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https://api.github.com/repos/huggingface/datasets/issues/5820 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5820/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5820/comments | https://api.github.com/repos/huggingface/datasets/issues/5820/events | https://github.com/huggingface/datasets/issues/5820 | 1,695,892,811 | I_kwDODunzps5lFUVL | 5,820 | Incomplete docstring for `BuilderConfig` | {
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] | closed | false | null | [] | null | 1 | "2023-05-04T12:14:34Z" | "2023-05-05T12:31:56Z" | "2023-05-05T12:31:56Z" | CONTRIBUTOR | null | null | null | Hi guys !
I stumbled upon this docstring while working on a project.
Some of the attributes have missing descriptions.
https://github.com/huggingface/datasets/blob/bc5fef5b6d91f009e4101684adcb374df2c170f6/src/datasets/builder.py#L104-L117 | {
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https://api.github.com/repos/huggingface/datasets/issues/5819 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5819/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5819/comments | https://api.github.com/repos/huggingface/datasets/issues/5819/events | https://github.com/huggingface/datasets/issues/5819 | 1,695,536,738 | I_kwDODunzps5lD9Zi | 5,819 | Cannot pickle error in Dataset.from_generator() | {
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} | [] | closed | false | null | [] | null | 2 | "2023-05-04T08:39:09Z" | "2023-05-05T19:20:59Z" | "2023-05-05T19:20:58Z" | NONE | null | null | null | ### Describe the bug
I'm trying to use Dataset.from_generator() to generate a large dataset.
### Steps to reproduce the bug
Code to reproduce:
```
from transformers import T5Tokenizer, T5ForConditionalGeneration, GenerationConfig
import torch
from tqdm import tqdm
from datasets import load_dataset
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small", device_map="auto")
model = torch.compile(model)
def generate_data(data_loader):
model.eval()
for batch in tqdm(data_loader):
input_ids = tokenizer(batch['instruction'], return_tensors='pt', padding=True, truncation=True).input_ids.to("cuda:0")
with torch.no_grad():
outputs = model.generate(input_ids, generation_config=generation_config)
decoder_hidden_states = outputs.decoder_hidden_states
for i, h in zip(batch['instruction'], decoder_hidden_states):
yield {"instruction": i, "decoder_hidden_states": h}
generation_config = GenerationConfig(
temperature=1,
max_new_tokens=1024,
do_sample=False,
num_return_sequences=1,
return_dict_in_generate=True,
output_scores=True,
output_hidden_states=True,
)
from datasets import Dataset, load_dataset
from torch.utils.data import DataLoader
dataset = load_dataset("HuggingFaceH4/databricks_dolly_15k")
train_loader = DataLoader(dataset['train'], batch_size=2, shuffle=True)
dataset = Dataset.from_generator(generator=generate_data, gen_kwargs={"data_loader": train_loader})
dataset.save_to_disk("data/flant5_small_generation")
```
### Expected behavior
The dataset should be generated and saved.
But the following error occurred:
```
Traceback (most recent call last):
File "/remote-home/xhwang/alpaca-lora/data_collection_t5.py", line 46, in <module>
dataset = Dataset.from_generator(generator=generate_data, gen_kwargs={"data_loader": train_loader})
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1035, in from_generator
return GeneratorDatasetInputStream(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/io/generator.py", line 28, in __init__
self.builder = Generator(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/builder.py", line 336, in __init__
self.config, self.config_id = self._create_builder_config(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/builder.py", line 505, in _create_builder_config
config_id = builder_config.create_config_id(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/builder.py", line 179, in create_config_id
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/fingerprint.py", line 236, in hash
return cls.hash_default(value)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/fingerprint.py", line 229, in hash_default
return cls.hash_bytes(dumps(value))
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 726, in dumps
dump(obj, file)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 701, in dump
Pickler(file, recurse=True).dump(obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 394, in dump
StockPickler.dump(self, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 487, in dump
self.save(obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1311, in save_function
dill._dill._save_with_postproc(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1084, in _save_with_postproc
pickler._batch_setitems(iter(source.items()))
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 603, in save
self.save_reduce(obj=obj, *rv)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 717, in save_reduce
save(state)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 603, in save
self.save_reduce(obj=obj, *rv)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 717, in save_reduce
save(state)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1311, in save_function
dill._dill._save_with_postproc(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1070, in _save_with_postproc
pickler.save_reduce(*reduction, obj=obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 717, in save_reduce
save(state)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 887, in save_tuple
save(element)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1311, in save_function
dill._dill._save_with_postproc(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1070, in _save_with_postproc
pickler.save_reduce(*reduction, obj=obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 717, in save_reduce
save(state)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 887, in save_tuple
save(element)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 1003, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1311, in save_function
dill._dill._save_with_postproc(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1084, in _save_with_postproc
pickler._batch_setitems(iter(source.items()))
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 578, in save
rv = reduce(self.proto)
TypeError: cannot pickle 'ConfigModuleInstance' object
```
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-4.15.0-156-generic-x86_64-with-glibc2.31
- Python version: 3.10.10
- Huggingface_hub version: 0.13.2
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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{
"color": "a2eeef",
"default": true,
"description": "New feature or request",
"id": 1935892871,
"name": "enhancement",
"node_id": "MDU6TGFiZWwxOTM1ODkyODcx",
"url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement"
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] | open | false | null | [] | null | 3 | "2023-05-04T01:08:13Z" | "2023-05-04T20:43:39Z" | null | NONE | null | null | null | ### Feature request
The ability to load a dataset, add or change something, and save it back to disk.
Maybe it's possible, but I can't work out how to do it, e.g. this fails:
```py
import datasets
dataset = datasets.load_from_disk("data/test1")
dataset = dataset.add_item({"text": "A new item"})
dataset.save_to_disk("data/test1")
```
With the error:
```
PermissionError: Tried to overwrite /mnt/c/Users/david/py/learning/mini_projects/data_sorting_and_filtering/data/test1 but a dataset can't overwrite itself.
```
### Motivation
My use case is that I want to process a dataset in a particular way but it doesn't fit in memory if I do it in one go. So I want to perform a loop and at each step in the loop, process one shard and append it to an ever-growing dataset. The code in the loop will load a dataset, add some rows, then save it again.
Maybe I'm just thinking about things incorrectly and there's a better approach. FWIW I can't use `dataset.map()` to do the task because that doesn't work with `num_proc` when adding rows, so is confined to a single process which is too slow.
The only other way I can think of is to create a new file each time, but surely that's not how people do this sort of thing.
### Your contribution
na | {
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https://api.github.com/repos/huggingface/datasets/issues/5817 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5817/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5817/comments | https://api.github.com/repos/huggingface/datasets/issues/5817/events | https://github.com/huggingface/datasets/issues/5817 | 1,694,891,866 | I_kwDODunzps5lBf9a | 5,817 | Setting `num_proc` errors when `.map` returns additional items. | {
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} | [] | closed | false | null | [] | null | 3 | "2023-05-03T21:46:53Z" | "2023-05-04T21:14:21Z" | "2023-05-04T20:22:25Z" | NONE | null | null | null | ### Describe the bug
I'm using a map function that returns more rows than are passed in.
If I try to use `num_proc` I get:
```
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 563, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 528, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3097, in map
for rank, done, content in iflatmap_unordered(
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1372, in iflatmap_unordered
yield queue.get(timeout=0.05)
File "<string>", line 2, in get
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/multiprocess/managers.py", line 818, in _callmethod
kind, result = conn.recv()
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/multiprocess/connection.py", line 258, in recv
buf = self._recv_bytes()
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/multiprocess/connection.py", line 422, in _recv_bytes
buf = self._recv(4)
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/multiprocess/connection.py", line 391, in _recv
raise EOFError
EOFError
```
### Steps to reproduce the bug
This is copied from the [Datasets docs](https://huggingface.co/docs/datasets/v2.12.0/en/process#batch-processing), with `num_proc` added, and will error.
```py
import datasets
dataset = ... # any old dataset
def chunk_examples(examples):
chunks = []
for sentence in examples["text"]:
chunks += [sentence[i : i + 50] for i in range(0, len(sentence), 50)]
return {"chunks": chunks}
chunked_dataset = dataset.map(
chunk_examples,
batched=True,
remove_columns=dataset.column_names,
num_proc=2, # Remove and it works
)
```
### Expected behavior
Should work fine. On a related note, multi-processing also fails if there is a Meta class anywhere in scope (and there are plenty in the standard library). This is the fault of `dill` and is a long standing issue.
Have you considered using Loky for multiprocessing? I've found that the built-in `datasets` multi-processing breaks more than it works so have written my own function using `loky`, for reference:
```py
import datasets
import loky
def fast_loop(dataset: datasets.Dataset, func, num_proc=None):
if num_proc is None:
import os
num_proc = len(os.sched_getaffinity(0))
shards = [
dataset.shard(num_shards=num_proc, index=i, contiguous=True)
for i in range(num_proc)
]
executor = loky.get_reusable_executor(max_workers=num_proc)
results = executor.map(func, shards)
return datasets.combine.concatenate_datasets(list(results))
```
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- Huggingface_hub version: 0.12.1
- PyArrow version: 11.0.0
- Pandas version: 2.0.1 | {
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} | [] | closed | false | null | [] | null | 3 | "2023-05-03T18:34:18Z" | "2023-05-04T14:31:55Z" | "2023-05-04T14:24:49Z" | CONTRIBUTOR | null | 0 | {
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} | Preserve the `stopping_strategy` in the `RandomlyCyclingMultiSourcesExamplesIterable.shard_data_sources` to fix shuffling a dataset interleaved (from multiple sources) with probabilities.
Fix #5812
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} | [] | open | false | null | [] | null | 4 | "2023-05-02T21:43:33Z" | "2023-07-26T16:13:31Z" | null | NONE | null | null | null | I'm not sure whether this is more appropriately addressed with HuggingFace or Kaggle. I would like to somehow directly create a Kaggle dataset from a HuggingFace Dataset.
While Kaggle does provide the option to create a dataset from a URI, that URI must point to a single file. For example:
Is there some mechanism from huggingface to represent a dataset (such as that from `load_dataset('wmt14', 'de-en', split='train')` as a single file? Or, some other way to get that into a Kaggle dataset so that I can use the huggingface `datasets` module to process and consume it inside of a Kaggle notebook?
Thanks in advance!
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] | null | 0 | "2023-05-02T05:26:17Z" | "2023-05-04T14:24:51Z" | "2023-05-04T14:24:51Z" | NONE | null | null | null | ### Describe the bug
Shuffling interleaved `IterableDataset` with "all_exhausted" strategy yields non-exhaustive sampling.
### Steps to reproduce the bug
```py
from datasets import IterableDataset, interleave_datasets
def gen(bias, length):
for i in range(length):
yield dict(a=bias+i)
seed = 42
probabilities = [0.2, 0.6, 0.2]
d1 = IterableDataset.from_generator(lambda: gen(0, 3))
d2 = IterableDataset.from_generator(lambda: gen(10, 4))
d3 = IterableDataset.from_generator(lambda: gen(20, 3))
ds = interleave_datasets([d1, d2, d3], probabilities=probabilities, seed=seed, stopping_strategy='all_exhausted')
ds = ds.shuffle(buffer_size=1000)
for x in ds:
print(x)
```
This code produces
```
{'a': 0}
{'a': 22}
{'a': 20}
{'a': 21}
{'a': 10}
{'a': 1}
```
### Expected behavior
It should produce a longer list of examples to exhaust all the datasets.
If you comment out the shuffle line, it will exhaust all the datasets properly.
Here is the output if you comment out shuffling:
```
{'a': 10}
{'a': 11}
{'a': 20}
{'a': 12}
{'a': 0}
{'a': 21}
{'a': 13}
{'a': 10}
{'a': 1}
{'a': 11}
{'a': 12}
{'a': 22}
{'a': 13}
{'a': 20}
{'a': 10}
{'a': 11}
{'a': 12}
{'a': 2}
```
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.31
- Python version: 3.10.11
- Huggingface_hub version: 0.14.1
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
This was run on Google Colab. | {
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https://api.github.com/repos/huggingface/datasets/issues/5811 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5811/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5811/comments | https://api.github.com/repos/huggingface/datasets/issues/5811/events | https://github.com/huggingface/datasets/issues/5811 | 1,689,919,046 | I_kwDODunzps5kuh5G | 5,811 | load_dataset: TypeError: 'NoneType' object is not callable, on local dataset filename changes | {
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I've adapted Databrick's [train_dolly.py](/databrickslabs/dolly/blob/master/train_dolly.py) to train using a local dataset, which has been working. Upon changing the filenames of the `.json` & `.py` files in my local dataset directory, `dataset = load_dataset(path_or_dataset)["train"]` throws the error:
```python
2023-04-30 09:10:52 INFO [training.trainer] Loading dataset from dushowxa-characters
Traceback (most recent call last):
File "/data/dushowxa-dolly/train_dushowxa.py", line 26, in <module>
load_training_dataset()
File "/data/dushowxa-dolly/training/trainer.py", line 89, in load_training_dataset
dataset = load_dataset(path_or_dataset)["train"]
File "/data/dushowxa-dolly/.venv/lib/python3.10/site-packages/datasets/load.py", line 1773, in load_dataset
builder_instance = load_dataset_builder(
File "/data/dushowxa-dolly/.venv/lib/python3.10/site-packages/datasets/load.py", line 1528, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
TypeError: 'NoneType' object is not callable
```
The local dataset filenames were of the form `dushowxa-characters/expanse-dushowxa-characters.json` and are now of the form `dushowxa-characters/dushowxa-characters.json` (the word `expanse-` was removed from the filenames). Is this perhaps a dataset caching issue?
I have attempted to manually clear caches, but to no effect:
```sh
rm -rfv ~/.cache/huggingface/datasets/*
rm -rfv ~/.cache/huggingface/modules/*
```
### Steps to reproduce the bug
Run `python3 train_dushowxa.py` (adapted from Databrick's [train_dolly.py](/databrickslabs/dolly/blob/master/train_dolly.py)).
### Expected behavior
Training succeeds as before local dataset filenames were changed.
### Environment info
Ubuntu 22.04, Python 3.10.6, venv
```python
accelerate>=0.16.0,<1
click>=8.0.4,<9
datasets>=2.10.0,<3
deepspeed>=0.9.0,<1
transformers[torch]>=4.28.1,<5
langchain>=0.0.139
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5810 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5810/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5810/comments | https://api.github.com/repos/huggingface/datasets/issues/5810/events | https://github.com/huggingface/datasets/pull/5810 | 1,689,917,822 | PR_kwDODunzps5PdJHI | 5,810 | Add `fn_kwargs` to `map` and `filter` of `IterableDataset` and `IterableDatasetDict` | {
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} | [] | closed | false | null | [] | null | 9 | "2023-04-30T13:23:01Z" | "2023-05-22T08:12:39Z" | "2023-05-22T08:05:31Z" | CONTRIBUTOR | null | 0 | {
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} | # Overview
I've added an argument`fn_kwargs` for map and filter methods of `IterableDataset` and `IterableDatasetDict` classes.
# Details
Currently, the map and filter methods of some classes related to `IterableDataset` do not allow specifing the arguments passed to the function. This pull request adds `fn_kwargs` to pass arguments to the mapping function. This allows users to preprocess data more flexibly.
Added `fn_kwargs` to the following classes and methods (description of the argument is also added).
1. class `FilteredExamplesIterable`
2. method `filter` of class `IterableDataset`
3. method `map` of class `IterableDatasetDict`
4. method `filter` of class `IterableDatasetDict`
# Example of changes
Here's an example of how to use the new functionality:
```python
from datasets import IterableDatasetDict
def preprocess_function(example, a=None, b=None):
# do something
return example
dataset = IterableDatasetDict(...)
dataset = dataset.map(preprocess_function, fn_kwargs={"a": 1, "b": 2})
```
# Related Issues
This pull request is related to the following issue:
https://github.com/huggingface/datasets/issues/3444 .
# Testing
I have added unit tests to test the new functionality.
In test_iterable_dataset.py
- Added `test_filtered_examples_iterable_with_fn_kwargs` for [1](#details).
- Added `test_iterable_dataset_filter` for [2](#details).
- Added `test_iterable_dataset_map_with_fn_kwargs`. This is not a newly added feature, but was added because it was not tested.
In test_dataset_dict.py
- Added `_create_dummy_iterable_dataset` for [3](#details) and [4](#details).
- Added `_create_dummy_iterable_dataset_dict` for [3](#details) and [4](#details).
- Added `test_iterable_map` for [3](#details).
- Added `test_iterable_filter` for [4](#details).
Note that, there is no test for `IterableDatasetDict` at the current main branch. I thought about writing tests for `IterableDatasetDict` in a new file, but I decided to add them in the test file for `DatasetDict` (test_dataset_dict.py).
# Checklist
- [x] Format the code.
- [x] Added tests.
- [x] Passed tests locally. | {
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} | [] | closed | false | null | [] | null | 1 | "2023-04-30T06:12:04Z" | "2023-07-21T14:11:00Z" | "2023-07-21T14:11:00Z" | NONE | null | null | null | Hey guys!
Thanks for creating the wiki_dpr dataset!
I am currently trying to combine wiki_dpr and my own datasets. but I don't know how to make the embedding value the same way as wiki_dpr.
As an experiment, I embeds the text of id="7" of wiki_dpr, but this result was very different from wiki_dpr. | {
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Parallelizing dataset processing is not supported in this PR. | {
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Add an optional parameter return_file_name in the load_dataset function. When it is set to True, the function will include the name of the file corresponding to the current line as a feature in the returned output.
### Motivation
When training large language models, machine problems may interrupt the training process. In such cases, it is common to load a previously saved checkpoint to resume training. I would like to be able to obtain the names of the previously trained data shards, so that I can skip these parts of the data during continued training to avoid overfitting and redundant training time.
### Your contribution
I currently use a dataset in jsonl format, so I am primarily interested in the json format. I suggest adding the file name to the returned table here https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/json/json.py#L92. | {
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] | open | false | null | [] | null | 2 | "2023-04-28T13:26:22Z" | "2023-06-23T14:58:44Z" | null | CONTRIBUTOR | null | null | null | Our [tutorial on how to create a dataset](https://huggingface.co/docs/datasets/create_dataset) is a bit misleading.
1. In **Folder-based builders** section it says that we have two folder-based builders as standard builders, but we also have similar builders (that can be created from directory with data of required format) for `csv`, `json/jsonl`, `parquet` and `txt` files. We have info about these loaders in separate [guide for loading](https://huggingface.co/docs/datasets/loading#local-and-remote-files) but it's worth briefly mentioning them in the beginning tutorial because they are more common and for consistency. Would be helpful to add the link to the full guide.
2. **From local files** section lists methods for creating a dataset from in-memory data which are also described in [loading guide](https://huggingface.co/docs/datasets/loading#inmemory-data).
Maybe we should actually rethink and restructure this tutorial somehow. | {
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See: https://github.com/huggingface/datasets/pull/5787#discussion_r1178862327 | {
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Related to:
- #2157
Fix #5799.
CC: @stas00 | {
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```bash
$ ls -l /path/to/cache/datasets/downloads/
-rw------- 1 uername username 150M Apr 25 16:41 5e646c1d600f065adaeb134e536f6f2f296a6d804bd1f0e1fdcd20ee28c185c6
```
Related to:
- #2065 | {
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] | open | false | null | [] | null | 17 | "2023-04-27T00:16:11Z" | "2023-05-25T14:11:41Z" | null | CONTRIBUTOR | null | null | null | ### Feature request
When calling `load_dataset` for datasets that have multiple files, support using Spark to distribute the downloading and processing job to worker nodes when `cache_dir` is a cloud file system shared among nodes.
```python
load_dataset(..., use_spark=True)
```
### Motivation
Further speed up `dl_manager.download` and `_prepare_split` by distributing the workloads to worker nodes.
### Your contribution
I can submit a PR to support this. | {
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} | [] | open | false | null | [] | null | 2 | "2023-04-26T18:19:04Z" | "2023-04-27T11:56:58Z" | null | NONE | null | null | null | ### Describe the bug
load_dataset() function is case sensitive?
### Steps to reproduce the bug
The following two code, get totally different behavior.
1. load_dataset('mbzuai/bactrian-x','en')
2. load_dataset('MBZUAI/Bactrian-X','en')
### Expected behavior
Compare 1 and 2.
1 will download all 52 subsets, shell output:
```Downloading and preparing dataset json/MBZUAI--bactrian-X to xxx```
2 will only download single subset, shell output
```Downloading and preparing dataset bactrian-x/en to xxx```
### Environment info
Python 3.10.11
datasets Version: 2.11.0 | {
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} | Added a "Use with Spark" doc page to document `Dataset.from_spark` following https://github.com/huggingface/datasets/pull/5701
cc @maddiedawson | {
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] | open | false | null | [] | null | 0 | "2023-04-26T14:55:23Z" | "2023-04-26T14:55:23Z" | null | MEMBER | null | null | null | Sometimes when running our CI on Windows, we get a ZeroDivisionError:
```
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_frugalscore - ZeroDivisionError: float division by zero
```
See for example:
- https://github.com/huggingface/datasets/actions/runs/4809358266/jobs/8560513110
- https://github.com/huggingface/datasets/actions/runs/4798359836/jobs/8536573688
```
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
split = 'test', start_time = 1682516718.8236516, num_samples = 2, num_steps = 1
def speed_metrics(split, start_time, num_samples=None, num_steps=None):
"""
Measure and return speed performance metrics.
This function requires a time snapshot `start_time` before the operation to be measured starts and this function
should be run immediately after the operation to be measured has completed.
Args:
- split: name to prefix metric (like train, eval, test...)
- start_time: operation start time
- num_samples: number of samples processed
"""
runtime = time.time() - start_time
result = {f"{split}_runtime": round(runtime, 4)}
if num_samples is not None:
> samples_per_second = num_samples / runtime
E ZeroDivisionError: float division by zero
C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\transformers\trainer_utils.py:354: ZeroDivisionError
``` | {
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After calling the with_format("torch") method on an IterableDataset instance, the data format is unchanged.
### Steps to reproduce the bug
```python
from datasets import IterableDataset
def gen():
for i in range(4):
yield {"a": [i] * 4}
dataset = IterableDataset.from_generator(gen).with_format("torch")
next(iter(dataset))
```
### Expected behavior
`{"a": torch.tensor([0, 0, 0, 0])}` is expected, but `{"a": [0, 0, 0, 0]}` is observed.
### Environment info
```bash
platform==ubuntu 22.04.01
python==3.10.9
datasets==2.11.0
``` | {
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I currently have a dataset (with tiff and json files) where I have to do this:
`wget path_to_data/images.zip && unzip images.zip`
`wget path_to_data/annotations.zip && unzip annotations.zip`
Would it make sense a contribution that supports these type of files?
### Motivation
instead of using `load_dataset` have to use wget as these files are not supported for annotations with JSON and images with TIFF files.
Additionally to this, the PIL formatting from datasets does not read correctly the image channels with TIFF format, besides multichannel adaptation might be necessary as well (as my data e.g has more than 3 channels)
### Your contribution
1. Support TIFF images over multi channel format
2. Support JSON annotations | {
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} | This PR allows to run the CI on push to a branch named "ci-*", without needing to open a PR.
- This will allow to make CI tests without opening a PR, e.g., for future `huggingface-hub` releases, future dependency releases (like `fsspec`, `pandas`,...)
Note that to build the documentation, we already allow it on push to a branch named "doc-builder*".
See:
- #5788
CC: @Wauplin | {
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] | null | 0 | "2023-04-25T07:40:02Z" | "2023-04-25T07:40:03Z" | null | MEMBER | null | null | null | Extend support for streaming datasets that use `jsonlines.open`.
Currently, if `jsonlines` is installed, `datasets` raises a `FileNotFoundError`:
```
FileNotFoundError: [Errno 2] No such file or directory: 'https://...'
```
See:
- https://huggingface.co/datasets/masakhane/afriqa/discussions/1 | {
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} | Related to the coming release of `huggingface_hub==0.14.0`. It will break some internal tests. The PR fixes these tests. Let's double-check the CI but I expect the fixed tests to be running fine with both `hfh<=0.13.4` and `hfh==0.14`. Worth case scenario, existing PRs will have to be rebased once this fix is merged.
See related [discussion](https://huggingface.slack.com/archives/C02V5EA0A95/p1682337463368609?thread_ts=1681994202.635609&cid=C02V5EA0A95) (private slack).
cc @lhoestq | {
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} | This PR raises a FileNotFoundError instead:
```
FileNotFoundError: No (supported) data files or dataset script found in <dataset_name>
```
Fix #5785. | {
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I am trying to use a Pytorch model loaded on CPUs with multiple processes with a `.map` or a `.filter` method.
Usually, when dealing with models that are non-pickable, creating a class such that the `map` function is the method `__call__`, and adding `reduce` helps to solve the problem.
However, here, the command hangs without throwing an error.
### Steps to reproduce the bug
```
from datasets import Dataset
import torch
from torch import nn
from torchvision import models
class FilterFunction:
#__slots__ = ("path_model", "model") # Doesn't change anything uncommented
def __init__(self, path_model):
self.path_model = path_model
model = models.resnet50()
model.fc = nn.Sequential(
nn.Linear(2048, 512),
nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(512, 10),
nn.LogSoftmax(dim=1)
)
model.load_state_dict(torch.load(path_model, map_location=torch.device("cpu")))
model.eval()
self.model = model
def __call__(self, batch):
return [True] * len(batch["id"])
# Comment this to have an error
def __reduce__(self):
return (self.__class__, (self.path_model,))
dataset = Dataset.from_dict({"id": [0, 1, 2, 4]})
# Download (100 MB) at https://github.com/emiliantolo/pytorch_nsfw_model/raw/master/ResNet50_nsfw_model.pth
path_model = "/fsx/hugo/nsfw_image/ResNet50_nsfw_model.pth"
filter_function = FilterFunction(path_model=path_model)
# Works
filtered_dataset = dataset.filter(filter_function, num_proc=1, batched=True, batch_size=2)
# Doesn't work
filtered_dataset = dataset.filter(filter_function, num_proc=2, batched=True, batch_size=2)
```
### Expected behavior
The command `filtered_dataset = dataset.filter(filter_function, num_proc=2, batched=True, batch_size=2)` should work and not hang.
### Environment info
Datasets: 2.11.0
Pyarrow: 11.0.0
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```
TypeError: 'NoneType' object is not iterable
```
See:
- https://github.com/huggingface/datasets-server/issues/1073
We should give a more informative error message. | {
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} | When a subprocess hangs in `filter` or `map`, one should be able to get the subprocess' traceback when interrupting the main process. Right now it shows nothing.
To do so I `.get()` the subprocesses async results even the main process is stopped with e.g. `KeyboardInterrupt`. I added a timeout in case the subprocess is hanging or crashed. | {
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https://api.github.com/repos/huggingface/datasets/issues/5783 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5783/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5783/comments | https://api.github.com/repos/huggingface/datasets/issues/5783/events | https://github.com/huggingface/datasets/issues/5783 | 1,679,664,393 | I_kwDODunzps5kHaUJ | 5,783 | Offset overflow while doing regex on a text column | {
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} | [] | open | false | null | [] | null | 7 | "2023-04-22T19:12:03Z" | "2023-09-22T06:44:07Z" | null | NONE | null | null | null | ### Describe the bug
`ArrowInvalid: offset overflow while concatenating arrays`
Same error as [here](https://github.com/huggingface/datasets/issues/615)
### Steps to reproduce the bug
Steps to reproduce: (dataset is a few GB big so try in colab maybe)
```
import datasets
import re
ds = datasets.load_dataset('nishanthc/dnd_map_dataset_v0.1', split = 'train')
def get_text_caption(example):
regex_pattern = r'\s\d+x\d+|,\sLQ|,\sgrid|\.\w+$'
example['text_caption'] = re.sub(regex_pattern, '', example['picture_text'])
return example
ds = ds.map(get_text_caption)
```
I am trying to apply a regex to remove certain patterns from a text column. Not sure why this error is showing up.
### Expected behavior
Dataset should have a new column with processed text
### Environment info
Datasets version - 2.11.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5782 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5782/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5782/comments | https://api.github.com/repos/huggingface/datasets/issues/5782/events | https://github.com/huggingface/datasets/issues/5782 | 1,679,622,367 | I_kwDODunzps5kHQDf | 5,782 | Support for various audio-loading backends instead of always relying on SoundFile | {
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] | closed | false | null | [] | null | 3 | "2023-04-22T17:09:25Z" | "2023-05-10T20:23:04Z" | "2023-05-10T20:23:04Z" | NONE | null | null | null | ### Feature request
Introduce an option to select from a variety of audio-loading backends rather than solely relying on the SoundFile library. For instance, if the ffmpeg library is installed, it can serve as a fallback loading option.
### Motivation
- The SoundFile library, used in [features/audio.py](https://github.com/huggingface/datasets/blob/649d5a3315f9e7666713b6affe318ee00c7163a0/src/datasets/features/audio.py#L185), supports only a [limited number of audio formats](https://pysoundfile.readthedocs.io/en/latest/index.html?highlight=supported#soundfile.available_formats).
- However, current methods for creating audio datasets permit the inclusion of audio files in formats not supported by SoundFile.
- As a result, developers may potentially create a dataset they cannot read back.
In my most recent project, I dealt with phone call recordings in `.amr` or `.gsm` formats and was genuinely surprised when I couldn't read the dataset I had just packaged a minute prior. Nonetheless, I can still accurately read these files using the librosa library, which employs the audioread library that internally leverages ffmpeg to read such files.
Example:
```python
audio_dataset_amr = Dataset.from_dict({"audio": ["audio_samples/audio.amr"]}).cast_column("audio", Audio())
audio_dataset_amr.save_to_disk("audio_dataset_amr")
audio_dataset_amr = Dataset.load_from_disk("audio_dataset_amr")
print(audio_dataset_amr[0])
```
Results in:
```
Traceback (most recent call last):
...
raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name))
soundfile.LibsndfileError: Error opening <_io.BytesIO object at 0x7f316323e4d0>: Format not recognised.
```
While I acknowledge that support for these rare file types may not be a priority, I believe it's quite unfortunate that it's possible to create an unreadable dataset in this manner.
### Your contribution
I've created a [simple demo repository](https://github.com/BoringDonut/hf-datasets-ffmpeg-audio) that highlights the mentioned issue. It demonstrates how to create an .amr dataset that results in an error when attempting to read it just a few lines later.
Additionally, I've made a [fork with a rudimentary solution](https://github.com/BoringDonut/datasets/blob/fea73a8fbbc8876467c7e6422c9360546c6372d8/src/datasets/features/audio.py#L189) that utilizes ffmpeg to load files not supported by SoundFile.
Here you may see github actions fails to read `.amr` dataset using the version of the current dataset, but will work with the patched version:
- https://github.com/BoringDonut/hf-datasets-ffmpeg-audio/actions/runs/4773780420/jobs/8487063785
- https://github.com/BoringDonut/hf-datasets-ffmpeg-audio/actions/runs/4773780420/jobs/8487063829
As evident from the GitHub action above, this solution resolves the previously mentioned problem.
I'd be happy to create a proper pull request, provide runtime benchmarks and tests if you could offer some guidance on the following:
- Where should I incorporate the ffmpeg (or other backends) code? For example, should I create a new file or simply add a function within the Audio class?
- Is it feasible to pass the audio-loading function as an argument within the current architecture? This would be useful if I know in advance that I'll be reading files not supported by SoundFile.
A few more notes:
- In theory, it's possible to load audio using librosa/audioread since librosa is already expected to be installed. However, librosa [will soon discontinue audioread support](https://github.com/librosa/librosa/blob/aacb4c134002903ae56bbd4b4a330519a5abacc0/librosa/core/audio.py#L227). Moreover, using audioread on its own seems inconvenient because it requires a file [path as input](https://github.com/beetbox/audioread/blob/ff9535df934c48038af7be9617fdebb12078cc07/audioread/__init__.py#L108) and cannot work with bytes already loaded into memory or an open file descriptor (as mentioned in [librosa docs](https://librosa.org/doc/main/generated/librosa.load.html#librosa.load), only SoundFile backend supports an open file descriptor as an input). | {
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https://api.github.com/repos/huggingface/datasets/issues/5781 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5781/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5781/comments | https://api.github.com/repos/huggingface/datasets/issues/5781/events | https://github.com/huggingface/datasets/issues/5781 | 1,679,580,460 | I_kwDODunzps5kHF0s | 5,781 | Error using `load_datasets` | {
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} | [] | closed | false | null | [] | null | 2 | "2023-04-22T15:10:44Z" | "2023-05-02T23:41:25Z" | "2023-05-02T23:41:25Z" | NONE | null | null | null | ### Describe the bug
I tried to load a dataset using the `datasets` library in a conda jupyter notebook and got the below error.
```
ImportError: dlopen(/Users/gilbertyoung/miniforge3/envs/review_sense/lib/python3.8/site-packages/scipy/sparse/linalg/_isolve/_iterative.cpython-38-darwin.so, 0x0002): Library not loaded: @rpath/liblapack.3.dylib
Referenced from: <65B094A2-59D7-31AC-A966-4DB9E11D2A15> /Users/gilbertyoung/miniforge3/envs/review_sense/lib/python3.8/site-packages/scipy/sparse/linalg/_isolve/_iterative.cpython-38-darwin.so
Reason: tried: '/Users/gilbertyoung/miniforge3/envs/review_sense/lib/python3.8/site-packages/scipy/sparse/linalg/_isolve/liblapack.3.dylib' (no such file), '/Users/gilbertyoung/miniforge3/envs/review_sense/lib/python3.8/site-packages/scipy/sparse/linalg/_isolve/../../../../../../liblapack.3.dylib' (no such file), '/Users/gilbertyoung/miniforge3/envs/review_sense/lib/python3.8/site-packages/scipy/sparse/linalg/_isolve/liblapack.3.dylib' (no such file), '/Users/gilbertyoung/miniforge3/envs/review_sense/lib/python3.8/site-packages/scipy/sparse/linalg/_isolve/../../../../../../liblapack.3.dylib' (no such file), '/Users/gilbertyoung/miniforge3/envs/review_sense/bin/../lib/liblapack.3.dylib' (no such file), '/Users/gilbertyoung/miniforge3/envs/review_sense/bin/../lib/liblapack.3.dylib' (no such file), '/usr/local/lib/liblapack.3.dylib' (no such file), '/usr/lib/liblapack.3.dylib' (no such file, not in dyld cache)
```
### Steps to reproduce the bug
Run the `load_datasets` function
### Expected behavior
I expected the dataset to be loaded into my notebook.
### Environment info
name: review_sense
channels:
- apple
- conda-forge
dependencies:
- python=3.8
- pip>=19.0
- jupyter
- tensorflow-deps
#- scikit-learn
#- scipy
- pandas
- pandas-datareader
- matplotlib
- pillow
- tqdm
- requests
- h5py
- pyyaml
- flask
- boto3
- ipykernel
- seaborn
- pip:
- tensorflow-macos==2.9
- tensorflow-metal==0.5.0
- bayesian-optimization
- gym
- kaggle
- huggingface_hub
- datasets
- numpy
- huggingface
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} | [] | closed | false | null | [] | null | 0 | "2023-04-22T06:22:43Z" | "2023-04-23T08:49:18Z" | "2023-04-23T08:49:18Z" | NONE | null | null | null | command：
```
def load_datasets(formats, data_dir=datadir, data_files=datafile）：
dataset = load_dataset(formats, data_dir=datadir, data_files=datafile, split=split, streaming=True, **kwargs)
return dataset
raw_datasets = DatasetDict()
raw_datasets["train"] = load_datasets(“csv”, args.datadir, "train.csv", split=train_split)
raw_datasets["test"] = load_datasets(“csv”, args.datadir, "dev.csv", split=test_split)
raw_datasets = raw_datasets.cast_column("audio", Audio(sampling_rate=16000))
```
error：
```
main()
File "peft_adalora_whisper_large_training.py", line 502, in main
raw_datasets = raw_datasets.cast_column("audio", Audio(sampling_rate=16000))
File "/home/ybZhang/miniconda3/envs/whister/lib/python3.8/site-packages/datasets/dataset_dict.py", line 2015, in cast_column
info.features[column] = feature
TypeError: 'NoneType' object does not support item assignment
```
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} | We need to call `fs.makedirs` when saving a dataset using `save_to_disk`, because some fs implementations have actual directories (S3 and others don't)
Close https://github.com/huggingface/datasets/issues/5775 | {
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} | [] | closed | false | null | [] | null | 1 | "2023-04-21T08:38:12Z" | "2023-07-24T15:15:14Z" | "2023-07-24T15:15:14Z" | NONE | null | null | null | ### Describe the bug
If you use load_dataset('json', data_files="path/test.json"), it will return DatasetDict({train:...}).
And if you use load_dataset("path"), it will return DatasetDict({test:...}).
Why can't the output behavior be unified?
### Steps to reproduce the bug
as description above.
### Expected behavior
consistent predictable output.
### Environment info
'2.11.0' | {
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] | null | 6 | "2023-04-21T02:08:07Z" | "2023-06-05T05:49:52Z" | "2023-05-11T11:51:56Z" | NONE | null | null | null | ### Describe the bug
While checking out the [tokenizer tutorial](https://huggingface.co/course/chapter6/2?fw=pt), i noticed getting an error while initially downloading the python dataset used in the examples.
The [collab with the error is here](https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter6/section2.ipynb#scrollTo=hGb69Yo3eV8S)
```
from datasets import load_dataset
import os
os.environ["HF_DATASETS_CACHE"] = "/workspace"
# This can take a few minutes to load, so grab a coffee or tea while you wait!
raw_datasets = load_dataset("code_search_net", "python")
```
yeilds:
```
ile /opt/conda/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:524, in xlistdir(path, use_auth_token)
522 main_hop, *rest_hops = _as_str(path).split("::")
523 if is_local_path(main_hop):
--> 524 return os.listdir(path)
525 else:
526 # globbing inside a zip in a private repo requires authentication
527 if not rest_hops and (main_hop.startswith("http://") or main_hop.startswith("https://")):
NotADirectoryError: [Errno 20] Not a directory: '/workspace/downloads/25ceeb4c25ab737d688bd56ea92bfbb1f199fe572470456cf2d675479f342ac7/python/final/jsonl/train'
```
I was able to reproduce this erro both in the collab and on my own pytorch/pytorch container pulled from the dockerhub official pytorch image, so i think it may be a server side thing.
### Steps to reproduce the bug
Steps to reproduce the issue:
1. run `raw_datasets = load_dataset("code_search_net", "python")`
### Expected behavior
expect the code to not exception during dataset pull.
### Environment info
i tried both the default HF_DATASETS_CACHE on Collab, and on my local container. i then pointed to the HF_DATASETS_CACHE to a large capacity local storage and the problem was consisten across all 3 scenarios. | {
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] | null | 0 | "2023-04-20T17:15:49Z" | "2023-04-20T17:15:49Z" | null | CONTRIBUTOR | null | null | null | Instead of PyArrow's `read_json`, we should use `pd.read_json` in the JSON builder for consistency with the CSV and SQL builders (e.g., to address https://github.com/huggingface/datasets/issues/5725).
In Pandas2.0, to get the same performance, we can set the `engine` to "pyarrow". The issue is that Colab still doesn't install Pandas 2.0 by default, so I think it's best to wait for this to be resolved on their side to avoid downgrading decoding performance in scenarios when Pandas 2.0 is not installed. | {
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https://github.com/huggingface/datasets/blob/e7ce0ac60c7efc10886471932854903a7c19f172/src/datasets/arrow_dataset.py#L1371
Here is the bug point, when I want to save from a `DatasetDict` class and the items of the instance is like `[('train', Dataset({features: ..., num_rows: ...}))]` , there is no guarantee that there exists a directory name `train` under `dataset_dict_path`.
### Steps to reproduce the bug
1. Mock a DatasetDict with items like what I said.
2. using save_to_disk with storage_options, u can use local sftp. code may like below
```python
from datasets import load_dataset
dataset = load_dataset(...)
dataset.save_to_disk('sftp:///tmp', storage_options={'host': 'localhost', 'username': 'admin'})
```
I suppose u can reproduce the bug by these steps.
### Expected behavior
Should create the folder if it does not exists, just like we do locally.
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-6.2.10-arch1-1-x86_64-with-glibc2.35
- Python version: 3.10.9
- Huggingface_hub version: 0.13.2
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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`ValueError: The train_dataset does not implement __len__, max_steps has to be specified. The number of steps needs to be known in advance for the learning rate scheduler.` | {
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https://api.github.com/repos/huggingface/datasets/issues/5772 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5772/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5772/comments | https://api.github.com/repos/huggingface/datasets/issues/5772/events | https://github.com/huggingface/datasets/pull/5772 | 1,675,033,510 | PR_kwDODunzps5OreXV | 5,772 | Fix JSON builder when missing keys in first row | {
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} | Until now, the JSON builder only considered the keys present in the first element of the list:
- Either explicitly: by passing index 0 in `dataset[0].keys()`
- Or implicitly: `pa.Table.from_pylist(dataset)`, where "schema (default None): If not passed, will be inferred from the first row of the mapping values"
This PR fixes the bug by considering the union of the keys present in all the rows.
Fix #5726. | {
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https://api.github.com/repos/huggingface/datasets/issues/5771 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5771/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5771/comments | https://api.github.com/repos/huggingface/datasets/issues/5771/events | https://github.com/huggingface/datasets/issues/5771 | 1,674,828,380 | I_kwDODunzps5j09pc | 5,771 | Support cloud storage for loading datasets | {
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] | closed | false | null | [] | null | 1 | "2023-04-19T12:43:53Z" | "2023-05-07T17:47:41Z" | "2023-05-07T17:47:41Z" | CONTRIBUTOR | null | null | null | ### Feature request
It seems that the the current implementation supports cloud storage only for `load_from_disk`. It would be nice if a similar functionality existed in `load_dataset`.
### Motivation
Motivation is pretty clear -- let users work with datasets located in the cloud.
### Your contribution
I can help implementing this. | {
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https://api.github.com/repos/huggingface/datasets/issues/5770 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5770/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5770/comments | https://api.github.com/repos/huggingface/datasets/issues/5770/events | https://github.com/huggingface/datasets/pull/5770 | 1,673,581,555 | PR_kwDODunzps5OmntV | 5,770 | Add IterableDataset.from_spark | {
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} | Follow-up from https://github.com/huggingface/datasets/pull/5701
Related issue: https://github.com/huggingface/datasets/issues/5678 | {
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https://api.github.com/repos/huggingface/datasets/issues/5769 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5769/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5769/comments | https://api.github.com/repos/huggingface/datasets/issues/5769/events | https://github.com/huggingface/datasets/issues/5769 | 1,673,441,182 | I_kwDODunzps5jvq-e | 5,769 | Tiktoken tokenizers are not pickable | {
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Since tiktoken tokenizer is not pickable, it is not possible to use it inside `dataset.map()` with multiprocessing enabled. However, you [made](https://github.com/huggingface/datasets/issues/5536) tiktoken's tokenizers pickable in `datasets==2.10.0` for caching. For some reason, this logic does not work in dataset processing and raises `TypeError: cannot pickle 'builtins.CoreBPE' object`
### Steps to reproduce the bug
```
from datasets import load_dataset
import tiktoken
dataset = load_dataset("stas/openwebtext-10k")
enc = tiktoken.get_encoding("gpt2")
tokenized = dataset.map(
process,
remove_columns=['text'],
desc="tokenizing the OWT splits",
num_proc=2,
)
def process(example):
ids = enc.encode(example['text'])
ids.append(enc.eot_token)
out = {'ids': ids, 'len': len(ids)}
return out
```
### Expected behavior
starts processing dataset
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.15.0-1021-oracle-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.13.4
- PyArrow version: 9.0.0
- Pandas version: 2.0.0 | {
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] | null | 8 | "2023-04-18T07:10:56Z" | "2023-04-20T10:27:23Z" | "2023-04-20T10:27:22Z" | NONE | null | null | null | ### Describe the bug
There is an issue that seems to be unique to the "squad" dataset, in which it cannot be loaded using standard methods. This issue is most quickly reproduced from the command line, using the HF examples to verify a dataset is loaded properly.
This is not a problem with "squad_v2" dataset for example.
### Steps to reproduce the bug
cmd line
> $ python -c "from datasets import load_dataset; print(load_dataset('squad', split='train')[0])"
OR
Python IDE
> from datasets import load_dataset
> load_dataset("squad")
### Expected behavior
I expected to either see the output described here from running the very same command in command line ([https://huggingface.co/docs/datasets/installation]), or any output that does not raise Python's TypeError.
There is some funky behaviour in the dataset builder portion of the codebase that means it is trying to import the squad dataset with an incorrect path, or the squad dataset couldn't be downloaded. I'm not really sure what the problem is beyond that. Messing around with caching I did manage to get it to load the dataset once, and then couldn't repeat this.
### Environment info
datasets=2.7.1 **or** 2.10.1, python=3.10.8, Linux 3.10.0-1160.36.2.el7.x86_64 **or** Windows 10-64
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} | [] | closed | false | null | [] | null | 1 | "2023-04-18T06:25:12Z" | "2023-04-20T16:52:05Z" | "2023-04-20T16:52:05Z" | NONE | null | null | null | ### Describe the bug
- `transformers` version: 4.11.3
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyTorch version (GPU?): 1.12.0+cu102 (True)
- Tensorflow version (GPU?): 2.10.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
### Steps to reproduce the bug
I recently read [this](https://huggingface.co/docs/transformers/quicktour#train-with-tensorflow:~:text=The%20most%20important%20thing%20to%20remember%20is%20you%20need%20to%20instantiate%20a%20tokenizer%20with%20the%20same%20model%20name%20to%20ensure%20you%E2%80%99re%20using%20the%20same%20tokenization%20rules%20a%20model%20was%20pretrained%20with.) and was wondering how to use distill-BERT (which is pre-trained with imdb dataset) with a different dataset (for eg. [this](https://huggingface.co/datasets/yhavinga/imdb_dutch) dataset)?
### Expected behavior
Distill-BERT should work with different datasets.
### Environment info
- `datasets` version: 1.12.1
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 11.0.0 | {
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{
"color": "a2eeef",
"default": true,
"description": "New feature or request",
"id": 1935892871,
"name": "enhancement",
"node_id": "MDU6TGFiZWwxOTM1ODkyODcx",
"url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement"
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] | open | false | null | [] | null | 3 | "2023-04-17T15:46:41Z" | "2023-05-03T21:58:43Z" | null | NONE | null | null | null | ### Feature request
I think it would be nice to allow registering custom feature types with the 🤗 Datasets library. For example, allow to do something along the following lines:
```
from datasets.features import register_feature_type # this would be a new function
@register_feature_type
class CustomFeatureType:
def encode_example(self, value):
"""User-provided logic to encode an example of this feature."""
pass
def decode_example(self, value, token_per_repo_id=None):
"""User-provided logic to decode an example of this feature."""
pass
```
### Motivation
Users of 🤗 Datasets, such as myself, may want to use the library to load datasets with unsupported feature types (i.e., beyond `ClassLabel`, `Image`, or `Audio`). This would be useful for prototyping new feature types and for feature types that aren't used widely enough to warrant inclusion in 🤗 Datasets.
At the moment, this is only possible by monkey-patching 🤗 Datasets, which obfuscates the code and is prone to breaking with library updates. It also requires the user to write some custom code which could be easily avoided.
### Your contribution
I would be happy to contribute this feature. My proposed solution would involve changing the following call to `globals()` to an explicit feature type registry, which a user-facing `register_feature_type` decorator could update.
https://github.com/huggingface/datasets/blob/fd893098627230cc734f6009ad04cf885c979ac4/src/datasets/features/features.py#L1329
I would also provide an abstract base class for custom feature types which users could inherit. This would have at least an `encode_example` method and a `decode_example` method, similar to `Image` or `Audio`.
The existing `encode_nested_example` and `decode_nested_example` functions would also need to be updated to correctly call the corresponding functions for the new type. | {
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https://api.github.com/repos/huggingface/datasets/issues/5765 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5765/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5765/comments | https://api.github.com/repos/huggingface/datasets/issues/5765/events | https://github.com/huggingface/datasets/issues/5765 | 1,671,388,824 | I_kwDODunzps5jn16Y | 5,765 | ValueError: You should supply an encoding or a list of encodings to this method that includes input_ids, but you provided ['text'] | {
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} | [] | open | false | null | [] | null | 4 | "2023-04-17T15:00:50Z" | "2023-04-25T13:50:45Z" | null | NONE | null | null | null | ### Describe the bug
Following is my code that I am trying to run, but facing an error (have attached the whole error below):
My code:
```
from collections import OrderedDict
import warnings
import flwr as fl
import torch
import numpy as np
import random
from torch.utils.data import DataLoader
from datasets import load_dataset, load_metric
from transformers import AutoTokenizer, DataCollatorWithPadding
from transformers import AutoModelForSequenceClassification
from transformers import AdamW
#from transformers import tokenized_datasets
warnings.filterwarnings("ignore", category=UserWarning)
# DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
DEVICE = "cpu"
CHECKPOINT = "distilbert-base-uncased" # transformer model checkpoint
def load_data():
"""Load IMDB data (training and eval)"""
raw_datasets = load_dataset("yhavinga/imdb_dutch")
raw_datasets = raw_datasets.shuffle(seed=42)
# remove unnecessary data split
del raw_datasets["unsupervised"]
tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT)
def tokenize_function(examples):
return tokenizer(examples["text"], truncation=True)
# random 100 samples
population = random.sample(range(len(raw_datasets["train"])), 100)
tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
tokenized_datasets["train"] = tokenized_datasets["train"].select(population)
tokenized_datasets["test"] = tokenized_datasets["test"].select(population)
# tokenized_datasets = tokenized_datasets.remove_columns("text")
# tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
tokenized_datasets = tokenized_datasets.remove_columns("attention_mask")
tokenized_datasets = tokenized_datasets.remove_columns("input_ids")
tokenized_datasets = tokenized_datasets.remove_columns("label")
tokenized_datasets = tokenized_datasets.remove_columns("text_en")
# tokenized_datasets = tokenized_datasets.remove_columns(raw_datasets["train"].column_names)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
trainloader = DataLoader(
tokenized_datasets["train"],
shuffle=True,
batch_size=32,
collate_fn=data_collator,
)
testloader = DataLoader(
tokenized_datasets["test"], batch_size=32, collate_fn=data_collator
)
return trainloader, testloader
def train(net, trainloader, epochs):
optimizer = AdamW(net.parameters(), lr=5e-4)
net.train()
for _ in range(epochs):
for batch in trainloader:
batch = {k: v.to(DEVICE) for k, v in batch.items()}
outputs = net(**batch)
loss = outputs.loss
loss.backward()
optimizer.step()
optimizer.zero_grad()
def test(net, testloader):
metric = load_metric("accuracy")
loss = 0
net.eval()
for batch in testloader:
batch = {k: v.to(DEVICE) for k, v in batch.items()}
with torch.no_grad():
outputs = net(**batch)
logits = outputs.logits
loss += outputs.loss.item()
predictions = torch.argmax(logits, dim=-1)
metric.add_batch(predictions=predictions, references=batch["labels"])
loss /= len(testloader.dataset)
accuracy = metric.compute()["accuracy"]
return loss, accuracy
def main():
net = AutoModelForSequenceClassification.from_pretrained(
CHECKPOINT, num_labels=2
).to(DEVICE)
trainloader, testloader = load_data()
# Flower client
class IMDBClient(fl.client.NumPyClient):
def get_parameters(self, config):
return [val.cpu().numpy() for _, val in net.state_dict().items()]
def set_parameters(self, parameters):
params_dict = zip(net.state_dict().keys(), parameters)
state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
net.load_state_dict(state_dict, strict=True)
def fit(self, parameters, config):
self.set_parameters(parameters)
print("Training Started...")
train(net, trainloader, epochs=1)
print("Training Finished.")
return self.get_parameters(config={}), len(trainloader), {}
def evaluate(self, parameters, config):
self.set_parameters(parameters)
loss, accuracy = test(net, testloader)
return float(loss), len(testloader), {"accuracy": float(accuracy)}
# Start client
fl.client.start_numpy_client(server_address="localhost:8080", client=IMDBClient())
if __name__ == "__main__":
main()
```
Error:
```
Traceback (most recent call last):
File "client_2.py", line 136, in <module>
main()
File "client_2.py", line 132, in main
fl.client.start_numpy_client(server_address="localhost:8080", client=IMDBClient())
File "/home/saurav/.local/lib/python3.8/site-packages/flwr/client/app.py", line 208, in start_numpy_client
start_client(
File "/home/saurav/.local/lib/python3.8/site-packages/flwr/client/app.py", line 142, in start_client
client_message, sleep_duration, keep_going = handle(
File "/home/saurav/.local/lib/python3.8/site-packages/flwr/client/grpc_client/message_handler.py", line 68, in handle
return _fit(client, server_msg.fit_ins), 0, True
File "/home/saurav/.local/lib/python3.8/site-packages/flwr/client/grpc_client/message_handler.py", line 157, in _fit
fit_res = client.fit(fit_ins)
File "/home/saurav/.local/lib/python3.8/site-packages/flwr/client/app.py", line 252, in _fit
results = self.numpy_client.fit(parameters, ins.config) # type: ignore
File "client_2.py", line 122, in fit
train(net, trainloader, epochs=1)
File "client_2.py", line 76, in train
for batch in trainloader:
File "/home/saurav/.local/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 652, in __next__
data = self._next_data()
File "/home/saurav/.local/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 692, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/home/saurav/.local/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch
return self.collate_fn(data)
File "/home/saurav/.local/lib/python3.8/site-packages/transformers/data/data_collator.py", line 221, in __call__
batch = self.tokenizer.pad(
File "/home/saurav/.local/lib/python3.8/site-packages/transformers/tokenization_utils_base.py", line 2713, in pad
raise ValueError(
ValueError: You should supply an encoding or a list of encodings to this method that includes input_ids, but you provided ['text']
```
### Steps to reproduce the bug
Run the above code.
### Expected behavior
Don't know, doing it for the first time.
### Environment info
- `datasets` version: 1.12.1
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 11.0.0
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https://api.github.com/repos/huggingface/datasets/issues/5764 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5764/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5764/comments | https://api.github.com/repos/huggingface/datasets/issues/5764/events | https://github.com/huggingface/datasets/issues/5764 | 1,670,740,198 | I_kwDODunzps5jlXjm | 5,764 | ConnectionError: Couldn't reach https://www.dropbox.com/s/zts98j4vkqtsns6/aclImdb_v2.tar?dl=1 | {
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] | null | 7 | "2023-04-17T09:08:18Z" | "2023-04-18T07:18:20Z" | "2023-04-18T07:18:20Z" | NONE | null | null | null | ### Describe the bug
I want to use this (https://huggingface.co/datasets/josianem/imdb) dataset therefore I am trying to load it using the following code:
```
dataset = load_dataset("josianem/imdb")
```
The dataset is not getting loaded and gives the error message as the following:
```
Traceback (most recent call last):
File "sample.py", line 3, in <module>
dataset = load_dataset("josianem/imdb")
File "/home/saurav/.local/lib/python3.8/site-packages/datasets/load.py", line 1112, in load_dataset
builder_instance.download_and_prepare(
File "/home/saurav/.local/lib/python3.8/site-packages/datasets/builder.py", line 636, in download_and_prepare
self._download_and_prepare(
File "/home/saurav/.local/lib/python3.8/site-packages/datasets/builder.py", line 704, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/saurav/.cache/huggingface/modules/datasets_modules/datasets/imdb/cc6ab4acab2799be15d5d217c24548b856156dafdc850165fdc4f2031f27ff2f/imdb.py", line 79, in _split_generators
archive = dl_manager.download(_DOWNLOAD_URL)
File "/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 197, in map_nested
return function(data_struct)
File "/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 289, in cached_path
output_path = get_from_cache(
File "/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 606, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://www.dropbox.com/s/zts98j4vkqtsns6/aclImdb_v2.tar?dl=1
```
### Steps to reproduce the bug
You can reproduce the error by using the following code:
```
from datasets import load_dataset, load_metric
dataset = load_dataset("josianem/imdb")
```
### Expected behavior
The dataset should get loaded (I am using this dataset for the first time so not much aware of the exact behavior).
### Environment info
- `datasets` version: 1.12.1
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 11.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5763 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5763/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5763/comments | https://api.github.com/repos/huggingface/datasets/issues/5763/events | https://github.com/huggingface/datasets/pull/5763 | 1,670,476,302 | PR_kwDODunzps5OcMI7 | 5,763 | fix typo: "mow" -> "now" | {
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} | [] | closed | false | null | [] | null | 2 | "2023-04-17T06:03:44Z" | "2023-04-17T15:01:53Z" | "2023-04-17T14:54:46Z" | CONTRIBUTOR | null | 0 | {
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} | I noticed a typo as I was reading the datasets documentation. This PR contains a trivial fix changing "mow" to "now." | {
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https://api.github.com/repos/huggingface/datasets/issues/5762 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5762/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5762/comments | https://api.github.com/repos/huggingface/datasets/issues/5762/events | https://github.com/huggingface/datasets/issues/5762 | 1,670,326,470 | I_kwDODunzps5jjyjG | 5,762 | Not able to load the pile | {
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] | null | 1 | "2023-04-17T03:09:10Z" | "2023-04-17T09:37:27Z" | "2023-04-17T09:37:27Z" | NONE | null | null | null | ### Describe the bug
Got this error when I am trying to load the pile dataset
```
TypeError: Couldn't cast array of type
struct<file: string, id: string>
to
{'id': Value(dtype='string', id=None)}
```
### Steps to reproduce the bug
Please visit the following sample notebook
https://colab.research.google.com/drive/1JHcjawcHL6QHhi5VcqYd07W2QCEj2nWK#scrollTo=ulJP3eJCI-tB
### Expected behavior
The pile should work
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.31
- Python version: 3.9.16
- Huggingface_hub version: 0.13.4
- PyArrow version: 9.0.0
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] | null | 3 | "2023-04-16T16:21:55Z" | "2023-04-19T11:53:24Z" | null | NONE | null | null | null | ### Describe the bug
An attempt to generate a dataset from a zip archive using imagefolder and metadata.jsonl does not lead to the expected result. Tried all possible locations of the json file: the file in the archive is ignored (generated dataset contains only images), the file next to the archive like [here](https://huggingface.co/docs/datasets/image_dataset#imagefolder) leads to an error:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\builder.py:1610, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)
1609 _time = time.time()
-> 1610 for key, record in generator:
1611 if max_shard_size is not None and writer._num_bytes > max_shard_size:
File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\packaged_modules\folder_based_builder\folder_based_builder.py:370, in FolderBasedBuilder._generate_examples(self, files, metadata_files, split_name, add_metadata, add_labels)
369 else:
--> 370 raise ValueError(
371 f"One or several metadata.{metadata_ext} were found, but not in the same directory or in a parent directory of {downloaded_dir_file}."
372 )
373 if metadata_dir is not None and downloaded_metadata_file is not None:
ValueError: One or several metadata.jsonl were found, but not in the same directory or in a parent directory of C:\Users\User\.cache\huggingface\datasets\downloads\extracted\f7fb7de25fb28ae63089974524f2d271a39d83888bc456d04aa3b3d45f33e6a6\ff0745a0-a741-4d9e-b228-a93b851adf61.png.
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
Cell In[3], line 1
----> 1 dataset = load_dataset("imagefolder", data_dir=r'C:\Users\User\data')
File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\load.py:1791, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)
1788 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1790 # Download and prepare data
-> 1791 builder_instance.download_and_prepare(
1792 download_config=download_config,
1793 download_mode=download_mode,
1794 verification_mode=verification_mode,
1795 try_from_hf_gcs=try_from_hf_gcs,
1796 num_proc=num_proc,
1797 storage_options=storage_options,
1798 )
1800 # Build dataset for splits
1801 keep_in_memory = (
1802 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1803 )
File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\builder.py:891, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
889 if num_proc is not None:
890 prepare_split_kwargs["num_proc"] = num_proc
--> 891 self._download_and_prepare(
892 dl_manager=dl_manager,
893 verification_mode=verification_mode,
894 **prepare_split_kwargs,
895 **download_and_prepare_kwargs,
896 )
897 # Sync info
898 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\builder.py:1651, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs)
1650 def _download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs):
-> 1651 super()._download_and_prepare(
1652 dl_manager,
1653 verification_mode,
1654 check_duplicate_keys=verification_mode == VerificationMode.BASIC_CHECKS
1655 or verification_mode == VerificationMode.ALL_CHECKS,
1656 **prepare_splits_kwargs,
1657 )
File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\builder.py:986, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
982 split_dict.add(split_generator.split_info)
984 try:
985 # Prepare split will record examples associated to the split
--> 986 self._prepare_split(split_generator, **prepare_split_kwargs)
987 except OSError as e:
988 raise OSError(
989 "Cannot find data file. "
990 + (self.manual_download_instructions or "")
991 + "\nOriginal error:\n"
992 + str(e)
993 ) from None
File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\builder.py:1490, in GeneratorBasedBuilder._prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)
1488 gen_kwargs = split_generator.gen_kwargs
1489 job_id = 0
-> 1490 for job_id, done, content in self._prepare_split_single(
1491 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
1492 ):
1493 if done:
1494 result = content
File ~\PycharmProjects\testproj\venv\lib\site-packages\datasets\builder.py:1646, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)
1644 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
1645 e = e.__context__
-> 1646 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1648 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset
```
### Steps to reproduce the bug
1. Organize directory structure like in the docs:
folder/metadata.jsonl
folder/train.zip
2. Run load_dataset("imagefolder", data_dir='folder/metadata.jsonl', split='train')
### Expected behavior
Dataset generated with all additional features from metadata.jsonl
### Environment info
- `datasets` version: 2.11.0
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.9.0
- Huggingface_hub version: 0.13.4
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
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] | null | 3 | "2023-04-16T16:01:05Z" | "2023-11-30T12:06:20Z" | null | NONE | null | null | null | ### Feature request
Extend the `imagefolder` dataloading script to support loading multiple images per dataset entry.
This only really makes sense if a metadata file is present.
Currently you can use the following format (example `metadata.jsonl`:
```
{'file_name': 'path_to_image.png', 'metadata': ...}
...
```
which will return a batch with key `image` and any other metadata.
I would propose extending `file_name` to also accept a list of files, which would return a batch with key `images` and any other metadata.
### Motivation
This is useful for example in segmentation tasks in computer vision models, or in text-to-image models that also accept conditioning signals such as another image, feature map, or similar. Currently if I want to do this, I would need to write a custom dataset, rather than just use `imagefolder`.
### Your contribution
Would be open to doing a PR, but also happy for someone else to take it as I am not familiar with the datasets library. | {
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] | null | 1 | "2023-04-16T13:50:14Z" | "2023-04-19T12:04:36Z" | null | NONE | null | null | null | ### Feature request
my jsonl dataset has following format:
```
[{'input':xxx, 'output':xxx},{'input:xxx,'output':xxx},...]
[{'input':xxx, 'output':xxx},{'input:xxx,'output':xxx},...]
```
I try to use `datasets.load_dataset('json', data_files=path)` or `datasets.Dataset.from_json`, it raises
```
File "site-packages/datasets/arrow_dataset.py", line 1078, in from_json
).read()
File "site-packages/datasets/io/json.py", line 59, in read
self.builder.download_and_prepare(
File "site-packages/datasets/builder.py", line 872, in download_and_prepare
self._download_and_prepare(
File "site-packages/datasets/builder.py", line 967, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "site-packages/datasets/builder.py", line 1749, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "site-packages/datasets/builder.py", line 1892, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
```
### Motivation
I wanna use features like `Datasets.map` or `Datasets.shuffle`, so i need the dataset in memory to be `arrow_dataset.Datasets` format
### Your contribution
PR | {
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} | [] | closed | false | null | [] | null | 0 | "2023-04-16T11:48:10Z" | "2023-04-20T15:30:51Z" | "2023-04-20T15:30:51Z" | CONTRIBUTOR | null | null | null | ### Describe the bug
It seems that `~` is not recognized correctly in local paths. Whenever I try to use it I get an exception
### Steps to reproduce the bug
```python
load_dataset("imagefolder", data_dir="~/data/my_dataset")
```
Will generate the following error:
```
EmptyDatasetError: The directory at /path/to/cwd/~/data/datasets/clementine_tagged_per_cam doesn't contain any data files
```
### Expected behavior
Load the dataset.
### Environment info
datasets==2.11.0 | {
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} | [] | closed | false | null | [] | null | 2 | "2023-04-16T04:59:47Z" | "2023-04-18T03:40:56Z" | "2023-04-18T03:40:56Z" | NONE | null | null | null | ### Describe the bug
When calling shuffle on a IterableDataset with streaming=True, I get the following error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/administrator/Documents/Projects/huggingface/jax-diffusers-sprint-consistency-models/virtualenv/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 937, in __iter__
for key, example in ex_iterable:
File "/home/administrator/Documents/Projects/huggingface/jax-diffusers-sprint-consistency-models/virtualenv/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 627, in __iter__
for x in self.ex_iterable:
File "/home/administrator/Documents/Projects/huggingface/jax-diffusers-sprint-consistency-models/virtualenv/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 138, in __iter__
yield from self.generate_examples_fn(**kwargs_with_shuffled_shards)
File "/home/administrator/.cache/huggingface/modules/datasets_modules/datasets/mnist/fda16c03c4ecfb13f165ba7e29cf38129ce035011519968cdaf74894ce91c9d4/mnist.py", line 111, in _generate_examples
images = np.frombuffer(f.read(), dtype=np.uint8).reshape(size, 28, 28)
ValueError: cannot reshape array of size 59992 into shape (60000,28,28)
```
Tested with the fashion_mnist and mnist datasets
### Steps to reproduce the bug
Code to reproduce
```python
from datasets import load_dataset
SHUFFLE_SEED = 42
SHUFFLE_BUFFER_SIZE = 10_000
dataset = load_dataset('fashion_mnist', streaming=True).shuffle(seed=SHUFFLE_SEED, buffer_size=SHUFFLE_BUFFER_SIZE)
next(iter(dataset['train']))
```
### Expected behavior
A random item from the dataset and no error
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.15.0-69-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- Huggingface_hub version: 0.13.4
- PyArrow version: 11.0.0
- Pandas version: 2.0.0
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} | [] | closed | false | null | [] | null | 1 | "2023-04-14T23:28:54Z" | "2023-04-14T23:36:19Z" | "2023-04-14T23:36:19Z" | NONE | null | null | null | ### Describe the bug
The module moved to new place?
### Steps to reproduce the bug
in the import step,
```python
from datasets.utils.deprecation_utils import DeprecatedEnum
```
error:
```
ImportError: cannot import name 'DeprecatedEnum' from 'datasets.utils.deprecation_utils'
```
### Expected behavior
import successfully
### Environment info
python==3.9.16
datasets==1.18.3 | {
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} | [] | closed | false | null | [] | null | 2 | "2023-04-14T18:15:14Z" | "2023-04-20T15:27:58Z" | "2023-04-20T15:21:00Z" | CONTRIBUTOR | null | 0 | {
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} | `GeneratorBasedBuilder`'s TQDM bars were not used as context managers. This PR fixes that (missed these bars in https://github.com/huggingface/datasets/pull/5560).
Also, this PR modifies the single-proc `save_to_disk` to fix the issue with the TQDM bar not accumulating the progress in the multi-shard setting (again, this bug was introduced by me in the linked PR 😎) | {
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} | [] | closed | false | null | [] | null | 1 | "2023-04-14T17:32:31Z" | "2023-04-14T17:45:52Z" | "2023-04-14T17:36:37Z" | CONTRIBUTOR | null | null | null | ### Describe the bug
If we add a new column to our iterable dataset using the hack described in #5752, when we then interleave datasets the new column is pinned to one value.
### Steps to reproduce the bug
What we're going to do here is:
1. Load an iterable dataset in streaming mode (`original_dataset`)
2. Add a new column to this dataset using the hack in #5752 (`modified_dataset_1`)
3. Create another new dataset by adding a column with the same key but different values (`modified_dataset_2`)
4. Interleave our new datasets (`modified_dataset_1` + `modified_dataset_2`)
5. Check the value of our newly added column (`new_column`)
```python
from datasets import load_dataset
# load an iterable dataset
original_dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
# now add a new column to our streaming dataset using our hack from 5752
name = "new_column"
column = [f"new dataset 1, row {i}" for i in range(50)]
new_features = original_dataset.features.copy()
new_features[name] = new_features["file"] # I know that "file" has the right column type to match our new feature
def add_column_fn(example, idx):
if name in example:
raise ValueError(f"Error when adding {name}: column {name} is already in the dataset.")
return {name: column[idx]}
modified_dataset_1 = original_dataset.map(add_column_fn, with_indices=True, features=new_features)
# now create a second modified dataset using the same trick
column = [f"new dataset 2, row {i}" for i in range(50)]
def add_column_fn(example, idx):
if name in example:
raise ValueError(f"Error when adding {name}: column {name} is already in the dataset.")
return {name: column[idx]}
modified_dataset_2 = original_dataset.map(add_column_fn, with_indices=True, features=new_features)
# interleave these datasets
interleaved_dataset = interleave_datasets([modified_dataset_1, modified_dataset_2])
# now check what the value of the added column is
for i, sample in enumerate(interleaved_dataset):
print(sample["new_column"])
if i == 10:
break
```
**Print Output:**
```
new dataset 2, row 0
new dataset 2, row 0
new dataset 2, row 1
new dataset 2, row 1
new dataset 2, row 2
new dataset 2, row 2
new dataset 2, row 3
new dataset 2, row 3
new dataset 2, row 4
new dataset 2, row 4
new dataset 2, row 5
```
We see that we only get outputs from our second dataset.
### Expected behavior
We should interleave between dataset 1 and 2 and increase in row value:
```
new dataset 1, row 0
new dataset 2, row 0
new dataset 1, row 1
new dataset 2, row 1
new dataset 1, row 2
new dataset 2, row 2
...
```
### Environment info
- datasets version: 2.10.2.dev0
- Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.16
- Huggingface_hub version: 0.13.3
- PyArrow version: 10.0.1
- Pandas version: 1.5.2 | {
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] | open | false | null | [] | null | 2 | "2023-04-14T16:39:50Z" | "2024-01-18T10:15:20Z" | null | CONTRIBUTOR | null | null | null | ### Describe the bug
After appending a new column to a streaming dataset using `.add_column`, we can no longer access the list of dataset features using the `.feature` method.
### Steps to reproduce the bug
```python
from datasets import load_dataset
original_dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
print(original_dataset.features.keys())
# now add a new column to our streaming dataset
modified_dataset = original_dataset.add_column("new_column", ["some random text" for _ in range(50)])
print(modified_dataset.features.keys())
```
**Print Output:**
```
dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'])
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[1], line 8
6 # now add a new column to our streaming dataset
7 modified_dataset = original_dataset.add_column("new_column", ["some random text" for _ in range(50)])
----> 8 print(modified_dataset.features.keys())
AttributeError: 'NoneType' object has no attribute 'keys'
```
We see that we get the features for the original dataset, but not the modified one with the added column.
### Expected behavior
Features should be persevered after adding a new column, i.e. calling:
```python
print(modified_dataset.features.keys())
```
Should return:
```
dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id', 'new_column'])
```
### Environment info
- `datasets` version: 2.10.2.dev0
- Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.16
- Huggingface_hub version: 0.13.3
- PyArrow version: 10.0.1
- Pandas version: 1.5.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/5751 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5751/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5751/comments | https://api.github.com/repos/huggingface/datasets/issues/5751/events | https://github.com/huggingface/datasets/pull/5751 | 1,668,333,316 | PR_kwDODunzps5OVMuT | 5,751 | Consistent ArrayXD Python formatting + better NumPy/Pandas formatting | {
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} | [] | closed | false | null | [] | null | 4 | "2023-04-14T14:13:59Z" | "2023-04-20T14:43:20Z" | "2023-04-20T14:40:34Z" | CONTRIBUTOR | null | 0 | {
"diff_url": "https://github.com/huggingface/datasets/pull/5751.diff",
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} | Return a list of lists instead of a list of NumPy arrays when converting the variable-shaped `ArrayXD` to Python. Additionally, improve the NumPy conversion by returning a numeric NumPy array when the offsets are equal or a NumPy object array when they aren't, and allow converting the variable-shaped `ArrayXD` to Pandas.
(Reported in https://github.com/huggingface/datasets/issues/5719#issuecomment-1507579671) | {
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https://api.github.com/repos/huggingface/datasets/issues/5750 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5750/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5750/comments | https://api.github.com/repos/huggingface/datasets/issues/5750/events | https://github.com/huggingface/datasets/issues/5750 | 1,668,289,067 | I_kwDODunzps5jcBIr | 5,750 | Fail to create datasets from a generator when using Google Big Query | {
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} | [] | closed | false | null | [] | null | 4 | "2023-04-14T13:50:59Z" | "2023-04-17T12:20:43Z" | "2023-04-17T12:20:43Z" | NONE | null | null | null | ### Describe the bug
Creating a dataset from a generator using `Dataset.from_generator()` fails if the generator is the [Google Big Query Python client](https://cloud.google.com/python/docs/reference/bigquery/latest). The problem is that the Big Query client is not pickable. And the function `create_config_id` tries to get a hash of the generator by pickling it. So the following error is generated:
```
_pickle.PicklingError: Pickling client objects is explicitly not supported.
Clients have non-trivial state that is local and unpickleable.
```
### Steps to reproduce the bug
1. Install the big query client and datasets `pip install google-cloud-bigquery datasets`
2. Run the following code:
```py
from datasets import Dataset
from google.cloud import bigquery
client = bigquery.Client()
# Perform a query.
QUERY = (
'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` '
'WHERE state = "TX" '
'LIMIT 100')
query_job = client.query(QUERY) # API request
rows = query_job.result() # Waits for query to finish
ds = Dataset.from_generator(rows)
for r in ds:
print(r)
```
### Expected behavior
Two options:
1. Ignore the pickle errors when computing the hash
2. Provide a scape hutch so that we can avoid calculating the hash for the generator. For example, allowing to provide a hash from the user.
### Environment info
python 3.9
google-cloud-bigquery 3.9.0
datasets 2.11.0
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] | null | 8 | "2023-04-14T10:48:06Z" | "2023-06-30T11:31:17Z" | "2023-04-18T12:57:08Z" | NONE | null | null | null | ### Describe the bug
When I run
from datasets import load_dataset
data = load_dataset("visual_genome", 'region_descriptions_v1.2.0')
AttributeError: 'Version' object has no attribute 'match'
### Steps to reproduce the bug
from datasets import load_dataset
data = load_dataset("visual_genome", 'region_descriptions_v1.2.0')
### Expected behavior
This is error trace:
Downloading and preparing dataset visual_genome/region_descriptions_v1.2.0 to C:/Users/Acer/.cache/huggingface/datasets/visual_genome/region_descriptions_v1.2.0/1.2.0/136fe5b83f6691884566c5530313288171e053a3b33bfe3ea2e4c8b39abaf7f3...
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[6], line 1
----> 1 data = load_dataset("visual_genome", 'region_descriptions_v1.2.0')
File ~\.conda\envs\aai\Lib\site-packages\datasets\load.py:1791, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)
1788 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1790 # Download and prepare data
-> 1791 builder_instance.download_and_prepare(
1792 download_config=download_config,
1793 download_mode=download_mode,
1794 verification_mode=verification_mode,
1795 try_from_hf_gcs=try_from_hf_gcs,
1796 num_proc=num_proc,
1797 storage_options=storage_options,
1798 )
1800 # Build dataset for splits
1801 keep_in_memory = (
1802 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1803 )
File ~\.conda\envs\aai\Lib\site-packages\datasets\builder.py:891, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
889 if num_proc is not None:
890 prepare_split_kwargs["num_proc"] = num_proc
--> 891 self._download_and_prepare(
892 dl_manager=dl_manager,
893 verification_mode=verification_mode,
894 **prepare_split_kwargs,
895 **download_and_prepare_kwargs,
896 )
897 # Sync info
898 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~\.conda\envs\aai\Lib\site-packages\datasets\builder.py:1651, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs)
1650 def _download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs):
-> 1651 super()._download_and_prepare(
1652 dl_manager,
1653 verification_mode,
1654 check_duplicate_keys=verification_mode == VerificationMode.BASIC_CHECKS
1655 or verification_mode == VerificationMode.ALL_CHECKS,
1656 **prepare_splits_kwargs,
1657 )
File ~\.conda\envs\aai\Lib\site-packages\datasets\builder.py:964, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
962 split_dict = SplitDict(dataset_name=self.name)
963 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 964 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
966 # Checksums verification
967 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums:
File ~\.cache\huggingface\modules\datasets_modules\datasets\visual_genome\136fe5b83f6691884566c5530313288171e053a3b33bfe3ea2e4c8b39abaf7f3\visual_genome.py:377, in VisualGenome._split_generators(self, dl_manager)
375 def _split_generators(self, dl_manager):
376 # Download image meta datas.
--> 377 image_metadatas_dir = dl_manager.download_and_extract(self.config.image_metadata_url)
378 image_metadatas_file = os.path.join(
379 image_metadatas_dir, _get_decompressed_filename_from_url(self.config.image_metadata_url)
380 )
382 # Download annotations
File ~\.cache\huggingface\modules\datasets_modules\datasets\visual_genome\136fe5b83f6691884566c5530313288171e053a3b33bfe3ea2e4c8b39abaf7f3\visual_genome.py:328, in VisualGenomeConfig.image_metadata_url(self)
326 @property
327 def image_metadata_url(self):
--> 328 if not self.version.match(_LATEST_VERSIONS["image_metadata"]):
329 logger.warning(
330 f"Latest image metadata version is {_LATEST_VERSIONS['image_metadata']}. Trying to generate a dataset of version: {self.version}. Please double check that image data are unchanged between the two versions."
331 )
332 return f"{_BASE_ANNOTATION_URL}/image_data.json.zip"
### Environment info
datasets 2.11.0
python 3.11.3 | {
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For your convenience, I opened a draft Pull Request to fix it quickly: https://github.com/huggingface/datasets/pull/5745
---
* The FutureWarning mentioned above:
```
FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'
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"e:\Krish_naik\FSDSRegression\venv\Lib\dataclasses.py" is overriding the stdlib module "dataclasses"
### Steps to reproduce the bug
module issue
### Expected behavior
overriding the stdlib module "dataclasses"
### Environment info
VS code | {
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- #5733 | {
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} | [] | open | false | null | [] | null | 4 | "2023-04-12T04:51:35Z" | "2023-04-21T14:20:59Z" | null | NONE | null | null | null | ### Describe the bug
The regex defined here https://github.com/huggingface/datasets/blob/f2607935c4e45c70c44fcb698db0363ca7ba83d4/src/datasets/utils/py_utils.py#L158
will cause a weird result during dataset split when data path starts with `/data`
### Steps to reproduce the bug
1. clone dataset into local path
```
cd /data/train/raw/
git lfs clone https://huggingface.co/datasets/deepmind/code_contests.git
ls /data/train/raw/code_contests
# README.md data dataset_infos.json
ls /data/train/raw/code_contests/data
# test-00000-of-00001-9c49eeff30aacaa8.parquet
# train-[0-9]+-of-[0-9]+-xx.parquet
# valid-00000-of-00001-5e672c5751f060d3.parquet
```
2. loading data from local
```
from datasets import load_dataset
dataset = load_dataset('/data/train/raw/code_contests')
FileNotFoundError: Unable to resolve any data file that matches '['data/train/raw/code_contests/data/train-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*']' at /data/train/raw/code_contests with any supported extension
```
weird path `data/train/raw/code_contests/data/train-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*`
While dive deep into `LocalDatasetModuleFactoryWithoutScript` defined in [load.py](https://github.com/huggingface/datasets/blob/f2607935c4e45c70c44fcb698db0363ca7ba83d4/src/datasets/load.py#L627) and _get_data_files_patterns https://github.com/huggingface/datasets/blob/f2607935c4e45c70c44fcb698db0363ca7ba83d4/src/datasets/data_files.py#L228. I found the weird behavior caused by `string_to_dict`
3. check `string_to_dict`
```
p = '/data/train/raw/code_contests/data/test-00000-of-00001-9c49eeff30aacaa8.parquet'
split_pattern = 'data/{split}-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*'
string_to_dict(p, split_pattern)
# {'split': 'train/raw/code_contests/data/test'}
p = '/data2/train/raw/code_contests/data/test-00000-of-00001-9c49eeff30aacaa8.parquet'
string_to_dict(p, split_pattern)
{'split': 'test'}
```
go deep into string_to_dict https://github.com/huggingface/datasets/blob/f2607935c4e45c70c44fcb698db0363ca7ba83d4/src/datasets/utils/py_utils.py#L158.
4. test the regex:
<img width="680" alt="image" src="https://user-images.githubusercontent.com/1772912/231351129-75179f01-fb9f-4f12-8fa9-0dfcc3d5f3bd.png">
<img width="679" alt="image" src="https://user-images.githubusercontent.com/1772912/231351025-009f3d83-2cf3-4e15-9ed4-6b9663dcb2ee.png">
### Expected behavior
statement in `steps to reproduce the bug`
3. check `string_to_dict`
```
p = '/data/train/raw/code_contests/data/test-00000-of-00001-9c49eeff30aacaa8.parquet'
split_pattern = 'data/{split}-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*'
string_to_dict(p, split_pattern)
# {'split': 'train/raw/code_contests/data/test'}
p = '/data2/train/raw/code_contests/data/test-00000-of-00001-9c49eeff30aacaa8.parquet'
string_to_dict(p, split_pattern)
{'split': 'test'}
```
### Environment info
- linux(debian)
- python 3.7
- datasets 2.8.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5738 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5738/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5738/comments | https://api.github.com/repos/huggingface/datasets/issues/5738/events | https://github.com/huggingface/datasets/issues/5738 | 1,663,477,690 | I_kwDODunzps5jJqe6 | 5,738 | load_dataset("text","dataset.txt") loads the wrong dataset! | {
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} | [] | closed | false | null | [] | null | 1 | "2023-04-12T01:07:46Z" | "2023-04-19T12:08:27Z" | "2023-04-19T12:08:27Z" | NONE | null | null | null | ### Describe the bug
I am trying to load my own custom text dataset using the load_dataset function. My dataset is a bunch of ordered text, think along the lines of shakespeare plays. However, after I load the dataset and I inspect it, the dataset is a table with a bunch of latitude and longitude values! What in the world??
### Steps to reproduce the bug
my_dataset = load_dataset("text","TextFile.txt")
my_dataset
### Expected behavior
I expected the dataset to contain the actual data from the text document that I used.
### Environment info
Google Colab | {
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https://api.github.com/repos/huggingface/datasets/issues/5737 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5737/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5737/comments | https://api.github.com/repos/huggingface/datasets/issues/5737/events | https://github.com/huggingface/datasets/issues/5737 | 1,662,919,811 | I_kwDODunzps5jHiSD | 5,737 | ClassLabel Error | {
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} | [] | closed | false | null | [] | null | 2 | "2023-04-11T17:14:13Z" | "2023-04-13T16:49:57Z" | "2023-04-13T16:49:57Z" | NONE | null | null | null | ### Describe the bug
I still getting the error "call() takes 1 positional argument but 2 were given" even after ensuring that the value being passed to the label object is a single value and that the ClassLabel object has been created with the correct number of label classes
### Steps to reproduce the bug
from datasets import ClassLabel, Dataset
1. Create the ClassLabel object with 3 label values and their corresponding names
label_test = ClassLabel(num_classes=3, names=["label_1", "label_2", "label_3"])
2. Define a dictionary with text and label fields
data = {
'text': ['text_1', 'text_2', 'text_3'],
'label': [1, 2, 3],
}
3. Create a Hugging Face dataset from the dictionary
dataset = Dataset.from_dict(data)
print(dataset.features)
4. Map the label values to their corresponding label names using the label object
dataset = dataset.map(lambda example: {'text': example['text'], 'label': label_test(example['label'])})
5. Print the resulting dataset
print(dataset)
### Expected behavior
I hope my label type is class label instead int.
### Environment info
python 3.9
google colab | {
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https://api.github.com/repos/huggingface/datasets/issues/5736 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5736/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5736/comments | https://api.github.com/repos/huggingface/datasets/issues/5736/events | https://github.com/huggingface/datasets/issues/5736 | 1,662,286,061 | I_kwDODunzps5jFHjt | 5,736 | FORCE_REDOWNLOAD raises "Directory not empty" exception on second run | {
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} | [] | open | false | null | [] | null | 3 | "2023-04-11T11:29:15Z" | "2023-11-30T07:16:58Z" | null | NONE | null | null | null | ### Describe the bug
Running `load_dataset(..., download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD)` twice raises a `Directory not empty` exception on the second run.
### Steps to reproduce the bug
I cannot test this on datasets v2.11.0 due to #5711, but this happens in v2.10.1.
1. Set up a script `my_dataset.py` to generate and load an offline dataset.
2. Load it with
```python
ds = datasets.load_dataset(path=/path/to/my_dataset.py,
name='toy',
data_dir=/path/to/my_dataset.py,
cache_dir=cache_dir,
download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD,
)
```
It loads fine
```
Dataset my_dataset downloaded and prepared to /path/to/cache/toy-..e05e/1.0.0/...5b4c. Subsequent calls will reuse this data.
```
3. Try to load it again with the same snippet and the splits are generated, but at the end of the loading process it raises the error
```
2023-04-11 12:10:19,965: DEBUG: open file: /path/to/cache/toy-..e05e/1.0.0/...5b4c.incomplete/dataset_info.json
Traceback (most recent call last):
File "<string>", line 2, in <module>
File "/path/to/conda/environment/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset
builder_instance.download_and_prepare(
File "/path/to/conda/environment/lib/python3.10/site-packages/datasets/builder.py", line 852, in download_and_prepare
with incomplete_dir(self._output_dir) as tmp_output_dir:
File "/path/to/conda/environment/lib/python3.10/contextlib.py", line 142, in __exit__
next(self.gen)
File "/path/to/conda/environment/lib/python3.10/site-packages/datasets/builder.py", line 826, in incomplete_dir
shutil.rmtree(dirname)
File "/path/to/conda/environment/lib/python3.10/shutil.py", line 730, in rmtree
onerror(os.rmdir, path, sys.exc_info())
File "/path/to/conda/environment/lib/python3.10/shutil.py", line 728, in rmtree
os.rmdir(path)
OSError: [Errno 39] Directory not empty: '/path/to/cache/toy-..e05e/1.0.0/...5b4c'
```
### Expected behavior
Regenerate the dataset from scratch and reload it.
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-4.18.0-483.el8.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.8
- PyArrow version: 11.0.0
- Pandas version: 1.5.2
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} | [] | closed | false | null | [] | null | 11 | "2023-04-11T10:02:25Z" | "2023-04-27T16:39:04Z" | "2023-04-27T16:32:09Z" | CONTRIBUTOR | null | 0 | {
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} | This PR allows sharding of merged iterable datasets.
Merged iterable datasets with for instance the `interleave_datasets` command are comprised of multiple sub-iterable, one for each dataset that has been merged.
With this PR, sharding a merged iterable will result in multiple merged datasets each comprised of sharded sub-iterable, ensuring that there is no duplication of data.
As a result it is now possible to set any amount of workers in the dataloader as long as it is lower or equal to the lowest amount of shards amongst the datasets. Before it had to be set to 0.
I previously talked about this issue on the forum [here](https://discuss.huggingface.co/t/interleaving-iterable-dataset-with-num-workers-0/35801) | {
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- #5731 | {
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} | In `fsspec--2023.4.0` default value for clobber when registering an implementation was changed from True to False. See:
- https://github.com/fsspec/filesystem_spec/pull/1237
This PR recovers previous behavior by passing clobber True when registering mock implementations.
This PR also removes the temporary pin introduced by:
- #5731
Fix #5734. | {
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The HuggingFace Datasets library currently supports two BuilderConfigs for Enwik8. One config yields individual lines as examples, while the other config yields the entire dataset as a single example. Both support only a monolithic split: it is all grouped as "train".
The HuggingFace Datasets library should include a BuilderConfig for Enwik8 with train, validation, and test sets derived from the first 90 million bytes, next 5 million bytes, and last 5 million bytes, respectively. This Enwik8 split is standard practice in LM papers, as elaborated and motivated below.
### Motivation
Enwik8 is commonly split into 90M, 5M, 5M consecutive bytes. This is done in the Transformer-XL [codebase](https://github.com/kimiyoung/transformer-xl/blob/44781ed21dbaec88b280f74d9ae2877f52b492a5/getdata.sh#L34), and is additionally mentioned in the Sparse Transformers [paper](https://arxiv.org/abs/1904.10509) and the Compressive Transformers [paper](https://arxiv.org/abs/1911.05507). This split is pretty much universal among language modeling papers.
One may obtain the splits by manual wrangling, using the data yielded by the ```enwik8-raw``` BuilderConfig. However, this undermines the seamless functionality of the library: one must slice the single raw example, extract it into three tensors, and wrap each in a separate dataset.
This becomes even more of a nuisance if using the current Enwik8 HuggingFace dataset as a TfdsDataSource with [SeqIO](https://github.com/google/seqio), where a pipeline of preprocessors is typically included in a SeqIO Task definition, to be applied immediately after loading the data with TFDS.
### Your contribution
Supporting this functionality in HuggingFace Datasets will only require an additional BuilderConfig for Enwik8 and a few additional lines of code. I will submit a PR. | {
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See: https://github.com/huggingface/datasets/actions/runs/4665326892/jobs/8258580948
```
=========================== short test summary info ============================
ERROR tests/test_builder.py::test_builder_with_filesystem_download_and_prepare - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_builder.py::test_builder_with_filesystem_download_and_prepare_reload - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_dataset_dict.py::test_dummy_datasetdict_serialize_fs - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_file_utils.py::test_get_from_cache_fsspec - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_filesystem.py::test_is_remote_filesystem - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xexists[tmp_path/file.txt-True] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xexists[tmp_path/file_that_doesnt_exist.txt-False] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xexists[mock://top_level/second_level/date=2019-10-01/a.parquet-True] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xexists[mock://top_level/second_level/date=2019-10-01/file_that_doesnt_exist.parquet-False] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xlistdir[tmp_path-expected_paths0] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xlistdir[mock://-expected_paths1] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xlistdir[mock://top_level-expected_paths2] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xlistdir[mock://top_level/second_level/date=2019-10-01-expected_paths3] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xisdir[tmp_path-True] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xisdir[tmp_path/file.txt-False] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xisdir[mock://-True] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xisdir[mock://top_level-True] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xisdir[mock://dir_that_doesnt_exist-False] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xisfile[tmp_path/file.txt-True] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xisfile[tmp_path/file_that_doesnt_exist.txt-False] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xisfile[mock://-False] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xisfile[mock://top_level/second_level/date=2019-10-01/a.parquet-True] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xgetsize[tmp_path/file.txt-100] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xgetsize[mock://-0] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xgetsize[mock://top_level/second_level/date=2019-10-01/a.parquet-100] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xglob[tmp_path/*.txt-expected_paths0] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xglob[mock://*-expected_paths1] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xglob[mock://top_*-expected_paths2] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xglob[mock://top_level/second_level/date=2019-10-0[1-4]-expected_paths3] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xglob[mock://top_level/second_level/date=2019-10-0[1-4]/*-expected_paths4] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xwalk[tmp_path-expected_outputs0] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::test_xwalk[mock://top_level/second_level-expected_outputs1] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_exists[tmp_path/file.txt-True] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_exists[tmp_path/file_that_doesnt_exist.txt-False] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_exists[mock://top_level/second_level/date=2019-10-01/a.parquet-True] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_exists[mock://top_level/second_level/date=2019-10-01/file_that_doesnt_exist.parquet-False] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_glob[tmp_path-*.txt-expected_paths0] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_glob[mock://-*-expected_paths1] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_glob[mock://-top_*-expected_paths2] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_glob[mock://top_level/second_level-date=2019-10-0[1-4]-expected_paths3] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_glob[mock://top_level/second_level-date=2019-10-0[1-4]/*-expected_paths4] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[tmp_path-*.txt-expected_paths0] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[mock://-date=2019-10-0[1-4]-expected_paths1] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[mock://top_level-date=2019-10-0[1-4]-expected_paths2] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[mock://-date=2019-10-0[1-4]/*-expected_paths3] - ValueError: Name (mock) already in the registry and clobber is False
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[mock://top_level-date=2019-10-0[1-4]/*-expected_paths4] - ValueError: Name (mock) already in the registry and clobber is False
===== 2105 passed, 18 skipped, 38 warnings, 46 errors in 236.22s (0:03:56) =====
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} | This PR makes the order of the split names deterministic. Before it was nondeterministic because we were iterating over `set` elements.
Fix #5728. | {
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https://api.github.com/repos/huggingface/datasets/issues/5728 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5728/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5728/comments | https://api.github.com/repos/huggingface/datasets/issues/5728/events | https://github.com/huggingface/datasets/issues/5728 | 1,661,925,932 | I_kwDODunzps5jDvos | 5,728 | The order of data split names is nondeterministic | {
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```
FAILED tests/test_data_files.py::test_get_data_files_patterns[data_file_per_split4] - AssertionError: assert ['random', 'train'] == ['train', 'random']
At index 0 diff: 'random' != 'train'
Full diff:
- ['train', 'random']
+ ['random', 'train']
```
I have checked locally and found out that the data split order is nondeterministic.
This is caused by the use of `set` for sharded splits. | {
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https://api.github.com/repos/huggingface/datasets/issues/5727 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5727/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5727/comments | https://api.github.com/repos/huggingface/datasets/issues/5727/events | https://github.com/huggingface/datasets/issues/5727 | 1,661,536,363 | I_kwDODunzps5jCQhr | 5,727 | load_dataset fails with FileNotFound error on Windows | {
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} | [] | closed | false | null | [] | null | 4 | "2023-04-10T23:21:12Z" | "2023-07-21T14:08:20Z" | "2023-07-21T14:08:19Z" | NONE | null | null | null | ### Describe the bug
Although I can import and run the datasets library in a Colab environment, I cannot successfully load any data on my own machine (Windows 10) despite following the install steps:
(1) create conda environment
(2) activate environment
(3) install with: ``conda` install -c huggingface -c conda-forge datasets`
Then
```
from datasets import load_dataset
# this or any other example from the website fails with the FileNotFoundError
glue = load_dataset("glue", "ax")
```
**Below I have pasted the error omitting the full path**:
```
raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at C:\Users\...\glue\glue.py or any data file in the same directory. Couldn't find 'glue' on the Hugging Face Hub either: FileNotFoundError: [WinError 3] The system cannot find the path specified:
'C:\\Users\\...\\.cache\\huggingface'
```
### Steps to reproduce the bug
On Windows 10
1) create a minimal conda environment (with just Python)
(2) activate environment
(3) install datasets with: ``conda` install -c huggingface -c conda-forge datasets`
(4) import load_dataset and follow example usage from any dataset card.
### Expected behavior
The expected behavior is to load the file into the Python session running on my machine without error.
### Environment info
```
# Name Version Build Channel
aiohttp 3.8.4 py311ha68e1ae_0 conda-forge
aiosignal 1.3.1 pyhd8ed1ab_0 conda-forge
arrow-cpp 11.0.0 h57928b3_13_cpu conda-forge
async-timeout 4.0.2 pyhd8ed1ab_0 conda-forge
attrs 22.2.0 pyh71513ae_0 conda-forge
aws-c-auth 0.6.26 h1262f0c_1 conda-forge
aws-c-cal 0.5.21 h7cda486_2 conda-forge
aws-c-common 0.8.14 hcfcfb64_0 conda-forge
aws-c-compression 0.2.16 h8a79959_5 conda-forge
aws-c-event-stream 0.2.20 h5f78564_4 conda-forge
aws-c-http 0.7.6 h2545be9_0 conda-forge
aws-c-io 0.13.19 h0d2781e_3 conda-forge
aws-c-mqtt 0.8.6 hd211e0c_12 conda-forge
aws-c-s3 0.2.7 h8113e7b_1 conda-forge
aws-c-sdkutils 0.1.8 h8a79959_0 conda-forge
aws-checksums 0.1.14 h8a79959_5 conda-forge
aws-crt-cpp 0.19.8 he6d3b81_12 conda-forge
aws-sdk-cpp 1.10.57 h64004b3_8 conda-forge
brotlipy 0.7.0 py311ha68e1ae_1005 conda-forge
bzip2 1.0.8 h8ffe710_4 conda-forge
c-ares 1.19.0 h2bbff1b_0
ca-certificates 2023.01.10 haa95532_0
certifi 2022.12.7 pyhd8ed1ab_0 conda-forge
cffi 1.15.1 py311h7d9ee11_3 conda-forge
charset-normalizer 2.1.1 pyhd8ed1ab_0 conda-forge
colorama 0.4.6 pyhd8ed1ab_0 conda-forge
cryptography 40.0.1 py311h28e9c30_0 conda-forge
dataclasses 0.8 pyhc8e2a94_3 conda-forge
datasets 2.11.0 py_0 huggingface
dill 0.3.6 pyhd8ed1ab_1 conda-forge
filelock 3.11.0 pyhd8ed1ab_0 conda-forge
frozenlist 1.3.3 py311ha68e1ae_0 conda-forge
fsspec 2023.4.0 pyh1a96a4e_0 conda-forge
gflags 2.2.2 ha925a31_1004 conda-forge
glog 0.6.0 h4797de2_0 conda-forge
huggingface_hub 0.13.4 py_0 huggingface
idna 3.4 pyhd8ed1ab_0 conda-forge
importlib-metadata 6.3.0 pyha770c72_0 conda-forge
importlib_metadata 6.3.0 hd8ed1ab_0 conda-forge
intel-openmp 2023.0.0 h57928b3_25922 conda-forge
krb5 1.20.1 heb0366b_0 conda-forge
libabseil 20230125.0 cxx17_h63175ca_1 conda-forge
libarrow 11.0.0 h04c43f8_13_cpu conda-forge
libblas 3.9.0 16_win64_mkl conda-forge
libbrotlicommon 1.0.9 hcfcfb64_8 conda-forge
libbrotlidec 1.0.9 hcfcfb64_8 conda-forge
libbrotlienc 1.0.9 hcfcfb64_8 conda-forge
libcblas 3.9.0 16_win64_mkl conda-forge
libcrc32c 1.1.2 h0e60522_0 conda-forge
libcurl 7.88.1 h68f0423_1 conda-forge
libexpat 2.5.0 h63175ca_1 conda-forge
libffi 3.4.2 h8ffe710_5 conda-forge
libgoogle-cloud 2.8.0 hf2ff781_1 conda-forge
libgrpc 1.52.1 h32da247_1 conda-forge
libhwloc 2.9.0 h51c2c0f_0 conda-forge
libiconv 1.17 h8ffe710_0 conda-forge
liblapack 3.9.0 16_win64_mkl conda-forge
libprotobuf 3.21.12 h12be248_0 conda-forge
libsqlite 3.40.0 hcfcfb64_0 conda-forge
libssh2 1.10.0 h9a1e1f7_3 conda-forge
libthrift 0.18.1 h9ce19ad_0 conda-forge
libutf8proc 2.8.0 h82a8f57_0 conda-forge
libxml2 2.10.3 hc3477c8_6 conda-forge
libzlib 1.2.13 hcfcfb64_4 conda-forge
lz4-c 1.9.4 hcfcfb64_0 conda-forge
mkl 2022.1.0 h6a75c08_874 conda-forge
multidict 6.0.4 py311ha68e1ae_0 conda-forge
multiprocess 0.70.14 py311ha68e1ae_3 conda-forge
numpy 1.24.2 py311h0b4df5a_0 conda-forge
openssl 3.1.0 hcfcfb64_0 conda-forge
orc 1.8.3 hada7b9e_0 conda-forge
packaging 23.0 pyhd8ed1ab_0 conda-forge
pandas 2.0.0 py311hf63dbb6_0 conda-forge
parquet-cpp 1.5.1 2 conda-forge
pip 23.0.1 pyhd8ed1ab_0 conda-forge
pthreads-win32 2.9.1 hfa6e2cd_3 conda-forge
pyarrow 11.0.0 py311h6a6099b_13_cpu conda-forge
pycparser 2.21 pyhd8ed1ab_0 conda-forge
pyopenssl 23.1.1 pyhd8ed1ab_0 conda-forge
pysocks 1.7.1 pyh0701188_6 conda-forge
python 3.11.3 h2628c8c_0_cpython conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python-tzdata 2023.3 pyhd8ed1ab_0 conda-forge
python-xxhash 3.2.0 py311ha68e1ae_0 conda-forge
python_abi 3.11 3_cp311 conda-forge
pytz 2023.3 pyhd8ed1ab_0 conda-forge
pyyaml 6.0 py311ha68e1ae_5 conda-forge
re2 2023.02.02 h63175ca_0 conda-forge
requests 2.28.2 pyhd8ed1ab_1 conda-forge
setuptools 67.6.1 pyhd8ed1ab_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
snappy 1.1.10 hfb803bf_0 conda-forge
tbb 2021.8.0 h91493d7_0 conda-forge
tk 8.6.12 h8ffe710_0 conda-forge
tqdm 4.65.0 pyhd8ed1ab_1 conda-forge
typing-extensions 4.5.0 hd8ed1ab_0 conda-forge
typing_extensions 4.5.0 pyha770c72_0 conda-forge
tzdata 2023c h71feb2d_0 conda-forge
ucrt 10.0.22621.0 h57928b3_0 conda-forge
urllib3 1.26.15 pyhd8ed1ab_0 conda-forge
vc 14.3 hb6edc58_10 conda-forge
vs2015_runtime 14.34.31931 h4c5c07a_10 conda-forge
wheel 0.40.0 pyhd8ed1ab_0 conda-forge
win_inet_pton 1.1.0 pyhd8ed1ab_6 conda-forge
xxhash 0.8.1 hcfcfb64_0 conda-forge
xz 5.2.10 h8cc25b3_1
yaml 0.2.5 h8ffe710_2 conda-forge
yarl 1.8.2 py311ha68e1ae_0 conda-forge
zipp 3.15.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.13 hcfcfb64_4 conda-forge
zstd 1.5.4 hd43e919_0
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https://api.github.com/repos/huggingface/datasets/issues/5726 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5726/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5726/comments | https://api.github.com/repos/huggingface/datasets/issues/5726/events | https://github.com/huggingface/datasets/issues/5726 | 1,660,944,807 | I_kwDODunzps5jAAGn | 5,726 | Fallback JSON Dataset loading does not load all values when features specified manually | {
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] | null | 1 | "2023-04-10T15:22:14Z" | "2023-04-21T06:35:28Z" | "2023-04-21T06:35:28Z" | NONE | null | null | null | ### Describe the bug
The fallback JSON dataset loader located here:
https://github.com/huggingface/datasets/blob/1c4ec00511868bd881e84a6f7e0333648d833b8e/src/datasets/packaged_modules/json/json.py#L130-L153
does not load the values of features correctly when features are specified manually and not all features have a value in the first entry of the dataset. I'm pretty sure this is not supposed to be expected bahavior?
To fix this you'd have to change this line:
https://github.com/huggingface/datasets/blob/1c4ec00511868bd881e84a6f7e0333648d833b8e/src/datasets/packaged_modules/json/json.py#L140
To pass a schema to pyarrow which has the same structure as the features argument passed to the load_dataset() method.
### Steps to reproduce the bug
Consider a dataset JSON like this:
```
[
{
"instruction": "Do stuff",
"output": "Answer stuff"
},
{
"instruction": "Do stuff2",
"input": "Additional Input2",
"output": "Answer stuff2"
}
]
```
Using this code to load the dataset:
```
from datasets import load_dataset, Features, Value
features = {
"instruction": Value("string"),
"input": Value("string"),
"output": Value("string")
}
features = Features(features)
ds = load_dataset("json", data_files="./ds.json", features=features)
for row in ds["train"]:
print(row)
```
we get a dataset that looks like this:
| **Instruction** | **Input** | **Output** |
|-----------------|--------------------|-----------------|
| "Do stuff" | None | "Answer Stuff" |
| "Do stuff2" | None | "Answer Stuff2" |
### Expected behavior
The input column should contain values other than None for dataset entries that have the "input" attribute set:
| **Instruction** | **Input** | **Output** |
|-----------------|--------------------|-----------------|
| "Do stuff" | None | "Answer Stuff" |
| "Do stuff2" | "Additional Input2" | "Answer Stuff2" |
### Environment info
Python 3.10.10
Datasets 2.11.0
Windows 10 | {
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https://api.github.com/repos/huggingface/datasets/issues/5725 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5725/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5725/comments | https://api.github.com/repos/huggingface/datasets/issues/5725/events | https://github.com/huggingface/datasets/issues/5725 | 1,660,455,202 | I_kwDODunzps5i-Iki | 5,725 | How to limit the number of examples in dataset, for testing? | {
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} | [] | closed | false | null | [] | null | 3 | "2023-04-10T08:41:43Z" | "2023-04-21T06:16:24Z" | "2023-04-21T06:16:24Z" | NONE | null | null | null | ### Describe the bug
I am using this command:
`data = load_dataset("json", data_files=data_path)`
However, I want to add a parameter, to limit the number of loaded examples to be 10, for development purposes, but can't find this simple parameter.
### Steps to reproduce the bug
In the description.
### Expected behavior
To be able to limit the number of examples
### Environment info
Nothing special | {
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https://api.github.com/repos/huggingface/datasets/issues/5724 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5724/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5724/comments | https://api.github.com/repos/huggingface/datasets/issues/5724/events | https://github.com/huggingface/datasets/issues/5724 | 1,659,938,135 | I_kwDODunzps5i8KVX | 5,724 | Error after shuffling streaming IterableDatasets with downloaded dataset | {
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} | [] | closed | false | null | [] | null | 1 | "2023-04-09T16:58:44Z" | "2023-04-20T20:37:30Z" | "2023-04-20T20:37:30Z" | NONE | null | null | null | ### Describe the bug
I downloaded the C4 dataset, and used streaming IterableDatasets to read it. Everything went normal until I used `dataset = dataset.shuffle(seed=42, buffer_size=10_000)` to shuffle the dataset. Shuffled dataset will throw the following error when it is used by `next(iter(dataset))`:
```
File "/data/miniconda3/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 937, in __iter__
for key, example in ex_iterable:
File "/data/miniconda3/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 627, in __iter__
for x in self.ex_iterable:
File "/data/miniconda3/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 138, in __iter__
yield from self.generate_examples_fn(**kwargs_with_shuffled_shards)
File "/data/miniconda3/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 763, in wrapper
for key, table in generate_tables_fn(**kwargs):
File "/data/miniconda3/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 101, in _generate_tables
batch = f.read(self.config.chunksize)
File "/data/miniconda3/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 372, in read_with_retries
out = read(*args, **kwargs)
File "/data/miniconda3/lib/python3.9/gzip.py", line 300, in read
return self._buffer.read(size)
File "/data/miniconda3/lib/python3.9/_compression.py", line 68, in readinto
data = self.read(len(byte_view))
File "/data/miniconda3/lib/python3.9/gzip.py", line 487, in read
if not self._read_gzip_header():
File "/data/miniconda3/lib/python3.9/gzip.py", line 435, in _read_gzip_header
raise BadGzipFile('Not a gzipped file (%r)' % magic)
gzip.BadGzipFile: Not a gzipped file (b've')
```
I found that there is no problem to use the dataset in this way without shuffling. Also, use `dataset = datasets.load_dataset('c4', 'en', split='train', streaming=True)`, which will download the dataset on-the-fly instead of loading from the local file, will also not have problems even after shuffle.
### Steps to reproduce the bug
1. Download C4 dataset from https://huggingface.co/datasets/allenai/c4
2.
```
import datasets
dataset = datasets.load_dataset('/path/to/your/data/dir', 'en', streaming=True, split='train')
dataset = dataset.shuffle(buffer_size=10_000, seed=42)
next(iter(dataset))
```
### Expected behavior
`next(iter(dataset))` should give me a sample from the dataset
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.4.32-1-tlinux4-0001-x86_64-with-glibc2.28
- Python version: 3.9.16
- Huggingface_hub version: 0.13.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5722 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5722/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5722/comments | https://api.github.com/repos/huggingface/datasets/issues/5722/events | https://github.com/huggingface/datasets/issues/5722 | 1,659,837,510 | I_kwDODunzps5i7xxG | 5,722 | Distributed Training Error on Customized Dataset | {
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} | [] | closed | false | null | [] | null | 1 | "2023-04-09T11:04:59Z" | "2023-07-24T14:50:46Z" | "2023-07-24T14:50:46Z" | NONE | null | null | null | Hi guys, recently I tried to use `datasets` to train a dual encoder.
I finish my own datasets according to the nice [tutorial](https://huggingface.co/docs/datasets/v2.11.0/en/dataset_script)
Here are my code:
```python
class RetrivalDataset(datasets.GeneratorBasedBuilder):
"""CrossEncoder dataset."""
BUILDER_CONFIGS = [RetrivalConfig(name="DuReader")]
# DEFAULT_CONFIG_NAME = "DuReader"
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features(
{
"id": datasets.Value("string"),
"question": datasets.Value("string"),
"documents": Sequence(datasets.Value("string")),
}
),
supervised_keys=None,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
train_file = self.config.data_dir + self.config.train_file
valid_file = self.config.data_dir + self.config.valid_file
logger.info(f"Training on {self.config.train_file}")
logger.info(f"Evaluating on {self.config.valid_file}")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"file_path": train_file}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"file_path": valid_file}
),
]
def _generate_examples(self, file_path):
with jsonlines.open(file_path, "r") as f:
for record in f:
label = record["label"]
question = record["question"]
# dual encoder
all_documents = record["all_documents"]
positive_paragraph = all_documents.pop(label)
all_documents = [positive_paragraph] + all_documents
u_id = "{}_#_{}".format(
md5_hash(question + "".join(all_documents)),
"".join(random.sample(string.ascii_letters + string.digits, 7)),
)
item = {
"question": question,
"documents": all_documents,
"id": u_id,
}
yield u_id, item
```
It works well on single GPU, but got errors as follows when used DDP:
```python
Detected mismatch between collectives on ranks. Rank 1 is running collective: CollectiveFingerPrint(OpType=BARRIER), but Rank 0 is running collective: CollectiveFingerPrint(OpType=ALLGATHER_COALESCED)
```
Here are my train script on a two A100 mechine:
```bash
export TORCH_DISTRIBUTED_DEBUG=DETAIL
export TORCH_SHOW_CPP_STACKTRACES=1
export NCCL_DEBUG=INFO
export NCCL_DEBUG_SUBSYS=INIT,COLL,ENV
nohup torchrun --nproc_per_node 2 train.py experiments/de-big.json >logs/de-big.log 2>&1&
```
I am not sure if this error below related to my dataset code when use DDP. And I notice the PR(#5369 ), but I don't know when and where should I used the function(`split_dataset_by_node`) .
@lhoestq hope you could help me?
| {
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https://api.github.com/repos/huggingface/datasets/issues/5721 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5721/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5721/comments | https://api.github.com/repos/huggingface/datasets/issues/5721/events | https://github.com/huggingface/datasets/issues/5721 | 1,659,680,682 | I_kwDODunzps5i7Leq | 5,721 | Calling datasets.load_dataset("text" ...) results in a wrong split. | {
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} | [] | open | false | null | [] | null | 0 | "2023-04-08T23:55:12Z" | "2023-04-08T23:55:12Z" | null | NONE | null | null | null | ### Describe the bug
When creating a text dataset, the training split should have the bulk of the examples by default. Currently, testing does.
### Steps to reproduce the bug
I have a folder with 18K text files in it. Each text file essentially consists in a document or article scraped from online. Calling the following codeL
```
folder_path = "/home/cyril/Downloads/llama_dataset"
data = datasets.load_dataset("text", data_dir=folder_path)
data.save_to_disk("/home/cyril/Downloads/data.hf")
data = datasets.load_from_disk("/home/cyril/Downloads/data.hf")
print(data)
```
Results in the following split:
```
DatasetDict({
train: Dataset({
features: ['text'],
num_rows: 2114
})
test: Dataset({
features: ['text'],
num_rows: 200882
})
validation: Dataset({
features: ['text'],
num_rows: 152
})
})
```
It seems to me like the train/test/validation splits are in the wrong order since test split >>>> train_split
### Expected behavior
Train split should have the bulk of the training examples.
### Environment info
datasets 2.11.0, python 3.10.6 | {
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https://api.github.com/repos/huggingface/datasets/issues/5720 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5720/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5720/comments | https://api.github.com/repos/huggingface/datasets/issues/5720/events | https://github.com/huggingface/datasets/issues/5720 | 1,659,610,705 | I_kwDODunzps5i66ZR | 5,720 | Streaming IterableDatasets do not work with torch DataLoaders | {
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} | [] | open | false | null | [] | null | 7 | "2023-04-08T18:45:48Z" | "2023-05-27T12:57:08Z" | null | NONE | null | null | null | ### Describe the bug
When using streaming datasets set up with train/val split using `.skip()` and `.take()`, the following error occurs when iterating over a torch dataloader:
```
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 363, in __iter__
self._iterator = self._get_iterator()
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 314, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 927, in __init__
w.start()
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/process.py", line 121, in start
self._popen = self._Popen(self)
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/context.py", line 284, in _Popen
return Popen(process_obj)
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 32, in __init__
super().__init__(process_obj)
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/popen_fork.py", line 19, in __init__
self._launch(process_obj)
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 47, in _launch
reduction.dump(process_obj, fp)
File "/Users/julian/miniconda3/envs/sims/lib/python3.9/multiprocessing/reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
AttributeError: Can't pickle local object '_generate_examples_from_tables_wrapper.<locals>.wrapper'
```
To reproduce, run the code
```
from datasets import load_dataset
data = load_dataset(args.dataset_name, split="train", streaming=True)
train_len = 5000
val_len = 100
train, val = data.take(train_len), data.skip(train_len).take(val_len)
traindata = IterableClipDataset(data, context_length=args.max_len, tokenizer=tokenizer, image_key="url", text_key="text")
traindata = DataLoader(traindata, batch_size=args.batch_size, num_workers=args.num_workers, persistent_workers=True)
```
Where the class IterableClipDataset is a simple wrapper to cast the dataset to a torch iterabledataset, defined via
```
from torch.utils.data import Dataset, IterableDataset
from torchvision.transforms import Compose, Resize, ToTensor
from transformers import AutoTokenizer
import requests
from PIL import Image
class IterableClipDataset(IterableDataset):
def __init__(self, dataset, context_length: int, image_transform=None, tokenizer=None, image_key="image", text_key="text"):
self.dataset = dataset
self.context_length = context_length
self.image_transform = Compose([Resize((224, 224)), ToTensor()]) if image_transform is None else image_transform
self.tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") if tokenizer is None else tokenizer
self.image_key = image_key
self.text_key = text_key
def read_image(self, url: str):
try: # Try to read the image
image = Image.open(requests.get(url, stream=True).raw)
except:
image = Image.new("RGB", (224, 224), (0, 0, 0))
return image
def process_sample(self, image, text):
if isinstance(image, str):
image = self.read_image(image)
if self.image_transform is not None:
image = self.image_transform(image)
text = self.tokenizer.encode(
text, add_special_tokens=True, max_length=self.context_length, truncation=True, padding="max_length"
)
text = torch.tensor(text, dtype=torch.long)
return image, text
def __iter__(self):
for sample in self.dataset:
image, text = sample[self.image_key], sample[self.text_key]
yield self.process_sample(image, text)
```
### Steps to reproduce the bug
Steps to reproduce
1. Install `datasets`, `torch`, and `PIL` (if you want to reproduce exactly)
2. Run the code above
### Expected behavior
Batched data is produced from the dataloader
### Environment info
```
datasets == 2.9.0
python == 3.9.12
torch == 1.11.0
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https://api.github.com/repos/huggingface/datasets/issues/5719 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5719/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5719/comments | https://api.github.com/repos/huggingface/datasets/issues/5719/events | https://github.com/huggingface/datasets/issues/5719 | 1,659,203,222 | I_kwDODunzps5i5W6W | 5,719 | Array2D feature creates a list of list instead of a numpy array | {
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} | [] | closed | false | null | [] | null | 4 | "2023-04-07T21:04:08Z" | "2023-04-20T15:34:41Z" | "2023-04-20T15:34:41Z" | NONE | null | null | null | ### Describe the bug
I'm not sure if this is expected behavior or not. When I create a 2D array using `Array2D`, the data has list type instead of numpy array. I think it should not be the expected behavior especially when I feed a numpy array as input to the data creation function. Why is it converting my array into a list?
Also if I change the first dimension of the `Array2D` shape to None, it's returning array correctly.
### Steps to reproduce the bug
Run this code:
```py
from datasets import Dataset, Features, Array2D
import numpy as np
# you have to change the first dimension of the shape to None to make it return an array
features = Features(dict(seq=Array2D((2,2), 'float32')))
ds = Dataset.from_dict(dict(seq=[np.random.rand(2,2)]), features=features)
a = ds[0]['seq']
print(a)
print(type(a))
```
The following will be printed in stdout:
```
[[0.8127174377441406, 0.3760348856449127], [0.7510159611701965, 0.4322739541530609]]
<class 'list'>
```
### Expected behavior
Each indexed item should be a list or numpy array. Currently, `Array((2,2))` yields a list but `Array((None,2))` yields an array.
### Environment info
- `datasets` version: 2.11.0
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.9.13
- Huggingface_hub version: 0.13.4
- PyArrow version: 11.0.0
- Pandas version: 1.4.4
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} | [] | closed | false | null | [] | null | 3 | "2023-04-07T16:01:26Z" | "2023-04-27T14:43:13Z" | "2023-04-27T14:35:52Z" | MEMBER | null | 0 | {
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The default order becomes: [train, validation, test] instead of [train, test, validation]. | {
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] | null | 8 | "2023-04-07T11:59:17Z" | "2023-11-08T11:08:18Z" | null | CONTRIBUTOR | null | null | null | ### Describe the bug
Hello!
I have an issue when I try to save on disk my dataset of images. The error I get is:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1442, in save_to_disk
for job_id, done, content in Dataset._save_to_disk_single(**kwargs):
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1473, in _save_to_disk_single
writer.write_table(pa_table)
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/arrow_writer.py", line 570, in write_table
pa_table = embed_table_storage(pa_table)
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 2268, in embed_table_storage
arrays = [
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 2269, in <listcomp>
embed_array_storage(table[name], feature) if require_storage_embed(feature) else table[name]
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 1817, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 1817, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/table.py", line 2142, in embed_array_storage
return feature.embed_storage(array)
File "/home/jplu/miniconda3/envs/image-xp/lib/python3.10/site-packages/datasets/features/image.py", line 269, in embed_storage
storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
File "pyarrow/array.pxi", line 2766, in pyarrow.lib.StructArray.from_arrays
File "pyarrow/array.pxi", line 2961, in pyarrow.lib.c_mask_inverted_from_obj
TypeError: Mask must be a pyarrow.Array of type boolean
```
My dataset is around 50K images, is this error might be due to a bad image?
Thanks for the help.
### Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("imagefolder", data_dir="/path/to/dataset")
dataset["train"].save_to_disk("./myds", num_shards=40)
```
### Expected behavior
Having my dataset properly saved to disk.
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.10
- Huggingface_hub version: 0.13.3
- PyArrow version: 11.0.0
- Pandas version: 2.0.0 | {
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