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https://github.com/huggingface/datasets/issues/2252 | I’m facing the same issue when loading a 900GB dataset (stored via `save_to_disk`): `load_from_disk(path_to_dir)` takes 1.5 hours and htop consistently shows high IO rates > 120 M/s. | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 27 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | @tsproisl same here, smells like ~~teen spirit~~ intended generator inadvertently ending up iterator
@lhoestq perhaps solution to detect bug location in code is to track its signature via HD read usage monitoring, option is to add tracking decorator on top each function and sequentially close all hatches from top to... | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 57 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | I wasn't able to reproduce this on a toy dataset of around 300GB:
```python
import datasets as ds
s = ds.load_dataset("squad", split="train")
s4000 = ds.concatenate_datasets([s] * 4000)
print(ds.utils.size_str(s4000.data.nbytes)) # '295.48 GiB'
s4000.save_to_disk("tmp/squad_4000")
```
```python
import... | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 130 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | Just tried on google colab and got ~1min for a 15GB dataset (only 200 times SQuAD), while it should be instantaneous. The time is spent reading the Apache Arrow table from the memory mapped file. This might come a virtual disk management issue. I'm trying to see if I can still speed it up on colab. | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 56 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | @lhoestq what is Google Colab's HD read speed, is it possible to introspect incl. make like SSD or HDD ? | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 20 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | @lhoestq Thank you! The issue is getting more interesting. The second script is still running, but it's definitely taking much longer than 15 seconds. | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 24 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | Okay, here’s the ouput:
Blocks read 158396
Elapsed time: 529.10s
Also using datasets 1.6.2. Do you have any ideas, how to pinpoint the problem? | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 24 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | @lhoestq, @tsproisl mmmh still writing on my side about 1h to go, thinking on it are your large datasets all monoblock unsharded ? mine is 335 times 1.18Gb shards. | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 29 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | The 529.10s was a bit too optimistic. I cancelled the reading process once before running it completely, therefore the harddrive cache probably did its work.
Here are three consecutive runs
First run (freshly written to disk):
Blocks read 309702
Elapsed time: 1267.74s
Second run (immediately after):
Blocks read... | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 62 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | @lhoestq
First test
> elapsed time: 11219.05s
Second test running bear with me, for Windows users slight trick to modify original "disk0" string:
First find physical unit relevant key in dictionnary
```
import psutil
psutil.disk_io_counters(perdisk=True)
```
> {'PhysicalDrive0': sdiskio(read_count=18453... | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 115 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | Unfortunately no. Thanks for running the benchmark though, it shows that you machine does a lot of read operations. This is not expected: in other machines it does almost no read operations which enables a very fast loading.
I did some tests on google colab and have the same issue. The first time the dataset arrow f... | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 177 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | Just want to say that I am seeing the same issue. Dataset size if 268GB and it takes **3 hours** to load `load_from_disk`, using dataset version `1.9.0`. Filesystem underneath is `Lustre` | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 31 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | Hi @lhoestq, confirmed Windows issue, exact same code running on Linux OS total loading time about 3 minutes. | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 18 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | Hmm that's different from what I got. I was on Ubuntu when reporting the initial issue. | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 16 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2250 | Hi,
1. try
```python
dataset = load_dataset("text", data_files={"train": ["a1.txt", "b1.txt"], "test": ["c1.txt"]})
```
instead.
Sadly, I can't reproduce the error on my machine. If the above code doesn't resolve the issue, try to update the library to the
newest version (`pip instal... | some issue in loading local txt file as Dataset for run_mlm.py | 
first of all, I tried to load 3 .txt files as a dataset (sure that the directory and permission is OK.), I face with the below error.
> FileNotFoundError: [Errno 2] No such file or directory: 'c'
by ... | 110 | some issue in loading local txt file as Dataset for run_mlm.py
first of all, I tried to load 3 .txt files as a dataset (sure that the directory and permission is OK.), I face with the below error.
> F... | [
-0.2187606394290924,
-0.16674190759658813,
0.03139013797044754,
0.42028746008872986,
0.4102848768234253,
0.2086268663406372,
0.35995063185691833,
0.3321823477745056,
0.09591799974441528,
-0.08650436997413635,
0.14329557120800018,
0.11218497902154922,
-0.31302616000175476,
0.115405872464179... |
https://github.com/huggingface/datasets/issues/2243 | Hi @villmow, thanks for reporting.
Could you please try with the Datasets version 1.6? We released it yesterday and it fixes some issues about the processing speed. You can see the fix implemented by @lhoestq here: #2122.
Once you update Datasets, please confirm if the problem persists. | Map is slow and processes batches one after another | ## Describe the bug
I have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 million samples) this problem occurs. Thats why I can't giv... | 47 | Map is slow and processes batches one after another
## Describe the bug
I have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 milli... | [
-0.35363948345184326,
-0.41076385974884033,
-0.09764096885919571,
0.257049560546875,
-0.007619311101734638,
0.11197598278522491,
0.18846529722213745,
0.39104968309402466,
0.4355856478214264,
-0.01323872059583664,
0.2567102611064911,
0.3540889620780945,
0.009038824588060379,
-0.151752740144... |
https://github.com/huggingface/datasets/issues/2243 | Hi @albertvillanova, thanks for the reply. I just tried the new version and the problem still persists.
Do I need to rebuild the saved dataset (which I load from disk) with the 1.6.0 version of datasets? My script loads this dataset and creates new datasets from it. I tried it without rebuilding.
See this short ... | Map is slow and processes batches one after another | ## Describe the bug
I have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 million samples) this problem occurs. Thats why I can't giv... | 70 | Map is slow and processes batches one after another
## Describe the bug
I have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 milli... | [
-0.3630147874355316,
-0.3355691730976105,
-0.0869322121143341,
0.2914978861808777,
0.004804702475667,
0.08502470701932907,
0.20765292644500732,
0.39800596237182617,
0.43489474058151245,
-0.008963964879512787,
0.21259453892707825,
0.2832154631614685,
-0.025046586990356445,
-0.20425862073898... |
https://github.com/huggingface/datasets/issues/2243 | There can be a bit of delay between the creations of the processes but this delay should be the same for both your `map` calls. We should look into this.
Also if you hav some code that reproduces this issue on google colab that'd be really useful !
Regarding the speed differences:
This looks like a similar issue a... | Map is slow and processes batches one after another | ## Describe the bug
I have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 million samples) this problem occurs. Thats why I can't giv... | 103 | Map is slow and processes batches one after another
## Describe the bug
I have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 milli... | [
-0.3194854259490967,
-0.47740575671195984,
-0.0747578963637352,
0.33022087812423706,
-0.0672013908624649,
0.04124841094017029,
0.2097327560186386,
0.32634982466697693,
0.43672388792037964,
0.029365800321102142,
0.241693377494812,
0.3490331172943115,
0.019093848764896393,
-0.038851182907819... |
https://github.com/huggingface/datasets/issues/2243 | Upgrade to 1.6.1 solved my problem somehow. I did not change any of my code, but now it starts all processes around the same time. | Map is slow and processes batches one after another | ## Describe the bug
I have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 million samples) this problem occurs. Thats why I can't giv... | 25 | Map is slow and processes batches one after another
## Describe the bug
I have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 milli... | [
-0.3285199701786041,
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... |
https://github.com/huggingface/datasets/issues/2243 | Nice ! I'm glad this works now.
Closing for now, but feel free to re-open if you experience this issue again. | Map is slow and processes batches one after another | ## Describe the bug
I have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 million samples) this problem occurs. Thats why I can't giv... | 21 | Map is slow and processes batches one after another
## Describe the bug
I have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 milli... | [
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https://github.com/huggingface/datasets/issues/2239 | Hi @odellus, thanks for reporting.
The `wikihow` dataset has 2 versions:
- `all`: Consisting of the concatenation of all paragraphs as the articles and the bold lines as the reference summaries.
- `sep`: Consisting of each paragraph and its summary.
Therefore, in order to load it, you have to specify which vers... | Error loading wikihow dataset | ## Describe the bug
When attempting to load wikihow into a dataset with
```python
from datasets import load_dataset
dataset = load_dataset('wikihow', data_dir='./wikihow')
```
I get the message:
```
AttributeError: 'BuilderConfig' object has no attribute 'filename'
```
at the end of a [full stack trace](htt... | 71 | Error loading wikihow dataset
## Describe the bug
When attempting to load wikihow into a dataset with
```python
from datasets import load_dataset
dataset = load_dataset('wikihow', data_dir='./wikihow')
```
I get the message:
```
AttributeError: 'BuilderConfig' object has no attribute 'filename'
```
at the... | [
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0.1863868236541748... |
https://github.com/huggingface/datasets/issues/2239 | Good call out. I did try that and that's when it told me to download the
dataset. Don't believe I have tried it with local files. Will try first
thing in the morning and get back to you.
On Mon, Apr 19, 2021, 11:17 PM Albert Villanova del Moral <
***@***.***> wrote:
> Hi @odellus <https://github.com/odellus>, thanks ... | Error loading wikihow dataset | ## Describe the bug
When attempting to load wikihow into a dataset with
```python
from datasets import load_dataset
dataset = load_dataset('wikihow', data_dir='./wikihow')
```
I get the message:
```
AttributeError: 'BuilderConfig' object has no attribute 'filename'
```
at the end of a [full stack trace](htt... | 168 | Error loading wikihow dataset
## Describe the bug
When attempting to load wikihow into a dataset with
```python
from datasets import load_dataset
dataset = load_dataset('wikihow', data_dir='./wikihow')
```
I get the message:
```
AttributeError: 'BuilderConfig' object has no attribute 'filename'
```
at the... | [
-0.22749951481819153,
0.37022316455841064,
0.02380962297320366,
0.39879512786865234,
0.25370973348617554,
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0.42765361070632935,
0.439177930355072,
0.245843768119812,
0.09350825846195221,
0.21628537774085999,
0.38537782430648804,
-0.012894676998257637,
0.1863868236541748... |
https://github.com/huggingface/datasets/issues/2239 | Hi @odellus, yes you are right.
Due to the server where the `wikihow` dataset is hosted, the dataset can't be downloaded automatically by `huggingface` and you have to download it manually as you did.
Nevertheless, you have to specify which dataset version you would like to load anyway:
```python
dataset = load... | Error loading wikihow dataset | ## Describe the bug
When attempting to load wikihow into a dataset with
```python
from datasets import load_dataset
dataset = load_dataset('wikihow', data_dir='./wikihow')
```
I get the message:
```
AttributeError: 'BuilderConfig' object has no attribute 'filename'
```
at the end of a [full stack trace](htt... | 90 | Error loading wikihow dataset
## Describe the bug
When attempting to load wikihow into a dataset with
```python
from datasets import load_dataset
dataset = load_dataset('wikihow', data_dir='./wikihow')
```
I get the message:
```
AttributeError: 'BuilderConfig' object has no attribute 'filename'
```
at the... | [
-0.22749951481819153,
0.37022316455841064,
0.02380962297320366,
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0.09350825846195221,
0.21628537774085999,
0.38537782430648804,
-0.012894676998257637,
0.1863868236541748... |
https://github.com/huggingface/datasets/issues/2237 | @albertvillanova I would like to take this up. It would be great if you could point me as to how the dataset size is calculated in HF. Thanks! | Update Dataset.dataset_size after transformed with map | After loading a dataset, if we transform it by using `.map` its `dataset_size` attirbute is not updated. | 28 | Update Dataset.dataset_size after transformed with map
After loading a dataset, if we transform it by using `.map` its `dataset_size` attirbute is not updated.
@albertvillanova I would like to take this up. It would be great if you could point me as to how the dataset size is calculated in HF. Thanks! | [
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https://github.com/huggingface/datasets/issues/2230 | Hi ! Indeed there's no verification on the uniqueness nor the types of the keys.
Do you already have some ideas of what you would like to implement and how ? | Keys yielded while generating dataset are not being checked | The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:
https... | 31 | Keys yielded while generating dataset are not being checked
The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for a... | [
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-0.020680442452430725,
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https://github.com/huggingface/datasets/issues/2230 | Hey @lhoestq, thank you so much for the opportunity.
Although I haven't had much experience with the HF Datasets code, after a careful look at how the `ArrowWriter` functions, I think we can implement this as follows:
1. First, we would have to update the `ArrowWriter.write()` function here:
https://github.com/hu... | Keys yielded while generating dataset are not being checked | The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:
https... | 235 | Keys yielded while generating dataset are not being checked
The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for a... | [
0.027536839246749878,
-0.2159307450056076,
0.02917030081152916,
0.4762759804725647,
0.06841948628425598,
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0.13391147553920746,
0.16721907258033752,
0.35025668144226074,
-0.020680442452430725,
0.206516683101... |
https://github.com/huggingface/datasets/issues/2230 | Interesting !
We keep the dataset sorted in the order examples are generated by the builder (we expect the dataset builders to generate examples in deterministic order). Therefore I don't think we should shuffle the examples with the hashing. Let me know what you think.
Other that that, I really like the idea of chec... | Keys yielded while generating dataset are not being checked | The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:
https... | 86 | Keys yielded while generating dataset are not being checked
The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for a... | [
0.027536839246749878,
-0.2159307450056076,
0.02917030081152916,
0.4762759804725647,
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0.43124350905418396,
0.13391147553920746,
0.16721907258033752,
0.35025668144226074,
-0.020680442452430725,
0.206516683101... |
https://github.com/huggingface/datasets/issues/2230 | @lhoestq I'm glad you liked the idea!
I think that since the keys will be unique and deterministic in the nature themselves, so even if we shuffle the examples according to the hash, a deterministic order would still be maintained (as the keys will always have the same hash, whenever the dataset is generated).
And s... | Keys yielded while generating dataset are not being checked | The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:
https... | 171 | Keys yielded while generating dataset are not being checked
The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for a... | [
0.027536839246749878,
-0.2159307450056076,
0.02917030081152916,
0.4762759804725647,
0.06841948628425598,
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0.41689780354499817,
0.08041272312402725,
0.43124350905418396,
0.13391147553920746,
0.16721907258033752,
0.35025668144226074,
-0.020680442452430725,
0.206516683101... |
https://github.com/huggingface/datasets/issues/2230 | When users load their own data, they expect the order to stay the same. I think that shuffling the data can make things inconvenient.
> I think that this is also what was originally envisioned as mentioned in the documentation here:
This part was originally developed by tensorflow datasets, and tensorflow dataset... | Keys yielded while generating dataset are not being checked | The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:
https... | 224 | Keys yielded while generating dataset are not being checked
The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for a... | [
0.027536839246749878,
-0.2159307450056076,
0.02917030081152916,
0.4762759804725647,
0.06841948628425598,
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0.41689780354499817,
0.08041272312402725,
0.43124350905418396,
0.13391147553920746,
0.16721907258033752,
0.35025668144226074,
-0.020680442452430725,
0.206516683101... |
https://github.com/huggingface/datasets/issues/2230 | Yes I think we want to keep the original order by default and only shuffle when the user ask for it (for instance by calling `dataset.shuffle()`). That’s how I had it in mind originally. | Keys yielded while generating dataset are not being checked | The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:
https... | 34 | Keys yielded while generating dataset are not being checked
The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for a... | [
0.027536839246749878,
-0.2159307450056076,
0.02917030081152916,
0.4762759804725647,
0.06841948628425598,
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0.41689780354499817,
0.08041272312402725,
0.43124350905418396,
0.13391147553920746,
0.16721907258033752,
0.35025668144226074,
-0.020680442452430725,
0.206516683101... |
https://github.com/huggingface/datasets/issues/2230 | Hey @lhoestq, I just had a more in-depth look at the original TFDS code about why the keys and hash were used in the first place.
In my opinion, the only use that the `hash(key)` serves is that it allows us to shuffle the examples in a deterministic order (as each example will always yield the same key and thus, the... | Keys yielded while generating dataset are not being checked | The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:
https... | 160 | Keys yielded while generating dataset are not being checked
The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for a... | [
0.027536839246749878,
-0.2159307450056076,
0.02917030081152916,
0.4762759804725647,
0.06841948628425598,
-0.23636221885681152,
0.41689780354499817,
0.08041272312402725,
0.43124350905418396,
0.13391147553920746,
0.16721907258033752,
0.35025668144226074,
-0.020680442452430725,
0.206516683101... |
https://github.com/huggingface/datasets/issues/2230 | In `datasets` the keys are currently ignored.
For shuffling we don't use the keys. Instead we shuffle an array of indices. Since both the original order of the dataset and the indices shuffling are deterministic, then `dataset.shuffle` is deterministic as well.
We can use it to:
1. detect duplicates
2. verify that ... | Keys yielded while generating dataset are not being checked | The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:
https... | 62 | Keys yielded while generating dataset are not being checked
The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for a... | [
0.027536839246749878,
-0.2159307450056076,
0.02917030081152916,
0.4762759804725647,
0.06841948628425598,
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0.41689780354499817,
0.08041272312402725,
0.43124350905418396,
0.13391147553920746,
0.16721907258033752,
0.35025668144226074,
-0.020680442452430725,
0.206516683101... |
https://github.com/huggingface/datasets/issues/2230 | Thanks a lot @lhoestq. I think I understand what we need to do now. The keys can indeed be used for detecting duplicates in generated examples as well as ensuring the order.
> Maybe we cam simply keep track of the hashes of of each batch being written ? The size of the batch when the data are save in arrow is 10 000... | Keys yielded while generating dataset are not being checked | The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:
https... | 119 | Keys yielded while generating dataset are not being checked
The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for a... | [
0.027536839246749878,
-0.2159307450056076,
0.02917030081152916,
0.4762759804725647,
0.06841948628425598,
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0.41689780354499817,
0.08041272312402725,
0.43124350905418396,
0.13391147553920746,
0.16721907258033752,
0.35025668144226074,
-0.020680442452430725,
0.206516683101... |
https://github.com/huggingface/datasets/issues/2229 | Hi ! Sure sounds good. Also if you find other datasets that use tuples instead of str/int, you can also fix them !
thanks :) | `xnli` dataset creating a tuple key while yielding instead of `str` or `int` | When using `ds = datasets.load_dataset('xnli', 'ar')`, the dataset generation script uses the following section of code in the egging, which yields a tuple key instead of the specified `str` or `int` key:
https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/datasets/xnli/xnli.py#L196
... | 25 | `xnli` dataset creating a tuple key while yielding instead of `str` or `int`
When using `ds = datasets.load_dataset('xnli', 'ar')`, the dataset generation script uses the following section of code in the egging, which yields a tuple key instead of the specified `str` or `int` key:
https://github.com/huggingface/dat... | [
-0.0539383739233017,
0.0509747713804245,
0.0451682023704052,
0.12626685202121735,
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-... |
https://github.com/huggingface/datasets/issues/2229 | @lhoestq I have sent a PR for fixing the issue. Would be great if you could have a look! Thanks! | `xnli` dataset creating a tuple key while yielding instead of `str` or `int` | When using `ds = datasets.load_dataset('xnli', 'ar')`, the dataset generation script uses the following section of code in the egging, which yields a tuple key instead of the specified `str` or `int` key:
https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/datasets/xnli/xnli.py#L196
... | 20 | `xnli` dataset creating a tuple key while yielding instead of `str` or `int`
When using `ds = datasets.load_dataset('xnli', 'ar')`, the dataset generation script uses the following section of code in the egging, which yields a tuple key instead of the specified `str` or `int` key:
https://github.com/huggingface/dat... | [
-0.07023048400878906,
0.09619006514549255,
0.07785558700561523,
0.12735730409622192,
0.1900971233844757,
0.012610368430614471,
0.44976574182510376,
0.2994561791419983,
0.648856520652771,
0.1779719889163971,
0.08786265552043915,
0.4414197504520416,
0.06090214103460312,
0.20753467082977295,
... |
https://github.com/huggingface/datasets/issues/2226 | I found the problem. I called `set_format` on some columns before. This makes it crash. Here is a complete example to reproduce:
```python
from datasets import load_dataset
sst = load_dataset("sst")
sst.set_format("torch", columns=["label"], output_all_columns=True)
ds = sst["train"]
# crashes
ds.map(
l... | Batched map fails when removing all columns | Hi @lhoestq ,
I'm hijacking this issue, because I'm currently trying to do the approach you recommend:
> Currently the optimal setup for single-column computations is probably to do something like
>
> ```python
> result = dataset.map(f, input_columns="my_col", remove_columns=dataset.column_names)
> ```
He... | 49 | Batched map fails when removing all columns
Hi @lhoestq ,
I'm hijacking this issue, because I'm currently trying to do the approach you recommend:
> Currently the optimal setup for single-column computations is probably to do something like
>
> ```python
> result = dataset.map(f, input_columns="my_col", rem... | [
-0.18979886174201965,
0.11866959929466248,
0.01695692539215088,
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0.39863210916519165,
-0.2221595048904419,
-0.149761050939559... |
https://github.com/huggingface/datasets/issues/2226 | Thanks for reporting and for providing this code to reproduce the issue, this is really helpful ! | Batched map fails when removing all columns | Hi @lhoestq ,
I'm hijacking this issue, because I'm currently trying to do the approach you recommend:
> Currently the optimal setup for single-column computations is probably to do something like
>
> ```python
> result = dataset.map(f, input_columns="my_col", remove_columns=dataset.column_names)
> ```
He... | 17 | Batched map fails when removing all columns
Hi @lhoestq ,
I'm hijacking this issue, because I'm currently trying to do the approach you recommend:
> Currently the optimal setup for single-column computations is probably to do something like
>
> ```python
> result = dataset.map(f, input_columns="my_col", rem... | [
-0.18979886174201965,
0.11866959929466248,
0.01695692539215088,
0.03455643355846405,
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0.39863210916519165,
-0.2221595048904419,
-0.149761050939559... |
https://github.com/huggingface/datasets/issues/2226 | I merged a fix, it should work on `master` now :)
We'll do a new release soon ! | Batched map fails when removing all columns | Hi @lhoestq ,
I'm hijacking this issue, because I'm currently trying to do the approach you recommend:
> Currently the optimal setup for single-column computations is probably to do something like
>
> ```python
> result = dataset.map(f, input_columns="my_col", remove_columns=dataset.column_names)
> ```
He... | 18 | Batched map fails when removing all columns
Hi @lhoestq ,
I'm hijacking this issue, because I'm currently trying to do the approach you recommend:
> Currently the optimal setup for single-column computations is probably to do something like
>
> ```python
> result = dataset.map(f, input_columns="my_col", rem... | [
-0.18979886174201965,
0.11866959929466248,
0.01695692539215088,
0.03455643355846405,
0.29575449228286743,
0.19975577294826508,
0.7953291535377502,
0.34695446491241455,
0.26812225580215454,
0.5083374977111816,
0.12785503268241882,
0.39863210916519165,
-0.2221595048904419,
-0.149761050939559... |
https://github.com/huggingface/datasets/issues/2218 | Hi,
currently the datasets API doesn't have a dedicated function to remove duplicate rows, but since the LAMA dataset is not too big (it fits in RAM), we can leverage pandas to help us remove duplicates:
```python
>>> from datasets import load_dataset, Dataset
>>> dataset = load_dataset('lama', split='train')
>>... | Duplicates in the LAMA dataset | I observed duplicates in the LAMA probing dataset, see a minimal code below.
```
>>> import datasets
>>> dataset = datasets.load_dataset('lama')
No config specified, defaulting to: lama/trex
Reusing dataset lama (/home/anam/.cache/huggingface/datasets/lama/trex/1.1.0/97deffae13eca0a18e77dfb3960bb31741e973586f5c... | 94 | Duplicates in the LAMA dataset
I observed duplicates in the LAMA probing dataset, see a minimal code below.
```
>>> import datasets
>>> dataset = datasets.load_dataset('lama')
No config specified, defaulting to: lama/trex
Reusing dataset lama (/home/anam/.cache/huggingface/datasets/lama/trex/1.1.0/97deffae13e... | [
0.2615724205970764,
-0.3211718797683716,
-0.030378146097064018,
0.6542941927909851,
0.3174535632133484,
-0.13887396454811096,
0.31275177001953125,
0.3269731402397156,
-0.547285258769989,
0.33654361963272095,
-0.3454263508319855,
0.360064834356308,
0.081962451338768,
-0.2813017666339874,
... |
https://github.com/huggingface/datasets/issues/2218 | Oh, seems like my question wasn't specified well. I'm _not_ asking how to remove duplicates, but whether duplicates should be removed if I want to do the evaluation on the LAMA dataset as it was proposed in the original paper/repository? In other words, will I get the same result if evaluate on the de-duplicated datase... | Duplicates in the LAMA dataset | I observed duplicates in the LAMA probing dataset, see a minimal code below.
```
>>> import datasets
>>> dataset = datasets.load_dataset('lama')
No config specified, defaulting to: lama/trex
Reusing dataset lama (/home/anam/.cache/huggingface/datasets/lama/trex/1.1.0/97deffae13eca0a18e77dfb3960bb31741e973586f5c... | 77 | Duplicates in the LAMA dataset
I observed duplicates in the LAMA probing dataset, see a minimal code below.
```
>>> import datasets
>>> dataset = datasets.load_dataset('lama')
No config specified, defaulting to: lama/trex
Reusing dataset lama (/home/anam/.cache/huggingface/datasets/lama/trex/1.1.0/97deffae13e... | [
0.2615724205970764,
-0.3211718797683716,
-0.030378146097064018,
0.6542941927909851,
0.3174535632133484,
-0.13887396454811096,
0.31275177001953125,
0.3269731402397156,
-0.547285258769989,
0.33654361963272095,
-0.3454263508319855,
0.360064834356308,
0.081962451338768,
-0.2813017666339874,
... |
https://github.com/huggingface/datasets/issues/2218 | So it looks like the person who added LAMA to the library chose to have one item per piece of evidence rather than one per relation - and in this case, there are duplicate pieces of evidence for the target relation
If I understand correctly, to reproduce reported results, you would have to aggregate predictions for ... | Duplicates in the LAMA dataset | I observed duplicates in the LAMA probing dataset, see a minimal code below.
```
>>> import datasets
>>> dataset = datasets.load_dataset('lama')
No config specified, defaulting to: lama/trex
Reusing dataset lama (/home/anam/.cache/huggingface/datasets/lama/trex/1.1.0/97deffae13eca0a18e77dfb3960bb31741e973586f5c... | 77 | Duplicates in the LAMA dataset
I observed duplicates in the LAMA probing dataset, see a minimal code below.
```
>>> import datasets
>>> dataset = datasets.load_dataset('lama')
No config specified, defaulting to: lama/trex
Reusing dataset lama (/home/anam/.cache/huggingface/datasets/lama/trex/1.1.0/97deffae13e... | [
0.2615724205970764,
-0.3211718797683716,
-0.030378146097064018,
0.6542941927909851,
0.3174535632133484,
-0.13887396454811096,
0.31275177001953125,
0.3269731402397156,
-0.547285258769989,
0.33654361963272095,
-0.3454263508319855,
0.360064834356308,
0.081962451338768,
-0.2813017666339874,
... |
https://github.com/huggingface/datasets/issues/2214 | Hi @nsaphra, thanks for reporting.
This issue was fixed in `datasets` version 1.3.0. Could you please update `datasets` and tell me if the problem persists?
```shell
pip install -U datasets
``` | load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings' | I'm having the same problem as [Notebooks issue 10](https://github.com/huggingface/notebooks/issues/10) on datasets 1.2.1, and it seems to be an issue with the datasets package.
```python
>>> from datasets import load_metric
>>> metric = load_metric("glue", "sst2")
Traceback (most recent call last):
File "<std... | 31 | load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'
I'm having the same problem as [Notebooks issue 10](https://github.com/huggingface/notebooks/issues/10) on datasets 1.2.1, and it seems to be an issue with the datasets package.
```python
>>> from datasets import load_me... | [
-0.2723239064216614,
-0.2135584056377411,
0.019322305917739868,
0.18529240787029266,
0.41950955986976624,
0.06305506825447083,
0.2729538083076477,
0.18010589480400085,
0.07569650560617447,
-0.05244572460651398,
-0.19403822720050812,
0.15459610521793365,
-0.07444840669631958,
0.297414451837... |
https://github.com/huggingface/datasets/issues/2214 | There might be a bug in the conda version of `datasets` 1.2.1 where the datasets/metric scripts are downloaded from `master` instead of the `1.2.1` repo.
You can try setting the env var `HF_SCRIPTS_VERSION="1.2.1"` as a workaround. Let me know if that helps. | load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings' | I'm having the same problem as [Notebooks issue 10](https://github.com/huggingface/notebooks/issues/10) on datasets 1.2.1, and it seems to be an issue with the datasets package.
```python
>>> from datasets import load_metric
>>> metric = load_metric("glue", "sst2")
Traceback (most recent call last):
File "<std... | 42 | load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'
I'm having the same problem as [Notebooks issue 10](https://github.com/huggingface/notebooks/issues/10) on datasets 1.2.1, and it seems to be an issue with the datasets package.
```python
>>> from datasets import load_me... | [
-0.2723239064216614,
-0.2135584056377411,
0.019322305917739868,
0.18529240787029266,
0.41950955986976624,
0.06305506825447083,
0.2729538083076477,
0.18010589480400085,
0.07569650560617447,
-0.05244572460651398,
-0.19403822720050812,
0.15459610521793365,
-0.07444840669631958,
0.297414451837... |
https://github.com/huggingface/datasets/issues/2214 | I just faced the same issue. I was using 1.2.1 from conda and received the same AttributeError complaining about 'add_start_docstrings'. Uninstalling the conda installed datasets and then installing the latest datasets (version 1.5.0) using pip install solved the issue for me. I don't like mixing up conda and pip insta... | load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings' | I'm having the same problem as [Notebooks issue 10](https://github.com/huggingface/notebooks/issues/10) on datasets 1.2.1, and it seems to be an issue with the datasets package.
```python
>>> from datasets import load_metric
>>> metric = load_metric("glue", "sst2")
Traceback (most recent call last):
File "<std... | 69 | load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'
I'm having the same problem as [Notebooks issue 10](https://github.com/huggingface/notebooks/issues/10) on datasets 1.2.1, and it seems to be an issue with the datasets package.
```python
>>> from datasets import load_me... | [
-0.2723239064216614,
-0.2135584056377411,
0.019322305917739868,
0.18529240787029266,
0.41950955986976624,
0.06305506825447083,
0.2729538083076477,
0.18010589480400085,
0.07569650560617447,
-0.05244572460651398,
-0.19403822720050812,
0.15459610521793365,
-0.07444840669631958,
0.297414451837... |
https://github.com/huggingface/datasets/issues/2214 | Yep, seems to have fixed things! The conda package could really do with an update. Thanks! | load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings' | I'm having the same problem as [Notebooks issue 10](https://github.com/huggingface/notebooks/issues/10) on datasets 1.2.1, and it seems to be an issue with the datasets package.
```python
>>> from datasets import load_metric
>>> metric = load_metric("glue", "sst2")
Traceback (most recent call last):
File "<std... | 16 | load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'
I'm having the same problem as [Notebooks issue 10](https://github.com/huggingface/notebooks/issues/10) on datasets 1.2.1, and it seems to be an issue with the datasets package.
```python
>>> from datasets import load_me... | [
-0.2723239064216614,
-0.2135584056377411,
0.019322305917739868,
0.18529240787029266,
0.41950955986976624,
0.06305506825447083,
0.2729538083076477,
0.18010589480400085,
0.07569650560617447,
-0.05244572460651398,
-0.19403822720050812,
0.15459610521793365,
-0.07444840669631958,
0.297414451837... |
https://github.com/huggingface/datasets/issues/2212 | Hi ! Apparently the data are not available from this url anymore. We'll replace it with the new url when it's available | Can't reach "https://storage.googleapis.com/illuin/fquad/train.json.zip" when trying to load fquad dataset | I'm trying to load the [fquad dataset](https://huggingface.co/datasets/fquad) by running:
```Python
fquad = load_dataset("fquad")
```
which produces the following error:
```
Using custom data configuration default
Downloading and preparing dataset fquad/default (download: 3.14 MiB, generated: 6.62 MiB, ... | 22 | Can't reach "https://storage.googleapis.com/illuin/fquad/train.json.zip" when trying to load fquad dataset
I'm trying to load the [fquad dataset](https://huggingface.co/datasets/fquad) by running:
```Python
fquad = load_dataset("fquad")
```
which produces the following error:
```
Using custom data config... | [
-0.35207587480545044,
0.18505197763442993,
-0.10664557665586472,
0.24557927250862122,
0.39981576800346375,
0.033607110381126404,
0.38763561844825745,
0.20610418915748596,
0.30574941635131836,
0.1453927457332611,
-0.26835981011390686,
-0.19080816209316254,
0.2827513515949249,
0.017551228404... |
https://github.com/huggingface/datasets/issues/2212 | I saw this on their website when we request to download the dataset:
Can we still request them link for the dataset and make a PR? @lhoestq @yjernite | Can't reach "https://storage.googleapis.com/illuin/fquad/train.json.zip" when trying to load fquad dataset | I'm trying to load the [fquad dataset](https://huggingface.co/datasets/fquad) by running:
```Python
fquad = load_dataset("fquad")
```
which produces the following error:
```
Using custom data configuration default
Downloading and preparing dataset fquad/default (download: 3.14 MiB, generated: 6.62 MiB, ... | 29 | Can't reach "https://storage.googleapis.com/illuin/fquad/train.json.zip" when trying to load fquad dataset
I'm trying to load the [fquad dataset](https://huggingface.co/datasets/fquad) by running:
```Python
fquad = load_dataset("fquad")
```
which produces the following error:
```
Using custom data config... | [
-0.35207587480545044,
0.18505197763442993,
-0.10664557665586472,
0.24557927250862122,
0.39981576800346375,
0.033607110381126404,
0.38763561844825745,
0.20610418915748596,
0.30574941635131836,
0.1453927457332611,
-0.26835981011390686,
-0.19080816209316254,
0.2827513515949249,
0.017551228404... |
https://github.com/huggingface/datasets/issues/2212 | I've contacted Martin (first author of the fquad paper) regarding a possible new url. Hopefully we can get one soon ! | Can't reach "https://storage.googleapis.com/illuin/fquad/train.json.zip" when trying to load fquad dataset | I'm trying to load the [fquad dataset](https://huggingface.co/datasets/fquad) by running:
```Python
fquad = load_dataset("fquad")
```
which produces the following error:
```
Using custom data configuration default
Downloading and preparing dataset fquad/default (download: 3.14 MiB, generated: 6.62 MiB, ... | 21 | Can't reach "https://storage.googleapis.com/illuin/fquad/train.json.zip" when trying to load fquad dataset
I'm trying to load the [fquad dataset](https://huggingface.co/datasets/fquad) by running:
```Python
fquad = load_dataset("fquad")
```
which produces the following error:
```
Using custom data config... | [
-0.35207587480545044,
0.18505197763442993,
-0.10664557665586472,
0.24557927250862122,
0.39981576800346375,
0.033607110381126404,
0.38763561844825745,
0.20610418915748596,
0.30574941635131836,
0.1453927457332611,
-0.26835981011390686,
-0.19080816209316254,
0.2827513515949249,
0.017551228404... |
https://github.com/huggingface/datasets/issues/2212 | They now made a website to force people who want to use the dataset for commercial purposes to seek a commercial license from them ... | Can't reach "https://storage.googleapis.com/illuin/fquad/train.json.zip" when trying to load fquad dataset | I'm trying to load the [fquad dataset](https://huggingface.co/datasets/fquad) by running:
```Python
fquad = load_dataset("fquad")
```
which produces the following error:
```
Using custom data configuration default
Downloading and preparing dataset fquad/default (download: 3.14 MiB, generated: 6.62 MiB, ... | 25 | Can't reach "https://storage.googleapis.com/illuin/fquad/train.json.zip" when trying to load fquad dataset
I'm trying to load the [fquad dataset](https://huggingface.co/datasets/fquad) by running:
```Python
fquad = load_dataset("fquad")
```
which produces the following error:
```
Using custom data config... | [
-0.35207587480545044,
0.18505197763442993,
-0.10664557665586472,
0.24557927250862122,
0.39981576800346375,
0.033607110381126404,
0.38763561844825745,
0.20610418915748596,
0.30574941635131836,
0.1453927457332611,
-0.26835981011390686,
-0.19080816209316254,
0.2827513515949249,
0.017551228404... |
https://github.com/huggingface/datasets/issues/2212 | The script has been adopted to support manual download from the website, so I'm closing this issue. | Can't reach "https://storage.googleapis.com/illuin/fquad/train.json.zip" when trying to load fquad dataset | I'm trying to load the [fquad dataset](https://huggingface.co/datasets/fquad) by running:
```Python
fquad = load_dataset("fquad")
```
which produces the following error:
```
Using custom data configuration default
Downloading and preparing dataset fquad/default (download: 3.14 MiB, generated: 6.62 MiB, ... | 17 | Can't reach "https://storage.googleapis.com/illuin/fquad/train.json.zip" when trying to load fquad dataset
I'm trying to load the [fquad dataset](https://huggingface.co/datasets/fquad) by running:
```Python
fquad = load_dataset("fquad")
```
which produces the following error:
```
Using custom data config... | [
-0.35207587480545044,
0.18505197763442993,
-0.10664557665586472,
0.24557927250862122,
0.39981576800346375,
0.033607110381126404,
0.38763561844825745,
0.20610418915748596,
0.30574941635131836,
0.1453927457332611,
-0.26835981011390686,
-0.19080816209316254,
0.2827513515949249,
0.017551228404... |
https://github.com/huggingface/datasets/issues/2211 | Hi,
I've already opened a PR with the fix. If you are in a hurry, just build the project from source and run:
```bash
datasets-cli test datasets/lc_quad --save_infos --all_configs --ignore_verifications
```
| Getting checksum error when trying to load lc_quad dataset | I'm having issues loading the [lc_quad](https://huggingface.co/datasets/fquad) dataset by running:
```Python
lc_quad = load_dataset("lc_quad")
```
which is giving me the following error:
```
Using custom data configuration default
Downloading and preparing dataset lc_quad/default (download: 3.69 MiB, ge... | 31 | Getting checksum error when trying to load lc_quad dataset
I'm having issues loading the [lc_quad](https://huggingface.co/datasets/fquad) dataset by running:
```Python
lc_quad = load_dataset("lc_quad")
```
which is giving me the following error:
```
Using custom data configuration default
Downloading ... | [
-0.15864786505699158,
0.04307863116264343,
-0.0340239591896534,
0.3563595116138458,
0.2677669823169708,
0.014729112386703491,
0.07140357792377472,
0.24690186977386475,
0.3710915148258209,
-0.04227311164140701,
-0.10491621494293213,
0.06774492561817169,
-0.03777630254626274,
0.0753149986267... |
https://github.com/huggingface/datasets/issues/2210 | Hi ! Yes this is an issue with `datasets<=1.5.0`
This issue has been fixed by #2122 , we'll do a new release soon :)
For now you can test it on the `master` branch. | dataloading slow when using HUGE dataset | Hi,
When I use datasets with 600GB data, the dataloading speed increases significantly.
I am experimenting with two datasets, and one is about 60GB and the other 600GB.
Simply speaking, my code uses `datasets.set_format("torch")` function and let pytorch-lightning handle ddp training.
When looking at the pytorch... | 34 | dataloading slow when using HUGE dataset
Hi,
When I use datasets with 600GB data, the dataloading speed increases significantly.
I am experimenting with two datasets, and one is about 60GB and the other 600GB.
Simply speaking, my code uses `datasets.set_format("torch")` function and let pytorch-lightning handle... | [
-0.5256722569465637,
-0.2135467529296875,
-0.0631117969751358,
0.2832503616809845,
0.13892066478729248,
-0.02778436243534088,
0.15010300278663635,
0.2327975034713745,
-0.15769244730472565,
-0.09218961745500565,
-0.11914967745542526,
0.14390510320663452,
-0.14865930378437042,
-0.18230807781... |
https://github.com/huggingface/datasets/issues/2210 | Hi, thank you for your answer. I did not realize that my issue stems from the same problem. | dataloading slow when using HUGE dataset | Hi,
When I use datasets with 600GB data, the dataloading speed increases significantly.
I am experimenting with two datasets, and one is about 60GB and the other 600GB.
Simply speaking, my code uses `datasets.set_format("torch")` function and let pytorch-lightning handle ddp training.
When looking at the pytorch... | 18 | dataloading slow when using HUGE dataset
Hi,
When I use datasets with 600GB data, the dataloading speed increases significantly.
I am experimenting with two datasets, and one is about 60GB and the other 600GB.
Simply speaking, my code uses `datasets.set_format("torch")` function and let pytorch-lightning handle... | [
-0.5256722569465637,
-0.2135467529296875,
-0.0631117969751358,
0.2832503616809845,
0.13892066478729248,
-0.02778436243534088,
0.15010300278663635,
0.2327975034713745,
-0.15769244730472565,
-0.09218961745500565,
-0.11914967745542526,
0.14390510320663452,
-0.14865930378437042,
-0.18230807781... |
https://github.com/huggingface/datasets/issues/2207 | Hi ! The ClassLabel feature type encodes the labels as integers.
The integer corresponds to the index of the label name in the `names` list of the ClassLabel.
Here that means that the labels are 'entailment' (0), 'neutral' (1), 'contradiction' (2).
You can get the label names back by using `a.features['label'].int... | making labels consistent across the datasets | Hi
For accessing the labels one can type
```
>>> a.features['label']
ClassLabel(num_classes=3, names=['entailment', 'neutral', 'contradiction'], names_file=None, id=None)
```
The labels however are not consistent with the actual labels sometimes, for instance in case of XNLI, the actual labels are 0,1,2, but if ... | 51 | making labels consistent across the datasets
Hi
For accessing the labels one can type
```
>>> a.features['label']
ClassLabel(num_classes=3, names=['entailment', 'neutral', 'contradiction'], names_file=None, id=None)
```
The labels however are not consistent with the actual labels sometimes, for instance in cas... | [
0.016260862350463867,
-0.12873655557632446,
-0.06999009847640991,
0.40296947956085205,
0.382713258266449,
-0.1300281584262848,
0.4260283410549164,
0.02341703698039055,
0.08527481555938721,
0.2710852026939392,
-0.23050175607204437,
0.5326738357543945,
-0.015342081896960735,
0.40478700399398... |
https://github.com/huggingface/datasets/issues/2206 | Hi,
the output of the tokenizers is treated specially in the lib to optimize the dataset size (see the code [here](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_writer.py#L138-L141)). It looks like that one of the values in a dictionary returned by the tokenizer is out of the assumed range.
... | Got pyarrow error when loading a dataset while adding special tokens into the tokenizer | I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:
Traceback (most recent call last):
File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1687, in _map_sin... | 53 | Got pyarrow error when loading a dataset while adding special tokens into the tokenizer
I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:
Traceback (most recent call last):
File "/home/xuyan/anaconda3/... | [
-0.23382794857025146,
0.292187362909317,
-0.05882884934544563,
0.06909652799367905,
0.2734423875808716,
-0.08607140183448792,
0.1836448460817337,
0.3055635392665863,
-0.5368505716323853,
-0.19321945309638977,
-0.04617089033126831,
0.4354701340198517,
-0.0010597314685583115,
-0.238174408674... |
https://github.com/huggingface/datasets/issues/2206 | Hi @yana-xuyan, thanks for reporting.
As clearly @mariosasko explained, `datasets` performs some optimizations in order to reduce the size of the dataset cache files. And one of them is storing the field `special_tokens_mask` as `int8`, which means that this field can only contain integers between `-128` to `127`. A... | Got pyarrow error when loading a dataset while adding special tokens into the tokenizer | I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:
Traceback (most recent call last):
File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1687, in _map_sin... | 98 | Got pyarrow error when loading a dataset while adding special tokens into the tokenizer
I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:
Traceback (most recent call last):
File "/home/xuyan/anaconda3/... | [
-0.23382794857025146,
0.292187362909317,
-0.05882884934544563,
0.06909652799367905,
0.2734423875808716,
-0.08607140183448792,
0.1836448460817337,
0.3055635392665863,
-0.5368505716323853,
-0.19321945309638977,
-0.04617089033126831,
0.4354701340198517,
-0.0010597314685583115,
-0.238174408674... |
https://github.com/huggingface/datasets/issues/2206 | I'm facing same issue @mariosasko @albertvillanova
```
ArrowInvalid: Integer value 50260 not in range: -128 to 127
```
To reproduce:
```python
SPECIAL_TOKENS = ['<bos>','<eos>','<speaker1>','<speaker2>','<pad>']
ATTR_TO_SPECIAL_TOKEN = {
'bos_token': '<bos>',
'eos_token': '<eos>',
'pad_toke... | Got pyarrow error when loading a dataset while adding special tokens into the tokenizer | I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:
Traceback (most recent call last):
File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1687, in _map_sin... | 70 | Got pyarrow error when loading a dataset while adding special tokens into the tokenizer
I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:
Traceback (most recent call last):
File "/home/xuyan/anaconda3/... | [
-0.23382794857025146,
0.292187362909317,
-0.05882884934544563,
0.06909652799367905,
0.2734423875808716,
-0.08607140183448792,
0.1836448460817337,
0.3055635392665863,
-0.5368505716323853,
-0.19321945309638977,
-0.04617089033126831,
0.4354701340198517,
-0.0010597314685583115,
-0.238174408674... |
https://github.com/huggingface/datasets/issues/2206 | @mariosasko
I am hitting this bug in the Bert tokenizer too. I see that @albertvillanova labeled this as a bug back in April. Has there been a fix released yet?
What I did for now is to just disable the optimization in the HF library. @yana-xuyan and @thomas-happify, is that what you did and did that work for you... | Got pyarrow error when loading a dataset while adding special tokens into the tokenizer | I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:
Traceback (most recent call last):
File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1687, in _map_sin... | 59 | Got pyarrow error when loading a dataset while adding special tokens into the tokenizer
I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:
Traceback (most recent call last):
File "/home/xuyan/anaconda3/... | [
-0.23382794857025146,
0.292187362909317,
-0.05882884934544563,
0.06909652799367905,
0.2734423875808716,
-0.08607140183448792,
0.1836448460817337,
0.3055635392665863,
-0.5368505716323853,
-0.19321945309638977,
-0.04617089033126831,
0.4354701340198517,
-0.0010597314685583115,
-0.238174408674... |
https://github.com/huggingface/datasets/issues/2206 | Hi @gregg-ADP,
This is still a bug.
As @albertvillanova has suggested, maybe it's indeed worth adding a variable to `config.py` to have a way to disable this behavior.
In the meantime, this forced optimization can be disabled by specifying `features` (of the returned examples) in the `map` call:
```python
f... | Got pyarrow error when loading a dataset while adding special tokens into the tokenizer | I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:
Traceback (most recent call last):
File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1687, in _map_sin... | 76 | Got pyarrow error when loading a dataset while adding special tokens into the tokenizer
I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:
Traceback (most recent call last):
File "/home/xuyan/anaconda3/... | [
-0.23382794857025146,
0.292187362909317,
-0.05882884934544563,
0.06909652799367905,
0.2734423875808716,
-0.08607140183448792,
0.1836448460817337,
0.3055635392665863,
-0.5368505716323853,
-0.19321945309638977,
-0.04617089033126831,
0.4354701340198517,
-0.0010597314685583115,
-0.238174408674... |
https://github.com/huggingface/datasets/issues/2206 | Thanks for the quick reply @mariosasko. What I did was to changed the optimizer to use int32 instead of int8.
What you're suggesting specifies the type for each feature explicitly without changing the HF code. This is definitely a better option. However, we are hitting a new error later:
```
File "/Users/ccccc/... | Got pyarrow error when loading a dataset while adding special tokens into the tokenizer | I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:
Traceback (most recent call last):
File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1687, in _map_sin... | 111 | Got pyarrow error when loading a dataset while adding special tokens into the tokenizer
I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:
Traceback (most recent call last):
File "/home/xuyan/anaconda3/... | [
-0.23382794857025146,
0.292187362909317,
-0.05882884934544563,
0.06909652799367905,
0.2734423875808716,
-0.08607140183448792,
0.1836448460817337,
0.3055635392665863,
-0.5368505716323853,
-0.19321945309638977,
-0.04617089033126831,
0.4354701340198517,
-0.0010597314685583115,
-0.238174408674... |
https://github.com/huggingface/datasets/issues/2206 | Hi @gwc4github,
the fix was merged a few minutes ago, and it doesn't require any changes on the user side (e.g. no need for specifying `features`). If you find time, feel free to install `datasets` from master with:
```
pip install git+https://github.com/huggingface/datasets.git
```
and let us know if it works f... | Got pyarrow error when loading a dataset while adding special tokens into the tokenizer | I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:
Traceback (most recent call last):
File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1687, in _map_sin... | 54 | Got pyarrow error when loading a dataset while adding special tokens into the tokenizer
I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:
Traceback (most recent call last):
File "/home/xuyan/anaconda3/... | [
-0.23382794857025146,
0.292187362909317,
-0.05882884934544563,
0.06909652799367905,
0.2734423875808716,
-0.08607140183448792,
0.1836448460817337,
0.3055635392665863,
-0.5368505716323853,
-0.19321945309638977,
-0.04617089033126831,
0.4354701340198517,
-0.0010597314685583115,
-0.238174408674... |
https://github.com/huggingface/datasets/issues/2200 | Hi ! This might be related to #2153
You're right the ArrowWriter should be initialized with `features=self.info.features` ! Good catch
I'm opening a PR to fix this and also to figure out how it was not caught in the tests
EDIT: opened #2201 | _prepare_split will overwrite DatasetBuilder.info.features | Hi, here is my issue:
I initialized a Csv datasetbuilder with specific features:
```
def get_dataset_features(data_args):
features = {}
if data_args.text_features:
features.update({text_feature: hf_features.Value("string") for text_feature in data_args.text_features.strip().split(",")})
if da... | 43 | _prepare_split will overwrite DatasetBuilder.info.features
Hi, here is my issue:
I initialized a Csv datasetbuilder with specific features:
```
def get_dataset_features(data_args):
features = {}
if data_args.text_features:
features.update({text_feature: hf_features.Value("string") for text_featu... | [
-0.2406022846698761,
-0.03829100728034973,
-0.10776729881763458,
0.17833749949932098,
0.29829806089401245,
0.21979233622550964,
0.4546085298061371,
0.21586909890174866,
-0.33275464177131653,
0.11496534943580627,
0.12917578220367432,
0.1715128868818283,
0.08633491396903992,
0.48735326528549... |
https://github.com/huggingface/datasets/issues/2200 | > Hi ! This might be related to #2153
>
> You're right the ArrowWriter should be initialized with `features=self.info.features` ! Good catch
> I'm opening a PR to fix this and also to figure out how it was not caught in the tests
>
> EDIT: opened #2201
Glad to hear that! Thank you for your fix, I'm new to hug... | _prepare_split will overwrite DatasetBuilder.info.features | Hi, here is my issue:
I initialized a Csv datasetbuilder with specific features:
```
def get_dataset_features(data_args):
features = {}
if data_args.text_features:
features.update({text_feature: hf_features.Value("string") for text_feature in data_args.text_features.strip().split(",")})
if da... | 67 | _prepare_split will overwrite DatasetBuilder.info.features
Hi, here is my issue:
I initialized a Csv datasetbuilder with specific features:
```
def get_dataset_features(data_args):
features = {}
if data_args.text_features:
features.update({text_feature: hf_features.Value("string") for text_featu... | [
-0.2406022846698761,
-0.03829100728034973,
-0.10776729881763458,
0.17833749949932098,
0.29829806089401245,
0.21979233622550964,
0.4546085298061371,
0.21586909890174866,
-0.33275464177131653,
0.11496534943580627,
0.12917578220367432,
0.1715128868818283,
0.08633491396903992,
0.48735326528549... |
https://github.com/huggingface/datasets/issues/2196 | Hi ! Files that starts with `cache-*` are cached computation files, i.e. they are the cached results of map/filter/cast/etc. operations. For example if you used `map` on your dataset to transform it, then the resulting dataset is going to be stored and cached in a `cache-*` file. These files are used to avoid having to... | `load_dataset` caches two arrow files? | Hi,
I am using datasets to load large json file of 587G.
I checked the cached folder and found that there are two arrow files created:
* `cache-ed205e500a7dc44c.arrow` - 355G
* `json-train.arrow` - 582G
Why is the first file created?
If I delete it, would I still be able to `load_from_disk`? | 64 | `load_dataset` caches two arrow files?
Hi,
I am using datasets to load large json file of 587G.
I checked the cached folder and found that there are two arrow files created:
* `cache-ed205e500a7dc44c.arrow` - 355G
* `json-train.arrow` - 582G
Why is the first file created?
If I delete it, would I still be a... | [
-0.02062353491783142,
-0.1851760298013687,
-0.13287143409252167,
0.6645825505256653,
-0.08110955357551575,
0.3052113652229309,
0.17529593408107758,
0.2564815580844879,
0.28745025396347046,
-0.15786431729793549,
-0.009086757898330688,
0.19466397166252136,
0.08018291741609573,
-0.51300036907... |
https://github.com/huggingface/datasets/issues/2196 | Thanks @lhoestq! Hmm.. that's strange because I specifically turned off auto caching, and saved mapped result, using `save_to_disk`, to another location. At this location, the following file is created:`355G cache-ed205e500a7dc44c.arrow`
To my observation, both `load_dataset` and `map` creates `cache-*` files, and I... | `load_dataset` caches two arrow files? | Hi,
I am using datasets to load large json file of 587G.
I checked the cached folder and found that there are two arrow files created:
* `cache-ed205e500a7dc44c.arrow` - 355G
* `json-train.arrow` - 582G
Why is the first file created?
If I delete it, would I still be able to `load_from_disk`? | 61 | `load_dataset` caches two arrow files?
Hi,
I am using datasets to load large json file of 587G.
I checked the cached folder and found that there are two arrow files created:
* `cache-ed205e500a7dc44c.arrow` - 355G
* `json-train.arrow` - 582G
Why is the first file created?
If I delete it, would I still be a... | [
0.017275936901569366,
-0.1464010626077652,
-0.11490826308727264,
0.6492550373077393,
-0.06654659658670425,
0.31068456172943115,
0.24094067513942719,
0.257883220911026,
0.30213525891304016,
-0.2563411593437195,
-0.03561730310320854,
0.27878209948539734,
0.11617574095726013,
-0.4971510469913... |
https://github.com/huggingface/datasets/issues/2196 | This is a wrong report -- `cache-*` files are created only my `map`, not by `load_dataset`. | `load_dataset` caches two arrow files? | Hi,
I am using datasets to load large json file of 587G.
I checked the cached folder and found that there are two arrow files created:
* `cache-ed205e500a7dc44c.arrow` - 355G
* `json-train.arrow` - 582G
Why is the first file created?
If I delete it, would I still be able to `load_from_disk`? | 16 | `load_dataset` caches two arrow files?
Hi,
I am using datasets to load large json file of 587G.
I checked the cached folder and found that there are two arrow files created:
* `cache-ed205e500a7dc44c.arrow` - 355G
* `json-train.arrow` - 582G
Why is the first file created?
If I delete it, would I still be a... | [
-0.0013232901692390442,
-0.2079307585954666,
-0.1252865493297577,
0.7411251068115234,
-0.14882919192314148,
0.27003490924835205,
0.28123342990875244,
0.23481959104537964,
0.3605191111564636,
-0.23589080572128296,
-0.011621806770563126,
0.20675554871559143,
0.13411922752857208,
-0.479701668... |
https://github.com/huggingface/datasets/issues/2195 | Thanks @samsontmr this should be fixed on master now
Feel free to reopen if you're still having issues | KeyError: '_indices_files' in `arrow_dataset.py` | After pulling the latest master, I'm getting a crash when `load_from_disk` tries to load my local dataset.
Trace:
```
Traceback (most recent call last):
File "load_data.py", line 11, in <module>
dataset = load_from_disk(SRC)
File "/opt/conda/envs/py38/lib/python3.8/site-packages/datasets/load.py", line ... | 18 | KeyError: '_indices_files' in `arrow_dataset.py`
After pulling the latest master, I'm getting a crash when `load_from_disk` tries to load my local dataset.
Trace:
```
Traceback (most recent call last):
File "load_data.py", line 11, in <module>
dataset = load_from_disk(SRC)
File "/opt/conda/envs/py38/l... | [
-0.3437005281448364,
0.06250949203968048,
-0.06198839470744133,
0.6979849338531494,
-0.07629891484975815,
0.15042316913604736,
0.1940692961215973,
0.49317359924316406,
0.5131548047065735,
0.14809013903141022,
0.019221685826778412,
0.18392223119735718,
-0.3918607532978058,
0.068030565977096... |
https://github.com/huggingface/datasets/issues/2193 | Hi ! Yes we are working on making `filter` significantly faster. You can look at related PRs here: #2060 #2178
I think you can expect to have the fast version of `filter` available next week.
We'll make it only select one column, and we'll also make the overall filtering operation way faster by avoiding many arr... | Filtering/mapping on one column is very slow | I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_colu... | 68 | Filtering/mapping on one column is very slow
I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_toke... | [
-0.12373072654008865,
0.2983856797218323,
-0.020849332213401794,
-0.2533702850341797,
0.10165707021951675,
-0.1387869268655777,
0.3027840852737427,
0.6129099726676941,
0.24762609601020813,
-0.0328550785779953,
-0.09859929978847504,
0.48608189821243286,
0.03813595697283745,
-0.1835794001817... |
https://github.com/huggingface/datasets/issues/2193 | @lhoestq Thanks for the response— it's great to hear that we'll be getting a much faster `filter` method soon. However, my use case does also involve using `map` over a single column in order to pre-compute roughly uniformly sized batches, and right now that is also very slow. Is there any plan to make `map` faster for... | Filtering/mapping on one column is very slow | I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_colu... | 96 | Filtering/mapping on one column is very slow
I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_toke... | [
-0.18856164813041687,
0.3007327616214752,
-0.006220906972885132,
-0.29155653715133667,
0.11390604078769684,
-0.125190868973732,
0.37745401263237,
0.6256687045097351,
0.2875320017337799,
0.01728106662631035,
-0.03644745051860809,
0.5041142702102661,
0.029043909162282944,
-0.1641895920038223... |
https://github.com/huggingface/datasets/issues/2193 | Currently the optimal setup for single-column computations is probably to do something like
```python
result = dataset.map(f, input_columns="my_col", remove_columns=dataset.column_names)
```
This has two advantages:
- input_columns="my_col" allows to only read the column "my_col"
- remove_columns=dataset.column_n... | Filtering/mapping on one column is very slow | I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_colu... | 82 | Filtering/mapping on one column is very slow
I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_toke... | [
-0.1294831931591034,
0.3391117453575134,
0.01074577122926712,
-0.21831822395324707,
0.09912162274122238,
-0.09364161640405655,
0.4080077111721039,
0.575050950050354,
0.23245979845523834,
0.08215230703353882,
-0.06951182335615158,
0.46341919898986816,
0.028099630028009415,
-0.17973065376281... |
https://github.com/huggingface/datasets/issues/2193 | Hi @lhoestq ,
I'm hijacking this issue, because I'm currently trying to do the approach you recommend:
> Currently the optimal setup for single-column computations is probably to do something like
>
> ```python
> result = dataset.map(f, input_columns="my_col", remove_columns=dataset.column_names)
> ```
He... | Filtering/mapping on one column is very slow | I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_colu... | 285 | Filtering/mapping on one column is very slow
I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_toke... | [
-0.1297885775566101,
0.3438469171524048,
0.00562778115272522,
-0.23468327522277832,
0.10948629677295685,
-0.12536677718162537,
0.39552000164985657,
0.5808489918708801,
0.22432242333889008,
0.06910649687051773,
-0.06487774103879929,
0.4875029921531677,
0.024431120604276657,
-0.1736373305320... |
https://github.com/huggingface/datasets/issues/2193 | Hi ! Can you open a separate issue for that ?
Also if you could provide a google colab or a sample code to reproduce this issue that would be helpful.
On my side I was not able to reproduce this error. | Filtering/mapping on one column is very slow | I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_colu... | 42 | Filtering/mapping on one column is very slow
I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_toke... | [
-0.07392755150794983,
0.26226165890693665,
0.010453317314386368,
-0.14601364731788635,
0.17261521518230438,
-0.09211455285549164,
0.34226787090301514,
0.585539698600769,
0.2409636527299881,
0.013878971338272095,
-0.028818810358643532,
0.4858112335205078,
0.07479938864707947,
-0.24202488362... |
https://github.com/huggingface/datasets/issues/2193 | @lhoestq Sorry I'm just responding now. I'm currently using your recommendation for the map on a single column, and I've gotten it to be fast enough to sort of work for my use case by just setting `num_proc=10`, although it's still quite slow. It's clear that it is still loading the entirety of each row into memory and... | Filtering/mapping on one column is very slow | I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_colu... | 195 | Filtering/mapping on one column is very slow
I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_toke... | [
-0.11003413051366806,
0.2694859206676483,
-0.0035480596125125885,
-0.22678828239440918,
0.13483862578868866,
-0.15745589137077332,
0.32289499044418335,
0.5779768824577332,
0.2516379952430725,
0.0171540267765522,
-0.008435383439064026,
0.5262536406517029,
-0.0003767870366573334,
-0.21994690... |
https://github.com/huggingface/datasets/issues/2193 | Hi @norabelrose ! I'm glad you managed to make this work on your side.
Regarding memory usage, you can try to drop the columns that you don't want to use for your `map` for now.
In the future we'll try to find a way to not load unnecessary columns in memory in `map`. Currently the way it works is that it gets the b... | Filtering/mapping on one column is very slow | I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_colu... | 126 | Filtering/mapping on one column is very slow
I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_toke... | [
-0.10902213305234909,
0.28565317392349243,
-0.017312441021203995,
-0.13604870438575745,
0.17431552708148956,
-0.08466316759586334,
0.2768234610557556,
0.5508019328117371,
0.2953397333621979,
-0.028073109686374664,
-0.04300359636545181,
0.48475387692451477,
-0.010495387017726898,
-0.2168287... |
https://github.com/huggingface/datasets/issues/2193 | @lhoestq After looking through the source code, it looks like the following solution has at least some chance of working:
- refactor `Dataset.map()` so that the `input_columns` parameter is implemented by using the `self.formatted_as()` context manager with `columns=input_columns`
- change `Dataset._getitem()` so tha... | Filtering/mapping on one column is very slow | I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_colu... | 62 | Filtering/mapping on one column is very slow
I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_toke... | [
-0.12732218205928802,
0.3481041193008423,
-0.0064756013453006744,
-0.1535291075706482,
0.14380288124084473,
-0.1467355191707611,
0.3634430766105652,
0.6069023609161377,
0.1660749763250351,
-0.005787186324596405,
-0.10351894795894623,
0.5548499822616577,
0.0025605224072933197,
-0.1955349445... |
https://github.com/huggingface/datasets/issues/2193 | Looks like a great direction :)
Note that `query_table` doesn't bring data into memory. Only `format_table` does.
Also the dataset may already have a format with `columns=` already defined so we would need to define the formatted `input_dataset` like:
```python
# before the `map` main for loop
input_columns = inpu... | Filtering/mapping on one column is very slow | I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_colu... | 148 | Filtering/mapping on one column is very slow
I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_toke... | [
-0.12052248418331146,
0.29167187213897705,
-0.006857927888631821,
-0.1955767422914505,
0.11129971593618393,
-0.10715066641569138,
0.25454196333885193,
0.5994856953620911,
0.20434832572937012,
-0.028510861098766327,
-0.03400091826915741,
0.4793541431427002,
0.021737582981586456,
-0.21316979... |
https://github.com/huggingface/datasets/issues/2193 | Good to know about `query_table` not bringing anything into memory. I was under the impression that it did because a while back I looked at my `map` operation in pdb and it looked like it was spending forever in line 93 of formatting.py, `return pa.concat_tables(....)`, although that was before the `fast_slice` interpo... | Filtering/mapping on one column is very slow | I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_colu... | 181 | Filtering/mapping on one column is very slow
I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_toke... | [
-0.10460534691810608,
0.3224446773529053,
-0.01583385095000267,
-0.1899833232164383,
0.0996762216091156,
-0.09264684468507767,
0.22558365762233734,
0.5739006996154785,
0.13459131121635437,
-0.005186170339584351,
-0.0915139764547348,
0.4579325020313263,
0.0769859254360199,
-0.23750719428062... |
https://github.com/huggingface/datasets/issues/2193 | `query_table` simply slices/concatenates parts of the table. The actual data inside the table is not brought in memory.
Also I'm more in favor of declaring `input_dataset = self.with_format(...)` since `formatted_as` may update the dataset fingerprint of `self`, which is not expected when someone runs `map`.
> It's... | Filtering/mapping on one column is very slow | I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_colu... | 145 | Filtering/mapping on one column is very slow
I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_toke... | [
-0.09050972014665604,
0.2783811092376709,
-0.007305510342121124,
-0.17817333340644836,
0.14086726307868958,
-0.09612897038459778,
0.3184325397014618,
0.570652961730957,
0.1868228018283844,
-0.028076283633708954,
-0.058641985058784485,
0.48464781045913696,
0.09406687319278717,
-0.2658148109... |
https://github.com/huggingface/datasets/issues/2193 | That sounds good to me! I just submitted a PR (#2246) implementing your approach. I also changed how `_query_table` handles Iterable keys since it still seemed like `pa.concat_tables` was taking a long time to create the table for each batch. Now my whole `map()` operation takes 1 min 46 seconds where it used to take s... | Filtering/mapping on one column is very slow | I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_colu... | 62 | Filtering/mapping on one column is very slow
I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_toke... | [
-0.17371785640716553,
0.3303046226501465,
-0.015150163322687149,
-0.235260009765625,
0.09672686457633972,
-0.13735659420490265,
0.24519683420658112,
0.6020463109016418,
0.1215745285153389,
-0.05612526834011078,
-0.07012178003787994,
0.5131319761276245,
0.07048053294420242,
-0.2048010379076... |
https://github.com/huggingface/datasets/issues/2190 | Hi @anassalamah,
Could you please try with this:
```python
train_ds = load_dataset("news_commentary", lang1="ar", lang2="en", split='train[:98%]')
val_ds = load_dataset("news_commentary", lang1="ar", lang2="en", split='train[98%:]')
``` | News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs | I used load_dataset to load the news_commentary dataset for "ar-en" translation pairs but found translations from Arabic to Hindi.
```
train_ds = load_dataset("news_commentary", "ar-en", split='train[:98%]')
val_ds = load_dataset("news_commentary", "ar-en", split='train[98%:]')
# filtering out examples that a... | 22 | News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs
I used load_dataset to load the news_commentary dataset for "ar-en" translation pairs but found translations from Arabic to Hindi.
```
train_ds = load_dataset("news_commentary", "ar-en", split='train[:98%]')
val_ds = load_datas... | [
-0.160386323928833,
0.00444599986076355,
-0.21704232692718506,
0.24928423762321472,
-0.053791165351867676,
0.08853870630264282,
0.3200060725212097,
0.22939994931221008,
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-0.23579910397529602,
-0.24613550305366516,
0.19127428531646729,
0.1839802861213684,
0.27491360902... |
https://github.com/huggingface/datasets/issues/2190 | Hello @albertvillanova,
Thanks for the suggestion. I didn't know you could do that. however, it didn't resolve the issue
| News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs | I used load_dataset to load the news_commentary dataset for "ar-en" translation pairs but found translations from Arabic to Hindi.
```
train_ds = load_dataset("news_commentary", "ar-en", split='train[:98%]')
val_ds = load_dataset("news_commentary", "ar-en", split='train[98%:]')
# filtering out examples that a... | 20 | News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs
I used load_dataset to load the news_commentary dataset for "ar-en" translation pairs but found translations from Arabic to Hindi.
```
train_ds = load_dataset("news_commentary", "ar-en", split='train[:98%]')
val_ds = load_datas... | [
-0.2070920467376709,
0.1082228422164917,
-0.1750502586364746,
0.32578739523887634,
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0.2640419006347656,
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0.23029908537864685,
0.2286696881055832,
0.18732714653015137... |
https://github.com/huggingface/datasets/issues/2189 | Hi ! We refactored save_to_disk in #2025 so this doesn't happen.
Feel free to try it on master for now
We'll do a new release soon | save_to_disk doesn't work when we use concatenate_datasets function before creating the final dataset_object. | As you can see, it saves the entire dataset.
@lhoestq
You can check by going through the following example,
```
from datasets import load_from_disk,concatenate_datasets
loaded_data=load_from_disk('/home/gsir059/HNSW-ori/my_knowledge_dataset')
n=20
kb_list=[loaded_data.shard(n, i, contiguous=True) for i... | 26 | save_to_disk doesn't work when we use concatenate_datasets function before creating the final dataset_object.
As you can see, it saves the entire dataset.
@lhoestq
You can check by going through the following example,
```
from datasets import load_from_disk,concatenate_datasets
loaded_data=load_from_di... | [
-0.25447577238082886,
0.036903053522109985,
-0.07479158043861389,
0.023532014340162277,
0.2022772878408432,
0.20770138502120972,
0.3147450089454651,
0.3745853304862976,
-0.07030657678842545,
0.2908635437488556,
-0.10103657841682434,
0.1949494183063507,
0.002328505739569664,
0.2046324610710... |
https://github.com/huggingface/datasets/issues/2188 | Hi ! Thanks for reporting
If I recall correctly this has been recently fixed #1995
Can you try to upgrade your local version of `datasets` ?
```
pip install --upgrade datasets
``` | Duplicate data in Timit dataset | I ran a simple code to list all texts in Timit dataset and the texts were all the same.
Is this dataset corrupted?
**Code:**
timit = load_dataset("timit_asr")
print(*timit['train']['text'], sep='\n')
**Result:**
Would such an act of refusal be useful?
Would such an act of refusal be useful?
Would such an act of... | 32 | Duplicate data in Timit dataset
I ran a simple code to list all texts in Timit dataset and the texts were all the same.
Is this dataset corrupted?
**Code:**
timit = load_dataset("timit_asr")
print(*timit['train']['text'], sep='\n')
**Result:**
Would such an act of refusal be useful?
Would such an act of refusa... | [
-0.008560225367546082,
-0.3116271197795868,
-0.08459101617336273,
0.6408434510231018,
0.29728367924690247,
0.17986047267913818,
0.212722510099411,
0.3483658730983734,
-0.35509422421455383,
0.06406495720148087,
-0.18485760688781738,
0.3607737421989441,
-0.03706914559006691,
0.13774994015693... |
https://github.com/huggingface/datasets/issues/2187 | An educated guess: does this refer to the fact that depending on the custom column names in the dataset files (csv in this case), there is a dataset loader being created? and this dataset loader - using the "custom data configuration" is used among all jobs running using this particular csv files? (thinking out loud he... | Question (potential issue?) related to datasets caching | I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the following:
```
04/07/2021 18:34:42 - WARNING - datasets.build... | 95 | Question (potential issue?) related to datasets caching
I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the followi... | [
-0.07473989576101303,
0.032924577593803406,
-0.11125245690345764,
0.18403871357440948,
0.20810315012931824,
0.19958627223968506,
0.34123751521110535,
0.03536439687013626,
0.3006586730480194,
-0.14211532473564148,
0.1708119660615921,
0.09101544320583344,
-0.23205505311489105,
0.038795650005... |
https://github.com/huggingface/datasets/issues/2187 | Hi ! Currently disabling the caching means that all the dataset transform like `map`, `filter` etc. ignore the cache: it doesn't write nor read processed cache files.
However `load_dataset` reuses datasets that have already been prepared: it does reload prepared dataset files.
Indeed from the documentation:
> data... | Question (potential issue?) related to datasets caching | I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the following:
```
04/07/2021 18:34:42 - WARNING - datasets.build... | 202 | Question (potential issue?) related to datasets caching
I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the followi... | [
-0.09970839321613312,
0.06134238839149475,
-0.09445241093635559,
0.13757392764091492,
0.2868356704711914,
0.15693961083889008,
0.29463258385658264,
0.05762897804379463,
0.2189561426639557,
-0.22742904722690582,
0.05633820593357086,
0.1891588270664215,
-0.15384472906589508,
-0.1349176764488... |
https://github.com/huggingface/datasets/issues/2187 | Thank you for the clarification.
This is a bit confusing. On one hand, it says that cache files are always recreated and written to a temporary directory that is removed; on the other hand the last bullet point makes me think that since the default according to the docs for `download_mode (Optional datasets.Generat... | Question (potential issue?) related to datasets caching | I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the following:
```
04/07/2021 18:34:42 - WARNING - datasets.build... | 112 | Question (potential issue?) related to datasets caching
I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the followi... | [
0.023499615490436554,
-0.006731539964675903,
-0.1006588265299797,
0.22953388094902039,
0.21939018368721008,
0.16460660099983215,
0.14268316328525543,
-0.12215961515903473,
0.26811477541923523,
-0.221396803855896,
0.14639052748680115,
0.04763816297054291,
-0.08117969334125519,
-0.0989197567... |
https://github.com/huggingface/datasets/issues/2187 | It would be nice if the documentation elaborated on all the possible values for `download_mode` and/or a link to `datasets.GenerateMode`.
This info here:
```
"""`Enum` for how to treat pre-existing downloads and data.
The default mode is `REUSE_DATASET_IF_EXISTS`, which will reuse both
raw downloads a... | Question (potential issue?) related to datasets caching | I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the following:
```
04/07/2021 18:34:42 - WARNING - datasets.build... | 84 | Question (potential issue?) related to datasets caching
I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the followi... | [
-0.1325751543045044,
0.0042608678340911865,
-0.10032553970813751,
0.14501981437206268,
0.2611146569252014,
0.08856382220983505,
0.14978021383285522,
-0.005440217442810535,
0.05236496031284332,
-0.2209995985031128,
-0.010533791035413742,
-0.009321220219135284,
-0.078802689909935,
0.01480204... |
https://github.com/huggingface/datasets/issues/2187 | I have another question. Assuming that I understood correctly and there is reuse of datasets files when caching is disabled (!), I'm guessing there is a directory that is created based on some information on the dataset file. I'm interested in the situation where I'm loading a (custom) dataset from local disk. What inf... | Question (potential issue?) related to datasets caching | I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the following:
```
04/07/2021 18:34:42 - WARNING - datasets.build... | 139 | Question (potential issue?) related to datasets caching
I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the followi... | [
0.03961437940597534,
0.17447888851165771,
-0.1220533475279808,
0.3688904941082001,
0.12289872765541077,
0.09724793583154678,
0.3478941023349762,
-0.043518610298633575,
0.17479485273361206,
-0.11833678930997849,
0.08155406266450882,
-0.07398997247219086,
-0.09671056270599365,
-0.19974206387... |
https://github.com/huggingface/datasets/issues/2187 | Thanks for the feedback, we'll work in improving this aspect of the documentation.
> Where are these files stored? I guess not in the temporary directory that is removed...
We're using the Arrow file format to load datasets. Therefore each time you load a dataset, it is prepared as an arrow file on your disk. By ... | Question (potential issue?) related to datasets caching | I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the following:
```
04/07/2021 18:34:42 - WARNING - datasets.build... | 231 | Question (potential issue?) related to datasets caching
I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the followi... | [
0.04265274852514267,
0.11284616589546204,
-0.07477438449859619,
0.3651137351989746,
0.18751344084739685,
0.1657911092042923,
0.3618483543395996,
-0.07639756053686142,
0.1920938491821289,
-0.14571726322174072,
0.12443508207798004,
0.09013614058494568,
-0.26943618059158325,
-0.12038263678550... |
https://github.com/huggingface/datasets/issues/2187 | Thank you for all your clarifications, really helpful!
If you have the bandwidth, please do revisit the api wrt cache disabling. Anywhere in the computer stack (hardware included) where you disable the cache, one assumes there is no caching that happens. | Question (potential issue?) related to datasets caching | I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the following:
```
04/07/2021 18:34:42 - WARNING - datasets.build... | 41 | Question (potential issue?) related to datasets caching
I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the followi... | [
-0.08065550029277802,
-0.019092977046966553,
-0.0784590095281601,
0.18294356763362885,
0.2073647677898407,
0.2329125702381134,
0.17175573110580444,
0.012628395110368729,
0.20395886898040771,
-0.16847378015518188,
0.1043684333562851,
0.06517932564020157,
-0.14791619777679443,
-0.04860478639... |
https://github.com/huggingface/datasets/issues/2187 | I have another question about caching, this time in the case where FORCE_REDOWNLOAD is used to load the dataset, the datasets cache is one directory as defined by HF_HOME and there are multiple concurrent jobs running in a cluster using the same local dataset (i.e., same local files in the cluster). Does anything in th... | Question (potential issue?) related to datasets caching | I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the following:
```
04/07/2021 18:34:42 - WARNING - datasets.build... | 115 | Question (potential issue?) related to datasets caching
I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the followi... | [
-0.19765862822532654,
0.09977436065673828,
-0.12614718079566956,
0.2399424910545349,
0.028943993151187897,
0.043431080877780914,
0.4564795196056366,
0.09844562411308289,
0.4220704734325409,
-0.2127024531364441,
-0.024983137845993042,
-0.04001113772392273,
-0.03659185767173767,
0.0134084075... |
https://github.com/huggingface/datasets/issues/2187 | I got another error that convinces me there is a race condition (one of the test files had zero samples at prediction time). I think it comes down to the fact that the `config_id` above (used in the naming for the cache) has no information on who's touching the data. If I have 2 concurrent jobs, both loading the same d... | Question (potential issue?) related to datasets caching | I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the following:
```
04/07/2021 18:34:42 - WARNING - datasets.build... | 74 | Question (potential issue?) related to datasets caching
I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the followi... | [
-0.25176846981048584,
0.1913684606552124,
-0.12854310870170593,
0.22529497742652893,
-0.027027703821659088,
0.07782411575317383,
0.3286905884742737,
0.10052663087844849,
0.32229548692703247,
-0.16506610810756683,
0.12396398931741714,
-0.12795720994472504,
-0.19243623316287994,
-0.014436140... |
https://github.com/huggingface/datasets/issues/2187 | We're using a locking mechanism to prevent two processes from writing at the same time. The locking is based on the `filelock` module.
Also directories that are being written use a suffix ".incomplete" so that reading is not possible on a dataset being written.
Do you think you could provide a simple code to reprod... | Question (potential issue?) related to datasets caching | I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the following:
```
04/07/2021 18:34:42 - WARNING - datasets.build... | 61 | Question (potential issue?) related to datasets caching
I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the followi... | [
-0.196752667427063,
0.11761069297790527,
-0.08972495794296265,
0.3552556037902832,
0.15775974094867706,
0.15526741743087769,
0.1879120171070099,
0.1608639806509018,
0.3102248013019562,
-0.18814939260482788,
0.19101916253566742,
0.0179731547832489,
-0.273449182510376,
0.0268450528383255,
... |
https://github.com/huggingface/datasets/issues/2187 | I can provide details about the code I'm running (it's really-really close to some official samples from the huggingface transformers examples, I can point to the exact sample file, I kept a record of that). I can also describe in which conditions this race occurs (I'm convinced it has to do with forcing the redownload... | Question (potential issue?) related to datasets caching | I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the following:
```
04/07/2021 18:34:42 - WARNING - datasets.build... | 291 | Question (potential issue?) related to datasets caching
I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the followi... | [
-0.21844841539859772,
-0.09565655887126923,
-0.01374078169465065,
0.32557302713394165,
0.14435283839702606,
0.04012306034564972,
0.2102060317993164,
-0.0006146859377622604,
0.2891821265220642,
-0.23899443447589874,
-0.017255550250411034,
-0.1222883015871048,
-0.21927693486213684,
0.0667363... |
https://github.com/huggingface/datasets/issues/2187 | Hi! I have the same challenge with caching, where the **.cache** folder is required even though it isn't possible for me.
I'd like to run transformers in Snowflake, using Snowpark for Python, this would mean I could provide configurable transformers in real-time for business users without having data leave an enviro... | Question (potential issue?) related to datasets caching | I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the following:
```
04/07/2021 18:34:42 - WARNING - datasets.build... | 83 | Question (potential issue?) related to datasets caching
I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the followi... | [
-0.09467746317386627,
0.06660062074661255,
-0.02343551442027092,
0.3731372356414795,
0.3858664035797119,
0.11478756368160248,
0.20470580458641052,
-0.023724500089883804,
0.13180018961429596,
-0.1508674919605255,
0.12440101057291031,
0.060443148016929626,
-0.21300435066223145,
0.03396269679... |
https://github.com/huggingface/datasets/issues/2187 | You can try to change the location of the cache folder using the `HF_CACHE_HOME` environment variable, and set a location where you have read/write access. | Question (potential issue?) related to datasets caching | I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the following:
```
04/07/2021 18:34:42 - WARNING - datasets.build... | 25 | Question (potential issue?) related to datasets caching
I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the followi... | [
-0.0555344857275486,
0.05233977735042572,
-0.09127381443977356,
0.21403542160987854,
0.3289119303226471,
0.19444540143013,
0.19704373180866241,
0.06822448968887329,
0.18030977249145508,
-0.12082286924123764,
0.04178718477487564,
0.036759283393621445,
-0.1732834279537201,
-0.033403467386960... |
https://github.com/huggingface/datasets/issues/2187 | Thanks @lhoestq
I wanted to do that, however, snowflake does not allow it to write at all. I'm asking around to see if they can help me out with that issue 😅 | Question (potential issue?) related to datasets caching | I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the following:
```
04/07/2021 18:34:42 - WARNING - datasets.build... | 32 | Question (potential issue?) related to datasets caching
I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the followi... | [
-0.08300358057022095,
-0.11795224994421005,
-0.08759865909814835,
0.43335649371147156,
0.3144419491291046,
0.09293164312839508,
0.21593499183654785,
0.04500103369355202,
0.09018541872501373,
-0.21972981095314026,
0.21874475479125977,
0.08733386546373367,
-0.1604956090450287,
0.212824612855... |
https://github.com/huggingface/datasets/issues/2185 | Hi, one workaround would be to save the mapped(tokenized in your case) file using `save_to_disk`, and having each process load this file using `load_from_disk`. This is what I am doing, and in this case, I turn off the ability to automatically load from the cache.
Also, multiprocessing the map function seems to be s... | .map() and distributed training | Hi,
I have a question regarding distributed training and the `.map` call on a dataset.
I have a local dataset "my_custom_dataset" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.
`dataset` is then tokenized:
```python
datasets = load_from_disk(dataset_path=my_path)
[...]
def tokeni... | 62 | .map() and distributed training
Hi,
I have a question regarding distributed training and the `.map` call on a dataset.
I have a local dataset "my_custom_dataset" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.
`dataset` is then tokenized:
```python
datasets = load_from_disk(dataset_pa... | [
-0.25376176834106445,
-0.22071696817874908,
0.11777669191360474,
0.07457458972930908,
0.21161656081676483,
-0.10975947976112366,
0.5170727372169495,
0.010205188766121864,
0.280156672000885,
0.023762941360473633,
0.1822473704814911,
0.34344932436943054,
-0.22906403243541718,
-0.585545003414... |
https://github.com/huggingface/datasets/issues/2185 | Thanks @hwijeen for the workaround, feels a bit prototypical but it works! (it seems files are written twice then though)
(I haven't observed slowness using multiprocessed map function but I could be wrong) | .map() and distributed training | Hi,
I have a question regarding distributed training and the `.map` call on a dataset.
I have a local dataset "my_custom_dataset" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.
`dataset` is then tokenized:
```python
datasets = load_from_disk(dataset_path=my_path)
[...]
def tokeni... | 33 | .map() and distributed training
Hi,
I have a question regarding distributed training and the `.map` call on a dataset.
I have a local dataset "my_custom_dataset" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.
`dataset` is then tokenized:
```python
datasets = load_from_disk(dataset_pa... | [
-0.25376176834106445,
-0.22071696817874908,
0.11777669191360474,
0.07457458972930908,
0.21161656081676483,
-0.10975947976112366,
0.5170727372169495,
0.010205188766121864,
0.280156672000885,
0.023762941360473633,
0.1822473704814911,
0.34344932436943054,
-0.22906403243541718,
-0.585545003414... |
https://github.com/huggingface/datasets/issues/2185 | To my understanding, files are written twice anyhow(one after load_dataset, another aftet map). It's just that you now have it at the location where you can see, whereas it was secretlely saved at caching folder(.cache/huggingface/datasets by default)! Correct me if I'm wrong! | .map() and distributed training | Hi,
I have a question regarding distributed training and the `.map` call on a dataset.
I have a local dataset "my_custom_dataset" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.
`dataset` is then tokenized:
```python
datasets = load_from_disk(dataset_path=my_path)
[...]
def tokeni... | 42 | .map() and distributed training
Hi,
I have a question regarding distributed training and the `.map` call on a dataset.
I have a local dataset "my_custom_dataset" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.
`dataset` is then tokenized:
```python
datasets = load_from_disk(dataset_pa... | [
-0.25376176834106445,
-0.22071696817874908,
0.11777669191360474,
0.07457458972930908,
0.21161656081676483,
-0.10975947976112366,
0.5170727372169495,
0.010205188766121864,
0.280156672000885,
0.023762941360473633,
0.1822473704814911,
0.34344932436943054,
-0.22906403243541718,
-0.585545003414... |
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