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https://github.com/huggingface/datasets/issues/1728 | Add an entry to an arrow dataset | That's a great idea! Thank you so much!
When I try that solution, I get the following error when I try to concatenate `datasets` and `modified_dataset`. I have also attached the output I get when I print out those two variables. Am I missing something?
Code:
``` python
combined_dataset = concatenate_datasets([datasets, modified_dataset])
```
Error:
```
AttributeError: 'DatasetDict' object has no attribute 'features'
```
Output:
```
(Pdb) datasets
DatasetDict({
train: Dataset({
features: ['attention_mask', 'input_ids', 'special_tokens_mask'],
num_rows: 493
})
})
(Pdb) modified_dataset
DatasetDict({
train: Dataset({
features: ['attention_mask', 'input_ids', 'special_tokens_mask'],
num_rows: 493
})
})
```
The error is stemming from the fact that the attribute `datasets.features` does not exist. Would it not be possible to use `concatenate_datasets` in such a case? Is there an alternate solution? | Is it possible to add an entry to a dataset object?
**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**
For example, say we have the following code:
``` python
from datasets import load_dataset
# Load a dataset and print the first examples in the training set
squad_dataset = load_dataset('squad')
print(squad_dataset['train'][0])
```
Is it possible to add an entry to `squad_dataset`? Something like the following?
``` python
squad_dataset.append({'text': "This is a new sentence"})
```
The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.
If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
| 123 | Add an entry to an arrow dataset
Is it possible to add an entry to a dataset object?
**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**
For example, say we have the following code:
``` python
from datasets import load_dataset
# Load a dataset and print the first examples in the training set
squad_dataset = load_dataset('squad')
print(squad_dataset['train'][0])
```
Is it possible to add an entry to `squad_dataset`? Something like the following?
``` python
squad_dataset.append({'text': "This is a new sentence"})
```
The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.
If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
That's a great idea! Thank you so much!
When I try that solution, I get the following error when I try to concatenate `datasets` and `modified_dataset`. I have also attached the output I get when I print out those two variables. Am I missing something?
Code:
``` python
combined_dataset = concatenate_datasets([datasets, modified_dataset])
```
Error:
```
AttributeError: 'DatasetDict' object has no attribute 'features'
```
Output:
```
(Pdb) datasets
DatasetDict({
train: Dataset({
features: ['attention_mask', 'input_ids', 'special_tokens_mask'],
num_rows: 493
})
})
(Pdb) modified_dataset
DatasetDict({
train: Dataset({
features: ['attention_mask', 'input_ids', 'special_tokens_mask'],
num_rows: 493
})
})
```
The error is stemming from the fact that the attribute `datasets.features` does not exist. Would it not be possible to use `concatenate_datasets` in such a case? Is there an alternate solution? | [
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https://github.com/huggingface/datasets/issues/1728 | Add an entry to an arrow dataset | You should do `combined_dataset = concatenate_datasets([datasets['train'], modified_dataset['train']])`
Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases. | Is it possible to add an entry to a dataset object?
**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**
For example, say we have the following code:
``` python
from datasets import load_dataset
# Load a dataset and print the first examples in the training set
squad_dataset = load_dataset('squad')
print(squad_dataset['train'][0])
```
Is it possible to add an entry to `squad_dataset`? Something like the following?
``` python
squad_dataset.append({'text': "This is a new sentence"})
```
The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.
If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
| 42 | Add an entry to an arrow dataset
Is it possible to add an entry to a dataset object?
**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**
For example, say we have the following code:
``` python
from datasets import load_dataset
# Load a dataset and print the first examples in the training set
squad_dataset = load_dataset('squad')
print(squad_dataset['train'][0])
```
Is it possible to add an entry to `squad_dataset`? Something like the following?
``` python
squad_dataset.append({'text': "This is a new sentence"})
```
The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.
If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
You should do `combined_dataset = concatenate_datasets([datasets['train'], modified_dataset['train']])`
Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases. | [
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https://github.com/huggingface/datasets/issues/1728 | Add an entry to an arrow dataset | > Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.
My opinion is that users should always know in advance what type of objects they're going to get. Otherwise the development workflow on their side is going to be pretty chaotic with sometimes unexpected behaviors.
For instance is `split=` is not specified it's currently always returning a DatasetDict. And if `split="train"` is given for example it's always returning a Dataset. | Is it possible to add an entry to a dataset object?
**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**
For example, say we have the following code:
``` python
from datasets import load_dataset
# Load a dataset and print the first examples in the training set
squad_dataset = load_dataset('squad')
print(squad_dataset['train'][0])
```
Is it possible to add an entry to `squad_dataset`? Something like the following?
``` python
squad_dataset.append({'text': "This is a new sentence"})
```
The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.
If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
| 96 | Add an entry to an arrow dataset
Is it possible to add an entry to a dataset object?
**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**
For example, say we have the following code:
``` python
from datasets import load_dataset
# Load a dataset and print the first examples in the training set
squad_dataset = load_dataset('squad')
print(squad_dataset['train'][0])
```
Is it possible to add an entry to `squad_dataset`? Something like the following?
``` python
squad_dataset.append({'text': "This is a new sentence"})
```
The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.
If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
> Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.
My opinion is that users should always know in advance what type of objects they're going to get. Otherwise the development workflow on their side is going to be pretty chaotic with sometimes unexpected behaviors.
For instance is `split=` is not specified it's currently always returning a DatasetDict. And if `split="train"` is given for example it's always returning a Dataset. | [
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https://github.com/huggingface/datasets/issues/1727 | BLEURT score calculation raises UnrecognizedFlagError | And I have the same error with TF 2.4.1. I believe this issue should be reopened. Any ideas?! | Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
``` | 18 | BLEURT score calculation raises UnrecognizedFlagError
Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
```
And I have the same error with TF 2.4.1. I believe this issue should be reopened. Any ideas?! | [
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https://github.com/huggingface/datasets/issues/1727 | BLEURT score calculation raises UnrecognizedFlagError | I'm seeing the same issue with TF 2.4.1 when running the following in https://colab.research.google.com/github/huggingface/datasets/blob/master/notebooks/Overview.ipynb:
```
!pip install git+https://github.com/google-research/bleurt.git
references = ["foo bar baz", "one two three"]
bleurt_metric = load_metric('bleurt')
predictions = ["foo bar", "four five six"]
bleurt_metric.compute(predictions=predictions, references=references)
``` | Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
``` | 39 | BLEURT score calculation raises UnrecognizedFlagError
Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
```
I'm seeing the same issue with TF 2.4.1 when running the following in https://colab.research.google.com/github/huggingface/datasets/blob/master/notebooks/Overview.ipynb:
```
!pip install git+https://github.com/google-research/bleurt.git
references = ["foo bar baz", "one two three"]
bleurt_metric = load_metric('bleurt')
predictions = ["foo bar", "four five six"]
bleurt_metric.compute(predictions=predictions, references=references)
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https://github.com/huggingface/datasets/issues/1727 | BLEURT score calculation raises UnrecognizedFlagError | @aleSuglia @oscartackstrom - Are you getting the error when running your code in a Jupyter notebook ?
I tried reproducing this error again, and was unable to do so from the python command line console in a virtual environment similar to the one I originally used (and unfortunately no longer have access to) when I first got the error.
However, I've managed to reproduce the error by running the same code in a Jupyter notebook running a kernel from the same virtual environment.
This made me suspect that the problem is somehow related to the Jupyter notebook.
More environment details:
```
OS: Ubuntu Linux 18.04
conda==4.8.3
python==3.8.5
datasets==1.3.0
tensorflow==2.4.0
BLEURT==0.0.1
notebook==6.2.0
``` | Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
``` | 112 | BLEURT score calculation raises UnrecognizedFlagError
Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
```
@aleSuglia @oscartackstrom - Are you getting the error when running your code in a Jupyter notebook ?
I tried reproducing this error again, and was unable to do so from the python command line console in a virtual environment similar to the one I originally used (and unfortunately no longer have access to) when I first got the error.
However, I've managed to reproduce the error by running the same code in a Jupyter notebook running a kernel from the same virtual environment.
This made me suspect that the problem is somehow related to the Jupyter notebook.
More environment details:
```
OS: Ubuntu Linux 18.04
conda==4.8.3
python==3.8.5
datasets==1.3.0
tensorflow==2.4.0
BLEURT==0.0.1
notebook==6.2.0
``` | [
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https://github.com/huggingface/datasets/issues/1727 | BLEURT score calculation raises UnrecognizedFlagError | This happens when running the notebook on colab. The issue seems to be that colab populates sys.argv with arguments not handled by bleurt.
Running this before calling bleurt fixes it:
```
import sys
sys.argv = sys.argv[:1]
```
Not the most elegant solution. Perhaps it needs to be fixed in the bleurt code itself rather than huggingface?
This is the output of `print(sys.argv)` when running on colab:
```
['/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py', '-f', '/root/.local/share/jupyter/runtime/kernel-a857a78c-44d6-4b9d-b18a-030b858ee327.json']
``` | Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
``` | 71 | BLEURT score calculation raises UnrecognizedFlagError
Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
```
This happens when running the notebook on colab. The issue seems to be that colab populates sys.argv with arguments not handled by bleurt.
Running this before calling bleurt fixes it:
```
import sys
sys.argv = sys.argv[:1]
```
Not the most elegant solution. Perhaps it needs to be fixed in the bleurt code itself rather than huggingface?
This is the output of `print(sys.argv)` when running on colab:
```
['/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py', '-f', '/root/.local/share/jupyter/runtime/kernel-a857a78c-44d6-4b9d-b18a-030b858ee327.json']
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https://github.com/huggingface/datasets/issues/1727 | BLEURT score calculation raises UnrecognizedFlagError | I got the error when running it from the command line. It looks more like an error that should be fixed in the BLEURT codebase. | Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
``` | 25 | BLEURT score calculation raises UnrecognizedFlagError
Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
```
I got the error when running it from the command line. It looks more like an error that should be fixed in the BLEURT codebase. | [
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https://github.com/huggingface/datasets/issues/1725 | load the local dataset | You should rephrase your question or give more examples and details on what you want to do.
it’s not possible to understand it and help you with only this information. | your guidebook's example is like
>>>from datasets import load_dataset
>>> dataset = load_dataset('json', data_files='my_file.json')
but the first arg is path...
so how should i do if i want to load the local dataset for model training?
i will be grateful if you can help me handle this problem!
thanks a lot! | 30 | load the local dataset
your guidebook's example is like
>>>from datasets import load_dataset
>>> dataset = load_dataset('json', data_files='my_file.json')
but the first arg is path...
so how should i do if i want to load the local dataset for model training?
i will be grateful if you can help me handle this problem!
thanks a lot!
You should rephrase your question or give more examples and details on what you want to do.
it’s not possible to understand it and help you with only this information. | [
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https://github.com/huggingface/datasets/issues/1725 | load the local dataset | sorry for that.
i want to know how could i load the train set and the test set from the local ,which api or function should i use .
| your guidebook's example is like
>>>from datasets import load_dataset
>>> dataset = load_dataset('json', data_files='my_file.json')
but the first arg is path...
so how should i do if i want to load the local dataset for model training?
i will be grateful if you can help me handle this problem!
thanks a lot! | 29 | load the local dataset
your guidebook's example is like
>>>from datasets import load_dataset
>>> dataset = load_dataset('json', data_files='my_file.json')
but the first arg is path...
so how should i do if i want to load the local dataset for model training?
i will be grateful if you can help me handle this problem!
thanks a lot!
sorry for that.
i want to know how could i load the train set and the test set from the local ,which api or function should i use .
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https://github.com/huggingface/datasets/issues/1725 | load the local dataset | thanks a lot
i find that the problem is i dont use vpn...
so i have to keep my net work even if i want to load the local data ? | your guidebook's example is like
>>>from datasets import load_dataset
>>> dataset = load_dataset('json', data_files='my_file.json')
but the first arg is path...
so how should i do if i want to load the local dataset for model training?
i will be grateful if you can help me handle this problem!
thanks a lot! | 31 | load the local dataset
your guidebook's example is like
>>>from datasets import load_dataset
>>> dataset = load_dataset('json', data_files='my_file.json')
but the first arg is path...
so how should i do if i want to load the local dataset for model training?
i will be grateful if you can help me handle this problem!
thanks a lot!
thanks a lot
i find that the problem is i dont use vpn...
so i have to keep my net work even if i want to load the local data ? | [
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https://github.com/huggingface/datasets/issues/1724 | could not run models on a offline server successfully | Hi @lkcao !
Your issue is indeed related to `datasets`. In addition to installing the package manually, you will need to download the `text.py` script on your server. You'll find it (under `datasets/datasets/text`: https://github.com/huggingface/datasets/blob/master/datasets/text/text.py.
Then you can change the line 221 of `run_mlm_new.py` into:
```python
datasets = load_dataset('/path/to/text.py', data_files=data_files)
```
Where `/path/to/text.py` is the path on the server where you saved the `text.py` script. | Hi, I really need your help about this.
I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:
is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out... | 64 | could not run models on a offline server successfully
Hi, I really need your help about this.
I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:
is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...
Hi @lkcao !
Your issue is indeed related to `datasets`. In addition to installing the package manually, you will need to download the `text.py` script on your server. You'll find it (under `datasets/datasets/text`: https://github.com/huggingface/datasets/blob/master/datasets/text/text.py.
Then you can change the line 221 of `run_mlm_new.py` into:
```python
datasets = load_dataset('/path/to/text.py', data_files=data_files)
```
Where `/path/to/text.py` is the path on the server where you saved the `text.py` script. | [
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https://github.com/huggingface/datasets/issues/1724 | could not run models on a offline server successfully | We're working on including the local dataset builders (csv, text, json etc.) directly in the `datasets` package so that they can be used offline | Hi, I really need your help about this.
I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:
is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out... | 24 | could not run models on a offline server successfully
Hi, I really need your help about this.
I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:
is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...
We're working on including the local dataset builders (csv, text, json etc.) directly in the `datasets` package so that they can be used offline | [
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https://github.com/huggingface/datasets/issues/1724 | could not run models on a offline server successfully | The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)
You can now use them offline
```python
datasets = load_dataset('text', data_files=data_files)
```
We'll do a new release soon | Hi, I really need your help about this.
I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:
is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out... | 38 | could not run models on a offline server successfully
Hi, I really need your help about this.
I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:
is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...
The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)
You can now use them offline
```python
datasets = load_dataset('text', data_files=data_files)
```
We'll do a new release soon | [
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https://github.com/huggingface/datasets/issues/1724 | could not run models on a offline server successfully | > The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)
> You can now use them offline
>
> ```python
> datasets = load_dataset('text', data_files=data_files)
> ```
>
> We'll do a new release soon
so the new version release now? | Hi, I really need your help about this.
I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:
is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out... | 52 | could not run models on a offline server successfully
Hi, I really need your help about this.
I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:
is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...
> The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)
> You can now use them offline
>
> ```python
> datasets = load_dataset('text', data_files=data_files)
> ```
>
> We'll do a new release soon
so the new version release now? | [
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https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | Thanks for reporting !
I was able to reproduce thanks to your code and find the origin of the bug.
The cache was not reusing the same file because one object was not deterministic. It comes from a conversion from `set` to `list` in the `datasets.arrrow_dataset.transmit_format` function, where the resulting list would not always be in the same order and therefore the function that computes the hash used by the cache would not always return the same result.
I'm opening a PR to fix this.
Also we plan to do a new release in the coming days so you can expect the fix to be available soon.
Note that you can still specify `cache_file_name=` in the second `map()` call to name the cache file yourself if you want to. | Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 129 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
Thanks for reporting !
I was able to reproduce thanks to your code and find the origin of the bug.
The cache was not reusing the same file because one object was not deterministic. It comes from a conversion from `set` to `list` in the `datasets.arrrow_dataset.transmit_format` function, where the resulting list would not always be in the same order and therefore the function that computes the hash used by the cache would not always return the same result.
I'm opening a PR to fix this.
Also we plan to do a new release in the coming days so you can expect the fix to be available soon.
Note that you can still specify `cache_file_name=` in the second `map()` call to name the cache file yourself if you want to. | [
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] |
https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | Thanks for the fast reply, waiting for the fix :)
I tried to use `cache_file_names` and wasn't sure how, I tried to give it the following:
```
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets}
)
```
and got an error:
```
multiprocess.pool.RemoteTraceback:
"""
Traceback (most recent call last):
File "/venv/lib/python3.6/site-packages/multiprocess/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 157, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/venv/lib/python3.6/site-packages/datasets/fingerprint.py", line 163, in wrapper
out = func(self, *args, **kwargs)
File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1491, in _map_single
tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(cache_file_name), delete=False)
File "/usr/lib/python3.6/tempfile.py", line 690, in NamedTemporaryFile
(fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)
File "/usr/lib/python3.6/tempfile.py", line 401, in _mkstemp_inner
fd = _os.open(file, flags, 0o600)
FileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop'
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "test.py", line 48, in <module>
cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets}
File "/venv/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map
for k, dataset in self.items()
File "/venv/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp>
for k, dataset in self.items()
File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1317, in map
transformed_shards = [r.get() for r in results]
File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1317, in <listcomp>
transformed_shards = [r.get() for r in results]
File "/venv/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get
raise self._value
FileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop'
```
| Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 229 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
Thanks for the fast reply, waiting for the fix :)
I tried to use `cache_file_names` and wasn't sure how, I tried to give it the following:
```
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets}
)
```
and got an error:
```
multiprocess.pool.RemoteTraceback:
"""
Traceback (most recent call last):
File "/venv/lib/python3.6/site-packages/multiprocess/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 157, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/venv/lib/python3.6/site-packages/datasets/fingerprint.py", line 163, in wrapper
out = func(self, *args, **kwargs)
File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1491, in _map_single
tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(cache_file_name), delete=False)
File "/usr/lib/python3.6/tempfile.py", line 690, in NamedTemporaryFile
(fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)
File "/usr/lib/python3.6/tempfile.py", line 401, in _mkstemp_inner
fd = _os.open(file, flags, 0o600)
FileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop'
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "test.py", line 48, in <module>
cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets}
File "/venv/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in map
for k, dataset in self.items()
File "/venv/lib/python3.6/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp>
for k, dataset in self.items()
File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1317, in map
transformed_shards = [r.get() for r in results]
File "/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1317, in <listcomp>
transformed_shards = [r.get() for r in results]
File "/venv/lib/python3.6/site-packages/multiprocess/pool.py", line 644, in get
raise self._value
FileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop'
```
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https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | The documentation says
```
cache_file_names (`Optional[Dict[str, str]]`, defaults to `None`): Provide the name of a cache file to use to store the
results of the computation instead of the automatically generated cache file name.
You have to provide one :obj:`cache_file_name` per dataset in the dataset dictionary.
```
What is expected is simply the name of a file, not a path. The file will be located in the cache directory of the `wikitext` dataset. You can try again with something like
```python
cache_file_names = {k: f'tokenized_and_grouped_{str(k)}' for k in tokenized_datasets}
``` | Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 90 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
The documentation says
```
cache_file_names (`Optional[Dict[str, str]]`, defaults to `None`): Provide the name of a cache file to use to store the
results of the computation instead of the automatically generated cache file name.
You have to provide one :obj:`cache_file_name` per dataset in the dataset dictionary.
```
What is expected is simply the name of a file, not a path. The file will be located in the cache directory of the `wikitext` dataset. You can try again with something like
```python
cache_file_names = {k: f'tokenized_and_grouped_{str(k)}' for k in tokenized_datasets}
``` | [
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] |
https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | Managed to get `cache_file_names` working and caching works well with it
Had to make a small modification for it to work:
```
cache_file_names = {k: f'tokenized_and_grouped_{str(k)}.arrow' for k in tokenized_datasets}
``` | Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 31 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
Managed to get `cache_file_names` working and caching works well with it
Had to make a small modification for it to work:
```
cache_file_names = {k: f'tokenized_and_grouped_{str(k)}.arrow' for k in tokenized_datasets}
``` | [
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] |
https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | Another comment on `cache_file_names`, it doesn't save the produced cached files in the dataset's cache folder, it requires to give a path to an existing directory for it to work.
I can confirm that this is how it works in `datasets==1.1.3` | Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 41 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
Another comment on `cache_file_names`, it doesn't save the produced cached files in the dataset's cache folder, it requires to give a path to an existing directory for it to work.
I can confirm that this is how it works in `datasets==1.1.3` | [
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] |
https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | Oh yes indeed ! Maybe we need to update the docstring to mention that it is a path | Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 18 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
Oh yes indeed ! Maybe we need to update the docstring to mention that it is a path | [
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https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | I upgraded to the latest version and I encountered some strange behaviour, the script I posted in the OP doesn't trigger recalculation, however, if I add the following change it does trigger partial recalculation, I am not sure if its something wrong on my machine or a bug:
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
# CHANGE
print('hello')
# CHANGE
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
...
```
I am using datasets in the `run_mlm.py` script in the transformers examples and I found that if I change the script without touching any of the preprocessing. it still triggers recalculation which is very weird
Edit: accidently clicked the close issue button | Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 136 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
I upgraded to the latest version and I encountered some strange behaviour, the script I posted in the OP doesn't trigger recalculation, however, if I add the following change it does trigger partial recalculation, I am not sure if its something wrong on my machine or a bug:
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
# CHANGE
print('hello')
# CHANGE
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
...
```
I am using datasets in the `run_mlm.py` script in the transformers examples and I found that if I change the script without touching any of the preprocessing. it still triggers recalculation which is very weird
Edit: accidently clicked the close issue button | [
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] |
https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | This is because the `group_texts` line definition changes (it is defined 3 lines later than in the previous call). Currently if a function is moved elsewhere in a script we consider it to be different.
Not sure this is actually a good idea to keep this behavior though. We had this as a security in the early development of the lib but now the recursive hashing of objects is robust so we can probably remove that.
Moreover we're already ignoring the line definition for lambda functions. | Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 86 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
This is because the `group_texts` line definition changes (it is defined 3 lines later than in the previous call). Currently if a function is moved elsewhere in a script we consider it to be different.
Not sure this is actually a good idea to keep this behavior though. We had this as a security in the early development of the lib but now the recursive hashing of objects is robust so we can probably remove that.
Moreover we're already ignoring the line definition for lambda functions. | [
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] |
https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | Sounds great, thank you for your quick responses and help! Looking forward for the next release. | Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 16 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
Sounds great, thank you for your quick responses and help! Looking forward for the next release. | [
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] |
https://github.com/huggingface/datasets/issues/1718 | Possible cache miss in datasets | I am having a similar issue where only the grouped files are loaded from cache while the tokenized ones aren't. I can confirm both datasets are being stored to file, but only the grouped version is loaded from cache. Not sure what might be going on. But I've tried to remove all kinds of non deterministic behaviour, but still no luck. Thanks for the help!
```python
# Datasets
train = sorted(glob(args.data_dir + '*.{}'.format(args.ext)))
if args.dev_split >= len(train):
raise ValueError("Not enough dev files")
dev = []
state = random.Random(1001)
for _ in range(args.dev_split):
dev.append(train.pop(state.randint(0, len(train) - 1)))
max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)
def tokenize_function(examples):
return tokenizer(examples['text'], return_special_tokens_mask=True)
def group_texts(examples):
# Concatenate all texts from our dataset and generate chunks of max_seq_length
concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# Truncate (not implementing padding)
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_seq_length
result = {
k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
datasets = load_dataset(
'text', name='DBNL', data_files={'train': train[:10], 'dev': dev[:5]},
cache_dir=args.data_cache_dir)
datasets = datasets.map(tokenize_function,
batched=True, remove_columns=['text'],
cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-tokenized') for k in datasets},
load_from_cache_file=not args.overwrite_cache)
datasets = datasets.map(group_texts,
batched=True,
cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-grouped') for k in datasets},
load_from_cache_file=not args.overwrite_cache)
```
And this is the log
```
04/26/2021 10:26:59 - WARNING - datasets.builder - Using custom data configuration DBNL-f8d988ad33ccf2c1
04/26/2021 10:26:59 - WARNING - datasets.builder - Reusing dataset text (/home/manjavacasema/data/.cache/text/DBNL-f8d988ad33ccf2c1/0.0.0/e16f44aa1b321ece1f87b07977cc5d70be93d69b20486d6dacd62e12cf25c9a5)
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:00<00:00, 21.07ba/s]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 24.28ba/s]
04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/train-grouped
04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/dev-grouped
```
| Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | 274 | Possible cache miss in datasets
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
I am having a similar issue where only the grouped files are loaded from cache while the tokenized ones aren't. I can confirm both datasets are being stored to file, but only the grouped version is loaded from cache. Not sure what might be going on. But I've tried to remove all kinds of non deterministic behaviour, but still no luck. Thanks for the help!
```python
# Datasets
train = sorted(glob(args.data_dir + '*.{}'.format(args.ext)))
if args.dev_split >= len(train):
raise ValueError("Not enough dev files")
dev = []
state = random.Random(1001)
for _ in range(args.dev_split):
dev.append(train.pop(state.randint(0, len(train) - 1)))
max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)
def tokenize_function(examples):
return tokenizer(examples['text'], return_special_tokens_mask=True)
def group_texts(examples):
# Concatenate all texts from our dataset and generate chunks of max_seq_length
concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# Truncate (not implementing padding)
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_seq_length
result = {
k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
datasets = load_dataset(
'text', name='DBNL', data_files={'train': train[:10], 'dev': dev[:5]},
cache_dir=args.data_cache_dir)
datasets = datasets.map(tokenize_function,
batched=True, remove_columns=['text'],
cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-tokenized') for k in datasets},
load_from_cache_file=not args.overwrite_cache)
datasets = datasets.map(group_texts,
batched=True,
cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-grouped') for k in datasets},
load_from_cache_file=not args.overwrite_cache)
```
And this is the log
```
04/26/2021 10:26:59 - WARNING - datasets.builder - Using custom data configuration DBNL-f8d988ad33ccf2c1
04/26/2021 10:26:59 - WARNING - datasets.builder - Reusing dataset text (/home/manjavacasema/data/.cache/text/DBNL-f8d988ad33ccf2c1/0.0.0/e16f44aa1b321ece1f87b07977cc5d70be93d69b20486d6dacd62e12cf25c9a5)
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:00<00:00, 21.07ba/s]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 24.28ba/s]
04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/train-grouped
04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/dev-grouped
```
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https://github.com/huggingface/datasets/issues/1717 | SciFact dataset - minor changes | Hi Dave,
You are more than welcome to open a PR to make these changes! 🤗
You will find the relevant information about opening a PR in the [contributing guide](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md) and in the [dataset addition guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Pinging also @lhoestq for the Google cloud matter. | Hi,
SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!
I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
Thanks,
Dave | 44 | SciFact dataset - minor changes
Hi,
SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!
I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
Thanks,
Dave
Hi Dave,
You are more than welcome to open a PR to make these changes! 🤗
You will find the relevant information about opening a PR in the [contributing guide](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md) and in the [dataset addition guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Pinging also @lhoestq for the Google cloud matter. | [
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] |
https://github.com/huggingface/datasets/issues/1717 | SciFact dataset - minor changes | > I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
Sure ! Also feel free to ping us for reviews or if we can help :)
> It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
What makes you think that ?
Afaik there's no scifact on our google storage
| Hi,
SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!
I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
Thanks,
Dave | 91 | SciFact dataset - minor changes
Hi,
SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!
I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
Thanks,
Dave
> I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
Sure ! Also feel free to ping us for reviews or if we can help :)
> It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
What makes you think that ?
Afaik there's no scifact on our google storage
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] |
https://github.com/huggingface/datasets/issues/1717 | SciFact dataset - minor changes |
> > I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
>
> Sure ! Also feel free to ping us for reviews or if we can help :)
>
OK! We're organizing a [shared task](https://sdproc.org/2021/sharedtasks.html#sciver) based on the dataset, and I made some updates and changed the download URL - so the current code points to a dead URL. I'll update appropriately once the task is finalized and make a PR.
> > It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
>
> What makes you think that ?
> Afaik there's no scifact on our google storage
You're right, I had the data cached on my machine somewhere.
| Hi,
SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!
I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
Thanks,
Dave | 152 | SciFact dataset - minor changes
Hi,
SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!
I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
Thanks,
Dave
> > I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
>
> Sure ! Also feel free to ping us for reviews or if we can help :)
>
OK! We're organizing a [shared task](https://sdproc.org/2021/sharedtasks.html#sciver) based on the dataset, and I made some updates and changed the download URL - so the current code points to a dead URL. I'll update appropriately once the task is finalized and make a PR.
> > It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
>
> What makes you think that ?
> Afaik there's no scifact on our google storage
You're right, I had the data cached on my machine somewhere.
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] |
https://github.com/huggingface/datasets/issues/1713 | Installation using conda | Great! Did you guys have a timeframe in mind for the next release?
Thank you for all the great work in developing this library. | Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience. | 24 | Installation using conda
Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience.
Great! Did you guys have a timeframe in mind for the next release?
Thank you for all the great work in developing this library. | [
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https://github.com/huggingface/datasets/issues/1713 | Installation using conda | I think we can have `datasets` on conda by next week. Will see what I can do! | Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience. | 17 | Installation using conda
Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience.
I think we can have `datasets` on conda by next week. Will see what I can do! | [
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https://github.com/huggingface/datasets/issues/1713 | Installation using conda | `datasets` has been added to the huggingface channel thanks to @LysandreJik :)
It depends on conda-forge though
```
conda install -c huggingface -c conda-forge datasets
``` | Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience. | 26 | Installation using conda
Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience.
`datasets` has been added to the huggingface channel thanks to @LysandreJik :)
It depends on conda-forge though
```
conda install -c huggingface -c conda-forge datasets
``` | [
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https://github.com/huggingface/datasets/issues/1710 | IsADirectoryError when trying to download C4 | I haven't tested C4 on my side so there so there may be a few bugs in the code/adjustments to make.
Here it looks like in c4.py, line 190 one of the `files_to_download` is `'/'` which is invalid.
Valid files are paths to local files or URLs to remote files. | **TLDR**:
I fail to download C4 and see a stacktrace originating in `IsADirectoryError` as an explanation for failure.
How can the problem be fixed?
**VERBOSE**:
I use Python version 3.7 and have the following dependencies listed in my project:
```
datasets==1.2.0
apache-beam==2.26.0
```
When running the following code, where `/data/huggingface/unpacked/` contains a single unzipped `wet.paths` file manually downloaded as per the instructions for C4:
```
from datasets import load_dataset
load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner')
```
I get the following stacktrace:
```
/Users/fredriko/venv/misc/bin/python /Users/fredriko/source/misc/main.py
Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/fredriko/.cache/huggingface/datasets/c4/en/2.3.0/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283...
Traceback (most recent call last):
File "/Users/fredriko/source/misc/main.py", line 3, in <module>
load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner')
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/load.py", line 612, in load_dataset
ignore_verifications=ignore_verifications,
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 527, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 1066, in _download_and_prepare
pipeline=pipeline,
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 582, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/Users/fredriko/.cache/huggingface/modules/datasets_modules/datasets/c4/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283/c4.py", line 190, in _split_generators
file_paths = dl_manager.download_and_extract(files_to_download)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 258, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 189, in download
self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 117, in _record_sizes_checksums
self._recorded_sizes_checksums[str(url)] = get_size_checksum_dict(path)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 80, in get_size_checksum_dict
with open(path, "rb") as f:
IsADirectoryError: [Errno 21] Is a directory: '/'
Process finished with exit code 1
``` | 50 | IsADirectoryError when trying to download C4
**TLDR**:
I fail to download C4 and see a stacktrace originating in `IsADirectoryError` as an explanation for failure.
How can the problem be fixed?
**VERBOSE**:
I use Python version 3.7 and have the following dependencies listed in my project:
```
datasets==1.2.0
apache-beam==2.26.0
```
When running the following code, where `/data/huggingface/unpacked/` contains a single unzipped `wet.paths` file manually downloaded as per the instructions for C4:
```
from datasets import load_dataset
load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner')
```
I get the following stacktrace:
```
/Users/fredriko/venv/misc/bin/python /Users/fredriko/source/misc/main.py
Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/fredriko/.cache/huggingface/datasets/c4/en/2.3.0/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283...
Traceback (most recent call last):
File "/Users/fredriko/source/misc/main.py", line 3, in <module>
load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner')
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/load.py", line 612, in load_dataset
ignore_verifications=ignore_verifications,
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 527, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 1066, in _download_and_prepare
pipeline=pipeline,
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 582, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/Users/fredriko/.cache/huggingface/modules/datasets_modules/datasets/c4/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283/c4.py", line 190, in _split_generators
file_paths = dl_manager.download_and_extract(files_to_download)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 258, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 189, in download
self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 117, in _record_sizes_checksums
self._recorded_sizes_checksums[str(url)] = get_size_checksum_dict(path)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 80, in get_size_checksum_dict
with open(path, "rb") as f:
IsADirectoryError: [Errno 21] Is a directory: '/'
Process finished with exit code 1
```
I haven't tested C4 on my side so there so there may be a few bugs in the code/adjustments to make.
Here it looks like in c4.py, line 190 one of the `files_to_download` is `'/'` which is invalid.
Valid files are paths to local files or URLs to remote files. | [
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https://github.com/huggingface/datasets/issues/1706 | Error when downloading a large dataset on slow connection. | Hi ! Is this an issue you have with `openwebtext` specifically or also with other datasets ?
It looks like the downloaded file is corrupted and can't be extracted using `tarfile`.
Could you try loading it again with
```python
import datasets
datasets.load_dataset("openwebtext", download_mode="force_redownload")
``` | I receive the following error after about an hour trying to download the `openwebtext` dataset.
The code used is:
```python
import datasets
datasets.load_dataset("openwebtext")
```
> Traceback (most recent call last): [4/28]
> File "<stdin>", line 1, in <module>
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/load.py", line 610, in load_dataset
> ignore_verifications=ignore_verifications,
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/builder.py", line 515, in download_and_prepare
> dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/builder.py", line 570, in _download_and_prepare
> split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
> File "/home/lucadiliello/.cache/huggingface/modules/datasets_modules/datasets/openwebtext/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02/openwebtext.py", line 62, in _split_generators
> dl_dir = dl_manager.download_and_extract(_URL)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
> return self.extract(self.download(url_or_urls))
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 235, in extract
> num_proc=num_proc,
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
> return function(data_struct)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 343, in cached_path
> tar_file.extractall(output_path_extracted)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2000, in extractall
> numeric_owner=numeric_owner)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2042, in extract
> numeric_owner=numeric_owner)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2112, in _extract_member
> self.makefile(tarinfo, targetpath)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2161, in makefile
> copyfileobj(source, target, tarinfo.size, ReadError, bufsize)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 253, in copyfileobj
> buf = src.read(remainder)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/lzma.py", line 200, in read
> return self._buffer.read(size)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/_compression.py", line 68, in readinto
> data = self.read(len(byte_view))
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/_compression.py", line 99, in read
> raise EOFError("Compressed file ended before the "
> EOFError: Compressed file ended before the end-of-stream marker was reached
| 44 | Error when downloading a large dataset on slow connection.
I receive the following error after about an hour trying to download the `openwebtext` dataset.
The code used is:
```python
import datasets
datasets.load_dataset("openwebtext")
```
> Traceback (most recent call last): [4/28]
> File "<stdin>", line 1, in <module>
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/load.py", line 610, in load_dataset
> ignore_verifications=ignore_verifications,
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/builder.py", line 515, in download_and_prepare
> dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/builder.py", line 570, in _download_and_prepare
> split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
> File "/home/lucadiliello/.cache/huggingface/modules/datasets_modules/datasets/openwebtext/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02/openwebtext.py", line 62, in _split_generators
> dl_dir = dl_manager.download_and_extract(_URL)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
> return self.extract(self.download(url_or_urls))
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 235, in extract
> num_proc=num_proc,
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
> return function(data_struct)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 343, in cached_path
> tar_file.extractall(output_path_extracted)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2000, in extractall
> numeric_owner=numeric_owner)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2042, in extract
> numeric_owner=numeric_owner)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2112, in _extract_member
> self.makefile(tarinfo, targetpath)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2161, in makefile
> copyfileobj(source, target, tarinfo.size, ReadError, bufsize)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 253, in copyfileobj
> buf = src.read(remainder)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/lzma.py", line 200, in read
> return self._buffer.read(size)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/_compression.py", line 68, in readinto
> data = self.read(len(byte_view))
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/_compression.py", line 99, in read
> raise EOFError("Compressed file ended before the "
> EOFError: Compressed file ended before the end-of-stream marker was reached
Hi ! Is this an issue you have with `openwebtext` specifically or also with other datasets ?
It looks like the downloaded file is corrupted and can't be extracted using `tarfile`.
Could you try loading it again with
```python
import datasets
datasets.load_dataset("openwebtext", download_mode="force_redownload")
``` | [
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] |
https://github.com/huggingface/datasets/issues/1701 | Some datasets miss dataset_infos.json or dummy_data.zip | Thanks for reporting.
We should indeed add all the missing dummy_data.zip and also the dataset_infos.json at least for lm1b, reclor and wikihow.
For c4 I haven't tested the script and I think we'll require some optimizations regarding beam datasets before processing it.
| While working on dataset REAME generation script at https://github.com/madlag/datasets_readme_generator , I noticed that some datasets miss a dataset_infos.json :
```
c4
lm1b
reclor
wikihow
```
And some does not have a dummy_data.zip :
```
kor_nli
math_dataset
mlqa
ms_marco
newsgroup
qa4mre
qangaroo
reddit_tifu
super_glue
trivia_qa
web_of_science
wmt14
wmt15
wmt16
wmt17
wmt18
wmt19
xtreme
```
But it seems that some of those last do have a "dummy" directory .
| 42 | Some datasets miss dataset_infos.json or dummy_data.zip
While working on dataset REAME generation script at https://github.com/madlag/datasets_readme_generator , I noticed that some datasets miss a dataset_infos.json :
```
c4
lm1b
reclor
wikihow
```
And some does not have a dummy_data.zip :
```
kor_nli
math_dataset
mlqa
ms_marco
newsgroup
qa4mre
qangaroo
reddit_tifu
super_glue
trivia_qa
web_of_science
wmt14
wmt15
wmt16
wmt17
wmt18
wmt19
xtreme
```
But it seems that some of those last do have a "dummy" directory .
Thanks for reporting.
We should indeed add all the missing dummy_data.zip and also the dataset_infos.json at least for lm1b, reclor and wikihow.
For c4 I haven't tested the script and I think we'll require some optimizations regarding beam datasets before processing it.
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https://github.com/huggingface/datasets/issues/1687 | Question: Shouldn't .info be a part of DatasetDict? | We could do something. There is a part of `.info` which is split specific (cache files, split instructions) but maybe if could be made to work. | Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets.
For instance:
```
>>> ds = datasets.load_dataset("conll2002", "es")
>>> ds.info
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'DatasetDict' object has no attribute 'info'
```
I could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets.
Intuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset.
It is entirely possible that I am missing another perspective | 26 | Question: Shouldn't .info be a part of DatasetDict?
Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets.
For instance:
```
>>> ds = datasets.load_dataset("conll2002", "es")
>>> ds.info
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'DatasetDict' object has no attribute 'info'
```
I could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets.
Intuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset.
It is entirely possible that I am missing another perspective
We could do something. There is a part of `.info` which is split specific (cache files, split instructions) but maybe if could be made to work. | [
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https://github.com/huggingface/datasets/issues/1687 | Question: Shouldn't .info be a part of DatasetDict? | Yes this was kinda the idea I was going for. DatasetDict.info would be the shared info amongs the datasets (maybe even some info on how they differ). | Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets.
For instance:
```
>>> ds = datasets.load_dataset("conll2002", "es")
>>> ds.info
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'DatasetDict' object has no attribute 'info'
```
I could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets.
Intuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset.
It is entirely possible that I am missing another perspective | 27 | Question: Shouldn't .info be a part of DatasetDict?
Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets.
For instance:
```
>>> ds = datasets.load_dataset("conll2002", "es")
>>> ds.info
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'DatasetDict' object has no attribute 'info'
```
I could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets.
Intuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset.
It is entirely possible that I am missing another perspective
Yes this was kinda the idea I was going for. DatasetDict.info would be the shared info amongs the datasets (maybe even some info on how they differ). | [
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https://github.com/huggingface/datasets/issues/1686 | Dataset Error: DaNE contains empty samples at the end | One the PR is merged the fix will be available in the next release of `datasets`.
If you don't want to wait the next release you can still load the script from the master branch with
```python
load_dataset("dane", script_version="master")
``` | The dataset DaNE, contains empty samples at the end. It is naturally easy to remove using a filter but should probably not be there, to begin with as it can cause errors.
```python
>>> import datasets
[...]
>>> dataset = datasets.load_dataset("dane")
[...]
>>> dataset["test"][-1]
{'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []}
>>> dataset["train"][-1]
{'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []}
```
Best,
Kenneth | 40 | Dataset Error: DaNE contains empty samples at the end
The dataset DaNE, contains empty samples at the end. It is naturally easy to remove using a filter but should probably not be there, to begin with as it can cause errors.
```python
>>> import datasets
[...]
>>> dataset = datasets.load_dataset("dane")
[...]
>>> dataset["test"][-1]
{'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []}
>>> dataset["train"][-1]
{'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []}
```
Best,
Kenneth
One the PR is merged the fix will be available in the next release of `datasets`.
If you don't want to wait the next release you can still load the script from the master branch with
```python
load_dataset("dane", script_version="master")
``` | [
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https://github.com/huggingface/datasets/issues/1683 | `ArrowInvalid` occurs while running `Dataset.map()` function for DPRContext | Looks like the mapping function returns a dictionary with a 768-dim array in the `embeddings` field. Since the map is batched, we actually expect the `embeddings` field to be an array of shape (batch_size, 768) to have one embedding per example in the batch.
To fix that can you try to remove one of the `[0]` ? In my opinion you only need one of them, not two. | It seems to fail the final batch ):
steps to reproduce:
```
from datasets import load_dataset
from elasticsearch import Elasticsearch
import torch
from transformers import file_utils, set_seed
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
MAX_SEQ_LENGTH = 256
ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base", cache_dir="../datasets/")
ctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained(
"facebook/dpr-ctx_encoder-single-nq-base",
cache_dir="..datasets/"
)
dataset = load_dataset('text',
data_files='data/raw/ARC_Corpus.txt',
cache_dir='../datasets')
torch.set_grad_enabled(False)
ds_with_embeddings = dataset.map(
lambda example: {
'embeddings': ctx_encoder(
**ctx_tokenizer(
example["text"],
padding='max_length',
truncation=True,
max_length=MAX_SEQ_LENGTH,
return_tensors="pt"
)
)[0][0].numpy(),
},
batched=True,
load_from_cache_file=False,
batch_size=1000
)
```
ARC Corpus can be obtained from [here](https://ai2-datasets.s3-us-west-2.amazonaws.com/arc/ARC-V1-Feb2018.zip)
And then the error:
```
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
<ipython-input-13-67d139bb2ed3> in <module>
14 batched=True,
15 load_from_cache_file=False,
---> 16 batch_size=1000
17 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc)
301 num_proc=num_proc,
302 )
--> 303 for k, dataset in self.items()
304 }
305 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0)
301 num_proc=num_proc,
302 )
--> 303 for k, dataset in self.items()
304 }
305 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)
1257 fn_kwargs=fn_kwargs,
1258 new_fingerprint=new_fingerprint,
-> 1259 update_data=update_data,
1260 )
1261 else:
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
155 }
156 # apply actual function
--> 157 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
158 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
159 # re-apply format to the output
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
161 # Call actual function
162
--> 163 out = func(self, *args, **kwargs)
164
165 # Update fingerprint of in-place transforms + update in-place history of transforms
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, update_data)
1526 if update_data:
1527 batch = cast_to_python_objects(batch)
-> 1528 writer.write_batch(batch)
1529 if update_data:
1530 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)
276 typed_sequence = TypedSequence(batch_examples[col], type=col_type, try_type=col_try_type)
277 typed_sequence_examples[col] = typed_sequence
--> 278 pa_table = pa.Table.from_pydict(typed_sequence_examples)
279 self.write_table(pa_table)
280
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pydict()
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_arrays()
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.validate()
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Column 1 named text expected length 768 but got length 1000
``` | 68 | `ArrowInvalid` occurs while running `Dataset.map()` function for DPRContext
It seems to fail the final batch ):
steps to reproduce:
```
from datasets import load_dataset
from elasticsearch import Elasticsearch
import torch
from transformers import file_utils, set_seed
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
MAX_SEQ_LENGTH = 256
ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base", cache_dir="../datasets/")
ctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained(
"facebook/dpr-ctx_encoder-single-nq-base",
cache_dir="..datasets/"
)
dataset = load_dataset('text',
data_files='data/raw/ARC_Corpus.txt',
cache_dir='../datasets')
torch.set_grad_enabled(False)
ds_with_embeddings = dataset.map(
lambda example: {
'embeddings': ctx_encoder(
**ctx_tokenizer(
example["text"],
padding='max_length',
truncation=True,
max_length=MAX_SEQ_LENGTH,
return_tensors="pt"
)
)[0][0].numpy(),
},
batched=True,
load_from_cache_file=False,
batch_size=1000
)
```
ARC Corpus can be obtained from [here](https://ai2-datasets.s3-us-west-2.amazonaws.com/arc/ARC-V1-Feb2018.zip)
And then the error:
```
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
<ipython-input-13-67d139bb2ed3> in <module>
14 batched=True,
15 load_from_cache_file=False,
---> 16 batch_size=1000
17 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc)
301 num_proc=num_proc,
302 )
--> 303 for k, dataset in self.items()
304 }
305 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0)
301 num_proc=num_proc,
302 )
--> 303 for k, dataset in self.items()
304 }
305 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)
1257 fn_kwargs=fn_kwargs,
1258 new_fingerprint=new_fingerprint,
-> 1259 update_data=update_data,
1260 )
1261 else:
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
155 }
156 # apply actual function
--> 157 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
158 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
159 # re-apply format to the output
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
161 # Call actual function
162
--> 163 out = func(self, *args, **kwargs)
164
165 # Update fingerprint of in-place transforms + update in-place history of transforms
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, update_data)
1526 if update_data:
1527 batch = cast_to_python_objects(batch)
-> 1528 writer.write_batch(batch)
1529 if update_data:
1530 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)
276 typed_sequence = TypedSequence(batch_examples[col], type=col_type, try_type=col_try_type)
277 typed_sequence_examples[col] = typed_sequence
--> 278 pa_table = pa.Table.from_pydict(typed_sequence_examples)
279 self.write_table(pa_table)
280
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pydict()
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_arrays()
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.validate()
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Column 1 named text expected length 768 but got length 1000
```
Looks like the mapping function returns a dictionary with a 768-dim array in the `embeddings` field. Since the map is batched, we actually expect the `embeddings` field to be an array of shape (batch_size, 768) to have one embedding per example in the batch.
To fix that can you try to remove one of the `[0]` ? In my opinion you only need one of them, not two. | [
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https://github.com/huggingface/datasets/issues/1681 | Dataset "dane" missing | Hi @KennethEnevoldsen ,
I think the issue might be that this dataset was added during the community sprint and has not been released yet. It will be available with the v2 of datasets.
For now, you should be able to load the datasets after installing the latest (master) version of datasets using pip:
pip install git+https://github.com/huggingface/datasets.git@master | the `dane` dataset appear to be missing in the latest version (1.1.3).
```python
>>> import datasets
>>> datasets.__version__
'1.1.3'
>>> "dane" in datasets.list_datasets()
True
```
As we can see it should be present, but doesn't seem to be findable when using `load_dataset`.
```python
>>> datasets.load_dataset("dane")
Traceback (most recent call last):
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path
output_path = get_from_cache(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path
output_path = get_from_cache(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 588, in load_dataset
module_path, hash = prepare_module(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 280, in prepare_module
raise FileNotFoundError(
FileNotFoundError: Couldn't find file locally at dane/dane.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py
```
This issue might be relevant to @ophelielacroix from the Alexandra Institut whom created the data. | 56 | Dataset "dane" missing
the `dane` dataset appear to be missing in the latest version (1.1.3).
```python
>>> import datasets
>>> datasets.__version__
'1.1.3'
>>> "dane" in datasets.list_datasets()
True
```
As we can see it should be present, but doesn't seem to be findable when using `load_dataset`.
```python
>>> datasets.load_dataset("dane")
Traceback (most recent call last):
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path
output_path = get_from_cache(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path
output_path = get_from_cache(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 588, in load_dataset
module_path, hash = prepare_module(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 280, in prepare_module
raise FileNotFoundError(
FileNotFoundError: Couldn't find file locally at dane/dane.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py
```
This issue might be relevant to @ophelielacroix from the Alexandra Institut whom created the data.
Hi @KennethEnevoldsen ,
I think the issue might be that this dataset was added during the community sprint and has not been released yet. It will be available with the v2 of datasets.
For now, you should be able to load the datasets after installing the latest (master) version of datasets using pip:
pip install git+https://github.com/huggingface/datasets.git@master | [
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https://github.com/huggingface/datasets/issues/1681 | Dataset "dane" missing | The `dane` dataset was added recently, that's why it wasn't available yet. We did an intermediate release today just before the v2.0.
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `dane` with
```python
from datasets import load_dataset
dataset = load_dataset("dane")
``` | the `dane` dataset appear to be missing in the latest version (1.1.3).
```python
>>> import datasets
>>> datasets.__version__
'1.1.3'
>>> "dane" in datasets.list_datasets()
True
```
As we can see it should be present, but doesn't seem to be findable when using `load_dataset`.
```python
>>> datasets.load_dataset("dane")
Traceback (most recent call last):
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path
output_path = get_from_cache(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path
output_path = get_from_cache(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 588, in load_dataset
module_path, hash = prepare_module(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 280, in prepare_module
raise FileNotFoundError(
FileNotFoundError: Couldn't find file locally at dane/dane.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py
```
This issue might be relevant to @ophelielacroix from the Alexandra Institut whom created the data. | 52 | Dataset "dane" missing
the `dane` dataset appear to be missing in the latest version (1.1.3).
```python
>>> import datasets
>>> datasets.__version__
'1.1.3'
>>> "dane" in datasets.list_datasets()
True
```
As we can see it should be present, but doesn't seem to be findable when using `load_dataset`.
```python
>>> datasets.load_dataset("dane")
Traceback (most recent call last):
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path
output_path = get_from_cache(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path
output_path = get_from_cache(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 588, in load_dataset
module_path, hash = prepare_module(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 280, in prepare_module
raise FileNotFoundError(
FileNotFoundError: Couldn't find file locally at dane/dane.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py
```
This issue might be relevant to @ophelielacroix from the Alexandra Institut whom created the data.
The `dane` dataset was added recently, that's why it wasn't available yet. We did an intermediate release today just before the v2.0.
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `dane` with
```python
from datasets import load_dataset
dataset = load_dataset("dane")
``` | [
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https://github.com/huggingface/datasets/issues/1679 | Can't import cc100 dataset | cc100 was added recently, that's why it wasn't available yet.
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `cc100` with
```python
from datasets import load_dataset
lang = "en"
dataset = load_dataset("cc100", lang=lang, split="train")
``` | There is some issue to import cc100 dataset.
```
from datasets import load_dataset
dataset = load_dataset("cc100")
```
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
280 raise FileNotFoundError(
281 "Couldn't find file locally at {}, or remotely at {} or {}".format(
--> 282 combined_path, github_file_path, file_path
283 )
284 )
FileNotFoundError: Couldn't find file locally at cc100/cc100.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py | 45 | Can't import cc100 dataset
There is some issue to import cc100 dataset.
```
from datasets import load_dataset
dataset = load_dataset("cc100")
```
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
280 raise FileNotFoundError(
281 "Couldn't find file locally at {}, or remotely at {} or {}".format(
--> 282 combined_path, github_file_path, file_path
283 )
284 )
FileNotFoundError: Couldn't find file locally at cc100/cc100.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py
cc100 was added recently, that's why it wasn't available yet.
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `cc100` with
```python
from datasets import load_dataset
lang = "en"
dataset = load_dataset("cc100", lang=lang, split="train")
``` | [
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https://github.com/huggingface/datasets/issues/1675 | Add the 800GB Pile dataset? | The pile dataset would be very nice.
Benchmarks show that pile trained models achieve better results than most of actually trained models | ## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
| 22 | Add the 800GB Pile dataset?
## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
The pile dataset would be very nice.
Benchmarks show that pile trained models achieve better results than most of actually trained models | [
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https://github.com/huggingface/datasets/issues/1675 | Add the 800GB Pile dataset? | The pile can very easily be added and adapted using this [tfds implementation](https://github.com/EleutherAI/The-Pile/blob/master/the_pile/tfds_pile.py) from the repo.
However, the question is whether you'd be ok with 800GB+ cached in your local disk, since the tfds implementation was designed to offload the storage to Google Cloud Storage. | ## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
| 45 | Add the 800GB Pile dataset?
## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
The pile can very easily be added and adapted using this [tfds implementation](https://github.com/EleutherAI/The-Pile/blob/master/the_pile/tfds_pile.py) from the repo.
However, the question is whether you'd be ok with 800GB+ cached in your local disk, since the tfds implementation was designed to offload the storage to Google Cloud Storage. | [
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https://github.com/huggingface/datasets/issues/1675 | Add the 800GB Pile dataset? | With the dataset streaming feature (see #2375) it will be more convenient to play with such big datasets :)
I'm currently adding C4 (see #2511 ) but I can probably start working on this afterwards | ## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
| 35 | Add the 800GB Pile dataset?
## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
With the dataset streaming feature (see #2375) it will be more convenient to play with such big datasets :)
I'm currently adding C4 (see #2511 ) but I can probably start working on this afterwards | [
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https://github.com/huggingface/datasets/issues/1675 | Add the 800GB Pile dataset? | Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too! | ## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
| 47 | Add the 800GB Pile dataset?
## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too! | [
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https://github.com/huggingface/datasets/issues/1675 | Add the 800GB Pile dataset? | Hi folks, thanks to some awesome work by @lhoestq and @albertvillanova you can now stream the Pile as follows:
```python
# Install master branch of `datasets`
pip install git+https://github.com/huggingface/datasets.git#egg=datasets[streaming]
pip install zstandard
from datasets import load_dataset
dset = load_dataset("json", data_files="https://the-eye.eu/public/AI/pile/train/00.jsonl.zst", streaming=True, split="train")
next(iter(dset))
# {'meta': {'pile_set_name': 'Pile-CC'},
# 'text': 'It is done, and submitted. You can play “Survival of the Tastiest” on Android, and on the web ... '}
```
Next step is to add the Pile as a "canonical" dataset that can be streamed without specifying the file names explicitly :) | ## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
| 92 | Add the 800GB Pile dataset?
## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
Hi folks, thanks to some awesome work by @lhoestq and @albertvillanova you can now stream the Pile as follows:
```python
# Install master branch of `datasets`
pip install git+https://github.com/huggingface/datasets.git#egg=datasets[streaming]
pip install zstandard
from datasets import load_dataset
dset = load_dataset("json", data_files="https://the-eye.eu/public/AI/pile/train/00.jsonl.zst", streaming=True, split="train")
next(iter(dset))
# {'meta': {'pile_set_name': 'Pile-CC'},
# 'text': 'It is done, and submitted. You can play “Survival of the Tastiest” on Android, and on the web ... '}
```
Next step is to add the Pile as a "canonical" dataset that can be streamed without specifying the file names explicitly :) | [
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https://github.com/huggingface/datasets/issues/1675 | Add the 800GB Pile dataset? | > Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too!
Hi @siddk thanks to a tip from @richarddwang it seems we can access some of the partitions that EleutherAI created for the Pile [here](https://the-eye.eu/public/AI/pile_preliminary_components/). What's missing are links to the preprocessed versions of pre-existing datasets like DeepMind Mathematics and OpenSubtitles, but worst case we do the processing ourselves and host these components on the Hub.
My current idea is that we could provide 23 configs: one for each of the 22 datasets and an `all` config that links to the train / dev / test splits that EleutherAI released [here](https://the-eye.eu/public/AI/pile/), e.g.
```python
from datasets import load_dataset
# Load a single component
youtube_subtitles = load_dataset("the_pile", "youtube_subtitles")
# Load the train / dev / test splits of the whole corpus
dset = load_dataset("the_pile", "all")
```
Ideally we'd like everything to be compatible with the streaming API and there's ongoing work by @albertvillanova to make this happen for the various compression algorithms.
cc @lhoestq | ## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
| 199 | Add the 800GB Pile dataset?
## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
> Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too!
Hi @siddk thanks to a tip from @richarddwang it seems we can access some of the partitions that EleutherAI created for the Pile [here](https://the-eye.eu/public/AI/pile_preliminary_components/). What's missing are links to the preprocessed versions of pre-existing datasets like DeepMind Mathematics and OpenSubtitles, but worst case we do the processing ourselves and host these components on the Hub.
My current idea is that we could provide 23 configs: one for each of the 22 datasets and an `all` config that links to the train / dev / test splits that EleutherAI released [here](https://the-eye.eu/public/AI/pile/), e.g.
```python
from datasets import load_dataset
# Load a single component
youtube_subtitles = load_dataset("the_pile", "youtube_subtitles")
# Load the train / dev / test splits of the whole corpus
dset = load_dataset("the_pile", "all")
```
Ideally we'd like everything to be compatible with the streaming API and there's ongoing work by @albertvillanova to make this happen for the various compression algorithms.
cc @lhoestq | [
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https://github.com/huggingface/datasets/issues/1675 | Add the 800GB Pile dataset? | Ah I just saw that @lhoestq is already thinking about the specifying of one or more subsets in [this PR](https://github.com/huggingface/datasets/pull/2817#issuecomment-901874049) :) | ## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
| 21 | Add the 800GB Pile dataset?
## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
Ah I just saw that @lhoestq is already thinking about the specifying of one or more subsets in [this PR](https://github.com/huggingface/datasets/pull/2817#issuecomment-901874049) :) | [
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https://github.com/huggingface/datasets/issues/1674 | dutch_social can't be loaded | Hi @koenvandenberge and @alighofrani95!
The datasets you're experiencing issues with were most likely added recently to the `datasets` library, meaning they have not been released yet. They will be released with the v2 of the library.
Meanwhile, you can still load the datasets using one of the techniques described in this issue: #1641
Let me know if this helps! | Hi all,
I'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social).
However, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.
```
(base) Koens-MacBook-Pro:~ koenvandenberge$ python
Python 3.7.4 (default, Aug 13 2019, 15:17:50)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
dataset = load_dataset(
'dutch_social')
>>> dataset = load_dataset(
... 'dutch_social')
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
``` | 59 | dutch_social can't be loaded
Hi all,
I'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social).
However, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.
```
(base) Koens-MacBook-Pro:~ koenvandenberge$ python
Python 3.7.4 (default, Aug 13 2019, 15:17:50)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
dataset = load_dataset(
'dutch_social')
>>> dataset = load_dataset(
... 'dutch_social')
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
```
Hi @koenvandenberge and @alighofrani95!
The datasets you're experiencing issues with were most likely added recently to the `datasets` library, meaning they have not been released yet. They will be released with the v2 of the library.
Meanwhile, you can still load the datasets using one of the techniques described in this issue: #1641
Let me know if this helps! | [
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https://github.com/huggingface/datasets/issues/1674 | dutch_social can't be loaded | I just did the release :)
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `dutch_social` with
```python
from datasets import load_dataset
dataset = load_dataset("dutch_social")
``` | Hi all,
I'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social).
However, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.
```
(base) Koens-MacBook-Pro:~ koenvandenberge$ python
Python 3.7.4 (default, Aug 13 2019, 15:17:50)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
dataset = load_dataset(
'dutch_social')
>>> dataset = load_dataset(
... 'dutch_social')
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
``` | 36 | dutch_social can't be loaded
Hi all,
I'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social).
However, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.
```
(base) Koens-MacBook-Pro:~ koenvandenberge$ python
Python 3.7.4 (default, Aug 13 2019, 15:17:50)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
dataset = load_dataset(
'dutch_social')
>>> dataset = load_dataset(
... 'dutch_social')
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
```
I just did the release :)
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `dutch_social` with
```python
from datasets import load_dataset
dataset = load_dataset("dutch_social")
``` | [
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https://github.com/huggingface/datasets/issues/1674 | dutch_social can't be loaded | @lhoestq could you also shed light on the Hindi Wikipedia Dataset for issue number #1673. Will this also be available in the new release that you committed recently? | Hi all,
I'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social).
However, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.
```
(base) Koens-MacBook-Pro:~ koenvandenberge$ python
Python 3.7.4 (default, Aug 13 2019, 15:17:50)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
dataset = load_dataset(
'dutch_social')
>>> dataset = load_dataset(
... 'dutch_social')
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
``` | 28 | dutch_social can't be loaded
Hi all,
I'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social).
However, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.
```
(base) Koens-MacBook-Pro:~ koenvandenberge$ python
Python 3.7.4 (default, Aug 13 2019, 15:17:50)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
dataset = load_dataset(
'dutch_social')
>>> dataset = load_dataset(
... 'dutch_social')
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
```
@lhoestq could you also shed light on the Hindi Wikipedia Dataset for issue number #1673. Will this also be available in the new release that you committed recently? | [
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https://github.com/huggingface/datasets/issues/1674 | dutch_social can't be loaded | Okay. Could you comment on the #1673 thread? Actually @thomwolf had commented that if i use datasets library from source, it would allow me to download the Hindi Wikipedia Dataset but even the version 1.1.3 gave me the same issue. The details are there in the issue #1673 thread. | Hi all,
I'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social).
However, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.
```
(base) Koens-MacBook-Pro:~ koenvandenberge$ python
Python 3.7.4 (default, Aug 13 2019, 15:17:50)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
dataset = load_dataset(
'dutch_social')
>>> dataset = load_dataset(
... 'dutch_social')
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
``` | 49 | dutch_social can't be loaded
Hi all,
I'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social).
However, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.
```
(base) Koens-MacBook-Pro:~ koenvandenberge$ python
Python 3.7.4 (default, Aug 13 2019, 15:17:50)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
dataset = load_dataset(
'dutch_social')
>>> dataset = load_dataset(
... 'dutch_social')
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
```
Okay. Could you comment on the #1673 thread? Actually @thomwolf had commented that if i use datasets library from source, it would allow me to download the Hindi Wikipedia Dataset but even the version 1.1.3 gave me the same issue. The details are there in the issue #1673 thread. | [
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https://github.com/huggingface/datasets/issues/1673 | Unable to Download Hindi Wikipedia Dataset | Currently this dataset is only available when the library is installed from source since it was added after the last release.
We pin the dataset version with the library version so that people can have a reproducible dataset and processing when pinning the library.
We'll see if we can provide access to newer datasets with a warning that they are newer than your library version, that would help in cases like yours. | I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.
| 72 | Unable to Download Hindi Wikipedia Dataset
I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.
Currently this dataset is only available when the library is installed from source since it was added after the last release.
We pin the dataset version with the library version so that people can have a reproducible dataset and processing when pinning the library.
We'll see if we can provide access to newer datasets with a warning that they are newer than your library version, that would help in cases like yours. | [
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https://github.com/huggingface/datasets/issues/1673 | Unable to Download Hindi Wikipedia Dataset | So for now, should i try and install the library from source and then try out the same piece of code? Will it work then, considering both the versions will match then? | I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.
| 32 | Unable to Download Hindi Wikipedia Dataset
I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.
So for now, should i try and install the library from source and then try out the same piece of code? Will it work then, considering both the versions will match then? | [
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https://github.com/huggingface/datasets/issues/1673 | Unable to Download Hindi Wikipedia Dataset | Hey, so i tried installing the library from source using the commands : **git clone https://github.com/huggingface/datasets**, **cd datasets** and then **pip3 install -e .**. But i still am facing the same error that file is not found. Please advise.
The Datasets library version now is 1.1.3 by installing from source as compared to the earlier 1.0.3 that i had loaded using pip command but I am still getting same error
| I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.
| 71 | Unable to Download Hindi Wikipedia Dataset
I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.
Hey, so i tried installing the library from source using the commands : **git clone https://github.com/huggingface/datasets**, **cd datasets** and then **pip3 install -e .**. But i still am facing the same error that file is not found. Please advise.
The Datasets library version now is 1.1.3 by installing from source as compared to the earlier 1.0.3 that i had loaded using pip command but I am still getting same error
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https://github.com/huggingface/datasets/issues/1673 | Unable to Download Hindi Wikipedia Dataset | Looks like the wikipedia dump for hindi at the date of 05/05/2020 is not available anymore.
You can try to load a more recent version of wikipedia
```python
from datasets import load_dataset
d = load_dataset("wikipedia", language="hi", date="20210101", split="train", beam_runner="DirectRunner")
``` | I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.
| 40 | Unable to Download Hindi Wikipedia Dataset
I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.
Looks like the wikipedia dump for hindi at the date of 05/05/2020 is not available anymore.
You can try to load a more recent version of wikipedia
```python
from datasets import load_dataset
d = load_dataset("wikipedia", language="hi", date="20210101", split="train", beam_runner="DirectRunner")
``` | [
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] |
https://github.com/huggingface/datasets/issues/1672 | load_dataset hang on file_lock | Having the same issue with `datasets 1.1.3` of `1.5.0` (both tracebacks look the same) and `kilt_wikipedia`, Ubuntu 20.04
```py
In [1]: from datasets import load_dataset
In [2]: wikipedia = load_dataset('kilt_wikipedia')['full']
Downloading: 7.37kB [00:00, 2.74MB/s]
Downloading: 3.33kB [00:00, 1.44MB/s]
^C---------------------------------------------------------------------------
OSError Traceback (most recent call last)
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in _acquire(self)
380 try:
--> 381 fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
382 except (IOError, OSError):
OSError: [Errno 37] No locks available
During handling of the above exception, another exception occurred:
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-2-f412d3d46ec9> in <module>
----> 1 wikipedia = load_dataset('kilt_wikipedia')['full']
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, sav
e_infos, script_version, **config_kwargs)
601 hash=hash,
602 features=features,
--> 603 **config_kwargs,
604 )
605
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/builder.py in __init__(self, *args, **kwargs)
841 def __init__(self, *args, **kwargs):
842 self._writer_batch_size = kwargs.pop("writer_batch_size", self._writer_batch_size)
--> 843 super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
844
845 @abc.abstractmethod
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/builder.py in __init__(self, cache_dir, name, hash, features, **config_kwargs)
174 os.makedirs(self._cache_dir_root, exist_ok=True)
175 lock_path = os.path.join(self._cache_dir_root, self._cache_dir.replace(os.sep, "_") + ".lock")
--> 176 with FileLock(lock_path):
177 if os.path.exists(self._cache_dir): # check if data exist
178 if len(os.listdir(self._cache_dir)) > 0:
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in __enter__(self)
312
313 def __enter__(self):
--> 314 self.acquire()
315 return self
316
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in acquire(self, timeout, poll_intervall)
261 if not self.is_locked:
262 logger().debug("Attempting to acquire lock %s on %s", lock_id, lock_filename)
--> 263 self._acquire()
264
265 if self.is_locked:
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in _acquire(self)
379
380 try:
--> 381 fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
382 except (IOError, OSError):
383 os.close(fd)
KeyboardInterrupt:
``` | I am trying to load the squad dataset. Fails on Windows 10 but succeeds in Colab.
Transformers: 3.3.1
Datasets: 1.0.2
Windows 10 (also tested in WSL)
```
datasets.logging.set_verbosity_debug()
datasets.
train_dataset = load_dataset('squad', split='train')
valid_dataset = load_dataset('squad', split='validation')
train_dataset.features
```
```
https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py not found in cache or force_download set to True, downloading to C:\Users\simpl\.cache\huggingface\datasets\tmpzj_o_6u7
Downloading:
5.24k/? [00:00<00:00, 134kB/s]
storing https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py in cache at C:\Users\simpl\.cache\huggingface\datasets\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py
creating metadata file for C:\Users\simpl\.cache\huggingface\datasets\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py
Checking C:\Users\simpl\.cache\huggingface\datasets\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py for additional imports.
Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py at C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad
Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py at C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41
Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py to C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41\squad.py
Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad\dataset_infos.json
Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py at C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41\squad.json
No config specified, defaulting to first: squad/plain_text
```
Interrupting the jupyter kernel we are in a file lock.
In Google Colab the download is ok. In contrast to a local run in colab dataset_infos.json is downloaded
```
https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/dataset_infos.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/datasets/tmptl9ha_ad
Downloading:
2.19k/? [00:00<00:00, 26.2kB/s]
``` | 234 | load_dataset hang on file_lock
I am trying to load the squad dataset. Fails on Windows 10 but succeeds in Colab.
Transformers: 3.3.1
Datasets: 1.0.2
Windows 10 (also tested in WSL)
```
datasets.logging.set_verbosity_debug()
datasets.
train_dataset = load_dataset('squad', split='train')
valid_dataset = load_dataset('squad', split='validation')
train_dataset.features
```
```
https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py not found in cache or force_download set to True, downloading to C:\Users\simpl\.cache\huggingface\datasets\tmpzj_o_6u7
Downloading:
5.24k/? [00:00<00:00, 134kB/s]
storing https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py in cache at C:\Users\simpl\.cache\huggingface\datasets\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py
creating metadata file for C:\Users\simpl\.cache\huggingface\datasets\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py
Checking C:\Users\simpl\.cache\huggingface\datasets\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py for additional imports.
Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py at C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad
Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py at C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41
Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py to C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41\squad.py
Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad\dataset_infos.json
Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py at C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41\squad.json
No config specified, defaulting to first: squad/plain_text
```
Interrupting the jupyter kernel we are in a file lock.
In Google Colab the download is ok. In contrast to a local run in colab dataset_infos.json is downloaded
```
https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/dataset_infos.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/datasets/tmptl9ha_ad
Downloading:
2.19k/? [00:00<00:00, 26.2kB/s]
```
Having the same issue with `datasets 1.1.3` of `1.5.0` (both tracebacks look the same) and `kilt_wikipedia`, Ubuntu 20.04
```py
In [1]: from datasets import load_dataset
In [2]: wikipedia = load_dataset('kilt_wikipedia')['full']
Downloading: 7.37kB [00:00, 2.74MB/s]
Downloading: 3.33kB [00:00, 1.44MB/s]
^C---------------------------------------------------------------------------
OSError Traceback (most recent call last)
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in _acquire(self)
380 try:
--> 381 fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
382 except (IOError, OSError):
OSError: [Errno 37] No locks available
During handling of the above exception, another exception occurred:
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-2-f412d3d46ec9> in <module>
----> 1 wikipedia = load_dataset('kilt_wikipedia')['full']
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, sav
e_infos, script_version, **config_kwargs)
601 hash=hash,
602 features=features,
--> 603 **config_kwargs,
604 )
605
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/builder.py in __init__(self, *args, **kwargs)
841 def __init__(self, *args, **kwargs):
842 self._writer_batch_size = kwargs.pop("writer_batch_size", self._writer_batch_size)
--> 843 super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
844
845 @abc.abstractmethod
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/builder.py in __init__(self, cache_dir, name, hash, features, **config_kwargs)
174 os.makedirs(self._cache_dir_root, exist_ok=True)
175 lock_path = os.path.join(self._cache_dir_root, self._cache_dir.replace(os.sep, "_") + ".lock")
--> 176 with FileLock(lock_path):
177 if os.path.exists(self._cache_dir): # check if data exist
178 if len(os.listdir(self._cache_dir)) > 0:
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in __enter__(self)
312
313 def __enter__(self):
--> 314 self.acquire()
315 return self
316
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in acquire(self, timeout, poll_intervall)
261 if not self.is_locked:
262 logger().debug("Attempting to acquire lock %s on %s", lock_id, lock_filename)
--> 263 self._acquire()
264
265 if self.is_locked:
~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in _acquire(self)
379
380 try:
--> 381 fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
382 except (IOError, OSError):
383 os.close(fd)
KeyboardInterrupt:
``` | [
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https://github.com/huggingface/datasets/issues/1671 | connection issue | Also, mayjor issue for me is the format issue, even if I go through changing the whole code to use load_from_disk, then if I do
d = datasets.load_from_disk("imdb")
d = d["train"][:10] => the format of this is no more in datasets format
this is different from you call load_datasets("train[10]")
could you tell me how I can make the two datastes the same format @lhoestq
| Hi
I am getting this connection issue, resulting in large failure on cloud, @lhoestq I appreciate your help on this.
If I want to keep the codes the same, so not using save_to_disk, load_from_disk, but save the datastes in the way load_dataset reads from and copy the files in the same folder the datasets library reads from, could you assist me how this can be done, thanks
I tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks
```
imdb = datasets.load_dataset("imdb")
imdb.save_to_disk("/idiap/temp/rkarimi/hf_datasets/imdb")
>>> os.environ["HF_HOME"]="/idiap/temp/rkarimi/hf_datasets/"
>>> imdb = datasets.load_dataset("imdb")
Reusing dataset imdb (/idiap/temp/rkarimi/cache_home_2/datasets/imdb/plain_text/1.0.0/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3)
```
I tried afterwards to set HF_HOME in bash, this makes it read from it, but it cannot let dataset library load from the saved path and still downloading data. could you tell me how to fix this issue @lhoestq thanks
Also this is on cloud, so I save them in a path, copy it to "another machine" to load the data
### Error stack
```
Traceback (most recent call last):
File "./finetune_t5_trainer.py", line 344, in <module>
main()
File "./finetune_t5_trainer.py", line 232, in main
for task in data_args.eval_tasks} if training_args.do_test else None
File "./finetune_t5_trainer.py", line 232, in <dictcomp>
for task in data_args.eval_tasks} if training_args.do_test else None
File "/workdir/seq2seq/data/tasks.py", line 136, in get_dataset
split = self.get_sampled_split(split, n_obs)
File "/workdir/seq2seq/data/tasks.py", line 64, in get_sampled_split
dataset = self.load_dataset(split)
File "/workdir/seq2seq/data/tasks.py", line 454, in load_dataset
split=split, script_version="master")
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 263, in prepare_module
head_hf_s3(path, filename=name, dataset=dataset)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 200, in head_hf_s3
return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 403, in http_head
url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 104, in head
return request('head', url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 61, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 542, in request
resp = self.send(prep, **send_kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 655, in send
r = adapter.send(request, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/adapters.py", line 504, in send
raise ConnectTimeout(e, request=request)
requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/glue/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))
```
| 64 | connection issue
Hi
I am getting this connection issue, resulting in large failure on cloud, @lhoestq I appreciate your help on this.
If I want to keep the codes the same, so not using save_to_disk, load_from_disk, but save the datastes in the way load_dataset reads from and copy the files in the same folder the datasets library reads from, could you assist me how this can be done, thanks
I tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks
```
imdb = datasets.load_dataset("imdb")
imdb.save_to_disk("/idiap/temp/rkarimi/hf_datasets/imdb")
>>> os.environ["HF_HOME"]="/idiap/temp/rkarimi/hf_datasets/"
>>> imdb = datasets.load_dataset("imdb")
Reusing dataset imdb (/idiap/temp/rkarimi/cache_home_2/datasets/imdb/plain_text/1.0.0/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3)
```
I tried afterwards to set HF_HOME in bash, this makes it read from it, but it cannot let dataset library load from the saved path and still downloading data. could you tell me how to fix this issue @lhoestq thanks
Also this is on cloud, so I save them in a path, copy it to "another machine" to load the data
### Error stack
```
Traceback (most recent call last):
File "./finetune_t5_trainer.py", line 344, in <module>
main()
File "./finetune_t5_trainer.py", line 232, in main
for task in data_args.eval_tasks} if training_args.do_test else None
File "./finetune_t5_trainer.py", line 232, in <dictcomp>
for task in data_args.eval_tasks} if training_args.do_test else None
File "/workdir/seq2seq/data/tasks.py", line 136, in get_dataset
split = self.get_sampled_split(split, n_obs)
File "/workdir/seq2seq/data/tasks.py", line 64, in get_sampled_split
dataset = self.load_dataset(split)
File "/workdir/seq2seq/data/tasks.py", line 454, in load_dataset
split=split, script_version="master")
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 263, in prepare_module
head_hf_s3(path, filename=name, dataset=dataset)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 200, in head_hf_s3
return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 403, in http_head
url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 104, in head
return request('head', url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 61, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 542, in request
resp = self.send(prep, **send_kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 655, in send
r = adapter.send(request, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/adapters.py", line 504, in send
raise ConnectTimeout(e, request=request)
requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/glue/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))
```
Also, mayjor issue for me is the format issue, even if I go through changing the whole code to use load_from_disk, then if I do
d = datasets.load_from_disk("imdb")
d = d["train"][:10] => the format of this is no more in datasets format
this is different from you call load_datasets("train[10]")
could you tell me how I can make the two datastes the same format @lhoestq
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] |
https://github.com/huggingface/datasets/issues/1671 | connection issue | > `
requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/glue/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))`
Do you have an internet connection on the machine ? Is there a proxy that might block requests to aws ?
> I tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks
HF_HOME is used to specify the directory for the cache files of this library.
You can use save_to_disk and load_from_disk without changing the HF_HOME:
```python
imdb = datasets.load_dataset("imdb")
imdb.save_to_disk("/idiap/temp/rkarimi/hf_datasets/imdb")
imdb = datasets.load_from_disk("/idiap/temp/rkarimi/hf_datasets/imdb")
```
> could you tell me how I can make the two datastes the same format
Indeed they returns different things:
- `load_dataset` returns a `Dataset` object if the split is specified, or a `DatasetDict` if no split is given. Therefore `load_datasets("imdb", split="train[10]")` returns a `Dataset` object containing 10 elements.
- doing `d["train"][:10]` on a DatasetDict "d" gets the train split `d["train"]` as a `Dataset` object and then gets the first 10 elements as a dictionary | Hi
I am getting this connection issue, resulting in large failure on cloud, @lhoestq I appreciate your help on this.
If I want to keep the codes the same, so not using save_to_disk, load_from_disk, but save the datastes in the way load_dataset reads from and copy the files in the same folder the datasets library reads from, could you assist me how this can be done, thanks
I tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks
```
imdb = datasets.load_dataset("imdb")
imdb.save_to_disk("/idiap/temp/rkarimi/hf_datasets/imdb")
>>> os.environ["HF_HOME"]="/idiap/temp/rkarimi/hf_datasets/"
>>> imdb = datasets.load_dataset("imdb")
Reusing dataset imdb (/idiap/temp/rkarimi/cache_home_2/datasets/imdb/plain_text/1.0.0/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3)
```
I tried afterwards to set HF_HOME in bash, this makes it read from it, but it cannot let dataset library load from the saved path and still downloading data. could you tell me how to fix this issue @lhoestq thanks
Also this is on cloud, so I save them in a path, copy it to "another machine" to load the data
### Error stack
```
Traceback (most recent call last):
File "./finetune_t5_trainer.py", line 344, in <module>
main()
File "./finetune_t5_trainer.py", line 232, in main
for task in data_args.eval_tasks} if training_args.do_test else None
File "./finetune_t5_trainer.py", line 232, in <dictcomp>
for task in data_args.eval_tasks} if training_args.do_test else None
File "/workdir/seq2seq/data/tasks.py", line 136, in get_dataset
split = self.get_sampled_split(split, n_obs)
File "/workdir/seq2seq/data/tasks.py", line 64, in get_sampled_split
dataset = self.load_dataset(split)
File "/workdir/seq2seq/data/tasks.py", line 454, in load_dataset
split=split, script_version="master")
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 263, in prepare_module
head_hf_s3(path, filename=name, dataset=dataset)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 200, in head_hf_s3
return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 403, in http_head
url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 104, in head
return request('head', url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 61, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 542, in request
resp = self.send(prep, **send_kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 655, in send
r = adapter.send(request, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/adapters.py", line 504, in send
raise ConnectTimeout(e, request=request)
requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/glue/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))
```
| 210 | connection issue
Hi
I am getting this connection issue, resulting in large failure on cloud, @lhoestq I appreciate your help on this.
If I want to keep the codes the same, so not using save_to_disk, load_from_disk, but save the datastes in the way load_dataset reads from and copy the files in the same folder the datasets library reads from, could you assist me how this can be done, thanks
I tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks
```
imdb = datasets.load_dataset("imdb")
imdb.save_to_disk("/idiap/temp/rkarimi/hf_datasets/imdb")
>>> os.environ["HF_HOME"]="/idiap/temp/rkarimi/hf_datasets/"
>>> imdb = datasets.load_dataset("imdb")
Reusing dataset imdb (/idiap/temp/rkarimi/cache_home_2/datasets/imdb/plain_text/1.0.0/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3)
```
I tried afterwards to set HF_HOME in bash, this makes it read from it, but it cannot let dataset library load from the saved path and still downloading data. could you tell me how to fix this issue @lhoestq thanks
Also this is on cloud, so I save them in a path, copy it to "another machine" to load the data
### Error stack
```
Traceback (most recent call last):
File "./finetune_t5_trainer.py", line 344, in <module>
main()
File "./finetune_t5_trainer.py", line 232, in main
for task in data_args.eval_tasks} if training_args.do_test else None
File "./finetune_t5_trainer.py", line 232, in <dictcomp>
for task in data_args.eval_tasks} if training_args.do_test else None
File "/workdir/seq2seq/data/tasks.py", line 136, in get_dataset
split = self.get_sampled_split(split, n_obs)
File "/workdir/seq2seq/data/tasks.py", line 64, in get_sampled_split
dataset = self.load_dataset(split)
File "/workdir/seq2seq/data/tasks.py", line 454, in load_dataset
split=split, script_version="master")
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 263, in prepare_module
head_hf_s3(path, filename=name, dataset=dataset)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 200, in head_hf_s3
return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 403, in http_head
url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 104, in head
return request('head', url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 61, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 542, in request
resp = self.send(prep, **send_kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 655, in send
r = adapter.send(request, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/adapters.py", line 504, in send
raise ConnectTimeout(e, request=request)
requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/glue/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))
```
> `
requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/glue/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))`
Do you have an internet connection on the machine ? Is there a proxy that might block requests to aws ?
> I tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks
HF_HOME is used to specify the directory for the cache files of this library.
You can use save_to_disk and load_from_disk without changing the HF_HOME:
```python
imdb = datasets.load_dataset("imdb")
imdb.save_to_disk("/idiap/temp/rkarimi/hf_datasets/imdb")
imdb = datasets.load_from_disk("/idiap/temp/rkarimi/hf_datasets/imdb")
```
> could you tell me how I can make the two datastes the same format
Indeed they returns different things:
- `load_dataset` returns a `Dataset` object if the split is specified, or a `DatasetDict` if no split is given. Therefore `load_datasets("imdb", split="train[10]")` returns a `Dataset` object containing 10 elements.
- doing `d["train"][:10]` on a DatasetDict "d" gets the train split `d["train"]` as a `Dataset` object and then gets the first 10 elements as a dictionary | [
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https://github.com/huggingface/datasets/issues/1670 | wiki_dpr pre-processing performance | Hi ! And thanks for the tips :)
Indeed currently `wiki_dpr` takes some time to be processed.
Multiprocessing for dataset generation is definitely going to speed up things.
Regarding the index note that for the default configurations, the index is downloaded instead of being built, which avoid spending time on constructing the index. However in other cases it would be awesome to make the construction faster.
Any contribution that can help things faster are welcome. In particular in you have some code that can build a wiki_dpr IVF PQ index in a sharded GPU setup and would like to share it, we can add it to an `examples` folder. In particular since faiss is becoming the library of reference for dataset indexing for tasks like Open Domain Question Answering.
| I've been working with wiki_dpr and noticed that the dataset processing is seriously impaired in performance [1]. It takes about 12h to process the entire dataset. Most of this time is simply loading and processing the data, but the actual indexing is also quite slow (3h).
I won't repeat the concerns around multiprocessing as they are addressed in other issues (#786), but this is the first obvious thing to do. Using cython to speed up the text manipulation may be also help. Loading and processing a dataset of this size in under 15 minutes does not seem unreasonable on a modern multi-core machine. I have hit such targets myself on similar tasks. Would love to see this improve.
The other issue is that it takes 3h to construct the FAISS index. If only we could use GPUs with HNSW, but we can't. My sharded GPU indexing code can build an IVF + PQ index in 10 minutes on 20 million vectors. Still, 3h seems slow even for the CPU.
It looks like HF is adding only 1000 vectors at a time by default [2], whereas the faiss benchmarks adds 1 million vectors at a time (effectively) [3]. It's possible the runtime could be reduced with a larger batch. Also, it looks like project dependencies ultimately use OpenBLAS, but this is known to have issues when combined with OpenMP, which HNSW does [3]. A workaround is to set the environment variable `OMP_WAIT_POLICY=PASSIVE` via `os.environ` or similar.
References:
[1] https://github.com/huggingface/datasets/blob/master/datasets/wiki_dpr/wiki_dpr.py
[2] https://github.com/huggingface/datasets/blob/master/src/datasets/search.py
[3] https://github.com/facebookresearch/faiss/blob/master/benchs/bench_hnsw.py
[4] https://github.com/facebookresearch/faiss/issues/422 | 129 | wiki_dpr pre-processing performance
I've been working with wiki_dpr and noticed that the dataset processing is seriously impaired in performance [1]. It takes about 12h to process the entire dataset. Most of this time is simply loading and processing the data, but the actual indexing is also quite slow (3h).
I won't repeat the concerns around multiprocessing as they are addressed in other issues (#786), but this is the first obvious thing to do. Using cython to speed up the text manipulation may be also help. Loading and processing a dataset of this size in under 15 minutes does not seem unreasonable on a modern multi-core machine. I have hit such targets myself on similar tasks. Would love to see this improve.
The other issue is that it takes 3h to construct the FAISS index. If only we could use GPUs with HNSW, but we can't. My sharded GPU indexing code can build an IVF + PQ index in 10 minutes on 20 million vectors. Still, 3h seems slow even for the CPU.
It looks like HF is adding only 1000 vectors at a time by default [2], whereas the faiss benchmarks adds 1 million vectors at a time (effectively) [3]. It's possible the runtime could be reduced with a larger batch. Also, it looks like project dependencies ultimately use OpenBLAS, but this is known to have issues when combined with OpenMP, which HNSW does [3]. A workaround is to set the environment variable `OMP_WAIT_POLICY=PASSIVE` via `os.environ` or similar.
References:
[1] https://github.com/huggingface/datasets/blob/master/datasets/wiki_dpr/wiki_dpr.py
[2] https://github.com/huggingface/datasets/blob/master/src/datasets/search.py
[3] https://github.com/facebookresearch/faiss/blob/master/benchs/bench_hnsw.py
[4] https://github.com/facebookresearch/faiss/issues/422
Hi ! And thanks for the tips :)
Indeed currently `wiki_dpr` takes some time to be processed.
Multiprocessing for dataset generation is definitely going to speed up things.
Regarding the index note that for the default configurations, the index is downloaded instead of being built, which avoid spending time on constructing the index. However in other cases it would be awesome to make the construction faster.
Any contribution that can help things faster are welcome. In particular in you have some code that can build a wiki_dpr IVF PQ index in a sharded GPU setup and would like to share it, we can add it to an `examples` folder. In particular since faiss is becoming the library of reference for dataset indexing for tasks like Open Domain Question Answering.
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https://github.com/huggingface/datasets/issues/1670 | wiki_dpr pre-processing performance | I'd be happy to contribute something when I get the time, probably adding multiprocessing and / or cython support to wiki_dpr. I've written cythonized apache beam code before as well.
For sharded index building, I used the FAISS example code for indexing 1 billion vectors as a start. I'm sure you're aware that the documentation isn't great, but the source code is fairly easy to follow. | I've been working with wiki_dpr and noticed that the dataset processing is seriously impaired in performance [1]. It takes about 12h to process the entire dataset. Most of this time is simply loading and processing the data, but the actual indexing is also quite slow (3h).
I won't repeat the concerns around multiprocessing as they are addressed in other issues (#786), but this is the first obvious thing to do. Using cython to speed up the text manipulation may be also help. Loading and processing a dataset of this size in under 15 minutes does not seem unreasonable on a modern multi-core machine. I have hit such targets myself on similar tasks. Would love to see this improve.
The other issue is that it takes 3h to construct the FAISS index. If only we could use GPUs with HNSW, but we can't. My sharded GPU indexing code can build an IVF + PQ index in 10 minutes on 20 million vectors. Still, 3h seems slow even for the CPU.
It looks like HF is adding only 1000 vectors at a time by default [2], whereas the faiss benchmarks adds 1 million vectors at a time (effectively) [3]. It's possible the runtime could be reduced with a larger batch. Also, it looks like project dependencies ultimately use OpenBLAS, but this is known to have issues when combined with OpenMP, which HNSW does [3]. A workaround is to set the environment variable `OMP_WAIT_POLICY=PASSIVE` via `os.environ` or similar.
References:
[1] https://github.com/huggingface/datasets/blob/master/datasets/wiki_dpr/wiki_dpr.py
[2] https://github.com/huggingface/datasets/blob/master/src/datasets/search.py
[3] https://github.com/facebookresearch/faiss/blob/master/benchs/bench_hnsw.py
[4] https://github.com/facebookresearch/faiss/issues/422 | 66 | wiki_dpr pre-processing performance
I've been working with wiki_dpr and noticed that the dataset processing is seriously impaired in performance [1]. It takes about 12h to process the entire dataset. Most of this time is simply loading and processing the data, but the actual indexing is also quite slow (3h).
I won't repeat the concerns around multiprocessing as they are addressed in other issues (#786), but this is the first obvious thing to do. Using cython to speed up the text manipulation may be also help. Loading and processing a dataset of this size in under 15 minutes does not seem unreasonable on a modern multi-core machine. I have hit such targets myself on similar tasks. Would love to see this improve.
The other issue is that it takes 3h to construct the FAISS index. If only we could use GPUs with HNSW, but we can't. My sharded GPU indexing code can build an IVF + PQ index in 10 minutes on 20 million vectors. Still, 3h seems slow even for the CPU.
It looks like HF is adding only 1000 vectors at a time by default [2], whereas the faiss benchmarks adds 1 million vectors at a time (effectively) [3]. It's possible the runtime could be reduced with a larger batch. Also, it looks like project dependencies ultimately use OpenBLAS, but this is known to have issues when combined with OpenMP, which HNSW does [3]. A workaround is to set the environment variable `OMP_WAIT_POLICY=PASSIVE` via `os.environ` or similar.
References:
[1] https://github.com/huggingface/datasets/blob/master/datasets/wiki_dpr/wiki_dpr.py
[2] https://github.com/huggingface/datasets/blob/master/src/datasets/search.py
[3] https://github.com/facebookresearch/faiss/blob/master/benchs/bench_hnsw.py
[4] https://github.com/facebookresearch/faiss/issues/422
I'd be happy to contribute something when I get the time, probably adding multiprocessing and / or cython support to wiki_dpr. I've written cythonized apache beam code before as well.
For sharded index building, I used the FAISS example code for indexing 1 billion vectors as a start. I'm sure you're aware that the documentation isn't great, but the source code is fairly easy to follow. | [
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] |
https://github.com/huggingface/datasets/issues/1662 | Arrow file is too large when saving vector data | Hi !
The arrow file size is due to the embeddings. Indeed if they're stored as float32 then the total size of the embeddings is
20 000 000 vectors * 768 dimensions * 4 bytes per dimension ~= 60GB
If you want to reduce the size you can consider using quantization for example, or maybe using dimension reduction techniques.
| I computed the sentence embedding of each sentence of bookcorpus data using bert base and saved them to disk. I used 20M sentences and the obtained arrow file is about 59GB while the original text file is only about 1.3GB. Are there any ways to reduce the size of the arrow file? | 59 | Arrow file is too large when saving vector data
I computed the sentence embedding of each sentence of bookcorpus data using bert base and saved them to disk. I used 20M sentences and the obtained arrow file is about 59GB while the original text file is only about 1.3GB. Are there any ways to reduce the size of the arrow file?
Hi !
The arrow file size is due to the embeddings. Indeed if they're stored as float32 then the total size of the embeddings is
20 000 000 vectors * 768 dimensions * 4 bytes per dimension ~= 60GB
If you want to reduce the size you can consider using quantization for example, or maybe using dimension reduction techniques.
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https://github.com/huggingface/datasets/issues/1662 | Arrow file is too large when saving vector data | Thanks for your reply @lhoestq.
I want to save original embedding for these sentences for subsequent calculations. So does arrow have a way to save in a compressed format to reduce the size of the file? | I computed the sentence embedding of each sentence of bookcorpus data using bert base and saved them to disk. I used 20M sentences and the obtained arrow file is about 59GB while the original text file is only about 1.3GB. Are there any ways to reduce the size of the arrow file? | 36 | Arrow file is too large when saving vector data
I computed the sentence embedding of each sentence of bookcorpus data using bert base and saved them to disk. I used 20M sentences and the obtained arrow file is about 59GB while the original text file is only about 1.3GB. Are there any ways to reduce the size of the arrow file?
Thanks for your reply @lhoestq.
I want to save original embedding for these sentences for subsequent calculations. So does arrow have a way to save in a compressed format to reduce the size of the file? | [
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https://github.com/huggingface/datasets/issues/1647 | NarrativeQA fails to load with `load_dataset` | Hi @eric-mitchell,
I think the issue might be that this dataset was added during the community sprint and has not been released yet. It will be available with the v2 of `datasets`.
For now, you should be able to load the datasets after installing the latest (master) version of `datasets` using pip:
`pip install git+https://github.com/huggingface/datasets.git@master` | When loading the NarrativeQA dataset with `load_dataset('narrativeqa')` as given in the documentation [here](https://huggingface.co/datasets/narrativeqa), I receive a cascade of exceptions, ending with
FileNotFoundError: Couldn't find file locally at narrativeqa/narrativeqa.py, or remotely at
https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/narrativeqa/narrativeqa.py or
https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/narrativeqa/narrativeqa.py
Workaround: manually copy the `narrativeqa.py` builder into my local directory with
curl https://raw.githubusercontent.com/huggingface/datasets/master/datasets/narrativeqa/narrativeqa.py -o narrativeqa.py
and load the dataset as `load_dataset('narrativeqa.py')` everything works fine. I'm on datasets v1.1.3 using Python 3.6.10. | 55 | NarrativeQA fails to load with `load_dataset`
When loading the NarrativeQA dataset with `load_dataset('narrativeqa')` as given in the documentation [here](https://huggingface.co/datasets/narrativeqa), I receive a cascade of exceptions, ending with
FileNotFoundError: Couldn't find file locally at narrativeqa/narrativeqa.py, or remotely at
https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/narrativeqa/narrativeqa.py or
https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/narrativeqa/narrativeqa.py
Workaround: manually copy the `narrativeqa.py` builder into my local directory with
curl https://raw.githubusercontent.com/huggingface/datasets/master/datasets/narrativeqa/narrativeqa.py -o narrativeqa.py
and load the dataset as `load_dataset('narrativeqa.py')` everything works fine. I'm on datasets v1.1.3 using Python 3.6.10.
Hi @eric-mitchell,
I think the issue might be that this dataset was added during the community sprint and has not been released yet. It will be available with the v2 of `datasets`.
For now, you should be able to load the datasets after installing the latest (master) version of `datasets` using pip:
`pip install git+https://github.com/huggingface/datasets.git@master` | [
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https://github.com/huggingface/datasets/issues/1647 | NarrativeQA fails to load with `load_dataset` | Update: HuggingFace did an intermediate release yesterday just before the v2.0.
To load it you can just update `datasets`
`pip install --upgrade datasets` | When loading the NarrativeQA dataset with `load_dataset('narrativeqa')` as given in the documentation [here](https://huggingface.co/datasets/narrativeqa), I receive a cascade of exceptions, ending with
FileNotFoundError: Couldn't find file locally at narrativeqa/narrativeqa.py, or remotely at
https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/narrativeqa/narrativeqa.py or
https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/narrativeqa/narrativeqa.py
Workaround: manually copy the `narrativeqa.py` builder into my local directory with
curl https://raw.githubusercontent.com/huggingface/datasets/master/datasets/narrativeqa/narrativeqa.py -o narrativeqa.py
and load the dataset as `load_dataset('narrativeqa.py')` everything works fine. I'm on datasets v1.1.3 using Python 3.6.10. | 23 | NarrativeQA fails to load with `load_dataset`
When loading the NarrativeQA dataset with `load_dataset('narrativeqa')` as given in the documentation [here](https://huggingface.co/datasets/narrativeqa), I receive a cascade of exceptions, ending with
FileNotFoundError: Couldn't find file locally at narrativeqa/narrativeqa.py, or remotely at
https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/narrativeqa/narrativeqa.py or
https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/narrativeqa/narrativeqa.py
Workaround: manually copy the `narrativeqa.py` builder into my local directory with
curl https://raw.githubusercontent.com/huggingface/datasets/master/datasets/narrativeqa/narrativeqa.py -o narrativeqa.py
and load the dataset as `load_dataset('narrativeqa.py')` everything works fine. I'm on datasets v1.1.3 using Python 3.6.10.
Update: HuggingFace did an intermediate release yesterday just before the v2.0.
To load it you can just update `datasets`
`pip install --upgrade datasets` | [
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https://github.com/huggingface/datasets/issues/1644 | HoVeR dataset fails to load | Hover was added recently, that's why it wasn't available yet.
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `hover` with
```python
from datasets import load_dataset
dataset = load_dataset("hover")
``` | Hi! I'm getting an error when trying to load **HoVeR** dataset. Another one (**SQuAD**) does work for me. I'm using the latest (1.1.3) version of the library.
Steps to reproduce the error:
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("hover")
Traceback (most recent call last):
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/hover/hover.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/hover/hover.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at hover/hover.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/hover/hover.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/hover/hover.py
``` | 40 | HoVeR dataset fails to load
Hi! I'm getting an error when trying to load **HoVeR** dataset. Another one (**SQuAD**) does work for me. I'm using the latest (1.1.3) version of the library.
Steps to reproduce the error:
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("hover")
Traceback (most recent call last):
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/hover/hover.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/hover/hover.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at hover/hover.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/hover/hover.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/hover/hover.py
```
Hover was added recently, that's why it wasn't available yet.
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `hover` with
```python
from datasets import load_dataset
dataset = load_dataset("hover")
``` | [
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https://github.com/huggingface/datasets/issues/1641 | muchocine dataset cannot be dowloaded | I have encountered the same error with `v1.0.1` and `v1.0.2` on both Windows and Linux environments. However, cloning the repo and using the path to the dataset's root directory worked for me. Even after having the dataset cached - passing the path is the only way (for now) to load the dataset.
```python
from datasets import load_dataset
dataset = load_dataset("squad") # Works
dataset = load_dataset("code_search_net", "python") # Error
dataset = load_dataset("covid_qa_deepset") # Error
path = "/huggingface/datasets/datasets/{}/"
dataset = load_dataset(path.format("code_search_net"), "python") # Works
dataset = load_dataset(path.format("covid_qa_deepset")) # Works
```
| ```python
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
267 try:
--> 268 local_path = cached_path(file_path, download_config=download_config)
269 except FileNotFoundError:
7 frames
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
281 raise FileNotFoundError(
282 "Couldn't find file locally at {}, or remotely at {} or {}".format(
--> 283 combined_path, github_file_path, file_path
284 )
285 )
FileNotFoundError: Couldn't find file locally at muchocine/muchocine.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
``` | 88 | muchocine dataset cannot be dowloaded
```python
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
267 try:
--> 268 local_path = cached_path(file_path, download_config=download_config)
269 except FileNotFoundError:
7 frames
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
281 raise FileNotFoundError(
282 "Couldn't find file locally at {}, or remotely at {} or {}".format(
--> 283 combined_path, github_file_path, file_path
284 )
285 )
FileNotFoundError: Couldn't find file locally at muchocine/muchocine.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
```
I have encountered the same error with `v1.0.1` and `v1.0.2` on both Windows and Linux environments. However, cloning the repo and using the path to the dataset's root directory worked for me. Even after having the dataset cached - passing the path is the only way (for now) to load the dataset.
```python
from datasets import load_dataset
dataset = load_dataset("squad") # Works
dataset = load_dataset("code_search_net", "python") # Error
dataset = load_dataset("covid_qa_deepset") # Error
path = "/huggingface/datasets/datasets/{}/"
dataset = load_dataset(path.format("code_search_net"), "python") # Works
dataset = load_dataset(path.format("covid_qa_deepset")) # Works
```
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https://github.com/huggingface/datasets/issues/1641 | muchocine dataset cannot be dowloaded | Hi @mrm8488 and @amoux!
The datasets you are trying to load have been added to the library during the community sprint for v2 last month. They will be available with the v2 release!
For now, there are still a couple of solutions to load the datasets:
1. As suggested by @amoux, you can clone the git repo and pass the local path to the script
2. You can also install the latest (master) version of `datasets` using pip: `pip install git+https://github.com/huggingface/datasets.git@master` | ```python
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
267 try:
--> 268 local_path = cached_path(file_path, download_config=download_config)
269 except FileNotFoundError:
7 frames
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
281 raise FileNotFoundError(
282 "Couldn't find file locally at {}, or remotely at {} or {}".format(
--> 283 combined_path, github_file_path, file_path
284 )
285 )
FileNotFoundError: Couldn't find file locally at muchocine/muchocine.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
``` | 81 | muchocine dataset cannot be dowloaded
```python
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
267 try:
--> 268 local_path = cached_path(file_path, download_config=download_config)
269 except FileNotFoundError:
7 frames
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
281 raise FileNotFoundError(
282 "Couldn't find file locally at {}, or remotely at {} or {}".format(
--> 283 combined_path, github_file_path, file_path
284 )
285 )
FileNotFoundError: Couldn't find file locally at muchocine/muchocine.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
```
Hi @mrm8488 and @amoux!
The datasets you are trying to load have been added to the library during the community sprint for v2 last month. They will be available with the v2 release!
For now, there are still a couple of solutions to load the datasets:
1. As suggested by @amoux, you can clone the git repo and pass the local path to the script
2. You can also install the latest (master) version of `datasets` using pip: `pip install git+https://github.com/huggingface/datasets.git@master` | [
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https://github.com/huggingface/datasets/issues/1641 | muchocine dataset cannot be dowloaded | If you don't want to clone entire `datasets` repo, just download the `muchocine` directory and pass the local path to the directory. Cheers! | ```python
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
267 try:
--> 268 local_path = cached_path(file_path, download_config=download_config)
269 except FileNotFoundError:
7 frames
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
281 raise FileNotFoundError(
282 "Couldn't find file locally at {}, or remotely at {} or {}".format(
--> 283 combined_path, github_file_path, file_path
284 )
285 )
FileNotFoundError: Couldn't find file locally at muchocine/muchocine.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
``` | 23 | muchocine dataset cannot be dowloaded
```python
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
267 try:
--> 268 local_path = cached_path(file_path, download_config=download_config)
269 except FileNotFoundError:
7 frames
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
281 raise FileNotFoundError(
282 "Couldn't find file locally at {}, or remotely at {} or {}".format(
--> 283 combined_path, github_file_path, file_path
284 )
285 )
FileNotFoundError: Couldn't find file locally at muchocine/muchocine.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
```
If you don't want to clone entire `datasets` repo, just download the `muchocine` directory and pass the local path to the directory. Cheers! | [
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] |
https://github.com/huggingface/datasets/issues/1641 | muchocine dataset cannot be dowloaded | Muchocine was added recently, that's why it wasn't available yet.
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `muchocine` with
```python
from datasets import load_dataset
dataset = load_dataset("muchocine", split="train")
``` | ```python
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
267 try:
--> 268 local_path = cached_path(file_path, download_config=download_config)
269 except FileNotFoundError:
7 frames
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
281 raise FileNotFoundError(
282 "Couldn't find file locally at {}, or remotely at {} or {}".format(
--> 283 combined_path, github_file_path, file_path
284 )
285 )
FileNotFoundError: Couldn't find file locally at muchocine/muchocine.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
``` | 41 | muchocine dataset cannot be dowloaded
```python
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
267 try:
--> 268 local_path = cached_path(file_path, download_config=download_config)
269 except FileNotFoundError:
7 frames
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
281 raise FileNotFoundError(
282 "Couldn't find file locally at {}, or remotely at {} or {}".format(
--> 283 combined_path, github_file_path, file_path
284 )
285 )
FileNotFoundError: Couldn't find file locally at muchocine/muchocine.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
```
Muchocine was added recently, that's why it wasn't available yet.
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `muchocine` with
```python
from datasets import load_dataset
dataset = load_dataset("muchocine", split="train")
``` | [
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https://github.com/huggingface/datasets/issues/1639 | bug with sst2 in glue | Maybe you can use nltk's treebank detokenizer ?
```python
from nltk.tokenize.treebank import TreebankWordDetokenizer
TreebankWordDetokenizer().detokenize("it 's a charming and often affecting journey . ".split())
# "it's a charming and often affecting journey."
``` | Hi
I am getting very low accuracy on SST2 I investigate this and observe that for this dataset sentences are tokenized, while this is correct for the other datasets in GLUE, please see below.
Is there any alternatives I could get untokenized sentences? I am unfortunately under time pressure to report some results on this dataset. thank you for your help. @lhoestq
```
>>> a = datasets.load_dataset('glue', 'sst2', split="validation", script_version="master")
Reusing dataset glue (/julia/datasets/glue/sst2/1.0.0/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4)
>>> a[:10]
{'idx': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'label': [1, 0, 1, 1, 0, 1, 0, 0, 1, 0], 'sentence': ["it 's a charming and often affecting journey . ", 'unflinchingly bleak and desperate ', 'allows us to hope that nolan is poised to embark a major career as a commercial yet inventive filmmaker . ', "the acting , costumes , music , cinematography and sound are all astounding given the production 's austere locales . ", "it 's slow -- very , very slow . ", 'although laced with humor and a few fanciful touches , the film is a refreshingly serious look at young women . ', 'a sometimes tedious film . ', "or doing last year 's taxes with your ex-wife . ", "you do n't have to know about music to appreciate the film 's easygoing blend of comedy and romance . ", "in exactly 89 minutes , most of which passed as slowly as if i 'd been sitting naked on an igloo , formula 51 sank from quirky to jerky to utter turkey . "]}
``` | 32 | bug with sst2 in glue
Hi
I am getting very low accuracy on SST2 I investigate this and observe that for this dataset sentences are tokenized, while this is correct for the other datasets in GLUE, please see below.
Is there any alternatives I could get untokenized sentences? I am unfortunately under time pressure to report some results on this dataset. thank you for your help. @lhoestq
```
>>> a = datasets.load_dataset('glue', 'sst2', split="validation", script_version="master")
Reusing dataset glue (/julia/datasets/glue/sst2/1.0.0/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4)
>>> a[:10]
{'idx': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'label': [1, 0, 1, 1, 0, 1, 0, 0, 1, 0], 'sentence': ["it 's a charming and often affecting journey . ", 'unflinchingly bleak and desperate ', 'allows us to hope that nolan is poised to embark a major career as a commercial yet inventive filmmaker . ', "the acting , costumes , music , cinematography and sound are all astounding given the production 's austere locales . ", "it 's slow -- very , very slow . ", 'although laced with humor and a few fanciful touches , the film is a refreshingly serious look at young women . ', 'a sometimes tedious film . ', "or doing last year 's taxes with your ex-wife . ", "you do n't have to know about music to appreciate the film 's easygoing blend of comedy and romance . ", "in exactly 89 minutes , most of which passed as slowly as if i 'd been sitting naked on an igloo , formula 51 sank from quirky to jerky to utter turkey . "]}
```
Maybe you can use nltk's treebank detokenizer ?
```python
from nltk.tokenize.treebank import TreebankWordDetokenizer
TreebankWordDetokenizer().detokenize("it 's a charming and often affecting journey . ".split())
# "it's a charming and often affecting journey."
``` | [
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https://github.com/huggingface/datasets/issues/1639 | bug with sst2 in glue | I don't know if there exists a detokenized version somewhere. Even the version on kaggle is tokenized | Hi
I am getting very low accuracy on SST2 I investigate this and observe that for this dataset sentences are tokenized, while this is correct for the other datasets in GLUE, please see below.
Is there any alternatives I could get untokenized sentences? I am unfortunately under time pressure to report some results on this dataset. thank you for your help. @lhoestq
```
>>> a = datasets.load_dataset('glue', 'sst2', split="validation", script_version="master")
Reusing dataset glue (/julia/datasets/glue/sst2/1.0.0/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4)
>>> a[:10]
{'idx': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'label': [1, 0, 1, 1, 0, 1, 0, 0, 1, 0], 'sentence': ["it 's a charming and often affecting journey . ", 'unflinchingly bleak and desperate ', 'allows us to hope that nolan is poised to embark a major career as a commercial yet inventive filmmaker . ', "the acting , costumes , music , cinematography and sound are all astounding given the production 's austere locales . ", "it 's slow -- very , very slow . ", 'although laced with humor and a few fanciful touches , the film is a refreshingly serious look at young women . ', 'a sometimes tedious film . ', "or doing last year 's taxes with your ex-wife . ", "you do n't have to know about music to appreciate the film 's easygoing blend of comedy and romance . ", "in exactly 89 minutes , most of which passed as slowly as if i 'd been sitting naked on an igloo , formula 51 sank from quirky to jerky to utter turkey . "]}
``` | 17 | bug with sst2 in glue
Hi
I am getting very low accuracy on SST2 I investigate this and observe that for this dataset sentences are tokenized, while this is correct for the other datasets in GLUE, please see below.
Is there any alternatives I could get untokenized sentences? I am unfortunately under time pressure to report some results on this dataset. thank you for your help. @lhoestq
```
>>> a = datasets.load_dataset('glue', 'sst2', split="validation", script_version="master")
Reusing dataset glue (/julia/datasets/glue/sst2/1.0.0/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4)
>>> a[:10]
{'idx': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'label': [1, 0, 1, 1, 0, 1, 0, 0, 1, 0], 'sentence': ["it 's a charming and often affecting journey . ", 'unflinchingly bleak and desperate ', 'allows us to hope that nolan is poised to embark a major career as a commercial yet inventive filmmaker . ', "the acting , costumes , music , cinematography and sound are all astounding given the production 's austere locales . ", "it 's slow -- very , very slow . ", 'although laced with humor and a few fanciful touches , the film is a refreshingly serious look at young women . ', 'a sometimes tedious film . ', "or doing last year 's taxes with your ex-wife . ", "you do n't have to know about music to appreciate the film 's easygoing blend of comedy and romance . ", "in exactly 89 minutes , most of which passed as slowly as if i 'd been sitting naked on an igloo , formula 51 sank from quirky to jerky to utter turkey . "]}
```
I don't know if there exists a detokenized version somewhere. Even the version on kaggle is tokenized | [
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https://github.com/huggingface/datasets/issues/1636 | winogrande cannot be dowloaded | I have same issue for other datasets (`myanmar_news` in my case).
A version of `datasets` runs correctly on my local machine (**without GPU**) which looking for the dataset at
```
https://raw.githubusercontent.com/huggingface/datasets/master/datasets/myanmar_news/myanmar_news.py
```
Meanwhile, other version runs on Colab (**with GPU**) failed to download the dataset. It try to find the dataset at `1.1.3` instead of `master` . If I disable GPU on my Colab, the code can load the dataset without any problem.
Maybe there is some version missmatch with the GPU and CPU version of code for these datasets? | Hi,
I am getting this error when trying to run the codes on the cloud. Thank you for any suggestion and help on this @lhoestq
```
File "./finetune_trainer.py", line 318, in <module>
main()
File "./finetune_trainer.py", line 148, in main
for task in data_args.tasks]
File "./finetune_trainer.py", line 148, in <listcomp>
for task in data_args.tasks]
File "/workdir/seq2seq/data/tasks.py", line 65, in get_dataset
dataset = self.load_dataset(split=split)
File "/workdir/seq2seq/data/tasks.py", line 466, in load_dataset
return datasets.load_dataset('winogrande', 'winogrande_l', split=split)
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 487, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/winogrande/winogrande.py
yo/0 I1224 14:17:46.419031 31226 main shadow.py:122 > Traceback (most recent call last):
File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.6/dist-packages/torch/distributed/launch.py", line 260, in <module>
main()
File "/usr/local/lib/python3.6/dist-packages/torch/distributed/launch.py", line 256, in main
cmd=cmd)
``` | 90 | winogrande cannot be dowloaded
Hi,
I am getting this error when trying to run the codes on the cloud. Thank you for any suggestion and help on this @lhoestq
```
File "./finetune_trainer.py", line 318, in <module>
main()
File "./finetune_trainer.py", line 148, in main
for task in data_args.tasks]
File "./finetune_trainer.py", line 148, in <listcomp>
for task in data_args.tasks]
File "/workdir/seq2seq/data/tasks.py", line 65, in get_dataset
dataset = self.load_dataset(split=split)
File "/workdir/seq2seq/data/tasks.py", line 466, in load_dataset
return datasets.load_dataset('winogrande', 'winogrande_l', split=split)
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 487, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/winogrande/winogrande.py
yo/0 I1224 14:17:46.419031 31226 main shadow.py:122 > Traceback (most recent call last):
File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.6/dist-packages/torch/distributed/launch.py", line 260, in <module>
main()
File "/usr/local/lib/python3.6/dist-packages/torch/distributed/launch.py", line 256, in main
cmd=cmd)
```
I have same issue for other datasets (`myanmar_news` in my case).
A version of `datasets` runs correctly on my local machine (**without GPU**) which looking for the dataset at
```
https://raw.githubusercontent.com/huggingface/datasets/master/datasets/myanmar_news/myanmar_news.py
```
Meanwhile, other version runs on Colab (**with GPU**) failed to download the dataset. It try to find the dataset at `1.1.3` instead of `master` . If I disable GPU on my Colab, the code can load the dataset without any problem.
Maybe there is some version missmatch with the GPU and CPU version of code for these datasets? | [
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https://github.com/huggingface/datasets/issues/1636 | winogrande cannot be dowloaded | It looks like they're two different issues
----------
First for `myanmar_news`:
It must come from the way you installed `datasets`.
If you install `datasets` from source, then the `myanmar_news` script will be loaded from `master`.
However if you install from `pip` it will get it using the version of the lib (here `1.1.3`) and `myanmar_news` is not available in `1.1.3`.
The difference between your GPU and CPU executions must be the environment, one seems to have installed `datasets` from source and not the other.
----------
Then for `winogrande`:
The errors says that the url https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/winogrande/winogrande.py is not reachable.
However it works fine on my side.
Does your machine have an internet connection ? Are connections to github blocked by some sort of proxy ?
Can you also try again in case github had issues when you tried the first time ?
| Hi,
I am getting this error when trying to run the codes on the cloud. Thank you for any suggestion and help on this @lhoestq
```
File "./finetune_trainer.py", line 318, in <module>
main()
File "./finetune_trainer.py", line 148, in main
for task in data_args.tasks]
File "./finetune_trainer.py", line 148, in <listcomp>
for task in data_args.tasks]
File "/workdir/seq2seq/data/tasks.py", line 65, in get_dataset
dataset = self.load_dataset(split=split)
File "/workdir/seq2seq/data/tasks.py", line 466, in load_dataset
return datasets.load_dataset('winogrande', 'winogrande_l', split=split)
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 487, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/winogrande/winogrande.py
yo/0 I1224 14:17:46.419031 31226 main shadow.py:122 > Traceback (most recent call last):
File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.6/dist-packages/torch/distributed/launch.py", line 260, in <module>
main()
File "/usr/local/lib/python3.6/dist-packages/torch/distributed/launch.py", line 256, in main
cmd=cmd)
``` | 141 | winogrande cannot be dowloaded
Hi,
I am getting this error when trying to run the codes on the cloud. Thank you for any suggestion and help on this @lhoestq
```
File "./finetune_trainer.py", line 318, in <module>
main()
File "./finetune_trainer.py", line 148, in main
for task in data_args.tasks]
File "./finetune_trainer.py", line 148, in <listcomp>
for task in data_args.tasks]
File "/workdir/seq2seq/data/tasks.py", line 65, in get_dataset
dataset = self.load_dataset(split=split)
File "/workdir/seq2seq/data/tasks.py", line 466, in load_dataset
return datasets.load_dataset('winogrande', 'winogrande_l', split=split)
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 487, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/winogrande/winogrande.py
yo/0 I1224 14:17:46.419031 31226 main shadow.py:122 > Traceback (most recent call last):
File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.6/dist-packages/torch/distributed/launch.py", line 260, in <module>
main()
File "/usr/local/lib/python3.6/dist-packages/torch/distributed/launch.py", line 256, in main
cmd=cmd)
```
It looks like they're two different issues
----------
First for `myanmar_news`:
It must come from the way you installed `datasets`.
If you install `datasets` from source, then the `myanmar_news` script will be loaded from `master`.
However if you install from `pip` it will get it using the version of the lib (here `1.1.3`) and `myanmar_news` is not available in `1.1.3`.
The difference between your GPU and CPU executions must be the environment, one seems to have installed `datasets` from source and not the other.
----------
Then for `winogrande`:
The errors says that the url https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/winogrande/winogrande.py is not reachable.
However it works fine on my side.
Does your machine have an internet connection ? Are connections to github blocked by some sort of proxy ?
Can you also try again in case github had issues when you tried the first time ?
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https://github.com/huggingface/datasets/issues/1634 | Inspecting datasets per category | That's interesting, can you tell me what you think would be useful to access to inspect a dataset?
You can filter them in the hub with the search by the way: https://huggingface.co/datasets have you seen it? | Hi
Is there a way I could get all NLI datasets/all QA datasets to get some understanding of available datasets per category? this is hard for me to inspect the datasets one by one in the webpage, thanks for the suggestions @lhoestq | 36 | Inspecting datasets per category
Hi
Is there a way I could get all NLI datasets/all QA datasets to get some understanding of available datasets per category? this is hard for me to inspect the datasets one by one in the webpage, thanks for the suggestions @lhoestq
That's interesting, can you tell me what you think would be useful to access to inspect a dataset?
You can filter them in the hub with the search by the way: https://huggingface.co/datasets have you seen it? | [
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https://github.com/huggingface/datasets/issues/1634 | Inspecting datasets per category | Hi @thomwolf
thank you, I was not aware of this, I was looking into the data viewer linked into readme page.
This is exactly what I was looking for, but this does not work currently, please see the attached
I am selecting to see all nli datasets in english and it retrieves none. thanks
| Hi
Is there a way I could get all NLI datasets/all QA datasets to get some understanding of available datasets per category? this is hard for me to inspect the datasets one by one in the webpage, thanks for the suggestions @lhoestq | 55 | Inspecting datasets per category
Hi
Is there a way I could get all NLI datasets/all QA datasets to get some understanding of available datasets per category? this is hard for me to inspect the datasets one by one in the webpage, thanks for the suggestions @lhoestq
Hi @thomwolf
thank you, I was not aware of this, I was looking into the data viewer linked into readme page.
This is exactly what I was looking for, but this does not work currently, please see the attached
I am selecting to see all nli datasets in english and it retrieves none. thanks
| [
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https://github.com/huggingface/datasets/issues/1634 | Inspecting datasets per category | I see 4 results for NLI in English but indeed some are not tagged yet and missing (GLUE), we will focus on that in January (cc @yjernite): https://huggingface.co/datasets?filter=task_ids:natural-language-inference,languages:en | Hi
Is there a way I could get all NLI datasets/all QA datasets to get some understanding of available datasets per category? this is hard for me to inspect the datasets one by one in the webpage, thanks for the suggestions @lhoestq | 28 | Inspecting datasets per category
Hi
Is there a way I could get all NLI datasets/all QA datasets to get some understanding of available datasets per category? this is hard for me to inspect the datasets one by one in the webpage, thanks for the suggestions @lhoestq
I see 4 results for NLI in English but indeed some are not tagged yet and missing (GLUE), we will focus on that in January (cc @yjernite): https://huggingface.co/datasets?filter=task_ids:natural-language-inference,languages:en | [
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https://github.com/huggingface/datasets/issues/1633 | social_i_qa wrong format of labels | @lhoestq, should I raise a PR for this? Just a minor change while reading labels text file | Hi,
there is extra "\n" in labels of social_i_qa datasets, no big deal, but I was wondering if you could remove it to make it consistent.
so label is 'label': '1\n', not '1'
thanks
```
>>> import datasets
>>> from datasets import load_dataset
>>> dataset = load_dataset(
... 'social_i_qa')
cahce dir /julia/cache/datasets
Downloading: 4.72kB [00:00, 3.52MB/s]
cahce dir /julia/cache/datasets
Downloading: 2.19kB [00:00, 1.81MB/s]
Using custom data configuration default
Reusing dataset social_i_qa (/julia/datasets/social_i_qa/default/0.1.0/4a4190cc2d2482d43416c2167c0c5dccdd769d4482e84893614bd069e5c3ba06)
>>> dataset['train'][0]
{'answerA': 'like attending', 'answerB': 'like staying home', 'answerC': 'a good friend to have', 'context': 'Cameron decided to have a barbecue and gathered her friends together.', 'label': '1\n', 'question': 'How would Others feel as a result?'}
```
| 17 | social_i_qa wrong format of labels
Hi,
there is extra "\n" in labels of social_i_qa datasets, no big deal, but I was wondering if you could remove it to make it consistent.
so label is 'label': '1\n', not '1'
thanks
```
>>> import datasets
>>> from datasets import load_dataset
>>> dataset = load_dataset(
... 'social_i_qa')
cahce dir /julia/cache/datasets
Downloading: 4.72kB [00:00, 3.52MB/s]
cahce dir /julia/cache/datasets
Downloading: 2.19kB [00:00, 1.81MB/s]
Using custom data configuration default
Reusing dataset social_i_qa (/julia/datasets/social_i_qa/default/0.1.0/4a4190cc2d2482d43416c2167c0c5dccdd769d4482e84893614bd069e5c3ba06)
>>> dataset['train'][0]
{'answerA': 'like attending', 'answerB': 'like staying home', 'answerC': 'a good friend to have', 'context': 'Cameron decided to have a barbecue and gathered her friends together.', 'label': '1\n', 'question': 'How would Others feel as a result?'}
```
@lhoestq, should I raise a PR for this? Just a minor change while reading labels text file | [
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https://github.com/huggingface/datasets/issues/1630 | Adding UKP Argument Aspect Similarity Corpus | Adding a link to the guide on adding a dataset if someone want to give it a try: https://github.com/huggingface/datasets#add-a-new-dataset-to-the-hub
we should add this guide to the issue template @lhoestq | Hi, this would be great to have this dataset included.
## Adding a Dataset
- **Name:** UKP Argument Aspect Similarity Corpus
- **Description:** The UKP Argument Aspect Similarity Corpus (UKP ASPECT) includes 3,595 sentence pairs over 28 controversial topics. Each sentence pair was annotated via crowdsourcing as either “high similarity”, “some similarity”, “no similarity” or “not related” with respect to the topic.
- **Paper:** https://www.aclweb.org/anthology/P19-1054/
- **Data:** https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/1998
- **Motivation:** this is one of the datasets currently used frequently in recent adapter papers like https://arxiv.org/pdf/2005.00247.pdf
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Thank you | 29 | Adding UKP Argument Aspect Similarity Corpus
Hi, this would be great to have this dataset included.
## Adding a Dataset
- **Name:** UKP Argument Aspect Similarity Corpus
- **Description:** The UKP Argument Aspect Similarity Corpus (UKP ASPECT) includes 3,595 sentence pairs over 28 controversial topics. Each sentence pair was annotated via crowdsourcing as either “high similarity”, “some similarity”, “no similarity” or “not related” with respect to the topic.
- **Paper:** https://www.aclweb.org/anthology/P19-1054/
- **Data:** https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/1998
- **Motivation:** this is one of the datasets currently used frequently in recent adapter papers like https://arxiv.org/pdf/2005.00247.pdf
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Thank you
Adding a link to the guide on adding a dataset if someone want to give it a try: https://github.com/huggingface/datasets#add-a-new-dataset-to-the-hub
we should add this guide to the issue template @lhoestq | [
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https://github.com/huggingface/datasets/issues/1630 | Adding UKP Argument Aspect Similarity Corpus | thanks @thomwolf , this is added now. The template is correct, sorry my mistake not to include it. | Hi, this would be great to have this dataset included.
## Adding a Dataset
- **Name:** UKP Argument Aspect Similarity Corpus
- **Description:** The UKP Argument Aspect Similarity Corpus (UKP ASPECT) includes 3,595 sentence pairs over 28 controversial topics. Each sentence pair was annotated via crowdsourcing as either “high similarity”, “some similarity”, “no similarity” or “not related” with respect to the topic.
- **Paper:** https://www.aclweb.org/anthology/P19-1054/
- **Data:** https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/1998
- **Motivation:** this is one of the datasets currently used frequently in recent adapter papers like https://arxiv.org/pdf/2005.00247.pdf
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Thank you | 18 | Adding UKP Argument Aspect Similarity Corpus
Hi, this would be great to have this dataset included.
## Adding a Dataset
- **Name:** UKP Argument Aspect Similarity Corpus
- **Description:** The UKP Argument Aspect Similarity Corpus (UKP ASPECT) includes 3,595 sentence pairs over 28 controversial topics. Each sentence pair was annotated via crowdsourcing as either “high similarity”, “some similarity”, “no similarity” or “not related” with respect to the topic.
- **Paper:** https://www.aclweb.org/anthology/P19-1054/
- **Data:** https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/1998
- **Motivation:** this is one of the datasets currently used frequently in recent adapter papers like https://arxiv.org/pdf/2005.00247.pdf
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Thank you
thanks @thomwolf , this is added now. The template is correct, sorry my mistake not to include it. | [
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https://github.com/huggingface/datasets/issues/1627 | `Dataset.map` disable progress bar | Progress bar can be disabled like this:
```python
from datasets.utils.logging import set_verbosity_error
set_verbosity_error()
```
There is this line in `Dataset.map`:
```python
not_verbose = bool(logger.getEffectiveLevel() > WARNING)
```
So any logging level higher than `WARNING` turns off the progress bar. | I can't find anything to turn off the `tqdm` progress bars while running a preprocessing function using `Dataset.map`. I want to do akin to `disable_tqdm=True` in the case of `transformers`. Is there something like that? | 39 | `Dataset.map` disable progress bar
I can't find anything to turn off the `tqdm` progress bars while running a preprocessing function using `Dataset.map`. I want to do akin to `disable_tqdm=True` in the case of `transformers`. Is there something like that?
Progress bar can be disabled like this:
```python
from datasets.utils.logging import set_verbosity_error
set_verbosity_error()
```
There is this line in `Dataset.map`:
```python
not_verbose = bool(logger.getEffectiveLevel() > WARNING)
```
So any logging level higher than `WARNING` turns off the progress bar. | [
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https://github.com/huggingface/datasets/issues/1624 | Cannot download ade_corpus_v2 | Hi @him1411, the dataset you are trying to load has been added during the community sprint and has not been released yet. It will be available with the v2 of `datasets`.
For now, you should be able to load the datasets after installing the latest (master) version of `datasets` using pip:
`pip install git+https://github.com/huggingface/datasets.git@master` | I tried this to get the dataset following this url : https://huggingface.co/datasets/ade_corpus_v2
but received this error :
`Traceback (most recent call last):
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/ade_corpus_v2/ade_corpus_v2.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/ade_corpus_v2/ade_corpus_v2.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at ade_corpus_v2/ade_corpus_v2.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/ade_corpus_v2/ade_corpus_v2.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/ade_corpus_v2/ade_corpus_v2.py`
| 54 | Cannot download ade_corpus_v2
I tried this to get the dataset following this url : https://huggingface.co/datasets/ade_corpus_v2
but received this error :
`Traceback (most recent call last):
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/ade_corpus_v2/ade_corpus_v2.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/ade_corpus_v2/ade_corpus_v2.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at ade_corpus_v2/ade_corpus_v2.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/ade_corpus_v2/ade_corpus_v2.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/ade_corpus_v2/ade_corpus_v2.py`
Hi @him1411, the dataset you are trying to load has been added during the community sprint and has not been released yet. It will be available with the v2 of `datasets`.
For now, you should be able to load the datasets after installing the latest (master) version of `datasets` using pip:
`pip install git+https://github.com/huggingface/datasets.git@master` | [
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https://github.com/huggingface/datasets/issues/1624 | Cannot download ade_corpus_v2 | `ade_corpus_v2` was added recently, that's why it wasn't available yet.
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `ade_corpus_v2` with
```python
from datasets import load_dataset
dataset = load_dataset("ade_corpus_v2", "Ade_corpos_v2_drug_ade_relation")
```
(looks like there is a typo in the configuration name, we'll fix it for the v2.0 release of `datasets` soon) | I tried this to get the dataset following this url : https://huggingface.co/datasets/ade_corpus_v2
but received this error :
`Traceback (most recent call last):
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/ade_corpus_v2/ade_corpus_v2.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/ade_corpus_v2/ade_corpus_v2.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at ade_corpus_v2/ade_corpus_v2.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/ade_corpus_v2/ade_corpus_v2.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/ade_corpus_v2/ade_corpus_v2.py`
| 61 | Cannot download ade_corpus_v2
I tried this to get the dataset following this url : https://huggingface.co/datasets/ade_corpus_v2
but received this error :
`Traceback (most recent call last):
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/ade_corpus_v2/ade_corpus_v2.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/ade_corpus_v2/ade_corpus_v2.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at ade_corpus_v2/ade_corpus_v2.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/ade_corpus_v2/ade_corpus_v2.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/ade_corpus_v2/ade_corpus_v2.py`
`ade_corpus_v2` was added recently, that's why it wasn't available yet.
To load it you can just update `datasets`
```
pip install --upgrade datasets
```
and then you can load `ade_corpus_v2` with
```python
from datasets import load_dataset
dataset = load_dataset("ade_corpus_v2", "Ade_corpos_v2_drug_ade_relation")
```
(looks like there is a typo in the configuration name, we'll fix it for the v2.0 release of `datasets` soon) | [
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https://github.com/huggingface/datasets/issues/1618 | Can't filter language:EN on https://huggingface.co/datasets | Cool @mapmeld ! My 2 cents (for a next iteration), it would be cool to have a small search widget in the filter dropdown as you have a ton of languages now here! Closing this in the meantime. | When visiting https://huggingface.co/datasets, I don't see an obvious way to filter only English datasets. This is unexpected for me, am I missing something? I'd expect English to be selectable in the language widget. This problem reproduced on Mozilla Firefox and MS Edge:
| 38 | Can't filter language:EN on https://huggingface.co/datasets
When visiting https://huggingface.co/datasets, I don't see an obvious way to filter only English datasets. This is unexpected for me, am I missing something? I'd expect English to be selectable in the language widget. This problem reproduced on Mozilla Firefox and MS Edge:
Cool @mapmeld ! My 2 cents (for a next iteration), it would be cool to have a small search widget in the filter dropdown as you have a ton of languages now here! Closing this in the meantime. | [
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https://github.com/huggingface/datasets/issues/1615 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` | Hi @SapirWeissbuch,
When you are saying it freezes, at that time it is unzipping the file from the zip file it downloaded. Since it's a very heavy file it'll take some time. It was taking ~11GB after unzipping when it started reading examples for me. Hope that helps!
| Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
| 54 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`
Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
Hi @SapirWeissbuch,
When you are saying it freezes, at that time it is unzipping the file from the zip file it downloaded. Since it's a very heavy file it'll take some time. It was taking ~11GB after unzipping when it started reading examples for me. Hope that helps!
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https://github.com/huggingface/datasets/issues/1615 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` | Hi @bhavitvyamalik
Thanks for the reply!
Actually I let it run for 30 minutes before I killed the process. In this time, 30GB were extracted (much more than 11GB), I checked the size of the destination directory.
What version of Datasets are you using?
| Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
| 44 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`
Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
Hi @bhavitvyamalik
Thanks for the reply!
Actually I let it run for 30 minutes before I killed the process. In this time, 30GB were extracted (much more than 11GB), I checked the size of the destination directory.
What version of Datasets are you using?
| [
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https://github.com/huggingface/datasets/issues/1615 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` | I'm using datasets version: 1.1.3. I think you should drop `cache_dir` and use only
`dataset = datasets.load_dataset("trivia_qa", "rc")`
Tried that on colab and it's working there too
| Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
| 28 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`
Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
I'm using datasets version: 1.1.3. I think you should drop `cache_dir` and use only
`dataset = datasets.load_dataset("trivia_qa", "rc")`
Tried that on colab and it's working there too
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https://github.com/huggingface/datasets/issues/1615 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` | Train, Validation, and Test splits contain 138384, 18669, and 17210 samples respectively. It takes some time to read the samples. Even in your colab notebook it was reading the samples before you killed the process. Let me know if it works now! | Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
| 42 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`
Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
Train, Validation, and Test splits contain 138384, 18669, and 17210 samples respectively. It takes some time to read the samples. Even in your colab notebook it was reading the samples before you killed the process. Let me know if it works now! | [
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https://github.com/huggingface/datasets/issues/1615 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` | Hi, it works on colab but it still doesn't work on my computer, same problem as before - overly large and long extraction process.
I have to use a custom 'cache_dir' because I don't have any space left in my home directory where it is defaulted, maybe this could be the issue? | Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
| 52 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`
Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
Hi, it works on colab but it still doesn't work on my computer, same problem as before - overly large and long extraction process.
I have to use a custom 'cache_dir' because I don't have any space left in my home directory where it is defaulted, maybe this could be the issue? | [
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https://github.com/huggingface/datasets/issues/1615 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` | I tried running this again - More details of the problem:
Code:
```
datasets.load_dataset("trivia_qa", "rc", cache_dir="/path/to/cache")
```
The output:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to path/to/cache/trivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.67G/2.67G [03:38<00:00, 12.2MB/s]
```
The process continues (no progress bar is visible).
I tried `du -sh .` in `path/to/cache`, and the size keeps increasing, reached 35G before I killed the process.
Google Colab with custom `cache_dir` has same issue.
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing#scrollTo=2G2O0AeNIXan | Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
| 81 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`
Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
I tried running this again - More details of the problem:
Code:
```
datasets.load_dataset("trivia_qa", "rc", cache_dir="/path/to/cache")
```
The output:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to path/to/cache/trivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.67G/2.67G [03:38<00:00, 12.2MB/s]
```
The process continues (no progress bar is visible).
I tried `du -sh .` in `path/to/cache`, and the size keeps increasing, reached 35G before I killed the process.
Google Colab with custom `cache_dir` has same issue.
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing#scrollTo=2G2O0AeNIXan | [
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https://github.com/huggingface/datasets/issues/1615 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` | 1) You can clear the huggingface folder in your `.cache` directory to use default directory for datasets. Speed of extraction and loading of samples depends a lot on your machine's configurations too.
2) I tried on colab `dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")`. After memory usage reached around 42GB (starting from 32GB used already), the dataset was loaded in the memory. Even Your colab notebook shows
which means it's loaded now. | Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
| 73 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`
Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
1) You can clear the huggingface folder in your `.cache` directory to use default directory for datasets. Speed of extraction and loading of samples depends a lot on your machine's configurations too.
2) I tried on colab `dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")`. After memory usage reached around 42GB (starting from 32GB used already), the dataset was loaded in the memory. Even Your colab notebook shows
which means it's loaded now. | [
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https://github.com/huggingface/datasets/issues/1615 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` | Facing the same issue.
I am able to download datasets without `cache_dir`, however, when I specify the `cache_dir`, the process hangs indefinitely after partial download.
Tried for `data = load_dataset("cnn_dailymail", "3.0.0")` | Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
| 31 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`
Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
Facing the same issue.
I am able to download datasets without `cache_dir`, however, when I specify the `cache_dir`, the process hangs indefinitely after partial download.
Tried for `data = load_dataset("cnn_dailymail", "3.0.0")` | [
0.0042782011,
0.2504118979,
-0.0458030142,
0.320359081,
0.1740588993,
0.2203364521,
0.274595052,
0.0188669562,
0.1395895183,
-0.0835989118,
-0.2411099523,
0.2049974203,
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https://github.com/huggingface/datasets/issues/1615 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` | Hi @ashutoshml,
I tried this and it worked for me:
`data = load_dataset("cnn_dailymail", "3.0.0", cache_dir="./dummy")`
I'm using datasets==1.8.0. It took around 3-4 mins for dataset to unpack and start loading examples. | Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
| 31 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`
Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
Hi @ashutoshml,
I tried this and it worked for me:
`data = load_dataset("cnn_dailymail", "3.0.0", cache_dir="./dummy")`
I'm using datasets==1.8.0. It took around 3-4 mins for dataset to unpack and start loading examples. | [
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] |
https://github.com/huggingface/datasets/issues/1615 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` | Ok. I waited for 20-30 mins, and it still is stuck.
I am using datasets==1.8.0.
Is there anyway to check what is happening? like a` --verbose` flag?
| Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
| 34 | Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`
Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
Ok. I waited for 20-30 mins, and it still is stuck.
I am using datasets==1.8.0.
Is there anyway to check what is happening? like a` --verbose` flag?
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] |
https://github.com/huggingface/datasets/issues/1611 | shuffle with torch generator | Is there a way one can convert the two generator? not sure overall what alternatives I could have to shuffle the datasets with a torch generator, thanks | Hi
I need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help
@lhoestq | 27 | shuffle with torch generator
Hi
I need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help
@lhoestq
Is there a way one can convert the two generator? not sure overall what alternatives I could have to shuffle the datasets with a torch generator, thanks | [
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https://github.com/huggingface/datasets/issues/1611 | shuffle with torch generator | @lhoestq let me please expalin in more details, maybe you could help me suggesting an alternative to solve the issue for now, I have multiple large datasets using huggingface library, then I need to define a distributed sampler on top of it, for this I need to shard the datasets and give each shard to each core, but before sharding I need to shuffle the dataset, if you are familiar with distributed sampler in pytorch, this needs to be done based on seed+epoch generator to make it consistent across the cores they do it through defining a torch generator, I was wondering if you could tell me how I can shuffle the data for now, I am unfortunately blocked by this and have a limited time left, and I greatly appreciate your help on this. thanks | Hi
I need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help
@lhoestq | 136 | shuffle with torch generator
Hi
I need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help
@lhoestq
@lhoestq let me please expalin in more details, maybe you could help me suggesting an alternative to solve the issue for now, I have multiple large datasets using huggingface library, then I need to define a distributed sampler on top of it, for this I need to shard the datasets and give each shard to each core, but before sharding I need to shuffle the dataset, if you are familiar with distributed sampler in pytorch, this needs to be done based on seed+epoch generator to make it consistent across the cores they do it through defining a torch generator, I was wondering if you could tell me how I can shuffle the data for now, I am unfortunately blocked by this and have a limited time left, and I greatly appreciate your help on this. thanks | [
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https://github.com/huggingface/datasets/issues/1611 | shuffle with torch generator | @lhoestq Is there a way I could shuffle the datasets from this library with a custom defined shuffle function? thanks for your help on this. | Hi
I need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help
@lhoestq | 25 | shuffle with torch generator
Hi
I need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help
@lhoestq
@lhoestq Is there a way I could shuffle the datasets from this library with a custom defined shuffle function? thanks for your help on this. | [
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https://github.com/huggingface/datasets/issues/1611 | shuffle with torch generator | Right now the shuffle method only accepts the `seed` (optional int) or `generator` (optional `np.random.Generator`) parameters.
Here is a suggestion to shuffle the data using your own shuffle method using `select`.
`select` can be used to re-order the dataset samples or simply pick a few ones if you want.
It's what is used under the hood when you call `dataset.shuffle`.
To use `select` you must have the list of re-ordered indices of your samples.
Let's say you have a `shuffle` methods that you want to use. Then you can first build your shuffled list of indices:
```python
shuffled_indices = shuffle(range(len(dataset)))
```
Then you can shuffle your dataset using the shuffled indices with
```python
shuffled_dataset = dataset.select(shuffled_indices)
```
Hope that helps | Hi
I need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help
@lhoestq | 120 | shuffle with torch generator
Hi
I need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help
@lhoestq
Right now the shuffle method only accepts the `seed` (optional int) or `generator` (optional `np.random.Generator`) parameters.
Here is a suggestion to shuffle the data using your own shuffle method using `select`.
`select` can be used to re-order the dataset samples or simply pick a few ones if you want.
It's what is used under the hood when you call `dataset.shuffle`.
To use `select` you must have the list of re-ordered indices of your samples.
Let's say you have a `shuffle` methods that you want to use. Then you can first build your shuffled list of indices:
```python
shuffled_indices = shuffle(range(len(dataset)))
```
Then you can shuffle your dataset using the shuffled indices with
```python
shuffled_dataset = dataset.select(shuffled_indices)
```
Hope that helps | [
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https://github.com/huggingface/datasets/issues/1611 | shuffle with torch generator | thank you @lhoestq thank you very much for responding to my question, this greatly helped me and remove the blocking for continuing my work, thanks. | Hi
I need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help
@lhoestq | 25 | shuffle with torch generator
Hi
I need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help
@lhoestq
thank you @lhoestq thank you very much for responding to my question, this greatly helped me and remove the blocking for continuing my work, thanks. | [
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https://github.com/huggingface/datasets/issues/1611 | shuffle with torch generator | @lhoestq could you confirm the method proposed does not bring the whole data into memory? thanks | Hi
I need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help
@lhoestq | 16 | shuffle with torch generator
Hi
I need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help
@lhoestq
@lhoestq could you confirm the method proposed does not bring the whole data into memory? thanks | [
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