': B000EBKH5S
'3101': B000ECQ8B4
'3102': B000EDCJ3Y
'3103': B000EDOT82
'3104': B000EDOTCI
'3105': B000EDQR50
'3106': B000EDTS0G
'3107': B000EDUTOA
'3108': B000EEHI7U
'3109': B000EEU0IO
'3110': B000EEXBEY
'3111': B000EFCPMC
'3112': B000EFJGWY
'3113': B000EFLRYE
'3114': B000EFOCB4
'3115': B000EFQG1I
'3116': B000EGAR62
'3117': B000EGK4LK
'3118': B000EGMDR8
'3119': B000EGNZC0
'3120': B000EH4XZC
'3121': B000EH6G1G
'3122': B000EJQJBG
'3123': B000EJRB40
'3124': B000EJZIAE
'3125': B000ELORQ8
'3126': B000ELP8GQ
'3127': B000EMBK3A
'3128': B000EMGAG2
'3129': B000EMNK2Y
'3130': B000EMOBPY
'3131': B000EMPMTS
'3132': B000EMPMWA
'3133': B000EMQEIG
'3134': B000EMYJ88
'3135': B000ENQRBY
'3136': B000EOQYM0
'3137': B000EPS38M
'3138': B000EQ5ZNW
'3139': B000EQ9OF2
'3140': B000EQE4OI
'3141': B000EQQOI2
'3142': B000EQR602
'3143': B000EQRRVU
'3144': B000EQS0YS
'3145': B000EQVYA0
'3146': B000ES6KPM
'3147': B000ES7C5E
'3148': B000ESJGZS
'3149': B000ESR3CG
'3150': B000ET5TC6
'3151': B000EU1OUG
'3152': B000EUFJXE
'3153': B000EWNWAY
'3154': B000EXS2TY
'3155': B000EXUB52
'3156': B000F3Q6W8
'3157': B000F3QFX8
'3158': B000F4DKB2
'3159': B000F4GHP8
'3160': B000F4J75K
'3161': B000F4OYMQ
'3162': B000F6PKAE
'3163': B000F6Q4MW
'3164': B000F6RFX4
'3165': B000F6TK3C
'3166': B000F7VAJI
'3167': B000F7W1ZA
'3168': B000F8IXOW
'3169': B000F8RBKY
'3170': B000F8V25O
'3171': B000F8XF8Q
'3172': B000F9J886
'3173': B000F9JFXE
'3174': B000F9NXUK
'3175': B000FB67D8
'3176': B000FBATB4
'3177': B000FBLRWE
'3178': B000FBM6X8
'3179': B000FBNTJ8
'3180': B000FCCYV6
'3181': B000FCINB6
'3182': B000FD2AZ0
'3183': B000FD7CLC
'3184': B000FE5QR8
'3185': B000FGTSFW
'3186': B000FGZF5E
'3187': B000FHBG88
'3188': B000FHBHTQ
'3189': B000FHGFES
'3190': B000FIE45O
'3191': B000FIGHD6
'3192': B000FIMHNK
'3193': B000FIUQAQ
'3194': B000FJH662
'3195': B000FJN9O0
'3196': B000FJOVHO
'3197': B000FK6ZN6
'3198': B000FK87NC
'3199': B000FKBRXO
'3200': B000FKERMW
'3201': B000FKQDAQ
'3202': B000FMDIL6
'3203': B000FMNQOK
'3204': B000FMUYCW
'3205': B000FMUYN6
'3206': B000FN3XV0
'3207': B000FNK3Z4
'3208': B000FNTGZC
'3209': B000FNUFRA
'3210': B000FO1QOU
'3211': B000FP0ALY
'3212': B000FPCC74
'3213': B000FPGYTQ
'3214': B000FPM22Y
'3215': B000FPYHOU
'3216': B000FQ5MJ8
'3217': B000FRS9II
'3218': B000FRXHAS
'3219': B000FS9FLC
'3220': B000FSH4I8
'3221': B000FSQJ5M
'3222': B000FSS3A6
'3223': B000FSSQRG
'3224': B000FSTK1W
'3225': B000FT1LQ8
'3226': B000FU0U5U
'3227': B000FUVEM8
'3228': B000FV8ML8
'3229': B000FVDY2A
'3230': B000FW61Z6
'3231': B000FW7VNW
'3232': B000FYXBJI
'3233': B000FYXZNK
'3234': B000FZXI7C
'3235': B000G03RJA
'3236': B000G04ULE
'3237': B000G2JQB6
'3238': B000G35N26
'3239': B000G62HZY
'3240': B000GA53CO
'3241': B000GAQB4S
'3242': B000GAT6NG
'3243': B000GATFH8
'3244': B000GBGNZY
'3245': B000GBRD08
'3246': B000GBT8GA
'3247': B000GC9D0U
'3248': B000GCKD2C
'3249': B000GCLFAG
'3250': B000GCN130
'3251': B000GDF484
'3252': B000GDJJOY
'3253': B000GE9US8
'3254': B000GF3TTS
'3255': B000GFJJHY
'3256': B000GFPDEW
'3257': B000GFSU7O
'3258': B000GFYRHG
'3259': B000GFYRIK
'3260': B000GG0BPM
'3261': B000GG15UM
'3262': B000GG29GG
'3263': B000GG2HOU
'3264': B000GG2KB0
'3265': B000GG5IY6
'3266': B000GG5J18
'3267': B000GG5LWA
'3268': B000GG5OU4
'3269': B000GG5OUY
'3270': B000GGFZLC
'3271': B000GGKPLC
'3272': B000GH0PFM
'3273': B000GH3COS
'3274': B000GI0S4E
'3275': B000GIFOSO
'3276': B000GIIR9M
'3277': B000GIQSVG
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'3322': B000H6WEMO
'3323': B000H6Y954
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'3361': B000HHOBHY
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'3365': B000HIVIWY
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'3367': B000HJ99UQ
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'3456': B000JCJG36
'3457': B000JE9LD4
'3458': B000JF2W8Y
'3459': B000JFHMPC
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'3467': B000JJJ2AQ
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'3498': B000KAG9EG
'3499': B000KAWKFI
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'3502': B000KD3FPE
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'3509': B000KKKNT8
'3510': B000KKVOX2
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'3513': B000KL8UVK
'3514': B000KNEIUA
'3515': B000KNJTBI
'3516': B000KNNIOW
'3517': B000KOSCAG
'3518': B000KPO9QG
'3519': B000KQ9TME
'3520': B000KRB3UO
'3521': B000KRGHNM
'3522': B000KSSA6I
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'3526': B000L21F56
'3527': B000L44CYA
'3528': B000L6IX20
'3529': B000L6J3WO
'3530': B000LC2PD2
'3531': B000LDMDDS
'3532': B000LF0INI
'3533': B000LF42NK
'3534': B000LG7PVK
'3535': B000LGJ08Q
'3536': B000LGKKSU
'3537': B000LGV9R6
'3538': B000LI40IY
'3539': B000LIOQ9W
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'3546': B000LTQA6I
'3547': B000LTV19O
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'3568': B000MFF008
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'3570': B000MFOJR8
'3571': B000MGBSG2
'3572': B000MGGT66
'3573': B000MGR1JK
'3574': B000MGR38O
'3575': B000MGVCHC
'3576': B000MGXS88
'3577': B000MJHFC0
'3578': B000MJJIR0
'3579': B000MK4RAM
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'3583': B000MOCUKW
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'3590': B000MTB7JW
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'3650': B000NNTUHS
'3651': B000NONNKW
'3652': B000NOZ3XC
'3653': B000NP4832
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'3655': B000NPX26G
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'3678': B000OCT7GC
'3679': B000OCXKQK
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'3681': B000OFBPBY
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'3870': B000TETMVK
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'3899': B000ULHXMC
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'3901': B000UML980
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'3903': B000UTYDCM
'3904': B000UV7PZW
'3905': B000UVKE2I
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'3909': B000UXDDU6
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'3924': B000V9CLH0
'3925': B000VA1Y36
'3926': B000VA76EW
'3927': B000VAK4WS
'3928': B000VAPCK2
'3929': B000VAZGBW
'3930': B000VB7YWU
'3931': B000VBH8OE
'3932': B000VBNU4Q
'3933': B000VDCT3C
'3934': B000VDCTCI
'3935': B000VDX6TI
'3936': B000VESTZS
'3937': B000VH7XG6
'3938': B000VHXD8I
'3939': B000VI6OO2
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'18819': B014UN8TNC
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'23971': B01H8VPBZC
'23972': B01H8YVNN8
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'23976': B01HAXBH1A
'23977': B01HBACPFO
'23978': B01HBZL82A
'23979': B01HC6FHOS
'23980': B01HCLKREI
'23981': B01HE6KCW8
'23982': B01HEBGGUA
'23983': B01HEDNN3G
'23984': B01HERWXEW
'23985': B01HFA9IDW
'23986': B01HFC92ZO
'23987': B01HFCEAC4
'23988': B01HFJRNO4
'23989': B01HFXXQ7S
'23990': B01HGBWVE8
'23991': B01HGCHXVS
'23992': B01HGCNN6W
'23993': B01HGROEYW
'23994': B01HGWL7P6
'23995': B01HGZOKNY
'23996': B01HH7CIR6
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'23998': B01HHX8ON2
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'24008': B01HM1D21I
'24009': B01HNDBR4O
'24010': B01HOACCQS
'24011': B01HOLYDTG
'24012': B01HOWROVO
'24013': B01HPANNFQ
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'24015': B01HPTEVT4
'24016': B01HQB3M8M
'24017': B01HQIT9EG
'24018': B01HRLTY7O
'24019': B01HT5421G
'24020': B01HTFUDZA
'24021': B01HZBHOUU
'24022': B01I3OEJP6
splits:
- name: train
num_bytes: 2141699778.715
num_examples: 37397
download_size: 2119875753
dataset_size: 2141699778.715
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_0-hero__Matter-0.2-7B-DPO | ---
pretty_name: Evaluation run of 0-hero/Matter-0.2-7B-DPO
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [0-hero/Matter-0.2-7B-DPO](https://huggingface.co/0-hero/Matter-0.2-7B-DPO) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe 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.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_0-hero__Matter-0.2-7B-DPO\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-15T21:23:53.765896](https://huggingface.co/datasets/open-llm-leaderboard/details_0-hero__Matter-0.2-7B-DPO/blob/main/results_2024-04-15T21-23-53.765896.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6304635219670391,\n\
\ \"acc_stderr\": 0.03247611955077603,\n \"acc_norm\": 0.6324699949079374,\n\
\ \"acc_norm_stderr\": 0.033134141472393366,\n \"mc1\": 0.3488372093023256,\n\
\ \"mc1_stderr\": 0.016684419859986893,\n \"mc2\": 0.5030082038523329,\n\
\ \"mc2_stderr\": 0.01531844462963793\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5989761092150171,\n \"acc_stderr\": 0.014322255790719867,\n\
\ \"acc_norm\": 0.6331058020477816,\n \"acc_norm_stderr\": 0.014084133118104303\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6362278430591516,\n\
\ \"acc_stderr\": 0.004801009657690438,\n \"acc_norm\": 0.8316072495518821,\n\
\ \"acc_norm_stderr\": 0.003734498979207306\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\
\ \"acc_stderr\": 0.041539484047423976,\n \"acc_norm\": 0.6370370370370371,\n\
\ \"acc_norm_stderr\": 0.041539484047423976\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\
\ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6679245283018868,\n \"acc_stderr\": 0.02898545565233439,\n\
\ \"acc_norm\": 0.6679245283018868,\n \"acc_norm_stderr\": 0.02898545565233439\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7222222222222222,\n\
\ \"acc_stderr\": 0.03745554791462456,\n \"acc_norm\": 0.7222222222222222,\n\
\ \"acc_norm_stderr\": 0.03745554791462456\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\
: 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6127167630057804,\n\
\ \"acc_stderr\": 0.037143259063020656,\n \"acc_norm\": 0.6127167630057804,\n\
\ \"acc_norm_stderr\": 0.037143259063020656\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.04755129616062946,\n\
\ \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.04755129616062946\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.74,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.74,\n\
\ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108101,\n\
\ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108101\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\
\ \"acc_stderr\": 0.047028804320496165,\n \"acc_norm\": 0.5087719298245614,\n\
\ \"acc_norm_stderr\": 0.047028804320496165\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482758,\n\
\ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482758\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41798941798941797,\n \"acc_stderr\": 0.025402555503260912,\n \"\
acc_norm\": 0.41798941798941797,\n \"acc_norm_stderr\": 0.025402555503260912\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4126984126984127,\n\
\ \"acc_stderr\": 0.04403438954768177,\n \"acc_norm\": 0.4126984126984127,\n\
\ \"acc_norm_stderr\": 0.04403438954768177\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7419354838709677,\n\
\ \"acc_stderr\": 0.02489246917246283,\n \"acc_norm\": 0.7419354838709677,\n\
\ \"acc_norm_stderr\": 0.02489246917246283\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.47783251231527096,\n \"acc_stderr\": 0.035145285621750094,\n\
\ \"acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.035145285621750094\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\
: 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7393939393939394,\n \"acc_stderr\": 0.034277431758165236,\n\
\ \"acc_norm\": 0.7393939393939394,\n \"acc_norm_stderr\": 0.034277431758165236\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8080808080808081,\n \"acc_stderr\": 0.02805779167298902,\n \"\
acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.02805779167298902\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8601036269430051,\n \"acc_stderr\": 0.025033870583015178,\n\
\ \"acc_norm\": 0.8601036269430051,\n \"acc_norm_stderr\": 0.025033870583015178\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.617948717948718,\n \"acc_stderr\": 0.024635549163908234,\n \
\ \"acc_norm\": 0.617948717948718,\n \"acc_norm_stderr\": 0.024635549163908234\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3037037037037037,\n \"acc_stderr\": 0.028037929969114993,\n \
\ \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.028037929969114993\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6386554621848739,\n \"acc_stderr\": 0.03120469122515002,\n \
\ \"acc_norm\": 0.6386554621848739,\n \"acc_norm_stderr\": 0.03120469122515002\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31788079470198677,\n \"acc_stderr\": 0.03802039760107903,\n \"\
acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.03802039760107903\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8201834862385321,\n \"acc_stderr\": 0.01646534546739152,\n \"\
acc_norm\": 0.8201834862385321,\n \"acc_norm_stderr\": 0.01646534546739152\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4583333333333333,\n \"acc_stderr\": 0.03398110890294636,\n \"\
acc_norm\": 0.4583333333333333,\n \"acc_norm_stderr\": 0.03398110890294636\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8088235294117647,\n \"acc_stderr\": 0.027599174300640766,\n \"\
acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.027599174300640766\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8016877637130801,\n \"acc_stderr\": 0.02595502084162113,\n \
\ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.02595502084162113\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.031024411740572206,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.031024411740572206\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.03641297081313731,\n\
\ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.03641297081313731\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7355371900826446,\n \"acc_stderr\": 0.04026187527591207,\n \"\
acc_norm\": 0.7355371900826446,\n \"acc_norm_stderr\": 0.04026187527591207\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7300613496932515,\n \"acc_stderr\": 0.03487825168497892,\n\
\ \"acc_norm\": 0.7300613496932515,\n \"acc_norm_stderr\": 0.03487825168497892\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\
\ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\
\ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\
\ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\
\ \"acc_stderr\": 0.022509033937077795,\n \"acc_norm\": 0.8632478632478633,\n\
\ \"acc_norm_stderr\": 0.022509033937077795\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7956577266922095,\n\
\ \"acc_stderr\": 0.014419123980931894,\n \"acc_norm\": 0.7956577266922095,\n\
\ \"acc_norm_stderr\": 0.014419123980931894\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.024182427496577612,\n\
\ \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.024182427496577612\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.41564245810055866,\n\
\ \"acc_stderr\": 0.016482782187500673,\n \"acc_norm\": 0.41564245810055866,\n\
\ \"acc_norm_stderr\": 0.016482782187500673\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.026090162504279053,\n\
\ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.026090162504279053\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7041800643086816,\n\
\ \"acc_stderr\": 0.025922371788818763,\n \"acc_norm\": 0.7041800643086816,\n\
\ \"acc_norm_stderr\": 0.025922371788818763\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7006172839506173,\n \"acc_stderr\": 0.025483115601195448,\n\
\ \"acc_norm\": 0.7006172839506173,\n \"acc_norm_stderr\": 0.025483115601195448\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48936170212765956,\n \"acc_stderr\": 0.029820747191422466,\n \
\ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.029820747191422466\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4641460234680574,\n\
\ \"acc_stderr\": 0.012737361318730581,\n \"acc_norm\": 0.4641460234680574,\n\
\ \"acc_norm_stderr\": 0.012737361318730581\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5955882352941176,\n \"acc_stderr\": 0.029812630701569743,\n\
\ \"acc_norm\": 0.5955882352941176,\n \"acc_norm_stderr\": 0.029812630701569743\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6748366013071896,\n \"acc_stderr\": 0.01895088677080631,\n \
\ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.01895088677080631\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\
\ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\
\ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7020408163265306,\n \"acc_stderr\": 0.02927956741106568,\n\
\ \"acc_norm\": 0.7020408163265306,\n \"acc_norm_stderr\": 0.02927956741106568\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8208955223880597,\n\
\ \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n\
\ \"acc_norm_stderr\": 0.027113286753111837\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \
\ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n\
\ \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3488372093023256,\n\
\ \"mc1_stderr\": 0.016684419859986893,\n \"mc2\": 0.5030082038523329,\n\
\ \"mc2_stderr\": 0.01531844462963793\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8026835043409629,\n \"acc_stderr\": 0.011185026389050374\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5693707354056103,\n \
\ \"acc_stderr\": 0.013639285985979927\n }\n}\n```"
repo_url: https://huggingface.co/0-hero/Matter-0.2-7B-DPO
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|arc:challenge|25_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|gsm8k|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hellaswag|10_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T21-23-53.765896.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-15T21-23-53.765896.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- '**/details_harness|winogrande|5_2024-04-15T21-23-53.765896.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-15T21-23-53.765896.parquet'
- config_name: results
data_files:
- split: 2024_04_15T21_23_53.765896
path:
- results_2024-04-15T21-23-53.765896.parquet
- split: latest
path:
- results_2024-04-15T21-23-53.765896.parquet
---
# Dataset Card for Evaluation run of 0-hero/Matter-0.2-7B-DPO
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [0-hero/Matter-0.2-7B-DPO](https://huggingface.co/0-hero/Matter-0.2-7B-DPO) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding 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 configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_0-hero__Matter-0.2-7B-DPO",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-15T21:23:53.765896](https://huggingface.co/datasets/open-llm-leaderboard/details_0-hero__Matter-0.2-7B-DPO/blob/main/results_2024-04-15T21-23-53.765896.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6304635219670391,
"acc_stderr": 0.03247611955077603,
"acc_norm": 0.6324699949079374,
"acc_norm_stderr": 0.033134141472393366,
"mc1": 0.3488372093023256,
"mc1_stderr": 0.016684419859986893,
"mc2": 0.5030082038523329,
"mc2_stderr": 0.01531844462963793
},
"harness|arc:challenge|25": {
"acc": 0.5989761092150171,
"acc_stderr": 0.014322255790719867,
"acc_norm": 0.6331058020477816,
"acc_norm_stderr": 0.014084133118104303
},
"harness|hellaswag|10": {
"acc": 0.6362278430591516,
"acc_stderr": 0.004801009657690438,
"acc_norm": 0.8316072495518821,
"acc_norm_stderr": 0.003734498979207306
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6370370370370371,
"acc_stderr": 0.041539484047423976,
"acc_norm": 0.6370370370370371,
"acc_norm_stderr": 0.041539484047423976
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6679245283018868,
"acc_stderr": 0.02898545565233439,
"acc_norm": 0.6679245283018868,
"acc_norm_stderr": 0.02898545565233439
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.03745554791462456,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.03745554791462456
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.42,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6127167630057804,
"acc_stderr": 0.037143259063020656,
"acc_norm": 0.6127167630057804,
"acc_norm_stderr": 0.037143259063020656
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.35294117647058826,
"acc_stderr": 0.04755129616062946,
"acc_norm": 0.35294117647058826,
"acc_norm_stderr": 0.04755129616062946
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.74,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.74,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5617021276595745,
"acc_stderr": 0.03243618636108101,
"acc_norm": 0.5617021276595745,
"acc_norm_stderr": 0.03243618636108101
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5087719298245614,
"acc_stderr": 0.047028804320496165,
"acc_norm": 0.5087719298245614,
"acc_norm_stderr": 0.047028804320496165
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5586206896551724,
"acc_stderr": 0.04137931034482758,
"acc_norm": 0.5586206896551724,
"acc_norm_stderr": 0.04137931034482758
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.41798941798941797,
"acc_stderr": 0.025402555503260912,
"acc_norm": 0.41798941798941797,
"acc_norm_stderr": 0.025402555503260912
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4126984126984127,
"acc_stderr": 0.04403438954768177,
"acc_norm": 0.4126984126984127,
"acc_norm_stderr": 0.04403438954768177
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7419354838709677,
"acc_stderr": 0.02489246917246283,
"acc_norm": 0.7419354838709677,
"acc_norm_stderr": 0.02489246917246283
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.47783251231527096,
"acc_stderr": 0.035145285621750094,
"acc_norm": 0.47783251231527096,
"acc_norm_stderr": 0.035145285621750094
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7393939393939394,
"acc_stderr": 0.034277431758165236,
"acc_norm": 0.7393939393939394,
"acc_norm_stderr": 0.034277431758165236
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8080808080808081,
"acc_stderr": 0.02805779167298902,
"acc_norm": 0.8080808080808081,
"acc_norm_stderr": 0.02805779167298902
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8601036269430051,
"acc_stderr": 0.025033870583015178,
"acc_norm": 0.8601036269430051,
"acc_norm_stderr": 0.025033870583015178
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.617948717948718,
"acc_stderr": 0.024635549163908234,
"acc_norm": 0.617948717948718,
"acc_norm_stderr": 0.024635549163908234
},
"harness|hendrycksTest-high_school_mathematics|5": {
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}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### 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. -->
[More Information Needed]
#### 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. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### 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. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### 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. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] |
DAMO-NLP-SG/HyperlinkMRC | ---
license: mit
---
|
HANMAMANG/LlaMa2_Practice | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 5792
num_examples: 39
download_size: 2640
dataset_size: 5792
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
DataStudio/OCR_document_yellow_highligth_01 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2436004947.625
num_examples: 111411
download_size: 2438620404
dataset_size: 2436004947.625
---
# Dataset Card for "OCR_document_yellow_highligth_01"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tyzhu/find_first_sent_train_200_eval_40 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: title
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 570793
num_examples: 440
- name: validation
num_bytes: 40604
num_examples: 40
download_size: 0
dataset_size: 611397
---
# Dataset Card for "find_first_sent_train_200_eval_40"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
distilled-one-sec-cv12-each-chunk-uniq/chunk_207 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1132955716.0
num_examples: 220763
download_size: 1161751256
dataset_size: 1132955716.0
---
# Dataset Card for "chunk_207"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kheopss/ask_kheops_f4.0 | ---
dataset_info:
features:
- name: text
dtype: string
- name: text2
dtype: string
splits:
- name: train
num_bytes: 1480822
num_examples: 500
download_size: 683726
dataset_size: 1480822
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Circularmachines/batch_indexing_machine_movs | ---
license: cc-by-4.0
---
|
hippocrates/sharegpt_train | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 619993202
num_examples: 94145
download_size: 233806236
dataset_size: 619993202
---
# Dataset Card for "sharegpt_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TigerResearch/sft_en | ---
license: apache-2.0
language:
- en
---
[Tigerbot](https://github.com/TigerResearch/TigerBot) 开源项目中微调英文sft-en数据合集
本合集涵盖本组织下开源的其他中文sft-英文-数据集，不需要重复下载
<p align="center" width="40%">
## Usage
```python
import datasets
ds_sft = datasets.load_dataset('TigerResearch/sft_en')
```
## 文件细分
| 类型 | 语言 | 数据集文件 | 数量 |
| ------------ | ---- | -------------------------------------------------------------------------------------------------------------------------------- | ----------- |
| alpaca 英文 | 英文 | [tigerbot-alpaca-en-50k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-alpaca-en-50k.json) | 50k |
| 头脑风暴 | 英文 | [tigerbot-dolly-Brainstorming-en-1.7k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-dolly-Brainstorming-en-1.7k.json) | 1.7k |
| 分类 | 英文 | [tigerbot-dolly-Classification-en-2k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-dolly-Classification-en-2k.json) | 2k |
| 代码 | 英文 | [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-kaggle-leetcodesolutions-en-2k.json) | 2k |
| 食谱生成 | 英文 | [tigerbot-kaggle-recipes-en-2k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-kaggle-recipes-en-2k.json) | 2k |
| 病历生成 | 英文 | [tigerbot-mt-note-generation-en](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-mt-note-generation-en.json) | 450 |
| 多轮对话 | 英文 | [tigerbot-OIG-multichat-en-50k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-OIG-multichat-en-50k.json) | 50k |
| 综合问答 | 英文 | [tigerbot-stackexchange-qa-en-0.5m](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-stackexchange-qa-en-0.5m.json) | 0.5m |
| wiki 问答 | 英文 | [tigerbot-wiki-qa-bart-en-10k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-wiki-qa-bart-en-10k.json) | 10k |
| 如何做类教程 | 英文 | [tigerbot-youtube-howto-en-50k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-youtube-howto-en-50k.json) | 50k | |
FireNutter/Voices | ---
license: creativeml-openrail-m
---
|
chaocai/tiny_dataset | ---
license: apache-2.0
---
|
gmongaras/BERT_Base_Cased_512_GLUE_Mapped | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: label
dtype: float64
- name: dataset_name
dtype: string
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 232895922
num_examples: 949728
- name: validation
num_bytes: 17255970
num_examples: 69711
- name: test
num_bytes: 96102951
num_examples: 425205
download_size: 123150665
dataset_size: 346254843
---
Original Dataset from: https://huggingface.co/datasets/glue
This dataset is adapted from https://huggingface.co/datasets/gmongaras/BERT_Base_Cased_512_GLUE
Every split besides the ax split is in this dataset.
Lines above 512 tokens from the BERT-cased (bert-base-cased) tokenizer are removed in the original dataset
If in any case the sentences are longer than 512 tokens, they are subsetted.
Original labels and dataset categories are retained. |
ChaiML/20231206_chai_prize_reward_model_data | ---
dataset_info:
features:
- name: input_text
dtype: string
- name: labels
dtype: int64
splits:
- name: train
num_bytes: 147932875
num_examples: 87763
download_size: 83237596
dataset_size: 147932875
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "20231206_chai_prize_reward_model_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Ssunbell/boostcamp-docvqa-v3 | ---
dataset_info:
features:
- name: questionId
dtype: int64
- name: question
dtype: string
- name: image
sequence:
sequence:
sequence:
sequence: uint8
- name: docId
dtype: int64
- name: ucsf_document_id
dtype: string
- name: ucsf_document_page_no
dtype: string
- name: answers
sequence: string
- name: data_split
dtype: string
- name: words
sequence: string
- name: boxes
sequence:
sequence: int64
splits:
- name: train
num_bytes: 6381951489
num_examples: 39454
- name: val
num_bytes: 869383194
num_examples: 5349
download_size: 2582271242
dataset_size: 7251334683
---
# Dataset Card for "boostcamp-docvqa-v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
iulusoy/test-data-2 | ---
dataset_info:
features:
- name: Sentences
sequence: string
- name: Labels
sequence: int64
- name: Span_begin
sequence: int64
- name: Span_end
sequence: int64
- name: Span_label
sequence: string
splits:
- name: train
num_bytes: 36481
num_examples: 103
download_size: 11243
dataset_size: 36481
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "test-data-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
shidowake/augmxnt_ultra-orca-boros-en-ja-v1_split_16 | ---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: weight
dtype: float64
- name: source
dtype: string
splits:
- name: train
num_bytes: 20639999.933149945
num_examples: 9397
download_size: 10379041
dataset_size: 20639999.933149945
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
one-sec-cv12/chunk_41 | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
splits:
- name: train
num_bytes: 23969546784.25
num_examples: 249558
download_size: 21032064812
dataset_size: 23969546784.25
---
# Dataset Card for "chunk_41"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mhenrichsen/context-aware-splits | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 35235257
num_examples: 12255
download_size: 20336185
dataset_size: 35235257
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "context-aware-splits"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Cartinoe5930/KoRAE_original | ---
dataset_info:
features:
- name: source
dtype: string
- name: prompt
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 95068407
num_examples: 63724
download_size: 48931987
dataset_size: 95068407
---
# Dataset Card for "KoRAE_original_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ClassicGuiwu/guiwu | ---
license: openrail
---
|
Atipico1/popqa_test | ---
dataset_info:
- config_name: adversary
features:
- name: question
dtype: string
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dtype: string
- name: gpt_adv_sentence
dtype: string
- name: is_valid_sentence
dtype: bool
- name: gpt_adv_passage
dtype: string
- name: is_valid_passage
dtype: bool
splits:
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- config_name: adversary_v2
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dtype: string
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sequence: string
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dtype: string
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sequence: string
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dtype: bool
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sequence: string
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sequence: string
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dtype: bool
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sequence: string
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sequence: string
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dtype: string
- name: title
dtype: string
- name: gpt_answer_sentence
dtype: string
- name: entity_type
dtype: string
- name: similar_entity
dtype: string
- name: similar_entity_score
dtype: float32
- name: random_entity
dtype: string
- name: random_entity_score
dtype: float64
splits:
- name: train
num_bytes: 96452590
num_examples: 14267
download_size: 53863232
dataset_size: 96452590
- config_name: default
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: ctxs
list:
- name: hasanswer
dtype: bool
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 94205924
num_examples: 14267
download_size: 52652398
dataset_size: 94205924
configs:
- config_name: adversary
data_files:
- split: train
path: adversary/train-*
- config_name: adversary_v2
data_files:
- split: train
path: adversary_v2/train-*
- config_name: adversary_v2-sent
data_files:
- split: train
path: adversary_v2-sent/train-*
- config_name: conflict
data_files:
- split: train
path: conflict/train-*
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267105 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-2.7b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-2.7b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
04-07-22/wep-probes | ---
license: apache-2.0
---
|
santhosh97/demo | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input_image
dtype: image
- name: ground_truth_image
dtype: image
splits:
- name: train
num_bytes: 83633000.0
num_examples: 80
download_size: 41819821
dataset_size: 83633000.0
---
# Dataset Card for "demo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vikas-mehta-cohere-health/sample | ---
size_categories: n<1K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for sample
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("vikas-mehta-cohere-health/sample")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("vikas-mehta-cohere-health/sample")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/guides/llms/conceptual_guides/data_model.html) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| text | Text | TextField | True | False |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| sentiment | Sentiment | LabelQuestion | True | N/A | ['positive', 'neutral', 'negative'] |
| mixed-emotion | Mixed-emotion | MultiLabelQuestion | True | N/A | ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'] |
**✨ NEW** Additionally, we also have **suggestions**, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.
Finally, the **guidelines** are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"fields": {
"text": "i didnt feel humiliated"
},
"metadata": {},
"responses": [
{
"status": "submitted",
"user_id": "ca1b15e8-a86c-4cdf-8783-45d3ee4912f4",
"values": {
"mixed-emotion": {
"value": [
"fear",
"surprise"
]
},
"sentiment": {
"value": "positive"
}
}
}
],
"suggestions": []
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"external_id": null,
"metadata": "{}",
"mixed-emotion": [
{
"status": "submitted",
"user_id": "ca1b15e8-a86c-4cdf-8783-45d3ee4912f4",
"value": [
"fear",
"surprise"
]
}
],
"mixed-emotion-suggestion": null,
"mixed-emotion-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"sentiment": [
{
"status": "submitted",
"user_id": "ca1b15e8-a86c-4cdf-8783-45d3ee4912f4",
"value": "positive"
}
],
"sentiment-suggestion": null,
"sentiment-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"text": "i didnt feel humiliated"
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
* **text** is of type `TextField`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* **sentiment** is of type `LabelQuestion` with the following allowed values ['positive', 'neutral', 'negative'].
* **mixed-emotion** is of type `MultiLabelQuestion` with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].
* **✨ NEW** **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **sentiment-suggestion** is of type `label_selection` with the following allowed values ['positive', 'neutral', 'negative'].
* (optional) **mixed-emotion-suggestion** is of type `multi_label_selection` with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].
Additionally, we also have one more field which is optional and is the following:
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation guidelines
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise.
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
ThWu/preference_benchmark | ---
dataset_info:
features:
- name: question_id
dtype: string
- name: prompt
dtype: string
- name: response_a
dtype: string
- name: response_b
dtype: string
- name: winner
dtype: string
- name: ranked_responses
sequence: string
splits:
- name: train
num_bytes: 3547912
num_examples: 1000
download_size: 1949831
dataset_size: 3547912
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
patilranjit485/aitChatBot | ---
license: apache-2.0
---
|
instructkr/ko_elo_arena_0407 | ---
license: apache-2.0
---
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_267 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1006795332.0
num_examples: 197721
download_size: 1026487852
dataset_size: 1006795332.0
---
# Dataset Card for "chunk_267"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ruchit2801/finer | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: tags
sequence: int64
splits:
- name: train
num_bytes: 1359318
num_examples: 3262
- name: validation
num_bytes: 171403
num_examples: 402
- name: test
num_bytes: 433606
num_examples: 1075
download_size: 413643
dataset_size: 1964327
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
cakiki/test-ud | ---
dataset_info:
features:
- name: sent_id
dtype: string
- name: text
dtype: string
- name: tokens
sequence: string
- name: dataset
dtype: string
- name: file
dtype: string
splits:
- name: german
num_bytes: 73638294
num_examples: 208438
- name: french
num_bytes: 10644508
num_examples: 29735
download_size: 39222235
dataset_size: 84282802
---
# Dataset Card for "test-ud"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/ais_wallenstein_isitwrongtotrytopickupgirlsinadungeon | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of ais_wallenstein (Dungeon ni Deai wo Motomeru no wa Machigatteiru no Darou ka)
This is the dataset of ais_wallenstein (Dungeon ni Deai wo Motomeru no wa Machigatteiru no Darou ka), containing 200 images and their tags.
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)).
|
svjack/zh-idiom-in-deepl-google-eng | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: deepl
sequence: string
- name: id
dtype: int64
- name: chinese
dtype: string
- name: book
sequence: string
- name: google
sequence: string
splits:
- name: train
num_bytes: 1016912
num_examples: 4310
download_size: 668171
dataset_size: 1016912
---
# Dataset Card for "zh-idiom-in-deepl-google-eng"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
thomasjeon/dulls | ---
license: mit
---
|
liuyanchen1015/MULTI_VALUE_wnli_drop_copula_be_NP | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 140
num_examples: 1
- name: test
num_bytes: 556
num_examples: 3
- name: train
num_bytes: 2156
num_examples: 13
download_size: 9072
dataset_size: 2852
---
# Dataset Card for "MULTI_VALUE_wnli_drop_copula_be_NP"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yatsy/GPT-wiki-intro-extension | ---
annotations_creators:
- no-annotation
language_creators:
- machine-generated
language:
- en
license:
- cc
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- aadityaubhat/GPT-wiki-intro
task_categories:
- text-classification
task_ids: []
pretty_name: GPT-wiki-intro-extension
tags:
- facebook/opt-1.3b
- facebook/opt-2.7b
- facebook/opt-125m
- meta-llama/Llama-2-7b-chat-hf
- meta-llama/Llama-2-13b-chat-hf
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: int64
- name: generated
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 6375445
num_examples: 5000
download_size: 2174490
dataset_size: 6375445
---
# GPT Wiki Intro Extension
This dataset is extension of aadityaubhat/GPT-wiki-intro.
1000 promts processed through several LLM witout sampling. (opt-125m, opt-1.3b, opt-2.7b, llama2-7b-chat, and llama2-13b-chat)
Schema for the dataset
|Column |Datatype|Description |
|---------------------|--------|-------------------------------------------|
|id |int64 |ID from original dataset |
|generated |string |Model's output |
|source |string |opt-125m, opt-1.3b, opt-2.7b, llama2-7b or llama2-13b |
```
@misc {yatsy,
author = { {Kirill Safronov} },
title = { GPT-wiki-intro-extension },
year = 2023,
url = { https://huggingface.co/datasets/yatsy/GPT-wiki-intro-extension },
publisher = { Hugging Face }
}
``` |
QEEWDFHGFH/picc | ---
license: other
---
|
yangcci/baseball_players_another | ---
license: unknown
---
|
google/MusicCaps | ---
license:
- cc-by-sa-4.0
converted_from: kaggle
kaggle_id: googleai/musiccaps
task_categories:
- text-to-speech
language:
- en
---
# Dataset Card for MusicCaps
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://kaggle.com/datasets/googleai/musiccaps
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The MusicCaps dataset contains **5,521 music examples, each of which is labeled with an English *aspect list* and a *free text caption* written by musicians**. An aspect list is for example *"pop, tinny wide hi hats, mellow piano melody, high pitched female vocal melody, sustained pulsating synth lead"*, while the caption consists of multiple sentences about the music, e.g.,
*"A low sounding male voice is rapping over a fast paced drums playing a reggaeton beat along with a bass. Something like a guitar is playing the melody along. This recording is of poor audio-quality. In the background a laughter can be noticed. This song may be playing in a bar."*
The text is solely focused on describing *how* the music sounds, not the metadata like the artist name.
The labeled examples are 10s music clips from the [**AudioSet**](https://research.google.com/audioset/) dataset (2,858 from the eval and 2,663 from the train split).
Please cite the corresponding paper, when using this dataset: http://arxiv.org/abs/2301.11325 (DOI: `10.48550/arXiv.2301.11325`)
### Dataset Usage
The published dataset takes the form of a `.csv` file that contains the ID of YouTube videos and their start/end stamps. In order to use this dataset, one must download the corresponding YouTube videos and chunk them according to the start/end times.
The following repository has an example script and notebook to load the clips. The notebook also includes a Gradio demo that helps explore some samples: https://github.com/nateraw/download-musiccaps-dataset
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
#### ytid
YT ID pointing to the YouTube video in which the labeled music segment appears. You can listen to the segment by opening https://youtu.be/watch?v={ytid}&start={start_s}
#### start_s
Position in the YouTube video at which the music starts.
#### end_s
Position in the YouTube video at which the music end. All clips are 10s long.
#### audioset_positive_labels
Labels for this segment from the AudioSet (https://research.google.com/audioset/) dataset.
#### aspect_list
A list of aspects describing the music.
#### caption
A multi-sentence free text caption describing the music.
#### author_id
An integer for grouping samples by who wrote them.
#### is_balanced_subset
If this value is true, the row is a part of the 1k subset which is genre-balanced.
#### is_audioset_eval
If this value is true, the clip is from the AudioSet eval split. Otherwise it is from the AudioSet train split.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset was shared by [@googleai](https://ai.google/research/)
### Licensing Information
The license for this dataset is cc-by-sa-4.0
### Citation Information
```bibtex
[More Information Needed]
```
### Contributions
[More Information Needed] |
DKYoon/slimorca-200k-english | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 321592977
num_examples: 200000
download_size: 167935239
dataset_size: 321592977
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
kunal18/ScienceQA-filtered_v2 | ---
dataset_info:
features:
- name: image
dtype: image
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype: int8
- name: correct_answer
dtype: string
splits:
- name: train
num_bytes: 246244065.0
num_examples: 3914
- name: validation
num_bytes: 82542203.0
num_examples: 1328
- name: test
num_bytes: 74700536.0
num_examples: 1208
download_size: 389785235
dataset_size: 403486804.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
joseluhf11/oct-object-detection-v4-average | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: objects
struct:
- name: bbox
sequence:
sequence: int64
- name: categories
sequence: string
splits:
- name: train
num_bytes: 70990022.0
num_examples: 566
download_size: 70811672
dataset_size: 70990022.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
---
# Dataset Card for "oct-object-detection-v4-average"
Dataset is composed of images with multiples object detection box in coco format (xmin, ymin, xmax, ymax). Images are OCT (type of eye scaner) with boxes indicating some features associated to AMD disease.
The difference from v3 is images are grouped (not duplicated images in multiples row) and they can have multiples labels-boxes in the objects field. So there are, 566 unique images, there are 566 rows, one per image.
Also, overlapped boxes are joined as average function
[Source datataset](https://doi.org/10.1101/2023.03.29.534704) |
saibo/bookcorpus_compact_1024_shard3_of_10_meta | ---
dataset_info:
features:
- name: text
dtype: string
- name: concept_with_offset
dtype: string
- name: cid_arrangement
sequence: int32
- name: schema_lengths
sequence: int64
- name: topic_entity_mask
sequence: int64
- name: text_lengths
sequence: int64
splits:
- name: train
num_bytes: 7826091649
num_examples: 61605
download_size: 1726433976
dataset_size: 7826091649
---
# Dataset Card for "bookcorpus_compact_1024_shard3_meta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kardosdrur/hestenet-qa | ---
license: mit
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 1144206.5903728174
num_examples: 1695
- name: test
num_bytes: 286220.40962718264
num_examples: 424
download_size: 936129
dataset_size: 1430427.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Hestenet Question-Answer
The dataset is based on data from Hestenettet in the Danish Gigaword corpus.
Question-answer pairs are purely extracted on the basis of heuristics, and have not been manually evaluated.
The dataset was created for aiding the training of sentence transformer models in the Danish Foundation Models project.
The dataset is currently not production-ready.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
result-kand2-sdxl-wuerst-karlo/1f4e3f67 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 260
num_examples: 10
download_size: 1484
dataset_size: 260
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "1f4e3f67"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aengusl/noise0_alpaca_sleeper_agents_toy_test_SFT_v4 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 5458229
num_examples: 15662
download_size: 2533210
dataset_size: 5458229
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/akizuki_ritsuko_theidolmster | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of akizuki_ritsuko/秋月律子 (THE iDOLM@STER)
This is the dataset of akizuki_ritsuko/秋月律子 (THE iDOLM@STER), containing 500 images and their tags.
The core tags of this character are `brown_hair, glasses, brown_eyes, antenna_hair, breasts, braid, twin_braids, folded_ponytail, short_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 | 500 | 445.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akizuki_ritsuko_theidolmster/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 317.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akizuki_ritsuko_theidolmster/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1064 | 610.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akizuki_ritsuko_theidolmster/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 416.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akizuki_ritsuko_theidolmster/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1064 | 775.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akizuki_ritsuko_theidolmster/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/akizuki_ritsuko_theidolmster',
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 |  |  |  |  |  | 1girl, solo, smile, open_mouth |
| 1 | 12 |  |  |  |  |  | 1girl, formal, solo, suit, smile, adjusting_eyewear, looking_at_viewer |
| 2 | 6 |  |  |  |  |  | 1girl, crop_top, fingerless_gloves, midriff, navel, necktie, belt, garter_straps, smile, thighhighs, wrist_cuffs, bare_shoulders, boots, fishnets, solo, adjusting_eyewear, short_shorts |
| 3 | 17 |  |  |  |  |  | 1girl, nude, solo, nipples, blush, medium_breasts, navel, pussy, large_breasts |
| 4 | 11 |  |  |  |  |  | 1girl, nipples, hetero, solo_focus, 1boy, censored, large_breasts, blush, nude, open_mouth, penis, female_pubic_hair, cum_in_pussy, cum_on_breasts, vaginal, after_sex, facial, spread_legs, sweat |
| 5 | 5 |  |  |  |  |  | 1girl, cleavage, detached_collar, looking_at_viewer, medium_breasts, playboy_bunny, solo, wrist_cuffs, fake_animal_ears, rabbit_ears, strapless_leotard, black-framed_eyewear, red_bowtie, blush, brown_pantyhose, cowboy_shot, fishnet_pantyhose, large_breasts, long_hair, rabbit_tail, semi-rimless_eyewear, smile, standing, sweat, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | smile | open_mouth | formal | suit | adjusting_eyewear | looking_at_viewer | crop_top | fingerless_gloves | midriff | navel | necktie | belt | garter_straps | thighhighs | wrist_cuffs | bare_shoulders | boots | fishnets | short_shorts | nude | nipples | blush | medium_breasts | pussy | large_breasts | hetero | solo_focus | 1boy | censored | penis | female_pubic_hair | cum_in_pussy | cum_on_breasts | vaginal | after_sex | facial | spread_legs | sweat | cleavage | detached_collar | playboy_bunny | fake_animal_ears | rabbit_ears | strapless_leotard | black-framed_eyewear | red_bowtie | brown_pantyhose | cowboy_shot | fishnet_pantyhose | long_hair | rabbit_tail | semi-rimless_eyewear | standing | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:-------------|:---------|:-------|:--------------------|:--------------------|:-----------|:--------------------|:----------|:--------|:----------|:-------|:----------------|:-------------|:--------------|:-----------------|:--------|:-----------|:---------------|:-------|:----------|:--------|:-----------------|:--------|:----------------|:---------|:-------------|:-------|:-----------|:--------|:--------------------|:---------------|:-----------------|:----------|:------------|:---------|:--------------|:--------|:-----------|:------------------|:----------------|:-------------------|:--------------|:--------------------|:-----------------------|:-------------|:------------------|:--------------|:--------------------|:------------|:--------------|:-----------------------|:-----------|:-------------------|
| 0 | 17 |  |  |  |  |  | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 12 |  |  |  |  |  | X | X | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | X | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 17 |  |  |  |  |  | X | X | | | | | | | | | | X | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 11 |  |  |  |  |  | X | | | X | | | | | | | | | | | | | | | | | | X | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | 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 |
|
monmamo/perrine-pudlan | ---
license: cc
language:
- en
tags:
- woman
- anthrope
- gouros
pretty_name: Perrine Pudlan
size_categories:
- n<1K
--- |
Pankajric22/test | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
multilinguality: []
size_categories:
- 1K<n<10K
source_datasets:
- extended
task_categories:
- image-classification
task_ids: []
paperswithcode_id: imagenette
pretty_name: Imagenette
---
# Dataset Card for Imagenette
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/fastai/imagenette
- **Repository:** https://github.com/fastai/imagenette
- **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-imagenette
### Dataset Summary
A smaller subset of 10 easily classified classes from [Imagenet](https://huggingface.co/datasets/imagenet-1k#dataset-summary), and a little more French.
This dataset was created by [Jeremy Howard](https://twitter.com/jeremyphoward), and this repository is only there to share his work on this platform. The repository owner takes no credit of any kind in the creation, curation or packaging of the dataset.
### Supported Tasks and Leaderboards
- `image-classification`: The dataset can be used to train a model for Image Classification.
### Languages
The class labels in the dataset are in English.
## Dataset Structure
### Data Instances
A data point comprises an image URL and its classification label.
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=320x320 at 0x19FA12186D8>,
'label': 'tench',
}
```
### Data Fields
- `image`: A `PIL.Image.Image` object containing the image.
- `label`: the expected class label of the image.
### Data Splits
| |train|validation|
|----------|----:|---------:|
|imagenette| 9469| 3925|
## Dataset Creation
### Curation Rationale
cf. https://huggingface.co/datasets/imagenet-1k#curation-rationale
### Source Data
#### Initial Data Collection and Normalization
Imagenette is a subset of [ImageNet](https://huggingface.co/datasets/imagenet-1k). Information about data collection of the source data can be found [here](https://huggingface.co/datasets/imagenet-1k#initial-data-collection-and-normalization).
### Annotations
#### Annotation process
cf. https://huggingface.co/datasets/imagenet-1k#annotation-process
#### Who are the annotators?
cf. https://huggingface.co/datasets/imagenet-1k#who-are-the-annotators
### Personal and Sensitive Information
cf. https://huggingface.co/datasets/imagenet-1k#personal-and-sensitive-information
## Considerations for Using the Data
### Social Impact of Dataset
cf. https://huggingface.co/datasets/imagenet-1k#social-impact-of-dataset
### Discussion of Biases
cf. https://huggingface.co/datasets/imagenet-1k#discussion-of-biases
### Other Known Limitations
cf. https://huggingface.co/datasets/imagenet-1k#other-known-limitations
## Additional Information
### Dataset Curators
cf. https://huggingface.co/datasets/imagenet-1k#dataset-curators
and Jeremy Howard
### Licensing Information
[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
```
@software{Howard_Imagenette_2019,
title={Imagenette: A smaller subset of 10 easily classified classes from Imagenet},
author={Jeremy Howard},
year={2019},
month={March},
publisher = {GitHub},
url = {https://github.com/fastai/imagenette}
}
```
### Contributions
This dataset was created by [Jeremy Howard](https://twitter.com/jeremyphoward) and published on [Github](https://github.com/fastai/imagenette). It was then only integrated into HuggingFace Datasets by [@frgfm](https://huggingface.co/frgfm).
|
autoevaluate/autoeval-staging-eval-project-ea06fa04-9805305 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- acronym_identification
eval_info:
task: entity_extraction
model: lewtun/autotrain-acronym-identification-7324788
metrics: ['bleu']
dataset_name: acronym_identification
dataset_config: default
dataset_split: validation
col_mapping:
tokens: tokens
tags: labels
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: lewtun/autotrain-acronym-identification-7324788
* Dataset: acronym_identification
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Felixver](https://huggingface.co/Felixver) for evaluating this model. |
AGBonnet/augmented-clinical-notes | ---
license: mit
task_categories:
- text-generation
language:
- en
pretty_name: Augmented Clinical Notes
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: augmented_notes_30K.jsonl
tags:
- medical
- health
dataset_info:
features:
- name: idx
dtype: string
- name: note
dtype: string
- name: full_note
dtype: string
- name: conversation
dtype: string
- name: summary
dtype: string
---
# Augmented Clinical Notes
The Augmented Clinical Notes dataset is an extension of existing datasets containing 30,000 triplets from different sources:
- **Real clinical notes** (*[PMC-Patients](https://arxiv.org/abs/2202.13876)*): Clinical notes correspond to patient summaries from the PMC-Patients dataset, which are extracted from PubMed Central case studies.
- **Synthetic dialogues** (*[NoteChat](https://arxiv.org/abs/2310.15959)*): Synthetic patient-doctor conversations were generated from clinical notes using GPT 3.5.
- **Structured patient information** (*ours*): From clinical notes, we generate structured patient summaries using GPT-4 and a tailored medical information template (see details below).
This dataset was used to train [**MediNote-7B**](https://huggingface.co/AGBonnet/medinote-7b) and [**MediNote-13B**](https://huggingface.co/AGBonnet/medinote-13b), a set of clinical note generators fine-tuned from the [**MediTron**](https://huggingface.co/epfl-llm/meditron-7b) large language models.
Our full report is available [here](./report.pdf).
## Dataset Details
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** Antoine Bonnet and Paul Boulenger
- **Language(s):** English only
- **Repository:** [EPFL-IC-Make-Team/ClinicalNotes](https://github.com/EPFL-IC-Make-Team/ClinicalNotes)
- **Paper:** *[MediNote: Automated Clinical Notes](report.pdf)*
## Dataset Creation
**Clinical notes**. Our primary source of clinical notes is *[PMC-Patients](https://arxiv.org/abs/2202.13876)*. This large-scale dataset contains 167K patient summaries extracted from open-access case studies published in PubMed Central. Each note encapsulates a detailed case presentation as written by a doctor, presenting a thorough summary encompassing the patient’s visit, medical history, symptoms, administered treatments, as well as the discharge summary and outcome of the intervention. These comprehensive case presentations offer a rich and diverse collection of medical scenarios, forming a robust foundation for our model training and evaluation.
**Synthetic dialogues**. Distribution of confidential patient-doctor conversations is forbidden, so no large scale dataset is publicly available for training. We circumvent the lack of real dialogue data by building upon [NoteChat](https://huggingface.co/datasets/akemiH/NoteChat), an extension of PMC-Patients with 167K synthetic patient-doctor conversations. Each dialogue transcript within the NoteChat dataset was generated from a clinical note by ChatGPT (version `gpt-3.5-turbo-0613`).
**Patient information**. We augment the PMC-Patients and NoteChat datasets by extracting structured patient information from the 30K longest clinical notes. To do so, we prompt GPT-4 (version `gpt-4-turbo-0613`) with zero-shot instructions, providing clinical notes and a structured template of patient medical information with feature definitions. This template, shown below, encapsulates crucial aspects of a clinical note such as the patient’s admission to a care center, medical history, current symptoms, as well as the doctor’s diagnosis and treatment plan.
The full data pipeline is shown below.
<p align="center">
<img width=70% src="data_pipeline.pdf" alt="Data pipeline" title="Data pipeline">
</p>
### Medical information template
Here is shown the medical template we used to structurize clinical notes. A JSON version is also available as `template_definitions.json`.
<p align="center">
<img width=70% src="template.pdf" alt="Data pipeline" title="Data pipeline">
</p>
### Dialogue Quality
The primary aim of synthetic dialogues is to distill comprehensive information from the case presentation, transforming it into a plausible and engaging conversation.
Newer versions of the dataset include higher quality dialogues generated by GPT-4 and NoteChat, a multi-agent dialogue generation pipeline (see the [NoteChat repository](https://github.com/believewhat/Dr.NoteAid) for more information).
Dialogues produced by ChatGPT tend to lack realism and frequently adhere to a pattern where the doctor poses a series of questions mirroring the facts from the original clinical notes, receiving simple ’Yes’ responses from the patient. Nevertheless, we decided to use ChatGPT dialogues as they were the only ones available during the training phase.
Clinical notes within NoteChat were truncated prior to the dialogue generation process. Consequently, the information lost due to truncation from the clinical note is also missing in the resulting dialogue. While complete notes were accessible from PMC-Patients, a conscious decision was made to fine-tune our models using truncated notes. This decision aimed at preventing our fine-tuned models from being inadvertently trained to hallucinate information towards the conclusion of a note. Notably, certain ChatGPT dialogues involving scenarios where a patient passes away and a subsequent dialogue with a family member commences revealed instances of prompt leaks. These leaks manifested as the prompt used for synthetic dialogue generation being inadvertently repeated within the dialogue.
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
Each row of the dataset represents one dialogue-summary-note triplet, and consists of the following dataset fields (all strings):
| Field | Description | Source |
|-|-|-|
| `idx` | Unique identifier, index in the original NoteChat-ChatGPT dataset | NoteChat |
| `note` | Clinical note used by NoteChat (possibly truncated) | NoteChat |
| `full_note` | Full clinical note | PMC-Patients |
| `conversation` | Patient-doctor dialogue | NoteChat |
| `summary`| Patient information summary (JSON) | ours |
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
While this dataset was originally used to fine-tune LLMs to extract structured patient information from dialogue, it can also be used for diverse applications in the healthcare domain, such as training models to extract comprehensive tabular patient features from clinical notes.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
- **Synthetic Data**: NoteChat dialogues were synthetically generated from clinical notes; they are not completely realistic and therefore fail to accurately represent real patient-doctor conversations. Real patient-doctor conversations are of course preferred, but their distribution is forbidden in the US by the [Health Insurance Portability and Accountability Act of 1996](https://www.cdc.gov/phlp/publications/topic/hipaa.html).
- **Representation**: PMC-Patients clinical notes have been extracted from English PubMed Central publications, and therefore over-represent clinical settings from English-speaking countries.
## Acknowledgments
We thank Prof. Mary-Anne Hartley for her advice on the appropriate template for structured medical patient summaries.
<!--
## Citation
If you use the Augmented Clinical Notes dataset, please cite out work:
```
ADD CITATION
```
--!> |
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/1110d6f3 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 182
num_examples: 10
download_size: 1338
dataset_size: 182
---
# Dataset Card for "1110d6f3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
NekoJojo/modified_wider_face | ---
dataset_info:
features:
- name: image
dtype: image
- name: labels
sequence: int64
- name: bbox
sequence:
sequence: float64
- name: valid_length
dtype: int64
- name: original_size
sequence: int64
- name: resized_bbox
sequence:
sequence: float64
- name: patches
sequence:
sequence: uint8
splits:
- name: train
num_bytes: 9200473267.125
num_examples: 28735
- name: val
num_bytes: 1006939083.125
num_examples: 3167
download_size: 9498167529
dataset_size: 10207412350.25
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
---
|
C-MTEB/DuRetrieval-qrels | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
dataset_info:
features:
- name: qid
dtype: string
- name: pid
dtype: string
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 787120
num_examples: 9839
download_size: 420443
dataset_size: 787120
---
# Dataset Card for "DuRetrieval-qrels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mteb-pt/amazon_massive_scenario | ---
configs:
- config_name: pt-PT
data_files:
- split: train
path: pt-PT_/train*
- split: validation
path: pt-PT_/validation*
- split: test
path: pt-PT_/test*
--- |
okezieowen/english_to_spanish | ---
language:
- en
- es
tags:
- machine-translation
- English
- Spanish
---
# Dataset Card for Dataset Name
This dataset was culled from the English-Spanish plain-text section of the United Nations Parallel Corpus.
## Dataset Sources
https://conferences.unite.un.org/UNCORPUS/Home/DownloadOverview
## Uses
This dataset can be used for various tasks in NLP, including but not limited to: Machine Translation, Cross-lingual Transfer Learning, Linguistic Research, etc.
## Dataset Card Contact
For any queries or contributions, please contact Okezie OKOYE at okezieowen@gmail.com. |
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