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I_kwDODunzps5Cm7sq

Add IGLUE dataset

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## Adding a Dataset - **Name:** IGLUE - **Description:** IGLUE brings together 4 vision-and-language tasks across 20 languages (Twitter [thread](https://twitter.com/ebugliarello/status/1487045497583976455?s=20&t=SB4LZGDhhkUW83ugcX_m5w)) - **Paper:** https://arxiv.org/abs/2201.11732 - **Data:** https://github.com/e-bug/iglue - **Motivation:** This dataset would provide a nice example of combining the text and image features of `datasets` together for multimodal applications. Note: the data / code are not yet visible on the GitHub repo, so I've pinged the authors for more information. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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https://github.com/huggingface/datasets/pull/3648

PR_kwDODunzps4xvXig

Fix Windows CI: bump python to 3.7

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Python>=3.7 is needed to install `tokenizers` 0.11

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https://github.com/huggingface/datasets/pull/3647

PR_kwDODunzps4xvGDQ

Fix `add_column` on datasets with indices mapping

closed

CONTRIBUTOR

My initial idea was to avoid the `flatten_indices` call and reorder a new column instead, but in the end I decided to follow `concatenate_datasets` and use `flatten_indices` to avoid padding when `dataset._indices.num_rows != dataset._data.num_rows`. Fix #3599

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https://github.com/huggingface/datasets/pull/3646

PR_kwDODunzps4xsX66

Fix streaming datasets that are not reset correctly

closed

MEMBER

Streaming datasets that use `StreamingDownloadManager.iter_archive` and `StreamingDownloadManager.iter_files` had some issues. Indeed if you try to iterate over such dataset twice, then the second time it will be empty. This is because the two methods above are generator functions. I fixed this by making them return iterables that are reset properly instead. Close https://github.com/huggingface/datasets/issues/3645 cc @anton-l

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I_kwDODunzps5CjRFy

Streaming dataset based on dl_manager.iter_archive/iter_files are not reset correctly

closed

MEMBER

Hi ! When iterating over a streaming dataset once, it's not reset correctly because of some issues with `dl_manager.iter_archive` and `dl_manager.iter_files`. Indeed they are generator functions (so the iterator that is returned can be exhausted). They should be iterables instead, and be reset if we do a for loop again: ```python from datasets import load_dataset d = load_dataset("common_voice", "ab", split="test", streaming=True) i = 0 for i, _ in enumerate(d): pass print(i) # 8 # let's do it again i = 0 for i, _ in enumerate(d): pass print(i) # 0 ```

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I_kwDODunzps5CjLz2

Add a GROUP BY operator

open

NONE

**Is your feature request related to a problem? Please describe.** Using batch mapping, we can easily split examples. However, we lack an appropriate option for merging them back together by some key. Consider this example: ```python # features: # { # "example_id": datasets.Value("int32"), # "text": datasets.Value("string") # } ds = datasets.Dataset() def split(examples): sentences = [text.split(".") for text in examples["text"]] return { "example_id": [ example_id for example_id, sents in zip(examples["example_id"], sentences) for _ in sents ], "sentence": [sent for sents in sentences for sent in sents], "sentence_id": [i for sents in sentences for i in range(len(sents))], } split_ds = ds.map(split, batched=True) def process(examples): outputs = some_neural_network_that_works_on_sentences(examples["sentence"]) return {"outputs": outputs} split_ds = split_ds.map(process, batched=True) ``` I have a dataset consisting of texts that I would like to process sentence by sentence in a batched way. Afterwards, I would like to put it back together as it was, merging the outputs together. **Describe the solution you'd like** Ideally, it would look something like this: ```python def join(examples): order = np.argsort(examples["sentence_id"]) text = ".".join(examples["text"][i] for i in order) outputs = [examples["outputs"][i] for i in order] return {"text": text, "outputs": outputs} ds = split_ds.group_by("example_id", join) ``` **Describe alternatives you've considered** Right now, we can do this: ```python def merge(example): meeting_id = example["example_id"] parts = split_ds.filter(lambda x: x["example_id"] == meeting_id).sort("segment_no") return {"outputs": list(parts["outputs"])} ds = ds.map(merge) ``` Of course, we could process the dataset like this: ```python def process(example): outputs = some_neural_network_that_works_on_sentences(example["text"].split(".")) return {"outputs": outputs} ds = ds.map(process, batched=True) ``` However, that does not allow using an arbitrary batch size and may lead to very inefficient use of resources if the batch size is much larger than the number of sentences in one example. I would very much appreciate some kind of group by operator to merge examples based on the value of one column.

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https://github.com/huggingface/datasets/pull/3643

PR_kwDODunzps4xr8mX

Fix sem_eval_2018_task_1 download location

open

CONTRIBUTOR

As discussed with @lhoestq in https://github.com/huggingface/datasets/issues/3549#issuecomment-1020176931_ this is the new pull request to fix the download location.

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https://github.com/huggingface/datasets/pull/3642

PR_kwDODunzps4xrj2S

Fix dataset slicing with negative bounds when indices mapping is not `None`

closed

CONTRIBUTOR

Fix #3611

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https://github.com/huggingface/datasets/pull/3641

PR_kwDODunzps4xre7C

Fix numpy rngs when seed is None

closed

CONTRIBUTOR

Fixes the NumPy RNG when `seed` is `None`. The problem becomes obvious after reading the NumPy notes on RNG (returned by `np.random.get_state()`): > The MT19937 state vector consists of a 624-element array of 32-bit unsigned integers plus a single integer value between 0 and 624 that indexes the current position within the main array. `The MT19937 state vector`: the seed which we currently index, but this value stays the same for multiple rounds. `plus a single integer value`: the `pos` value in this PR (is 624 if `seed` is set to a fixed value with `np.random.seed`, so we take the first value in the `seed` array returned by `np.random.get_state()`: https://stackoverflow.com/questions/32172054/how-can-i-retrieve-the-current-seed-of-numpys-random-number-generator) NumPy notes: https://numpy.org/doc/stable/reference/random/bit_generators/mt19937.html Fix #3634

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I_kwDODunzps5ChtmJ

Issues with custom dataset in Wav2Vec2

closed

NONE

We are training Vav2Vec using the run_speech_recognition_ctc_bnb.py-script. This is working fine with Common Voice, however using our custom dataset and data loader at [NbAiLab/NPSC]( https://huggingface.co/datasets/NbAiLab/NPSC) it crashes after roughly 1 epoch with the following stack trace:  We are able to work around the issue, for instance by adding this check in line#222 in transformers/models/wav2vec2/modeling_wav2vec2.py: ```python if input_length - (mask_length - 1) < num_masked_span: num_masked_span = input_length - (mask_length - 1) ``` Interestingly, these are the variable values before the adjustment: ``` input_length=10 mask_length=10 num_masked_span=2 ```` After adjusting num_masked_spin to 1, the training script runs. The issue is also fixed by setting “replace=True” in the same function. Do you have any idea what is causing this, and how to fix this error permanently? If you do not think this is an Datasets issue, feel free to move the issue.

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https://github.com/huggingface/datasets/issues/3639

I_kwDODunzps5ChSKs

same value of precision, recall, f1 score at each epoch for classification task.

open

NONE

**1st Epoch:** 1/27/2022 09:30:48 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/f1/default/default_experiment-1-0.arrow.59it/s] 01/27/2022 09:30:48 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/precision/default/default_experiment-1-0.arrow 01/27/2022 09:30:49 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/recall/default/default_experiment-1-0.arrow PRECISION: {'precision': 0.7612903225806451} RECALL: {'recall': 0.7612903225806451} F1: {'f1': 0.7612903225806451} {'eval_loss': 1.4658324718475342, 'eval_accuracy': 0.7612903118133545, 'eval_runtime': 30.0054, 'eval_samples_per_second': 46.492, 'eval_steps_per_second': 46.492, 'epoch': 3.0} **4th Epoch:** 1/27/2022 09:56:55 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/f1/default/default_experiment-1-0.arrow.92it/s] 01/27/2022 09:56:56 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/precision/default/default_experiment-1-0.arrow 01/27/2022 09:56:56 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/recall/default/default_experiment-1-0.arrow PRECISION: {'precision': 0.7698924731182796} RECALL: {'recall': 0.7698924731182796} F1: {'f1': 0.7698924731182796} ## Environment info !git clone https://github.com/huggingface/transformers %cd transformers !pip install . !pip install -r /content/transformers/examples/pytorch/token-classification/requirements.txt !pip install datasets

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I_kwDODunzps5CgJ-H

AutoTokenizer hash value got change after datasets.map

open

NONE

## Describe the bug AutoTokenizer hash value got change after datasets.map ## Steps to reproduce the bug 1. trash huggingface datasets cache 2. run the following code: ```python from transformers import AutoTokenizer, BertTokenizer from datasets import load_dataset from datasets.fingerprint import Hasher tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') def tokenize_function(example): return tokenizer(example["sentence1"], example["sentence2"], truncation=True) raw_datasets = load_dataset("glue", "mrpc") print(Hasher.hash(tokenize_function)) print(Hasher.hash(tokenizer)) tokenized_datasets = raw_datasets.map(tokenize_function, batched=True) print(Hasher.hash(tokenize_function)) print(Hasher.hash(tokenizer)) ``` got ``` Reusing dataset glue (/home1/wts/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad) 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1112.35it/s] f4976bb4694ebc51 3fca35a1fd4a1251 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 6.96ba/s] 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 15.25ba/s] 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 5.81ba/s] d32837619b7d7d01 5fd925c82edd62b6 ``` 3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache. ## Expected results `AutoTokenizer` work like specific Tokenizer (The hash value don't change after map): ```python from transformers import AutoTokenizer, BertTokenizer from datasets import load_dataset from datasets.fingerprint import Hasher tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') def tokenize_function(example): return tokenizer(example["sentence1"], example["sentence2"], truncation=True) raw_datasets = load_dataset("glue", "mrpc") print(Hasher.hash(tokenize_function)) print(Hasher.hash(tokenizer)) tokenized_datasets = raw_datasets.map(tokenize_function, batched=True) print(Hasher.hash(tokenize_function)) print(Hasher.hash(tokenizer)) ``` ``` Reusing dataset glue (/home1/wts/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad) 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1091.22it/s] 46d4b31f54153fc7 5b8771afd8d43888 Loading cached processed dataset at /home1/wts/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6b07ff82ae9d5c51.arrow Loading cached processed dataset at /home1/wts/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-af738a6d84f3864b.arrow Loading cached processed dataset at /home1/wts/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-531d2a603ba713c1.arrow 46d4b31f54153fc7 5b8771afd8d43888 ``` ## Environment info - `datasets` version: 1.18.0 - Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27 - Python version: 3.9.7 - PyArrow version: 6.0.1

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I_kwDODunzps5CfZUm

[TypeError: Couldn't cast array of type] Cannot load dataset in v1.18

open

MEMBER

## Describe the bug I am trying to load the [`GEM/RiSAWOZ` dataset](https://huggingface.co/datasets/GEM/RiSAWOZ) in `datasets` v1.18.1 and am running into a type error when casting the features. The strange thing is that I can load the dataset with v1.17.0. Note that the error is also present if I install from `master` too. As far as I can tell, the dataset loading script is correct and the problematic features [here](https://huggingface.co/datasets/GEM/RiSAWOZ/blob/main/RiSAWOZ.py#L237) also look fine to me. ## Steps to reproduce the bug ```python from datasets import load_dataset dset = load_dataset("GEM/RiSAWOZ") ``` ## Expected results I can load the dataset without error. ## Actual results <details><summary>Traceback</summary> ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in _prepare_split(self, split_generator) 1083 example = self.info.features.encode_example(record) -> 1084 writer.write(example, key) 1085 finally: ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write(self, example, key, writer_batch_size) 445 --> 446 self.write_examples_on_file() 447 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_examples_on_file(self) 403 batch_examples[col] = [row[0][col] for row in self.current_examples] --> 404 self.write_batch(batch_examples=batch_examples) 405 self.current_examples = [] ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size) 496 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col) --> 497 arrays.append(pa.array(typed_sequence)) 498 inferred_features[col] = typed_sequence.get_inferred_type() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib.array() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type) 204 # We only do it if trying_type is False - since this is what the user asks for. --> 205 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type) 206 return out ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 919 else: --> 920 return func(array, *args, **kwargs) 921 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1064 if isinstance(feature, list): -> 1065 return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0])) 1066 elif isinstance(feature, Sequence): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 919 else: --> 920 return func(array, *args, **kwargs) 921 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0) 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 919 else: --> 920 return func(array, *args, **kwargs) 921 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0) 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 919 else: --> 920 return func(array, *args, **kwargs) 921 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1086 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) -> 1087 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") 1088 TypeError: Couldn't cast array of type struct<医院-3.0T MRI: string, 医院-CT: string, 医院-DSA: string, 医院-公交线路: string, 医院-区域: string, 医院-名称: string, 医院-地址: string, 医院-地铁可达: string, 医院-地铁线路: string, 医院-性质: string, 医院-挂号时间: string, 医院-电话: string, 医院-等级: string, 医院-类别: string, 医院-重点科室: string, 医院-门诊时间: string, 天气-城市: string, 天气-天气: string, 天气-日期: string, 天气-温度: string, 天气-紫外线强度: string, 天气-风力风向: string, 旅游景点-区域: string, 旅游景点-名称: string, 旅游景点-地址: string, 旅游景点-开放时间: string, 旅游景点-是否地铁直达: string, 旅游景点-景点类型: string, 旅游景点-最适合人群: string, 旅游景点-消费: string, 旅游景点-特点: string, 旅游景点-电话号码: string, 旅游景点-评分: string, 旅游景点-门票价格: string, 汽车-价格(万元): string, 汽车-倒车影像: string, 汽车-动力水平: string, 汽车-厂商: string, 汽车-发动机排量(L): string, 汽车-发动机马力(Ps): string, 汽车-名称: string, 汽车-定速巡航: string, 汽车-巡航系统: string, 汽车-座位数: string, 汽车-座椅加热: string, 汽车-座椅通风: string, 汽车-所属价格区间: string, 汽车-油耗水平: string, 汽车-环保标准: string, 汽车-级别: string, 汽车-综合油耗(L/100km): string, 汽车-能源类型: string, 汽车-车型: string, 汽车-车系: string, 汽车-车身尺寸(mm): string, 汽车-驱动方式: string, 汽车-驾驶辅助影像: string, 火车-出发地: string, 火车-出发时间: string, 火车-到达时间: string, 火车-坐席: string, 火车-日期: string, 火车-时长: string, 火车-目的地: string, 火车-票价: string, 火车-舱位档次: string, 火车-车型: string, 火车-车次信息: string, 电影-主演: string, 电影-主演名单: string, 电影-具体上映时间: string, 电影-制片国家/地区: string, 电影-导演: string, 电影-年代: string, 电影-片名: string, 电影-片长: string, 电影-类型: string, 电影-豆瓣评分: string, 电脑-CPU: string, 电脑-CPU型号: string, 电脑-产品类别: string, 电脑-价格: string, 电脑-价格区间: string, 电脑-内存容量: string, 电脑-分类: string, 电脑-品牌: string, 电脑-商品名称: string, 电脑-屏幕尺寸: string, 电脑-待机时长: string, 电脑-显卡型号: string, 电脑-显卡类别: string, 电脑-游戏性能: string, 电脑-特性: string, 电脑-硬盘容量: string, 电脑-系列: string, 电脑-系统: string, 电脑-色系: string, 电脑-裸机重量: string, 电视剧-主演: string, 电视剧-主演名单: string, 电视剧-制片国家/地区: string, 电视剧-单集片长: string, 电视剧-导演: string, 电视剧-年代: string, 电视剧-片名: string, 电视剧-类型: string, 电视剧-豆瓣评分: string, 电视剧-集数: string, 电视剧-首播时间: string, 辅导班-上课方式: string, 辅导班-上课时间: string, 辅导班-下课时间: string, 辅导班-价格: string, 辅导班-区域: string, 辅导班-年级: string, 辅导班-开始日期: string, 辅导班-教室地点: string, 辅导班-教师: string, 辅导班-教师网址: string, 辅导班-时段: string, 辅导班-校区: string, 辅导班-每周: string, 辅导班-班号: string, 辅导班-科目: string, 辅导班-结束日期: string, 辅导班-课时: string, 辅导班-课次: string, 辅导班-课程网址: string, 辅导班-难度: string, 通用-产品类别: string, 通用-价格区间: string, 通用-品牌: string, 通用-系列: string, 酒店-价位: string, 酒店-停车场: string, 酒店-区域: string, 酒店-名称: string, 酒店-地址: string, 酒店-房型: string, 酒店-房费: string, 酒店-星级: string, 酒店-电话号码: string, 酒店-评分: string, 酒店-酒店类型: string, 飞机-准点率: string, 飞机-出发地: string, 飞机-到达时间: string, 飞机-日期: string, 飞机-目的地: string, 飞机-票价: string, 飞机-航班信息: string, 飞机-舱位档次: string, 飞机-起飞时间: string, 餐厅-人均消费: string, 餐厅-价位: string, 餐厅-区域: string, 餐厅-名称: string, 餐厅-地址: string, 餐厅-推荐菜: string, 餐厅-是否地铁直达: string, 餐厅-电话号码: string, 餐厅-菜系: string, 餐厅-营业时间: string, 餐厅-评分: string> to {'旅游景点-名称': Value(dtype='string', id=None), '旅游景点-区域': Value(dtype='string', id=None), '旅游景点-景点类型': Value(dtype='string', id=None), '旅游景点-最适合人群': Value(dtype='string', id=None), '旅游景点-消费': Value(dtype='string', id=None), '旅游景点-是否地铁直达': Value(dtype='string', id=None), '旅游景点-门票价格': Value(dtype='string', id=None), '旅游景点-电话号码': Value(dtype='string', id=None), '旅游景点-地址': Value(dtype='string', id=None), '旅游景点-评分': Value(dtype='string', id=None), '旅游景点-开放时间': Value(dtype='string', id=None), '旅游景点-特点': Value(dtype='string', id=None), '餐厅-名称': Value(dtype='string', id=None), '餐厅-区域': Value(dtype='string', id=None), '餐厅-菜系': Value(dtype='string', id=None), '餐厅-价位': Value(dtype='string', id=None), '餐厅-是否地铁直达': Value(dtype='string', id=None), '餐厅-人均消费': Value(dtype='string', id=None), '餐厅-地址': Value(dtype='string', id=None), '餐厅-电话号码': Value(dtype='string', id=None), '餐厅-评分': Value(dtype='string', id=None), '餐厅-营业时间': Value(dtype='string', id=None), '餐厅-推荐菜': Value(dtype='string', id=None), '酒店-名称': Value(dtype='string', id=None), '酒店-区域': Value(dtype='string', id=None), '酒店-星级': Value(dtype='string', id=None), '酒店-价位': Value(dtype='string', id=None), '酒店-酒店类型': Value(dtype='string', id=None), '酒店-房型': Value(dtype='string', id=None), '酒店-停车场': Value(dtype='string', id=None), '酒店-房费': Value(dtype='string', id=None), '酒店-地址': Value(dtype='string', id=None), '酒店-电话号码': Value(dtype='string', id=None), '酒店-评分': Value(dtype='string', id=None), '电脑-品牌': Value(dtype='string', id=None), '电脑-产品类别': Value(dtype='string', id=None), '电脑-分类': Value(dtype='string', id=None), '电脑-内存容量': Value(dtype='string', id=None), '电脑-屏幕尺寸': Value(dtype='string', id=None), '电脑-CPU': Value(dtype='string', id=None), '电脑-价格区间': Value(dtype='string', id=None), '电脑-系列': Value(dtype='string', id=None), '电脑-商品名称': Value(dtype='string', id=None), '电脑-系统': Value(dtype='string', id=None), '电脑-游戏性能': Value(dtype='string', id=None), '电脑-CPU型号': Value(dtype='string', id=None), '电脑-裸机重量': Value(dtype='string', id=None), '电脑-显卡类别': Value(dtype='string', id=None), '电脑-显卡型号': Value(dtype='string', id=None), '电脑-特性': Value(dtype='string', id=None), '电脑-色系': Value(dtype='string', id=None), '电脑-待机时长': Value(dtype='string', id=None), '电脑-硬盘容量': Value(dtype='string', id=None), '电脑-价格': Value(dtype='string', id=None), '火车-出发地': Value(dtype='string', id=None), '火车-目的地': Value(dtype='string', id=None), '火车-日期': Value(dtype='string', id=None), '火车-车型': Value(dtype='string', id=None), '火车-坐席': Value(dtype='string', id=None), '火车-车次信息': Value(dtype='string', id=None), '火车-时长': Value(dtype='string', id=None), '火车-出发时间': Value(dtype='string', id=None), '火车-到达时间': Value(dtype='string', id=None), '火车-票价': Value(dtype='string', id=None), '飞机-出发地': Value(dtype='string', id=None), '飞机-目的地': Value(dtype='string', id=None), '飞机-日期': Value(dtype='string', id=None), '飞机-舱位档次': Value(dtype='string', id=None), '飞机-航班信息': Value(dtype='string', id=None), '飞机-起飞时间': Value(dtype='string', id=None), '飞机-到达时间': Value(dtype='string', id=None), '飞机-票价': Value(dtype='string', id=None), '飞机-准点率': Value(dtype='string', id=None), '天气-城市': Value(dtype='string', id=None), '天气-日期': Value(dtype='string', id=None), '天气-天气': Value(dtype='string', id=None), '天气-温度': Value(dtype='string', id=None), '天气-风力风向': Value(dtype='string', id=None), '天气-紫外线强度': Value(dtype='string', id=None), '电影-制片国家/地区': Value(dtype='string', id=None), '电影-类型': Value(dtype='string', id=None), '电影-年代': Value(dtype='string', id=None), '电影-主演': Value(dtype='string', id=None), '电影-导演': Value(dtype='string', id=None), '电影-片名': Value(dtype='string', id=None), '电影-主演名单': Value(dtype='string', id=None), '电影-具体上映时间': Value(dtype='string', id=None), '电影-片长': Value(dtype='string', id=None), '电影-豆瓣评分': Value(dtype='string', id=None), '电视剧-制片国家/地区': Value(dtype='string', id=None), '电视剧-类型': Value(dtype='string', id=None), '电视剧-年代': Value(dtype='string', id=None), '电视剧-主演': Value(dtype='string', id=None), '电视剧-导演': Value(dtype='string', id=None), '电视剧-片名': Value(dtype='string', id=None), '电视剧-主演名单': Value(dtype='string', id=None), '电视剧-首播时间': Value(dtype='string', id=None), '电视剧-集数': Value(dtype='string', id=None), '电视剧-单集片长': Value(dtype='string', id=None), '电视剧-豆瓣评分': Value(dtype='string', id=None), '辅导班-班号': Value(dtype='string', id=None), '辅导班-难度': Value(dtype='string', id=None), '辅导班-科目': Value(dtype='string', id=None), '辅导班-年级': Value(dtype='string', id=None), '辅导班-区域': Value(dtype='string', id=None), '辅导班-校区': Value(dtype='string', id=None), '辅导班-上课方式': Value(dtype='string', id=None), '辅导班-开始日期': Value(dtype='string', id=None), '辅导班-结束日期': Value(dtype='string', id=None), '辅导班-每周': Value(dtype='string', id=None), '辅导班-上课时间': Value(dtype='string', id=None), '辅导班-下课时间': Value(dtype='string', id=None), '辅导班-时段': Value(dtype='string', id=None), '辅导班-课次': Value(dtype='string', id=None), '辅导班-课时': Value(dtype='string', id=None), '辅导班-教室地点': Value(dtype='string', id=None), '辅导班-教师': Value(dtype='string', id=None), '辅导班-价格': Value(dtype='string', id=None), '辅导班-课程网址': Value(dtype='string', id=None), '辅导班-教师网址': Value(dtype='string', id=None), '汽车-名称': Value(dtype='string', id=None), '汽车-车型': Value(dtype='string', id=None), '汽车-级别': Value(dtype='string', id=None), '汽车-座位数': Value(dtype='string', id=None), '汽车-车身尺寸(mm)': Value(dtype='string', id=None), '汽车-厂商': Value(dtype='string', id=None), '汽车-能源类型': Value(dtype='string', id=None), '汽车-发动机排量(L)': Value(dtype='string', id=None), '汽车-发动机马力(Ps)': Value(dtype='string', id=None), '汽车-驱动方式': Value(dtype='string', id=None), '汽车-综合油耗(L/100km)': Value(dtype='string', id=None), '汽车-环保标准': Value(dtype='string', id=None), '汽车-驾驶辅助影像': Value(dtype='string', id=None), '汽车-巡航系统': Value(dtype='string', id=None), '汽车-价格(万元)': Value(dtype='string', id=None), '汽车-车系': Value(dtype='string', id=None), '汽车-动力水平': Value(dtype='string', id=None), '汽车-油耗水平': Value(dtype='string', id=None), '汽车-倒车影像': Value(dtype='string', id=None), '汽车-定速巡航': Value(dtype='string', id=None), '汽车-座椅加热': Value(dtype='string', id=None), '汽车-座椅通风': Value(dtype='string', id=None), '汽车-所属价格区间': Value(dtype='string', id=None), '医院-名称': Value(dtype='string', id=None), '医院-等级': Value(dtype='string', id=None), '医院-类别': Value(dtype='string', id=None), '医院-性质': Value(dtype='string', id=None), '医院-区域': Value(dtype='string', id=None), '医院-地址': Value(dtype='string', id=None), '医院-电话': Value(dtype='string', id=None), '医院-挂号时间': Value(dtype='string', id=None), '医院-门诊时间': Value(dtype='string', id=None), '医院-公交线路': Value(dtype='string', id=None), '医院-地铁可达': Value(dtype='string', id=None), '医院-地铁线路': Value(dtype='string', id=None), '医院-重点科室': Value(dtype='string', id=None), '医院-CT': Value(dtype='string', id=None), '医院-3.0T MRI': Value(dtype='string', id=None), '医院-DSA': Value(dtype='string', id=None)} During handling of the above exception, another exception occurred: TypeError Traceback (most recent call last) /var/folders/28/k4cy5q7s2hs92xq7_h89_vgm0000gn/T/ipykernel_44306/2896005239.py in <module> ----> 1 dset = load_dataset("GEM/RiSAWOZ") 2 dset ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1692 1693 # Download and prepare data -> 1694 builder_instance.download_and_prepare( 1695 download_config=download_config, 1696 download_mode=download_mode, ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 593 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 594 if not downloaded_from_gcs: --> 595 self._download_and_prepare( 596 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 597 ) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 682 try: 683 # Prepare split will record examples associated to the split --> 684 self._prepare_split(split_generator, **prepare_split_kwargs) 685 except OSError as e: 686 raise OSError( ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in _prepare_split(self, split_generator) 1084 writer.write(example, key) 1085 finally: -> 1086 num_examples, num_bytes = writer.finalize() 1087 1088 split_generator.split_info.num_examples = num_examples ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in finalize(self, close_stream) 525 # Re-intializing to empty list for next batch 526 self.hkey_record = [] --> 527 self.write_examples_on_file() 528 if self.pa_writer is None: 529 if self.schema: ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_examples_on_file(self) 402 # Since current_examples contains (example, key) tuples 403 batch_examples[col] = [row[0][col] for row in self.current_examples] --> 404 self.write_batch(batch_examples=batch_examples) 405 self.current_examples = [] 406 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size) 495 col_try_type = try_features[col] if try_features is not None and col in try_features else None 496 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col) --> 497 arrays.append(pa.array(typed_sequence)) 498 inferred_features[col] = typed_sequence.get_inferred_type() 499 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib.array() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type) 203 # Also, when trying type "string", we don't want to convert integers or floats to "string". 204 # We only do it if trying_type is False - since this is what the user asks for. --> 205 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type) 206 return out 207 except (TypeError, pa.lib.ArrowInvalid) as e: # handle type errors and overflows ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"): 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 946 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 919 else: --> 920 return func(array, *args, **kwargs) 921 922 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1063 # feature must be either [subfeature] or Sequence(subfeature) 1064 if isinstance(feature, list): -> 1065 return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0])) 1066 elif isinstance(feature, Sequence): 1067 if feature.length > -1: ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"): 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 946 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 919 else: --> 920 return func(array, *args, **kwargs) 921 922 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1058 } 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) 1062 elif pa.types.is_list(array.type): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0) 1058 } 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) 1062 elif pa.types.is_list(array.type): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"): 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 946 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 919 else: --> 920 return func(array, *args, **kwargs) 921 922 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1058 } 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) 1062 elif pa.types.is_list(array.type): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0) 1058 } 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) 1062 elif pa.types.is_list(array.type): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"): 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 946 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 919 else: --> 920 return func(array, *args, **kwargs) 921 922 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1085 elif not isinstance(feature, (Sequence, dict, list, tuple)): 1086 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) -> 1087 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") 1088 1089 TypeError: Couldn't cast array of type struct<医院-3.0T MRI: string, 医院-CT: string, 医院-DSA: string, 医院-公交线路: string, 医院-区域: string, 医院-名称: string, 医院-地址: string, 医院-地铁可达: string, 医院-地铁线路: string, 医院-性质: string, 医院-挂号时间: string, 医院-电话: string, 医院-等级: string, 医院-类别: string, 医院-重点科室: string, 医院-门诊时间: string, 天气-城市: string, 天气-天气: string, 天气-日期: string, 天气-温度: string, 天气-紫外线强度: string, 天气-风力风向: string, 旅游景点-区域: string, 旅游景点-名称: string, 旅游景点-地址: string, 旅游景点-开放时间: string, 旅游景点-是否地铁直达: string, 旅游景点-景点类型: string, 旅游景点-最适合人群: string, 旅游景点-消费: string, 旅游景点-特点: string, 旅游景点-电话号码: string, 旅游景点-评分: string, 旅游景点-门票价格: string, 汽车-价格(万元): string, 汽车-倒车影像: string, 汽车-动力水平: string, 汽车-厂商: string, 汽车-发动机排量(L): string, 汽车-发动机马力(Ps): string, 汽车-名称: string, 汽车-定速巡航: string, 汽车-巡航系统: string, 汽车-座位数: string, 汽车-座椅加热: string, 汽车-座椅通风: string, 汽车-所属价格区间: string, 汽车-油耗水平: string, 汽车-环保标准: string, 汽车-级别: string, 汽车-综合油耗(L/100km): string, 汽车-能源类型: string, 汽车-车型: string, 汽车-车系: string, 汽车-车身尺寸(mm): string, 汽车-驱动方式: string, 汽车-驾驶辅助影像: string, 火车-出发地: string, 火车-出发时间: string, 火车-到达时间: string, 火车-坐席: string, 火车-日期: string, 火车-时长: string, 火车-目的地: string, 火车-票价: string, 火车-舱位档次: string, 火车-车型: string, 火车-车次信息: string, 电影-主演: string, 电影-主演名单: string, 电影-具体上映时间: string, 电影-制片国家/地区: string, 电影-导演: string, 电影-年代: string, 电影-片名: string, 电影-片长: string, 电影-类型: string, 电影-豆瓣评分: string, 电脑-CPU: string, 电脑-CPU型号: string, 电脑-产品类别: string, 电脑-价格: string, 电脑-价格区间: string, 电脑-内存容量: string, 电脑-分类: string, 电脑-品牌: string, 电脑-商品名称: string, 电脑-屏幕尺寸: string, 电脑-待机时长: string, 电脑-显卡型号: string, 电脑-显卡类别: string, 电脑-游戏性能: string, 电脑-特性: string, 电脑-硬盘容量: string, 电脑-系列: string, 电脑-系统: string, 电脑-色系: string, 电脑-裸机重量: string, 电视剧-主演: string, 电视剧-主演名单: string, 电视剧-制片国家/地区: string, 电视剧-单集片长: string, 电视剧-导演: string, 电视剧-年代: string, 电视剧-片名: string, 电视剧-类型: string, 电视剧-豆瓣评分: string, 电视剧-集数: string, 电视剧-首播时间: string, 辅导班-上课方式: string, 辅导班-上课时间: string, 辅导班-下课时间: string, 辅导班-价格: string, 辅导班-区域: string, 辅导班-年级: string, 辅导班-开始日期: string, 辅导班-教室地点: string, 辅导班-教师: string, 辅导班-教师网址: string, 辅导班-时段: string, 辅导班-校区: string, 辅导班-每周: string, 辅导班-班号: string, 辅导班-科目: string, 辅导班-结束日期: string, 辅导班-课时: string, 辅导班-课次: string, 辅导班-课程网址: string, 辅导班-难度: string, 通用-产品类别: string, 通用-价格区间: string, 通用-品牌: string, 通用-系列: string, 酒店-价位: string, 酒店-停车场: string, 酒店-区域: string, 酒店-名称: string, 酒店-地址: string, 酒店-房型: string, 酒店-房费: string, 酒店-星级: string, 酒店-电话号码: string, 酒店-评分: string, 酒店-酒店类型: string, 飞机-准点率: string, 飞机-出发地: string, 飞机-到达时间: string, 飞机-日期: string, 飞机-目的地: string, 飞机-票价: string, 飞机-航班信息: string, 飞机-舱位档次: string, 飞机-起飞时间: string, 餐厅-人均消费: string, 餐厅-价位: string, 餐厅-区域: string, 餐厅-名称: string, 餐厅-地址: string, 餐厅-推荐菜: string, 餐厅-是否地铁直达: string, 餐厅-电话号码: string, 餐厅-菜系: string, 餐厅-营业时间: string, 餐厅-评分: string> to {'旅游景点-名称': Value(dtype='string', id=None), '旅游景点-区域': Value(dtype='string', id=None), '旅游景点-景点类型': Value(dtype='string', id=None), '旅游景点-最适合人群': Value(dtype='string', id=None), '旅游景点-消费': Value(dtype='string', id=None), '旅游景点-是否地铁直达': Value(dtype='string', id=None), '旅游景点-门票价格': Value(dtype='string', id=None), '旅游景点-电话号码': Value(dtype='string', id=None), '旅游景点-地址': Value(dtype='string', id=None), '旅游景点-评分': Value(dtype='string', id=None), '旅游景点-开放时间': Value(dtype='string', id=None), '旅游景点-特点': Value(dtype='string', id=None), '餐厅-名称': Value(dtype='string', id=None), '餐厅-区域': Value(dtype='string', id=None), '餐厅-菜系': Value(dtype='string', id=None), '餐厅-价位': Value(dtype='string', id=None), '餐厅-是否地铁直达': Value(dtype='string', id=None), '餐厅-人均消费': Value(dtype='string', id=None), '餐厅-地址': Value(dtype='string', id=None), '餐厅-电话号码': Value(dtype='string', id=None), '餐厅-评分': Value(dtype='string', id=None), '餐厅-营业时间': Value(dtype='string', id=None), '餐厅-推荐菜': Value(dtype='string', id=None), '酒店-名称': Value(dtype='string', id=None), '酒店-区域': Value(dtype='string', id=None), '酒店-星级': Value(dtype='string', id=None), '酒店-价位': Value(dtype='string', id=None), '酒店-酒店类型': Value(dtype='string', id=None), '酒店-房型': Value(dtype='string', id=None), '酒店-停车场': Value(dtype='string', id=None), '酒店-房费': Value(dtype='string', id=None), '酒店-地址': Value(dtype='string', id=None), '酒店-电话号码': Value(dtype='string', id=None), '酒店-评分': Value(dtype='string', id=None), '电脑-品牌': Value(dtype='string', id=None), '电脑-产品类别': Value(dtype='string', id=None), '电脑-分类': Value(dtype='string', id=None), '电脑-内存容量': Value(dtype='string', id=None), '电脑-屏幕尺寸': Value(dtype='string', id=None), '电脑-CPU': Value(dtype='string', id=None), '电脑-价格区间': Value(dtype='string', id=None), '电脑-系列': Value(dtype='string', id=None), '电脑-商品名称': Value(dtype='string', id=None), '电脑-系统': Value(dtype='string', id=None), '电脑-游戏性能': Value(dtype='string', id=None), '电脑-CPU型号': Value(dtype='string', id=None), '电脑-裸机重量': Value(dtype='string', id=None), '电脑-显卡类别': Value(dtype='string', id=None), '电脑-显卡型号': Value(dtype='string', id=None), '电脑-特性': Value(dtype='string', id=None), '电脑-色系': Value(dtype='string', id=None), '电脑-待机时长': Value(dtype='string', id=None), '电脑-硬盘容量': Value(dtype='string', id=None), '电脑-价格': Value(dtype='string', id=None), '火车-出发地': Value(dtype='string', id=None), '火车-目的地': Value(dtype='string', id=None), '火车-日期': Value(dtype='string', id=None), '火车-车型': Value(dtype='string', id=None), '火车-坐席': Value(dtype='string', id=None), '火车-车次信息': Value(dtype='string', id=None), '火车-时长': Value(dtype='string', id=None), '火车-出发时间': Value(dtype='string', id=None), '火车-到达时间': Value(dtype='string', id=None), '火车-票价': Value(dtype='string', id=None), '飞机-出发地': Value(dtype='string', id=None), '飞机-目的地': Value(dtype='string', id=None), '飞机-日期': Value(dtype='string', id=None), '飞机-舱位档次': Value(dtype='string', id=None), '飞机-航班信息': Value(dtype='string', id=None), '飞机-起飞时间': Value(dtype='string', id=None), '飞机-到达时间': Value(dtype='string', id=None), '飞机-票价': Value(dtype='string', id=None), '飞机-准点率': Value(dtype='string', id=None), '天气-城市': Value(dtype='string', id=None), '天气-日期': Value(dtype='string', id=None), '天气-天气': Value(dtype='string', id=None), '天气-温度': Value(dtype='string', id=None), '天气-风力风向': Value(dtype='string', id=None), '天气-紫外线强度': Value(dtype='string', id=None), '电影-制片国家/地区': Value(dtype='string', id=None), '电影-类型': Value(dtype='string', id=None), '电影-年代': Value(dtype='string', id=None), '电影-主演': Value(dtype='string', id=None), '电影-导演': Value(dtype='string', id=None), '电影-片名': Value(dtype='string', id=None), '电影-主演名单': Value(dtype='string', id=None), '电影-具体上映时间': Value(dtype='string', id=None), '电影-片长': Value(dtype='string', id=None), '电影-豆瓣评分': Value(dtype='string', id=None), '电视剧-制片国家/地区': Value(dtype='string', id=None), '电视剧-类型': Value(dtype='string', id=None), '电视剧-年代': Value(dtype='string', id=None), '电视剧-主演': Value(dtype='string', id=None), '电视剧-导演': Value(dtype='string', id=None), '电视剧-片名': Value(dtype='string', id=None), '电视剧-主演名单': Value(dtype='string', id=None), '电视剧-首播时间': Value(dtype='string', id=None), '电视剧-集数': Value(dtype='string', id=None), '电视剧-单集片长': Value(dtype='string', id=None), '电视剧-豆瓣评分': Value(dtype='string', id=None), '辅导班-班号': Value(dtype='string', id=None), '辅导班-难度': Value(dtype='string', id=None), '辅导班-科目': Value(dtype='string', id=None), '辅导班-年级': Value(dtype='string', id=None), '辅导班-区域': Value(dtype='string', id=None), '辅导班-校区': Value(dtype='string', id=None), '辅导班-上课方式': Value(dtype='string', id=None), '辅导班-开始日期': Value(dtype='string', id=None), '辅导班-结束日期': Value(dtype='string', id=None), '辅导班-每周': Value(dtype='string', id=None), '辅导班-上课时间': Value(dtype='string', id=None), '辅导班-下课时间': Value(dtype='string', id=None), '辅导班-时段': Value(dtype='string', id=None), '辅导班-课次': Value(dtype='string', id=None), '辅导班-课时': Value(dtype='string', id=None), '辅导班-教室地点': Value(dtype='string', id=None), '辅导班-教师': Value(dtype='string', id=None), '辅导班-价格': Value(dtype='string', id=None), '辅导班-课程网址': Value(dtype='string', id=None), '辅导班-教师网址': Value(dtype='string', id=None), '汽车-名称': Value(dtype='string', id=None), '汽车-车型': Value(dtype='string', id=None), '汽车-级别': Value(dtype='string', id=None), '汽车-座位数': Value(dtype='string', id=None), '汽车-车身尺寸(mm)': Value(dtype='string', id=None), '汽车-厂商': Value(dtype='string', id=None), '汽车-能源类型': Value(dtype='string', id=None), '汽车-发动机排量(L)': Value(dtype='string', id=None), '汽车-发动机马力(Ps)': Value(dtype='string', id=None), '汽车-驱动方式': Value(dtype='string', id=None), '汽车-综合油耗(L/100km)': Value(dtype='string', id=None), '汽车-环保标准': Value(dtype='string', id=None), '汽车-驾驶辅助影像': Value(dtype='string', id=None), '汽车-巡航系统': Value(dtype='string', id=None), '汽车-价格(万元)': Value(dtype='string', id=None), '汽车-车系': Value(dtype='string', id=None), '汽车-动力水平': Value(dtype='string', id=None), '汽车-油耗水平': Value(dtype='string', id=None), '汽车-倒车影像': Value(dtype='string', id=None), '汽车-定速巡航': Value(dtype='string', id=None), '汽车-座椅加热': Value(dtype='string', id=None), '汽车-座椅通风': Value(dtype='string', id=None), '汽车-所属价格区间': Value(dtype='string', id=None), '医院-名称': Value(dtype='string', id=None), '医院-等级': Value(dtype='string', id=None), '医院-类别': Value(dtype='string', id=None), '医院-性质': Value(dtype='string', id=None), '医院-区域': Value(dtype='string', id=None), '医院-地址': Value(dtype='string', id=None), '医院-电话': Value(dtype='string', id=None), '医院-挂号时间': Value(dtype='string', id=None), '医院-门诊时间': Value(dtype='string', id=None), '医院-公交线路': Value(dtype='string', id=None), '医院-地铁可达': Value(dtype='string', id=None), '医院-地铁线路': Value(dtype='string', id=None), '医院-重点科室': Value(dtype='string', id=None), '医院-CT': Value(dtype='string', id=None), '医院-3.0T MRI': Value(dtype='string', id=None), '医院-DSA': Value(dtype='string', id=None)} ``` </details> ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.1 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.10 - PyArrow version: 3.0.0

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PR_kwDODunzps4xohMB

Update index.rst

closed

CONTRIBUTOR

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PR_kwDODunzps4xobAe

Make `ted_talks_iwslt` dataset streamable

open

CONTRIBUTOR

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I_kwDODunzps5Cd5Vf

Dataset.shuffle(seed=None) gives fixed row permutation

closed

NONE

## Describe the bug Repeated attempts to `shuffle` a dataset without specifying a seed give the same results. ## Steps to reproduce the bug ```python import datasets # Some toy example data = datasets.Dataset.from_dict( {"feature": [1, 2, 3, 4, 5], "label": ["a", "b", "c", "d", "e"]} ) # Doesn't work as expected print("Shuffle dataset") for _ in range(3): print(data.shuffle(seed=None)[:]) # This seems to work with pandas print("\nShuffle via pandas") for _ in range(3): df = data.to_pandas().sample(frac=1.0) print(datasets.Dataset.from_pandas(df, preserve_index=False)[:]) ``` ## Expected results I assumed that the default setting would initialize a new/random state of a `np.random.BitGenerator` (see [docs](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=shuffle#datasets.Dataset.shuffle)). Wouldn't that reshuffle the rows each time I call `data.shuffle()`? ## Actual results ```bash Shuffle dataset {'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']} {'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']} {'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']} Shuffle via pandas {'feature': [4, 2, 3, 1, 5], 'label': ['d', 'b', 'c', 'a', 'e']} {'feature': [2, 5, 3, 4, 1], 'label': ['b', 'e', 'c', 'd', 'a']} {'feature': [5, 2, 3, 1, 4], 'label': ['e', 'b', 'c', 'a', 'd']} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.0 - Platform: Linux-5.13.0-27-generic-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 6.0.1

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PR_kwDODunzps4xng6E

Mirror canonical datasets in prod

closed

MEMBER

Push the datasets changes to the Hub in production by setting `HF_USE_PROD=1` I also added a fix that makes the script ignore the json, csv, text, parquet and pandas dataset builders. cc @SBrandeis

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I_kwDODunzps5Cdfbx

Adding CC-100: Monolingual Datasets from Web Crawl Data (Datasets links are invalid)

open

CONTRIBUTOR

## Describe the bug The dataset links are no longer valid for CC-100. It seems that the website which was keeping these files are no longer accessible and therefore this dataset became unusable. Check out the dataset [homepage](http://data.statmt.org/cc-100/) which isn't accessible. Also the URLs for dataset file per language isn't accessible: http://data.statmt.org/cc-100/<language code here>.txt.xz (language codes: am, sr, ka, etc.) ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("cc100", "ka") ``` It throws 503 error. ## Expected results It should successfully download and load dataset but it throws an exception because the dataset files are no longer accessible. ## Environment info Run from google colab. Just installed the library using pip: ```!pip install -U datasets```

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I_kwDODunzps5CcwL-

Labels conflict when loading a local CSV file.

open

NONE

## Describe the bug I am trying to load a local CSV file with a separate file containing label names. It is successfully loaded for the first time, but when I try to load it again, there is a conflict between provided labels and the cached dataset info. Disabling caching globally and/or using `download_mode="force_redownload"` did not help. ## Steps to reproduce the bug ```python load_dataset('csv', data_files='data/my_data.csv', features=Features(text=Value(dtype='string'), label=ClassLabel(names_file='data/my_data_labels.txt'))) ``` `my_data.csv` file has the following structure: ``` text,label "example1",0 "example2",1 ... ``` and the `my_data_labels.txt` looks like this: ``` label1 label2 ... ``` ## Expected results Successfully loaded dataset. ## Actual results ```python File "/usr/local/lib/python3.8/site-packages/datasets/load.py", line 1706, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/usr/local/lib/python3.8/site-packages/datasets/builder.py", line 766, in as_dataset datasets = utils.map_nested( File "/usr/local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 261, in map_nested mapped = [ File "/usr/local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 262, in <listcomp> _single_map_nested((function, obj, types, None, True)) File "/usr/local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.8/site-packages/datasets/builder.py", line 797, in _build_single_dataset ds = self._as_dataset( File "/usr/local/lib/python3.8/site-packages/datasets/builder.py", line 872, in _as_dataset return Dataset(fingerprint=fingerprint, **dataset_kwargs) File "/usr/local/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 638, in __init__ inferred_features = Features.from_arrow_schema(arrow_table.schema) File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1242, in from_arrow_schema return Features.from_dict(metadata["info"]["features"]) File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1271, in from_dict obj = generate_from_dict(dic) File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1076, in generate_from_dict return {key: generate_from_dict(value) for key, value in obj.items()} File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1076, in <dictcomp> return {key: generate_from_dict(value) for key, value in obj.items()} File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1083, in generate_from_dict return class_type(**{k: v for k, v in obj.items() if k in field_names}) File "<string>", line 7, in __init__ File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 776, in __post_init__ raise ValueError("Please provide either names or names_file but not both.") ValueError: Please provide either names or names_file but not both. ``` ## Environment info - `datasets` version: 1.18.0 - Python version: 3.18.2

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I_kwDODunzps5Cbx7B

DuplicatedKeysError of NewsQA dataset

open

NONE

After processing the dataset following official [NewsQA](https://github.com/Maluuba/newsqa), I used datasets to load it: ``` a = load_dataset('newsqa', data_dir='news') ``` and the following error occurred: ``` Using custom data configuration default-data_dir=news Downloading and preparing dataset newsqa/default to /root/.cache/huggingface/datasets/newsqa/default-data_dir=news/1.0.0/b0b23e22d94a3d352ad9d75aff2b71375264a122fae301463079ee8595e05ab9... Traceback (most recent call last): File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1084, in _prepare_split writer.write(example, key) File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 442, in write self.check_duplicate_keys() File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 453, in check_duplicate_keys raise DuplicatedKeysError(key) datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: ./cnn/stories/6a0f9c8a5d0c6e8949b37924163c92923fe5770d.story Keys should be unique and deterministic in nature During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 1694, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 595, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 684, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1086, in _prepare_split num_examples, num_bytes = writer.finalize() File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 524, in finalize self.check_duplicate_keys() File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 453, in check_duplicate_keys raise DuplicatedKeysError(key) datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: ./cnn/stories/6a0f9c8a5d0c6e8949b37924163c92923fe5770d.story Keys should be unique and deterministic in nature ```

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https://github.com/huggingface/datasets/pull/3629

PR_kwDODunzps4xkCZA

Fix Hub repos update when there's a new release

closed

MEMBER

It was not listing the full list of datasets correctly cc @SBrandeis this is why it failed for 1.18.0 We should be good now !

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I_kwDODunzps5CZTuU

Dataset Card Creator drops information for "Additional Information" Section

open

NONE

First of all, the card creator is a great addition and really helpful for streamlining dataset cards! ## Describe the bug I encountered an inconvenient bug when entering "Additional Information" in the react app, which drops already entered text when switching to a previous section, and then back again to "Additional Information". I was able to reproduce the issue in both Firefox and Chrome, so I suspect a problem with the React logic that doesn't expect users to switch back in the final section. Edit: I'm also not sure whether this is the right place to open the bug report on, since it's not clear to me which particular project it belongs to, or where I could find associated source code. ## Steps to reproduce the bug 1. Navigate to the Section "Additional Information" in the [dataset card creator](https://huggingface.co/datasets/card-creator/) 2. Enter text in an arbitrary field, e.g., "Dataset Curators". 3. Switch back to a previous section, like "Dataset Creation". 4. When switching back again to "Additional Information", the text has been deleted. Notably, this behavior can be reproduced again and again, it's not just problematic for the first "switch-back" from Additional Information. ## Expected results For step 4, the previously entered information should still be present in the boxes, similar to the behavior to all other sections (switching back there works as expected) ## Actual results The text boxes are empty again, and previously entered text got deleted. ## Environment info - `datasets` version: N/A - Platform: Firefox 96.0 / Chrome 97.0 - Python version: N/A - PyArrow version: N/A

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PR_kwDODunzps4xitGe

Fix host URL in The Pile datasets

open

MEMBER

This PR fixes the host URL in The Pile datasets, once they have mirrored their data in another server. Fix #3626.

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I_kwDODunzps5CXy_k

The Pile cannot connect to host

open

MEMBER

## Describe the bug The Pile had issues with their previous host server and have mirrored its content to another server. The new URL server should be updated.

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I_kwDODunzps5CV0yy

Add a metadata field for when source data was produced

open

CONTRIBUTOR

**Is your feature request related to a problem? Please describe.** The current problem is that information about when source data was produced is not easily visible. Though there are a variety of metadata fields available in the dataset viewer, time period information is not included. This feature request suggests making metadata relating to the time that the underlying *source* data was produced more prominent and outlines why this specific information is of particular importance, both in domain-specific historic research and more broadly. **Describe the solution you'd like** There are a variety of metadata fields exposed in the dataset viewer (license, task categories, etc.) These fields make this metadata more prominent both for human users and as potentially machine-actionable information (for example, through the API). I would propose to add a metadata field that says when some underlying data was produced. For example, a dataset would be labelled as being produced between `1800-1900`. **Describe alternatives you've considered** This information is sometimes available in the Datacard or a paper describing the dataset. However, it's often not that easy to identify or extract this information, particularly if you want to use this field as a filter to identify relevant datasets. **Additional context** I believe this feature is relevant for a number of reasons: - Increasingly, there is an interest in using historical data for training language models (for example, https://huggingface.co/dbmdz/bert-base-historic-dutch-cased), and datasets to support this task (for example, https://huggingface.co/datasets/bnl_newspapers). For these datasets, indicating the time periods covered is particularly relevant. - More broadly, time is likely a common source of domain drift. Datasets of movie reviews from the 90s may not work well for recent movie reviews. As the documentation and long-term management of ML data become more of a priority, quickly understanding the time when the underlying text (or other data types) is arguably more important. - time-series data: datasets are adding more support for time series data. Again, the periods covered might be particularly relevant here. **open questions** - I think some of my points above apply not only to the underlying data but also to annotations. As a result, there could also be an argument for encoding this information somewhere. However, I would argue (but could be persuaded otherwise) that this is probably less important for filtering. This type of context is already addressed in the datasheets template and often requires more narrative to discuss. - what level of granularity would make sense for this? e.g. assigning a decade, century or year? - how to encode this information? What formatting makes sense - what specific time to encode; a data range? (mean, modal, min, max value?) This is a slightly amorphous feature request - I would be happy to discuss further/try and propose a more concrete solution if this seems like something that could be worth considering. I realise this might also touch on other parts of the 🤗 hubs ecosystem.

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https://github.com/huggingface/datasets/pull/3623

PR_kwDODunzps4xgWig

Extend support for streaming datasets that use os.path.relpath

open

MEMBER

This PR extends the support in streaming mode for datasets that use `os.path.relpath`, by patching that function. This feature will also be useful to yield the relative path of audio or image files, within an archive or parent dir. Close #3622.

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https://github.com/huggingface/datasets/issues/3622

I_kwDODunzps5CVHat

Extend support for streaming datasets that use os.path.relpath

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Extend support for streaming datasets that use `os.path.relpath`. This feature will also be useful to yield the relative path of audio or image files.

https://api.github.com/repos/huggingface/datasets/issues/3622/timeline

https://api.github.com/repos/huggingface/datasets/issues/3621

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https://github.com/huggingface/datasets/issues/3621

I_kwDODunzps5CUsQy

Consider adding `ipywidgets` as a dependency.

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When I install `datasets` in a fresh virtualenv with jupyterlab I always see this error. ``` ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html ``` It's a bit of a nuisance, because I need to run shut down the jupyterlab server in order to install the required dependency. Might it be an option to just include it as a dependency here?

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https://api.github.com/repos/huggingface/datasets/issues/3620

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https://github.com/huggingface/datasets/pull/3620

PR_kwDODunzps4xf1J3

Add Fon language tag

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Add Fon language tag to resources.

https://api.github.com/repos/huggingface/datasets/issues/3620/timeline