Dataset Preview
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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowInvalid
Message: Failed to parse string: 'X' as a scalar of type int64
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2223, in cast_table_to_schema
arrays = [
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2224, in <listcomp>
cast_array_to_feature(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2086, in cast_array_to_feature
return array_cast(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1797, in wrapper
return func(array, *args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1949, in array_cast
return array.cast(pa_type)
File "pyarrow/array.pxi", line 996, in pyarrow.lib.Array.cast
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/compute.py", line 404, in cast
return call_function("cast", [arr], options, memory_pool)
File "pyarrow/_compute.pyx", line 590, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 385, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Failed to parse string: 'X' as a scalar of type int64
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1456, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1055, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
chromosome
int64 | position
int64 | ref
string | alt
string | label
int64 | consequence
string |
|---|---|---|---|---|---|
1
| 13,273
|
G
|
C
| 0
|
ncRNA
|
1
| 14,464
|
A
|
T
| 0
|
ncRNA
|
1
| 16,688
|
G
|
A
| 0
|
ncRNA
|
1
| 17,697
|
G
|
C
| 0
|
ncRNA
|
1
| 49,554
|
A
|
G
| 0
|
Enhancer
|
1
| 51,479
|
T
|
A
| 0
|
Promoter
|
1
| 51,803
|
T
|
C
| 0
|
Promoter
|
1
| 51,928
|
G
|
A
| 0
|
Promoter
|
1
| 52,058
|
G
|
C
| 0
|
Promoter
|
1
| 52,238
|
T
|
G
| 0
|
Promoter
|
1
| 54,366
|
A
|
G
| 0
|
Enhancer
|
1
| 54,380
|
T
|
C
| 0
|
Enhancer
|
1
| 54,421
|
A
|
G
| 0
|
Enhancer
|
1
| 54,490
|
G
|
A
| 0
|
Enhancer
|
1
| 54,586
|
T
|
C
| 0
|
Enhancer
|
1
| 54,676
|
C
|
T
| 0
|
Enhancer
|
1
| 54,844
|
G
|
A
| 0
|
Enhancer
|
1
| 55,164
|
C
|
A
| 0
|
Enhancer
|
1
| 55,545
|
C
|
T
| 0
|
Enhancer
|
1
| 55,926
|
T
|
C
| 0
|
Enhancer
|
1
| 58,771
|
T
|
C
| 0
|
ncRNA
|
1
| 63,268
|
T
|
C
| 0
|
ncRNA
|
1
| 63,516
|
A
|
G
| 0
|
ncRNA
|
1
| 63,527
|
T
|
C
| 0
|
ncRNA
|
1
| 63,671
|
G
|
A
| 0
|
ncRNA
|
1
| 63,697
|
T
|
C
| 0
|
ncRNA
|
1
| 64,025
|
T
|
C
| 0
|
ncRNA
|
1
| 64,764
|
C
|
T
| 0
|
Promoter
|
1
| 64,931
|
G
|
A
| 0
|
Promoter
|
1
| 73,649
|
C
|
G
| 0
|
Enhancer
|
1
| 75,767
|
G
|
A
| 0
|
Enhancer
|
1
| 76,057
|
A
|
G
| 0
|
Enhancer
|
1
| 76,206
|
T
|
C
| 0
|
Enhancer
|
1
| 76,240
|
A
|
G
| 0
|
Enhancer
|
1
| 76,838
|
T
|
G
| 0
|
Enhancer
|
1
| 76,854
|
A
|
G
| 0
|
Enhancer
|
1
| 77,089
|
C
|
T
| 0
|
Enhancer
|
1
| 77,866
|
C
|
T
| 0
|
Enhancer
|
1
| 77,961
|
G
|
A
| 0
|
Enhancer
|
1
| 79,772
|
C
|
G
| 0
|
Enhancer
|
1
| 80,323
|
G
|
C
| 0
|
Enhancer
|
1
| 80,619
|
G
|
A
| 0
|
Enhancer
|
1
| 80,857
|
C
|
T
| 0
|
Enhancer
|
1
| 81,260
|
C
|
T
| 0
|
Enhancer
|
1
| 81,343
|
A
|
G
| 0
|
Enhancer
|
1
| 82,163
|
G
|
A
| 0
|
Enhancer
|
1
| 82,400
|
G
|
A
| 0
|
Enhancer
|
1
| 82,562
|
T
|
C
| 0
|
Enhancer
|
1
| 82,609
|
C
|
G
| 0
|
Enhancer
|
1
| 82,676
|
T
|
G
| 0
|
Enhancer
|
1
| 82,734
|
T
|
C
| 0
|
Enhancer
|
1
| 83,084
|
T
|
A
| 0
|
Enhancer
|
1
| 83,443
|
C
|
T
| 0
|
Enhancer
|
1
| 83,795
|
G
|
A
| 0
|
Enhancer
|
1
| 84,002
|
G
|
A
| 0
|
Enhancer
|
1
| 84,244
|
A
|
C
| 0
|
Enhancer
|
1
| 84,307
|
C
|
A
| 0
|
Enhancer
|
1
| 84,683
|
A
|
G
| 0
|
Enhancer
|
1
| 85,529
|
G
|
A
| 0
|
Enhancer
|
1
| 85,597
|
A
|
C
| 0
|
Enhancer
|
1
| 86,018
|
C
|
G
| 0
|
Enhancer
|
1
| 86,065
|
G
|
C
| 0
|
Enhancer
|
1
| 86,303
|
G
|
T
| 0
|
Enhancer
|
1
| 86,331
|
A
|
G
| 0
|
Enhancer
|
1
| 87,190
|
G
|
A
| 0
|
Enhancer
|
1
| 88,136
|
G
|
A
| 0
|
Enhancer
|
1
| 88,169
|
C
|
T
| 0
|
Enhancer
|
1
| 88,172
|
G
|
A
| 0
|
Enhancer
|
1
| 88,177
|
G
|
C
| 0
|
Enhancer
|
1
| 89,599
|
A
|
T
| 0
|
ncRNA
|
1
| 89,946
|
A
|
T
| 0
|
ncRNA
|
1
| 90,007
|
G
|
A
| 0
|
ncRNA
|
1
| 91,190
|
G
|
A
| 0
|
ncRNA
|
1
| 91,421
|
T
|
C
| 0
|
ncRNA
|
1
| 91,605
|
C
|
T
| 0
|
ncRNA
|
1
| 131,310
|
G
|
C
| 0
|
ncRNA
|
1
| 133,160
|
G
|
A
| 0
|
ncRNA
|
1
| 133,483
|
G
|
T
| 0
|
ncRNA
|
1
| 136,437
|
T
|
C
| 0
|
Promoter
|
1
| 136,635
|
T
|
G
| 0
|
Promoter
|
1
| 136,817
|
T
|
C
| 0
|
Promoter
|
1
| 137,825
|
G
|
A
| 0
|
ncRNA
|
1
| 138,593
|
G
|
T
| 0
|
Promoter
|
1
| 181,321
|
C
|
G
| 0
|
Enhancer
|
1
| 181,583
|
C
|
G
| 0
|
Enhancer
|
1
| 181,706
|
G
|
A
| 0
|
Promoter
|
1
| 183,800
|
C
|
G
| 0
|
ncRNA
|
1
| 185,497
|
G
|
A
| 0
|
ncRNA
|
1
| 187,153
|
T
|
C
| 0
|
ncRNA
|
1
| 187,259
|
G
|
T
| 0
|
ncRNA
|
1
| 188,252
|
G
|
T
| 0
|
ncRNA
|
1
| 202,330
|
C
|
A
| 0
|
Enhancer
|
1
| 206,074
|
T
|
C
| 0
|
Enhancer
|
1
| 206,849
|
C
|
G
| 0
|
Enhancer
|
1
| 206,863
|
T
|
C
| 0
|
Enhancer
|
1
| 206,930
|
G
|
A
| 0
|
Enhancer
|
1
| 263,722
|
C
|
G
| 0
|
ncRNA
|
1
| 264,481
|
G
|
A
| 0
|
ncRNA
|
1
| 264,557
|
A
|
G
| 0
|
ncRNA
|
1
| 264,562
|
C
|
T
| 0
|
ncRNA
|
End of preview.
YAML Metadata
Warning:
The task_categories "sequence-modeling" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
YAML Metadata
Warning:
The task_ids "sequence-classification" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation
vep_clinvar_chr1_split
- 字段: ref, alt, label, chromosome, position
- 划分: chromosome=1为test，其余为train
- 支持自动生成ref/alt序列
用法
from datasets import load_dataset
ds = load_dataset(
"Bgoood/vep_mendelian_traits_chr11_split",
sequence_length=2048,
fasta_path="/path/to/hg38.fa.gz",
data_dir="."
)
---
## 5. 上传到 HuggingFace
1. **初始化git repo（如果还没有）**
```bash
git lfs install
git clone https://huggingface.co/datasets/Bgoood/vep_mendelian_traits_chr11_split
cd vep_mendelian_traits_chr11_split
# 把 train.csv, test.csv, vep_mendelian_traits_chr11_split.py, README.md 放到这个目录
git add .
git commit -m "init dataset with script"
git push
- 或者直接网页上传
在你的数据集页面，点击“Add file”，上传上述文件。
6. 用户使用方式
用户只需这样调用即可自动生成ref/alt序列：
from datasets import load_dataset
ds = load_dataset(
"Bgoood/vep_mendelian_traits_chr11_split",
sequence_length=2048,
fasta_path="/path/to/hg38.fa.gz",
data_dir="."
)
7. 依赖
确保用户环境已安装：
pip install datasets pyfaidx pandas
8. 注意事项
fasta_path必须是本地可访问的 hg38.fa.gz 路径。- 你上传到HF的数据集只需包含原始csv和脚本，不需要包含fasta文件。
如需自动化脚本生成csv、或有其他定制需求，请随时告知！
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