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DanielPFlorian

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[ "Thanks for reporting !\r\nThis has been fixed in #475 and the fix will be available in the next release", "What you can do instead to get the list of the datasets is call\r\n\r\n```python\r\nprint([dataset.id for dataset in nlp.list_datasets()])\r\n```", "Thanks @lhoestq . " ]

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[ "I am facing probably facing similar issues with \r\n\r\n`wiki40b_en_100_0`", "Could you try to run `dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")` once before calling the script ?\r\n\r\nIt looks like several processes try to create the dataset in arrow format at the same time. If the dataset is already created it should be fine", "Thanks! I tested on 328MB text data on `n1-standard-8 (8 vCPUs, 30 GB memory)`. The main script ran without any issue, but it seems to require a huge space in the drive.\r\n\r\nAs suggested, I ran the following script before running the pre-training command with `xla_spawn.py`.\r\n\r\n```python\r\nfrom nlp import load_dataset\r\n\r\nfile_path=\"your_file_name\"\r\nload_dataset(\"text\", data_files=file_path, split=\"train\")\r\n```\r\nThis will create `text-train.arrow` under the default cache directory. Then, I run the script with `xla_spawn.py`. It will load data from the cached file. My understanding is that there's no other way but to do this two-step process with the current version (0.4) of `nlp`.\r\n\r\nDuring another caching process that happens in the main script:\r\n\r\n```\r\n08/26/2020 09:19:51 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 09:19:53 - INFO - nlp.arrow_dataset - Caching processed dataset at /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d/cache-f90f341e5308a7469\r\n8d872bcc88f9c0e.arrow\r\n```\r\n\r\n`nlp` generates a temporary file per core, each of which is three times larger than the original text data. If each process is actually writing on the disk, you will need a huge amount of space in your drive. (Maybe I'm missing something.)\r\n\r\n```\r\n-rw-r--r-- 1 ***** ***** 674 Aug 26 09:19 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 09:19 LICENSE\r\n-rw-r--r-- 1 ***** ***** 332M Aug 26 09:10 text-train.arrow\r\n-rw------- 1 ***** ***** 940M Aug 26 09:31 tmp0k43sazw\r\n-rw------- 1 ***** ***** 940M Aug 26 09:31 tmp7sxs9mj5\r\n-rw------- 1 ***** ***** 939M Aug 26 09:31 tmpbbiqw2vp\r\n-rw------- 1 ***** ***** 937M Aug 26 09:31 tmpjxb5ptyu\r\n-rw------- 1 ***** ***** 933M Aug 26 09:31 tmpk3hkdh0e\r\n-rw------- 1 ***** ***** 944M Aug 26 09:31 tmpnoalwftz\r\n-rw------- 1 ***** ***** 931M Aug 26 09:31 tmpuxdr_dz3\r\n-rw------- 1 ***** ***** 945M Aug 26 09:31 tmpxjyuy6dk\r\n```\r\nAfter the caching process, they seem to be merged into one file.\r\n\r\n```\r\n-rw------- 1 ***** ***** 989M Aug 26 09:32 cache-f90f341e5308a74698d872bcc88f9c0e.arrow\r\n-rw-r--r-- 1 ***** ***** 674 Aug 26 09:19 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 09:19 LICENSE\r\n-rw-r--r-- 1 ***** ***** 332M Aug 26 09:10 text-train.arrow\r\n```", "Again it looks like every process tries to tokenize the full dataset at the same time.\r\nIf you do the tokenization before calling `xla_spawn.py` once, then each process will then use the tokenized cached file `cache-f90f341e5308a74698d872bcc88f9c0e.arrow` and not recompute it.\r\n\r\nNot sure if there's a better way to do that cc @julien-c @thomwolf ", "I wrote a separate script just for preparing a cached file, including tokenization. Each process did use the tokenized cached file.\r\n\r\nCurrently I'm testing the pipeline on 24GB text data. It took about 1.5 hour to create a cached file on `n1-highmem-16 (16 vCPUs, 104 GB memory)`. I assume loading this cached file in the main script with `xla_spawn.py` won't be an issue (even if there are 8 processes).\r\n\r\n```\r\ntotal 98G\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 13:38 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 12:24 ..\r\n-rw------- 1 ***** ***** 74G Aug 26 13:38 cache-a7aa04134ba7b1aff5d9710f14a4e334.arrow\r\n-rw-r--r-- 1 ***** ***** 681 Aug 26 12:24 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 12:24 LICENSE\r\n-rw-r--r-- 1 ***** ***** 25G Aug 26 12:24 text-train.arrow\r\n```", "Yes loading the cached file should be fine from different processes", "Sorry, I thought it was working, but actually the second call doesn't use the cached file that was generated separately, and it will generate another cache-****.arrorw file with a different name. If I run the training script again (with `xla_spawn.py`), it will use the second cached file, which was generated by the training script itself in the previous run.\r\n\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 15:35 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:29 ..\r\n-rw------- 1 ***** ***** 99M Aug 26 15:35 cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n-rw------- 1 ***** ***** 99M Aug 26 15:29 cache-69633651476e943b93c89ace715f9487.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 26 15:33 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 15:33 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 26 15:29 text-train.arrow\r\n```", "So if I understand correctly it means that the cached file generated by your separated script is different by the one used by the training script ?", "Yes.\r\n\r\n1. `cache-69633651476e943b93c89ace715f9487.arrow` was generated with a separate script. \r\n2. I ran the entire script with `xla_spawn.py`.\r\n3. `cache-69633651476e943b93c89ace715f9487.arrow` is not used.\r\n4. `cache-0d77dfce704493dbe63f071eed6a5431.arrow` is created.\r\n5. training starts...\r\n\r\nNow, if I kill the process at step 5, and do the step 2 again, it will use `cache-0d77dfce704493dbe63f071eed6a5431.arrow` (cached file created at step 4) without any issue.\r\n\r\nI used the following to generate the first cached file.\r\n```python\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\n```", "1. Here's the log from the first step.\r\n```\r\nDownloading and preparing dataset text/default-e84dd29acc4ad9ef (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDataset text downloaded and prepared to /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d. Subsequent calls will reuse this data.\r\n```\r\nThere's a file named `cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow`, so it did create a cached file.\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 15:59 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:58 ..\r\n-rw------- 1 ***** ***** 99M Aug 26 15:59 cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 26 15:58 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 15:58 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 26 15:58 text-train.arrow\r\n```\r\n2. Ideally, `cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow` should be used in `run_language_modeling.py` (modified version using `nlp`) with `xla_spawn.py`. But it looks like it's creating a new cached file.\r\n\r\n```\r\n08/26/2020 16:13:03 - INFO - filelock - Lock 139635836351096 released on /home/*****/.cache/huggingface/datasets/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.202fa4f84f552bff1f5400ae012663839c61efb3de068c6c8722d34ac0ea6192\r\n.py.lock\r\n08/26/2020 16:13:03 - WARNING - nlp.builder - Using custom data configuration default\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Overwrite dataset info from restored data version.\r\n08/26/2020 16:13:03 - INFO - nlp.info - Loading Dataset info from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Reusing dataset text (/home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Constructing Dataset for split train, from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Overwrite dataset info from restored data version.\r\n08/26/2020 16:13:03 - INFO - nlp.info - Loading Dataset info from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Reusing dataset text (/home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Constructing Dataset for split train, from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 16:13:05 - INFO - nlp.arrow_dataset - Caching processed dataset at /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d/cache-0d77dfce704493dbe\r\n63f071eed6a5431.arrow\r\n^M 0%| | 0/100 [00:00<?, ?it/s]08/26/2020 16:13:05 - INFO - nlp.arrow_dataset - Caching processed dataset at /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6\r\nfe661fe4d070d380d/cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n```\r\n\r\nThere are two cached files in the directory:\r\n\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 16:14 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:58 ..\r\n-rw------- 1 ***** ***** 99M Aug 26 16:14 cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n-rw------- 1 ***** ***** 99M Aug 26 15:59 cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 26 16:13 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 16:13 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 26 15:58 text-train.arrow\r\n```\r\n\r\nIf I kill the process, and run it again, it will use the second cached file.\r\n\r\n```\r\n08/26/2020 16:19:52 - WARNING - nlp.builder - Using custom data configuration default\r\n08/26/2020 16:19:52 - INFO - nlp.builder - Overwrite dataset info from restored data version.\r\n08/26/2020 16:19:52 - INFO - nlp.info - Loading Dataset info from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:19:52 - INFO - nlp.builder - Reusing dataset text (/home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08/26/2020 16:19:52 - INFO - nlp.builder - Constructing Dataset for split train, from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:19:52 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 16:19:53 - INFO - nlp.arrow_dataset - Loading cached processed dataset at /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d/cache-0d77dfce70\r\n4493dbe63f071eed6a5431.arrow\r\n08/26/2020 16:19:53 - INFO - nlp.arrow_dataset - Set __getitem__(key) output type to torch for ['input_ids'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```", "Thanks for all the details.\r\nThe two cached files are supposed to be the same. I suspect that the caching has a problem with the tokenizer.\r\nWhich tokenizer did you use ?", "I trained a byte-level BPE tokenizer on my data with `tokenziers` library following this [example](https://github.com/huggingface/tokenizers/blob/master/bindings/python/examples/train_bytelevel_bpe.py).\r\n\r\nAnd I put these model files in a directory named `\"model_name\"`. I also put config.json, which is the original RoBERTa config file.\r\n\r\n```bash\r\n%ls model_name\r\nconfig.json merges.txt vocab.json\r\n```\r\n\r\n[This](https://github.com/huggingface/transformers/blob/4bd7be9a4268221d2a0000c7e8033aaeb365c03b/examples/language-modeling/run_language_modeling.py#L196) is the line where `run_language_modeling.py` loads the tokenier.\r\n\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n```\r\n\r\nI use `\"model_name\"` for `model_args.tokenizer_name`. I don't specify `model_args.cache_dir`. It is 'None' by default.", "In my separated script for caching, I'm using `use_fast=True` when initializing a tokenizer.\r\n\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(args.config_name, use_fast=True)\r\n```\r\nI wasn't using that option in the main script. That could be the reason...", "Yea it could definitely explain why you have two different cache files.\r\nLet me know if using the same tokenizers on both sides fixes the issue", "It still creates a new file even if I remove `use_fast=True`... \r\n\r\nHere's the script used to create a cached file.\r\n```python \r\n#!/usr/bin/env python3\r\n\r\nimport argparse\r\n\r\nfrom transformers import AutoTokenizer\r\n\r\nfrom nlp import load_dataset\r\n\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser(description='description')\r\n parser.add_argument('--config_name', type=str, help='Pretrained config name or path if not the same as model_name')\r\n parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n args = parser.parse_args()\r\n\r\n tokenizer = AutoTokenizer.from_pretrained(args.config_name)\r\n\r\n dataset = load_dataset(\"text\", data_files=args.data_file, split=\"train\")\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\nHere's how the data is loaded in the modified `run_language_modeling.py`. [[original function](https://github.com/huggingface/transformers/blob/971d1802d009d9996b36a34a34477cee849ef39f/examples/language-modeling/run_language_modeling.py#L128-L135)]\r\n\r\n```python\r\ndef get_dataset(args: DataTrainingArguments, tokenizer: PreTrainedTokenizer, evaluate=False):\r\n file_path = args.eval_data_file if evaluate else args.train_data_file\r\n split = \"validation\" if evaluate else \"train\"\r\n if args.line_by_line:\r\n # return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\n dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n return dataset\r\n\r\n else:\r\n return TextDataset(\r\n tokenizer=tokenizer, file_path=file_path, block_size=args.block_size, overwrite_cache=args.overwrite_cache\r\n )\r\n```\r\n\r\nProbably I don't need this part in the main script,\r\n\r\n```python\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n```\r\nand simply do this?\r\n```python\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\nreturn dataset\r\n```", "You need this part in the main script or it will use the dataset that is not tokenized\r\n\r\n", "I can see that the tokenizer in `run_language_modeling.py` is not instantiated the same way as in your separated script.\r\nIndeed we can see L196:\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n```\r\nCould you try to make it so they are instantiated the exact same way please ?", "I updated my separated script, but it's creating a cached file again. If I don't use the `model_args.cache_dir`, both will get `None`, so they should be the same.\r\n\r\n```python\r\n#!/usr/bin/env python3\r\nimport argparse\r\n\r\nfrom transformers import AutoTokenizer\r\nfrom nlp import load_dataset\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser(description='description')\r\n parser.add_argument('--tokenizer_name', type=str, help='Pretrained tokenizer name or path if not the same as model_name')\r\n parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n parser.add_argument('--cache_dir', type=str, default=None, help='Where do you want to store the pretrained models downloaded from s3')\r\n parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n\r\n model_args = parser.parse_args()\r\n\r\n tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n\r\n dataset = load_dataset(\"text\", data_files=model_args.data_file, split=\"train\")\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=model_args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\nIs there a way to specify the cache file to load, and skip the re-computation?", "Could you also check that the `args.block_size` used in the lambda function is the same as well ?", "Here's a minimal working example to reproduce this issue.\r\n\r\nAssumption:\r\n- You have access to TPU.\r\n- You have installed `transformers` and `nlp`.\r\n- You have tokenizer files (`config.json`, `merges.txt`, `vocab.json`) under the directory named `model_name`.\r\n- You have `xla_spawn.py` (Download from https://github.com/huggingface/transformers/blob/master/examples/xla_spawn.py).\r\n- You have saved the following script as `prepare_cached_dataset.py`.\r\n\r\n```python\r\n#!/usr/bin/env python3\r\nimport argparse\r\nfrom transformers import AutoTokenizer\r\nfrom nlp import load_dataset\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser(description='description')\r\n parser.add_argument('--tokenizer_name', type=str, help='Pretrained tokenizer name or path if not the same as model_name')\r\n parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n parser.add_argument('--cache_dir', type=str, default=None, help='Where do you want to store the pretrained models downloaded from s3')\r\n parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n parser.add_argument('--tpu_num_cores', type=int, default=1, help='Number of TPU cores to use (1 or 8). For xla_apwan.py')\r\n model_args = parser.parse_args()\r\n \r\n tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=True)\r\n \r\n dataset = load_dataset(\"text\", data_files=model_args.data_file, split=\"train\")\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=model_args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\ndef _mp_fn(index):\r\n # For xla_spawn (TPUs)\r\n main()\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\n- Run the following command. Replace `your_training_data` with some text file.\r\n\r\n```bash\r\nexport TRAIN_DATA=your_training_data\r\n\r\npython prepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n- Check the cached directory.\r\n```bash\r\nls -lha /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 132M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:08 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** ***** 99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 28 13:08 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 28 13:08 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 28 13:08 text-train.arrow\r\n```\r\n\r\n- Run the same script again. (The output should be just `Using custom data configuration default`.)\r\n```\r\npython prepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n- Check the cached directory.\r\n```bash\r\nls -lha /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 132M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:08 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** ***** 99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 28 13:20 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 28 13:20 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 28 13:08 text-train.arrow\r\n```\r\n- The cached file (`cache-bfc7cb0702426d19242db5e8c079f04b.arrow`) is reused.\r\n- Now, run this script with `xla_spawn.py`. Ideally, it should reuse the cached file, however, you will see each process is creating a cache file again.\r\n\r\n```bash\r\npython xla_spawn.py --num_cores 8 \\\r\nprepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n\r\n- Check the cached directory. There are two arrrow files.\r\n```bash\r\nls -lha /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 230M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:25 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** ***** 99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw------- 1 ***** ***** 99M Aug 28 13:25 cache-e0e2313e49c8a110aafcc8133154c19a.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 28 13:24 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 28 13:24 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 28 13:08 text-train.arrow\r\n```\r\n", "I ended up specifying the `cache_file_name` argument when I call `map` function.\r\n\r\n```python\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True, truncation=True, max_length=args.block_size),\r\n batched=True,\r\n cache_file_name=cache_file_name)\r\n```\r\n\r\nNote:\r\n- `text` dataset in `nlp` does not strip `\"\\n\"`. If you want the same output as in [`LineByLineTextDataset`](https://github.com/huggingface/transformers/blob/afc4ece462ad83a090af620ff4da099a0272e171/src/transformers/data/datasets/language_modeling.py#L88-L111), you would need to create your own dataset class where you replace `line` to `line.strip()` [here](https://github.com/huggingface/nlp/blob/master/datasets/text/text.py#L35).\r\n" ]

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[ "Yes I agree. Maybe something that lets specify different format depending on the column ? Especially to better control dtype and shape (and ragged for tf)\r\n\r\nOh and I forgot: this one should also fix the second issue found in #477 for the next release", "I am running into the same issue with the error message on my local windows machine -\r\nAttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'to_tensor'. Tensorflow version is 2.6. Anything that I can do to fix it?\r\ntrain_features = {x: tf_train_dataset[x].to_tensor() for x in tokenizer.model_input_names}\r\ntrain_tf_dataset = tf.data.Dataset.from_tensor_slices((train_features, tf_train_dataset[\"label\"]))\r\ntrain_tf_dataset = train_tf_dataset.shuffle(len(tf_train_dataset)).batch(8)\r\n\r\neval_features = {x: tf_eval_dataset[x].to_tensor() for x in tokenizer.model_input_names}\r\neval_tf_dataset = tf.data.Dataset.from_tensor_slices((eval_features, tf_eval_dataset[\"label\"]))\r\neval_tf_dataset = eval_tf_dataset.batch(8)\r\n\r\nttributeError Traceback (most recent call last)\r\n<ipython-input-59-f50e45c2c0dc> in <module>\r\n----> 1 train_features = {x: tf_train_dataset[x].convert_to_tensor() for x in tokenizer.model_input_names}\r\n 2 train_tf_dataset = tf.data.Dataset.from_tensor_slices((train_features, tf_train_dataset[\"label\"]))\r\n 3 train_tf_dataset = train_tf_dataset.shuffle(len(tf_train_dataset)).batch(8)\r\n 4 \r\n 5 eval_features = {x: tf_eval_dataset[x].to_tensor() for x in tokenizer.model_input_names}\r\n\r\n<ipython-input-59-f50e45c2c0dc> in <dictcomp>(.0)\r\n----> 1 train_features = {x: tf_train_dataset[x].convert_to_tensor() for x in tokenizer.model_input_names}\r\n 2 train_tf_dataset = tf.data.Dataset.from_tensor_slices((train_features, tf_train_dataset[\"label\"]))\r\n 3 train_tf_dataset = train_tf_dataset.shuffle(len(tf_train_dataset)).batch(8)\r\n 4 \r\n 5 eval_features = {x: tf_eval_dataset[x].to_tensor() for x in tokenizer.model_input_names}\r\n\r\n~\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\framework\\ops.py in __getattr__(self, name)\r\n 399 from tensorflow.python.ops.numpy_ops import np_config\r\n 400 np_config.enable_numpy_behavior()\"\"\".format(type(self).__name__, name))\r\n--> 401 self.__getattribute__(name)\r\n 402 \r\n 403 @staticmethod\r\n\r\nAttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'convert_to_tensor'\r\n\r\n", "Hi ! Before calling `to_tensor`, make sure that your object is a RaggedTensor, because it may already be a regular Tensor if the shapes of your examples are all the same", "Okay. i am not familiar with how to check the difference between the two. I will research on this." ]

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[ "Could you test to run the test using the changes in #527 and let me know if it fixes the issue ? If so I'll merge it and we'll be good to go :)", "Hello, it does work on the fixing real dataset branch. Merci Quentin :)", "Nice, glad to hear that :)\r\nde rien !" ]

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[ "The problem came from `default: ` that is rendered differently and hides the parameter names. I changed `default: ...` to `defaults to ...`" ]

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[ "We have to change the tests to expect `None` instead of `python` then", "Merging!" ]

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[ "Thanks for creating the issue :)\r\nThe download link for wmt-en-de raw looks like a mirror. We should use that instead of the current url.\r\nIs this mirror official ?\r\n\r\nAlso it looks like for `ro-en` it tried to download other languages. If we manage to only download the one that is asked it'd be cool\r\n\r\nAlso cc @patrickvonplaten ", "Mirror is not official.", "Shall we host the files ourselves or it is fine to use this mirror in your opinion ?", "Should we add an argument in `load_dataset` to override some URL with a custom URL (e.g. mirror) or a local path?\r\n\r\nThis could also be used to provide local files instead of the original files as requested by some users (e.g. when you made a dataset with the same format than SQuAD and what to use it instead of the official dataset files).", "@lhoestq I think we should host it ourselves. I'll put the subset of wmt (without preprocessed files) that we need on s3 and post a link over the weekend.", "Is there a solution yet? The download speed is still too slow. 60-70kbps download for wmt16 and around 100kbps for wmt19. @sshleifer ", "I'm working on mirror links which will provide high download speed :)\r\nSee https://github.com/huggingface/datasets/issues/1892", "Resolved via https://github.com/huggingface/datasets/pull/1912" ]

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[ "Indeed, good catch!" ]

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[ "I took your comments into account and added tests for `cast_to_python_objects`" ]

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[ "Thanks!" ]

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[ "Hahah thanks Yonatan. It was not on purpose, we are just not very good at spelling :)" ]

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[ "I think I agree @lhoestq so I pushed a change.\r\nThanks for your work on the library!" ]

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[ "Update - maybe this is only failing on bleu because I was not tokenizing inputs to the metric", "Closing - seems to be just forgetting to tokenize. And found the helpful discussion in huggingface/evaluate#105 " ]

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[ "(test failure is unrelated)", "As discussed with @thomwolf merging since the hyperparameter-search has been merged in transformers." ]

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[ "Any updates on this?", "This request is still an open issue waiting to be addressed by any community member, @GuillemGSubies." ]

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[ "This seems to be fixed in #513 for the filter function, replacing `cache_file_name` with `indices_cache_file_name` in the assert. Although not for the `map()` function @thomwolf ", "Maybe I'm a bit tired but I fail to see the issue here.\r\n\r\nSince `cache_file_name` is `None` by default, if you set `keep_in_memory` to `True`, the assert should pass, no?", "I failed to realise that this only applies to `shuffle()`. Whenever `keep_in_memory` is set to True, this is passed on to the `select()` function. However, if `cache_file_name` is None, it will be defined in the `shuffle()` function before it is passed on to `select()`. \r\n\r\nThus, `select()` is called with `keep_in_memory=True` and a not None value for `cache_file_name`. \r\nThis is essentially fixed in #513 \r\n\r\nEasily reproducible:\r\n```python\r\n>>> import nlp\r\n>>> data = nlp.load_dataset(\"cosmos_qa\", split=\"train\")\r\nUsing custom data configuration default\r\n>>> data.shuffle(keep_in_memory=True)\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py\", line 1398, in shuffle\r\n verbose=verbose,\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py\", line 1178, in select\r\n ), \"Please use either `keep_in_memory` or `cache_file_name` but not both.\"\r\nAssertionError: Please use either `keep_in_memory` or `cache_file_name` but not both.\r\n>>>data.select([0], keep_in_memory=True)\r\n# No error\r\n```", "Oh yes ok got it thanks. Should be fixed if we are happy with #513 indeed.", "My bad. This is actually not fixed in #513. Sorry about that...\r\nThe new `indices_cache_file_name` is set to a non-None value in the new `shuffle()` as well. \r\n\r\nThe buffer and caching mechanisms used in the `select()` function are too intricate for me to understand why the check is there at all. I've removed it in my local build and it seems to be working fine for my project, without really considering other implications of the change. \r\n\r\n", "Ok I'll investigate and add a series of tests on the `keep_in_memory=True` settings which is under-tested atm", "Hey, still seeing this issue with the latest version.", "The same :(", "These are the steps needed to fix this issue:\r\n1. add the following check to `Dataset.shuffle`:\r\n```python\r\nif keep_in_memory and indices_cache_file_name is not None:\r\n raise ValueError(\"Please use either `keep_in_memory` or `indices_cache_file_name` but not both.\")\r\n```\r\n2. set `indices_cache_file_name` to `None` if `keep_in_memory` is True in the call to `select`\r\n3. add a test with `shuffle(keep_in_memory=True)`", "Hi @mariosasko , I have opened this PR #5082 " ]

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[ "Ok I fixed `concatenate_datasets` and added tests\r\nFeel free to merge if it's good for you @thomwolf ", "Ok, adding some benchmarks for map/filters and then I'll merge", "Warning from pytorch that we should maybe consider at some point @lhoestq:\r\n```\r\n/__w/nlp/nlp/src/nlp/arrow_dataset.py:648: UserWarning: The given NumPy array is not writeable,\r\nand PyTorch does not support non-writeable tensors. This means you can write to the underlying\r\n(supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to\r\nprotect its data or make it writeable before converting it to a tensor. This type of warning will be\r\nsuppressed for the rest of this program.\r\n(Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\r\n532\r\n return torch.tensor(x, **format_kwargs)\r\n```", "> Warning from pytorch that we should maybe consider at some point @lhoestq:\r\n> \r\n> ```\r\n> /__w/nlp/nlp/src/nlp/arrow_dataset.py:648: UserWarning: The given NumPy array is not writeable,\r\n> and PyTorch does not support non-writeable tensors. This means you can write to the underlying\r\n> (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to\r\n> protect its data or make it writeable before converting it to a tensor. This type of warning will be\r\n> suppressed for the rest of this program.\r\n> (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\r\n> 532\r\n> return torch.tensor(x, **format_kwargs)\r\n> ```\r\n\r\nNot sure why we have that, it's probably linked to zero copy from arrow to numpy" ]

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[ "😱", "Yeah, this is spammy behavior. I've reported the user handle." ]

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[ "Hi @vegarab yes feel free to open a discussion here.\r\n\r\nThis design choice was not very much thought about.\r\n\r\nSince `dataset.select()` (like all the method without a trailing underscore) is non-destructive and returns a new dataset it has most of its properties initialized from scratch (except the table and infos).\r\n\r\nThinking about it I don't see a strong reason against transmitting the format from the parent dataset to its newly created child. It's probably what's expected by the user in most cases. What do you think @lhoestq?\r\n\r\nBy the way, I've been working today on a refactoring of all the samples re-ordering/selection methods (`select`, `sort`, `shuffle`, `shard`, `train_test_split`). The idea is to speed them up by a lot (like, really a lot) by working as much as possible with an indices mapping table instead of doing a deep copy of the full dataset as we've been doing currently. You can give it a look and try it here: https://github.com/huggingface/nlp/pull/513\r\nFeedbacks are very much welcome", "I think it's ok to keep the format.\r\nIf we want to have this behavior for `.map` too we just have to make sure it doesn't keep a column that's been removed.", "Shall we have this in the coming release by the way @lhoestq ?", "Yes sure !", "Since datasets 1.0.0 the format is not reset anymore.\r\nClosing this one, but feel free to re-open if you have other questions" ]

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[ "Seems like this method was added in 1.17. I'll add a requirement on this.", "Thank you so much. After upgrading the numpy library, it worked." ]

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[ "Do you want to convert a dataset script to the tfds format ?\r\nIf so, we currently have a comversion script nlp/commands/convert.py but it is a conversion script that goes from tfds to nlp.\r\nI think it shouldn't be too hard to do the changes in reverse (at some manual adjustments).\r\nIf you manage to make it work in reverse, feel free to open a PR to share it with the community :)", "In our docs: [Using a Dataset with PyTorch/Tensorflow](https://huggingface.co/docs/datasets/torch_tensorflow.html)." ]

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[ "Which version of Apache Beam do you have (can you copy your full environment info here)?", "apache-beam==2.23.0\r\nnlp==0.4.0\r\n\r\nFor me this was resolved by running the same python script on Linux (or really WSL). ", "Do you manage to run a dummy beam pipeline with python on windows ? \r\nYou can test a dummy pipeline with [this code](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/wordcount_minimal.py)\r\n\r\nIf you get the same error, it means that the issue comes from apache beam.\r\nOtherwise we'll investigate what went wrong here", "Still, same error, so I guess it is on apache beam then. \r\nThanks for the investigation.", "Thanks for trying\r\nLet us know if you find clues of what caused this issue, or if you find a fix" ]

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[ "Looks like an issue with 3.0.2 transformers version. Works fine when I use \"master\" version of transformers." ]

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[ "Thanks for reporting the issue ! I'm creating a new PR to fix it and add tests.\r\n(I'm doing a new PR because I know there's some other place where it needs to be fixed)", "I'm closing this one as I created the other PR." ]

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[ "Thank you @ghomasHudson for making our dataset available! This is great!", "The test passes since #527 :)" ]

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[ "I don't see any significant change in the dataset script (except the version value update), can you check that again please ?", "Hi @aleSuglia , can you check that all the changes you wanted to do are in the dataset script ?", "Hey sorry but I'm in the middle of a conference deadline. I'll let you know asap!", "Ok np :)\r\nGood luck with your work for the conference", "I finally managed to find some time to complete this. The only weird thing about this release is that I had to run the tests with the ignore checksum flag. Could it be because the Dropbox link doesn't change but the file does? Sorry didn't have the time to check the code to see what's happening behind the scenes.\r\n", "Yes if the file changed, then the checksum verification won't pass as it expects to see the checksum of the old file.\r\nThe checksum is computed by hashing the complete file.\r\nYou can update the checksum by doing \r\n\r\n```\r\nnlp-cli test ./datasets/compguesswhat --save_infos --all_configs\r\n```", "Any updates on this?", "Hi :)\r\n\r\nI think what's left to do is\r\n1- rebase from master, since we changed the name of the library\r\n2- update the metadata file of the dataset using the command \r\n```\r\ndatasets-cli test ./datasets/compguesswhat --save_infos --all_configs --ignore_verifications\r\n```\r\n\r\nThis command should update the checksum of the dropbox file", "That's perfect. I'll have a look at it later today!", "Nice thanks !", "@lhoestq not sure why the quality check doesn't pass. Unfortunately CircleCI doesn't show the actual error. If I run `black` on my machine it works just fine. Ideas?", "@lhoestq any updates? :) ", "Your version of `black` might be outdated, or you run using `black` instead of `make style` since it reformatted 100+ files.\r\nCould you try to update black, then `make style` ?", "Yes I think my versions of isort and black were outdated. Thanks @lhoestq :)\r\n", "It still doesn't look right in terms of line-length.\r\nAre you running `black` or `make style` ?", "I'm running `make style`. This is the output of the command:\r\n\r\n```\r\nblack --line-length 119 --target-version py36 tests src benchmarks datasets metrics\r\nAll done! ✨ 🍰 ✨\r\n250 files left unchanged.\r\nisort tests src benchmarks datasets metrics\r\n```", "Weird I have the same output without file changes with black `20.8b1` and isort `5.6.4` using `make style` too", "I think that's because black doesn't revert the changes you first did with the old version.\r\nCould you open a new PR with only the ComGuessWhat files updated ? Hopefully now that black is up to date it should work directly (and to avoid 100+ files changes)", "I will have a look at it tomorrow. Thanks for your help!", "I'm closing this one and I'll open a new one." ]

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[ "This should fix #501 and also the issue you sent me on slack @sgugger ." ]

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[ "Thanks for reporting !\r\n\r\nTo store the cache file, we compute a hash of the function given in `.map`, using our own hashing function.\r\nThe hash doesn't seem to stay the same over sessions for the tokenizer.\r\nApparently this is because of the regex at `tokenizer.pat` is not well supported by our hashing function.\r\n\r\nI'm working on a fix", "Thanks everyone. Works great now.", "Hi. I believe the fix was for the nlp library. Is there a solution to handle compiled regex expressions in .map() with the caching. I want to run a simple regex pattern on a big dataset, but I am running into the issue of compiled expression not being cached. \r\n\r\nInstead of opening a new issue, I thought I would put my query here. Let me know if a new issue would be more suitable. Thanks", "Hi @MaveriQ! This fix is also included in the `datasets` library. Can you provide a reproducer?" ]

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[ "I took a look at the dummy data creation for this dataset.\r\n\r\nMaybe it didn't work on your side might be because `master.zip` and `narrativeqa_full_text.zip` are supposed to be directories and not acutal zip files in the dummy data folder.\r\n\r\nI managed to make it work with this `dummy_data.zip` file:\r\nhttps://drive.google.com/file/d/1G9ZHAjelazNApbFI0ep2dnSAWklXgGMd/view?usp=sharing", "@lhoestq Hmmm wasn't that. Must have been something else I missed.\r\n\r\nHave committed your working version though now.", "Ok thanks.\r\nCould you rebase from master to fix the CI please ?", "Hi @ghomasHudson, did you get the chance to add the test split and regenerate the dataset_infos.json file ?", "> Hi @ghomasHudson, did you get the chance to add the test split and regenerate the dataset_infos.json file ?\r\n\r\nHave added the test set code but getting an OverflowError when trying to regen the dataset_infos.json:\r\n\r\n---\r\nOverflowError: There was an overflow in the <class 'pyarrow.lib.StructArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB\r\n\r\n---\r\n", "Thanks for reporting @ghomasHudson , I'll look into it", "It looks like it's an issue with Pyarrow.\r\nBy changing the `DEFAULT_MAX_BATCH_SIZE` to 1000 instead of 10 000 in `arrow_writer.py` I was able to run the command.\r\n\r\nBasically it seems that is an Arrow StructArray has more than 1-2GB of data, then it shuffles some of its content.\r\nI can't find any issue on Apache Arrow's JIRA about this problem. It will require more investigation.\r\n\r\nMaybe we can simply automatically decrease the writer's batch size when this happens. We can just check if the arrow array is more than a certain amount of bytes. ", "@lhoestq I've finally got round to regenerating the `dataset_infos.json` for this and adding all 3 splits. I've done this and updated for the new version of datasets.\r\n\r\nThe CI tests still aren't passing though (they pass on my machine). `test_load_dataset_narrativeqa` seems to fail but I have no idea how. Would appreciate if you have any ideas - would be great to finally finish this one!", "The dummy data test fails, apparently it's because no examples are yielded for the dummy data.\r\n\r\nAlso it looks like the PR now show changes in many other files than the ones for NarrativeQA, could you create another branch and another PR please ?\r\n\r\nFeel free to ping me on the new PR so we can fi the dummy data together" ]

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[ "This PR also fixed a bug where numpy arrays were returned instead of pytorch tensors when getting with a clumn as a key." ]

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[ "this doesn't work. I can decompress the file after download locally." ]

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[ "In 0.4.0, the assertion in `concatenate_datasets ` is on the features, and not the schema.\r\nCould you try to update `nlp` ?\r\n\r\nAlso, since 0.4.0, you can use `dset_wikipedia.cast_(dset_books.features)` to avoid the schema cast hack.", "Or maybe the assertion comes from elsewhere ?", "I'm using the master branch. The assertion failure comes from the underlying `pa.concat_tables()`, which is in the pyarrow package. That method does check schemas.\r\n\r\nSince `features.type` does not contain information about nullable vs non-nullable features, the `cast_()` method won't resolve the schema mismatch. There is information in a schema which is not stored in features.", "I'm doing a refactor of type inference in #363 . Both text fields should match after that", "By default nullable will be set to True", "It should be good now. I was able to run\r\n\r\n```python\r\n>>> from nlp import concatenate_datasets, load_dataset\r\n>>>\r\n>>> bookcorpus = load_dataset(\"bookcorpus\", split=\"train\")\r\n>>> wiki = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\")\r\n>>> wiki.remove_columns_(\"title\") # only keep the text\r\n>>>\r\n>>> assert bookcorpus.features.type == wiki.features.type\r\n>>> bert_dataset = concatenate_datasets([bookcorpus, wiki])\r\n```", "Thanks!" ]

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[ "I did the release on github, and updated the doc :)\r\nSorry for the delay", "Thanks!" ]

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[ "whoops", "please delete this" ]

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[ "I found `UNv1.0.en-ru.tar.gz` here: https://conferences.unite.un.org/uncorpus/en/downloadoverview, so it can be reconstructed with:\r\n```\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.00\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.01\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.02\r\ncat UNv1.0.en-ru.tar.gz.0* > UNv1.0.en-ru.tar.gz\r\n```\r\nit has other languages as well, in case https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/ is gone", "Further, `nlp.load_dataset('wmt19', 'ru-en')` has only the `train` and `val` datasets. `test` is missing.\r\n\r\nFixed locally for summarization needs, by running:\r\n```\r\npip install sacrebleu\r\nsacrebleu -t wmt19 -l ru-en --echo src > test.source\r\nsacrebleu -t wmt19 -l ru-en --echo ref > test.target\r\n```\r\nh/t @sshleifer ", "Fixed in https://github.com/huggingface/datasets/pull/1912" ]

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[ "It looks like you need to rebase from master to fix the CI. Could you do that please ?" ]

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[ "Yes indeed it looks like some `'` and spaces are missing (for example in `dont` or `didnt`).\r\nDo you know if there exist some copies without this issue ?\r\nHow would you fix this issue on the current data exactly ? I can see that the data is raw text (not tokenized) so I'm not sure I understand how you would do it. Could you provide more details ?", "I'm afraid that I don't know how to obtain the original BookCorpus data. I believe this version came from an anonymous Google Drive link posted in another issue.\r\n\r\nGoing through the raw text in this version, it's apparent that NLTK's TreebankWordTokenizer was applied on it (I gave some examples in my original post), followed by:\r\n`' '.join(tokens)`\r\nYou can retrieve the tokenization by splitting on whitespace. You can then \"detokenize\" it with TreebankWordDetokenizer class of NLTK (though, as I suggested, use the fixed version in my repo). This will bring the text closer to its original form, but some steps of TreebankWordTokenizer are destructive, so it wouldn't be one-to-one. Something along the lines of the following should work:\r\n```\r\ntreebank_detokenizer = nltk.tokenize.treebank.TreebankWordDetokenizer()\r\ndb = nlp.load_dataset('bookcorpus', split=nlp.Split.TRAIN)\r\ndb = db.map(lambda x: treebank_detokenizer.detokenize(x['text'].split()))\r\n```\r\n\r\nRegarding other issues beyond the above, I'm afraid that I can't help with that.", "Ok I get it, that would be very cool indeed\r\n\r\nWhat kinds of patterns the detokenizer can't retrieve ?", "The TreebankTokenizer makes some assumptions about whitespace, parentheses, quotation marks, etc. For instance, while tokenizing the following text:\r\n```\r\nDwayne \"The Rock\" Johnson\r\n```\r\nwill result in:\r\n```\r\nDwayne `` The Rock '' Johnson\r\n```\r\nwhere the left and right quotation marks are turned into distinct symbols. Upon reconstruction, we can attach the left part to its token on the right, and respectively for the right part. However, the following texts would be tokenized exactly the same:\r\n```\r\nDwayne \" The Rock \" Johnson\r\nDwayne \" The Rock\" Johnson\r\nDwayne \" The Rock\" Johnson\r\n...\r\n```\r\nIn the above examples, the detokenizer would correct these inputs into the canonical text\r\n```\r\nDwayne \"The Rock\" Johnson\r\n```\r\nHowever, there are cases where there the solution cannot easily be inferred (at least without a true LM - this tokenizer is just a bunch of regexes). For instance, in cases where you have a fragment that contains the end of quote, but not its beginning, plus an accidental space:\r\n```\r\n... and it sounds fantastic, \" he said.\r\n```\r\nIn the above case, the tokenizer would assume that the quotes refer to the next token, and so upon detokenization it will result in the following mistake:\r\n```\r\n... and it sounds fantastic, \"he said.\r\n```\r\n\r\nWhile these are all odd edge cases (the basic assumptions do make sense), in noisy data they can occur, which is why I mentioned that the detokenizer cannot restore the original perfectly.\r\n", "To confirm, since this is preprocessed, this was not the exact version of the Book Corpus used to actually train the models described here (particularly Distilbert)? https://huggingface.co/datasets/bookcorpus\r\n\r\nOr does this preprocessing exactly match that of the papers?", "I believe these are just artifacts of this particular source. It might be better to crawl it again, or use another preprocessed source, as found here: https://github.com/soskek/bookcorpus ", "Yes actually the BookCorpus on hugginface is based on [this](https://github.com/soskek/bookcorpus/issues/24#issuecomment-643933352). And I kind of regret naming it as \"BookCorpus\" instead of something like \"BookCorpusLike\".\r\n\r\nBut there is a good news ! @shawwn has replicated BookCorpus in his way, and also provided a link to download the plain text files. see [here](https://github.com/soskek/bookcorpus/issues/27). There is chance we can have a \"OpenBookCorpus\" !", "Resolved via #856" ]

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[ "Thanks for adding this mirror link :)\r\n\r\nCould you run the following command to update the json file `dataset_infos.json` used to verify the integrity of the downloaded file ?\r\n\r\n```\r\nnlp-cli test ./datasets/rotten_tomatoes --save_infos --ignore_verifications\r\n```", "done! @lhoestq ", "the build_doc CI fail comes from master and has been fixed on master", "done @thomwolf @lhoestq " ]

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[ "found a mirror: https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz", "fixed in #484 ", "Closing this one. Thanks again @jxmorris12 for taking care of this :)" ]

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[ "This comes from an overflow in pyarrow's array.\r\nIt is stuck inside the loop that reduces the batch size to avoid the overflow.\r\nI'll take a look", "I created a PR to fix the issue.\r\nIt was due to an overflow check that handled badly an empty list.\r\n\r\nYou can try the changes by using \r\n```\r\n!pip install git+https://github.com/huggingface/nlp.git@fix-bad-type-in-overflow-check\r\n```\r\n\r\nAlso I noticed that the first 1000 examples have an empty list in the `title_urls` field. The feature type inference in `.map` will consider it `null` because of that, and it will crash when it encounter the next example with a `title_urls` that is not empty.\r\n\r\nTherefore to fix that, what you can do for now is increase the writer batch size so that the feature inference will take into account at least one example with a non-empty `title_urls`:\r\n\r\n```python\r\n# default batch size is 1_000 and it's not enough for feature type inference because of empty lists\r\nvalid_dataset = valid_dataset.map(make_input_target, writer_batch_size=3_000) \r\n```\r\n\r\nI was able to run the frozen cell with these changes.", "@lhoestq Perfect and thank you very much!!\r\nClose the issue.", "@lhoestq mapping the function `make_input_target` was passed by your fixing.\r\n\r\nHowever, there is another error in the final step of `valid_dataset.map(convert_to_features, batched=True)`\r\n\r\n`ArrowInvalid: Could not convert Thepiratebay.vg with type str: converting to null type`\r\n(The [same colab notebook above with new error message](https://colab.research.google.com/drive/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing#scrollTo=5sRrJ3_C8rLt))\r\n\r\nDo you have some ideas? (I am really sorry I could not debug it by myself since I never used `pyarrow` before) \r\nNote that `train_dataset.map(convert_to_features, batched=True)` can be run successfully even though train_dataset is 27x bigger than `valid_dataset` so I believe the problem lies in some field of `valid_dataset` again .", "I got this issue too and fixed it by specifying `writer_batch_size=3_000` in `.map`.\r\nThis is because Arrow didn't expect `Thepiratebay.vg` in `title_urls `, as all previous examples have empty lists in `title_urls `", "I am clear now . Thank so much again Quentin!", "I'm getting a hanging `dataset.map()` when running a gradio app with `gradio` for auto-reloading instead of `python`", "Maybe this is an issue with gradio, could you open an issue on their repo ? `Dataset.map` simply uses `multiprocess.Pool` for multiprocessing\r\n\r\nIf you interrupt the program mayeb the stack trace would give some information of where it was hanging in the code (maybe a lock somewhere ?)" ]

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[ "Not sure why the AWS test is failing - perhaps I made too many concurrent CI builds 😢. Can someone please rerun the CI to check the error is not on my end?", "I pushed an improved docstring and the unit tests now pass, which suggests the previous failure on AWS was simply a timeout error. \r\n\r\nFor some reason the docs are now failing to build, but does not seem related to my changes:\r\n```\r\nWarning, treated as error:\r\n/home/circleci/nlp/src/nlp/dataset_dict.py:docstring of nlp.DatasetDict.filter:27:Inline interpreted text or phrase reference start-string without end-string.\r\nmake: *** [Makefile:20: html] Error 2\r\n```\r\n\r\nAny ideas what's going wrong?", "The build_doc fail has been fixed on master.\r\nIt was due to the latest update of sphinx that has some issues, so I pinned the previous version for now.", "I noticed that you also changed the Apache Beam `open` to also use utf-8. However it doesn't have an `encoding` parameter.\r\nTherefore you should ignore lines like\r\n\r\n```python\r\nbeam.io.filesystems.FileSystems.open(filepath)\r\n```\r\n\r\nI guess you could add a rule to your regex to only include the `open` call that have a space right before it.", "Good catch @lhoestq! Your suggestion to match on `open(...)` with a whitespace was a great idea - it allowed me to simplify the regexp considerably 😄.\r\n\r\nI fixed the Apache Beam false positives and also caught a few problems in `json.load()`, e.g.\r\n```python\r\nrelation_name_map = json.load(open(rel_info), encoding='utf-8')\r\n```\r\n\r\nI've tested that the new regexp doesn't reintroduce these false positives, so I think the PR is ready for another review.", "Ok to merge this @lhoestq ?" ]

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[ "Looks good to me as well but we'll want to add a test indeed.\r\nYou can add one if you have time @TevenLeScao.\r\nOtherwise, we'll do it when we are back with Quentin. ", "I fixed it in #494 " ]

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[ "Really nice !\r\nThanks for adding this one.\r\n\r\nI noticed that there are some '-' that are left in the description in the middle of some workds. It migh come from copy-pasting the pdf paper. ex: `im-provement`. Could you fix that please ?", "@lhoestq \r\nLinebreaks have been removed! Note that there are still a few compound words that are hyphenated intentionally. ", "I think you just need to rebase from master to fix the CI :)", "Yes I made the mistake of simply merging master into this branch. A rebase seems to be neater :) Although all the commits ended up being added twice. I assume you just squash them into a single one on merge anyways?", "Yes indeed they'll be squashed" ]

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[ "Nevermind, I restarted my python session and it worked fine...\r\n\r\n---\r\n\r\nI had an authentification error, and I authenticated from another terminal. After that, no more error but it was not working. Restarting the sessions makes it work :)" ]

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[ "Thanks for reporting this issue\r\n\r\nThere was a bug where numpy arrays would get returned instead of tensorflow tensors.\r\nThis is fixed on master.\r\n\r\nI tried to re-run the colab and encountered this error instead:\r\n\r\n```\r\nAttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'to_tensor'\r\n```\r\n\r\nThis is because the dataset returns a Tensor and not a RaggedTensor.\r\nBut I think we should always return a RaggedTensor unless the length of the sequence is fixed (it that case they can be stack into a Tensor).", "Hi, I got another error (on Colab):\r\n\r\n```python\r\n# You can read a few attributes of the datasets before loading them (they are python dataclasses)\r\nfrom dataclasses import asdict\r\n\r\nfor key, value in asdict(datasets[6]).items():\r\n print('👉 ' + key + ': ' + str(value))\r\n\r\n---------------------------------------------------------------------------\r\n\r\nTypeError Traceback (most recent call last)\r\n\r\n<ipython-input-6-b8ace6c227a2> in <module>()\r\n 2 from dataclasses import asdict\r\n 3 \r\n----> 4 for key, value in asdict(datasets[6]).items():\r\n 5 print('👉 ' + key + ': ' + str(value))\r\n\r\n/usr/local/lib/python3.6/dist-packages/dataclasses.py in asdict(obj, dict_factory)\r\n 1008 \"\"\"\r\n 1009 if not _is_dataclass_instance(obj):\r\n-> 1010 raise TypeError(\"asdict() should be called on dataclass instances\")\r\n 1011 return _asdict_inner(obj, dict_factory)\r\n 1012 \r\n\r\nTypeError: asdict() should be called on dataclass instances\r\n```", "Indeed we'll update the cola with the new release coming up this week." ]

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[ "> Also, a little out of my depth there, but would there be a way to have the default pip install checklist command not require mysql and mariadb to be installed? Feels like that might be a source of confusion for users.\r\n\r\nI removed the pattern dependency, mysql is not a requirement anymore. I'm not sure where mariadb is coming from. ", "Thanks for your contribution, @marcotcr. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest you create this dataset there. Please, feel free to tell us if you need some help." ]

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[ "Cool thanks, I made those changes. LMK if you think it's ready for merge.", "Ok to merge for me" ]

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[ "The `data_dir` parameter has been removed. Now the error is `ValueError: Config name is missing`\r\n\r\nAs mentioned in #470 I think we can have one test with the first config of BUILDER_CONFIGS, and another test that runs all of the configs in BUILDER_CONFIGS", "This was fixed in #527 \r\n\r\nClosing this one, but feel free to re-open if you have other questions" ]

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[ "This PR was already approved by @lhoestq in #456 . This one just make style to remove some typos" ]

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[ "Ok I tried to add the dummy dataset (I actually modified the dummy_data command to generate them for me because it was too painful to do that manually).\r\n\r\nThe dummy_data test seems to work:\r\n```bash\r\nRUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_iwslt2017\r\n```\r\n\r\nHowever the test on the full data fails, because the `**config_kwargs` don't include `pair, multilingual`.\r\nI could add a default parameter for the Config (but that feels broken, how can one config be the \"default\" ?). If I do I still have errors, saying that something within the downloader is a directory so I'm not sure where that comes from.\r\n\r\nI can share my auto_zip dummy data code if you want (I tried to keep it clean). [Edit: it's [here](https://github.com/Narsil/nlp/tree/auto_zip)]. \r\nThe way it works is that it just keeps X line from the beginning of the original files, and Y lines at the end. It's good enough for my usage, but I guess it could work for most data files out there (as long as they're real text and not binary format)", "The slow test doesn't support dataset that require config parameters that don't have default values.\r\n\r\nTo improve that we can replace it by two tests:\r\n- one test that loads the default config (it can simply be the first config of the config lists for example)\r\n- one tests that iterate over all configs and load them all one by one\r\n\r\nBy using the configs inside the builder config lists, there is no need to instantiate new configs, so the missing parameter error doesn't happen.\r\n\r\nDoes that sound good to you ?", "Seems fair.\r\nHowever I'm unsure what I should do ?\r\n\r\nShould I wait for #527 to pass and rebase and the command will be the same ?\r\nShould I update something ?", "I think everything is fine on your side. Thanks for adding this dataset :)\r\n\r\nI think it's better to wait for the slow test to be updated if you don't mind.\r\n", "Sure ! :)", "Thanks for fixing the isort/black changes :)\r\nFeel free to merge if it's good for you @Narsil " ]

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[ "Hi ! Did you try to set the output format to pytorch ? (or tensorflow if you're using tensorflow)\r\nIt can be done with `dataset.set_format(\"torch\", columns=columns)` (or \"tensorflow\").\r\n\r\nNote that for pytorch, string columns can't be converted to `torch.Tensor`, so you have to specify in `columns=` the list of columns you want to keep (`input_ids` for example)", "Hello . Yes, I did set the output format as below for the two columns \r\n\r\n `train_dataset.set_format('torch',columns=['Text','Label'])`\r\n ", "I think you're having this issue because you try to format strings as pytorch tensors, which is not possible.\r\nIndeed by having \"Text\" in `columns=['Text','Label']`, you try to convert the text values to pytorch tensors.\r\n\r\nInstead I recommend you to first tokenize your dataset using a tokenizer from transformers. For example\r\n\r\n```python\r\nfrom transformers import BertTokenizer\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\n\r\ntrain_dataset.map(lambda x: tokenizer(x[\"Text\"]), batched=True)\r\ntrain_dataset.set_format(\"torch\", column=[\"input_ids\"])\r\n```\r\n\r\nAnother way to fix your issue would be to not set the format to pytorch, and leave the dataset as it is by default. In that case, the strings are returned normally when you get examples from your dataloader. It means that you would have to tokenize the examples in the training loop (or using a data collator) though.\r\n\r\nLet me know if you have other questions", "Hi, actually the thing is I am getting the same error and even after tokenizing them I am passing them through batch_encode_plus.\r\nI dont know what seems to be the problem is. I even converted it into 'pt' while passing them through batch_encode_plus but when I am evaluating my model , i am getting this error\r\n\r\n\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n<ipython-input-145-ca218223c9fc> in <module>()\r\n----> 1 val_loss, predictions, true_val = evaluate(dataloader_validation)\r\n 2 val_f1 = f1_score_func(predictions, true_val)\r\n 3 tqdm.write(f'Validation loss: {val_loss}')\r\n 4 tqdm.write(f'F1 Score (Weighted): {val_f1}')\r\n\r\n6 frames\r\n/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataset.py in <genexpr>(.0)\r\n 160 \r\n 161 def __getitem__(self, index):\r\n--> 162 return tuple(tensor[index] for tensor in self.tensors)\r\n 163 \r\n 164 def __len__(self):\r\n\r\nTypeError: new(): invalid data type 'str' ", "> Hi, actually the thing is I am getting the same error and even after tokenizing them I am passing them through batch_encode_plus.\r\n> I dont know what seems to be the problem is. I even converted it into 'pt' while passing them through batch_encode_plus but when I am evaluating my model , i am getting this error\r\n> \r\n> TypeError Traceback (most recent call last)\r\n> in ()\r\n> ----> 1 val_loss, predictions, true_val = evaluate(dataloader_validation)\r\n> 2 val_f1 = f1_score_func(predictions, true_val)\r\n> 3 tqdm.write(f'Validation loss: {val_loss}')\r\n> 4 tqdm.write(f'F1 Score (Weighted): {val_f1}')\r\n> \r\n> 6 frames\r\n> /usr/local/lib/python3.6/dist-packages/torch/utils/data/dataset.py in (.0)\r\n> 160\r\n> 161 def **getitem**(self, index):\r\n> --> 162 return tuple(tensor[index] for tensor in self.tensors)\r\n> 163\r\n> 164 def **len**(self):\r\n> \r\n> TypeError: new(): invalid data type 'str'\r\n\r\nI got the same error and fix it .\r\nyou can check your input where there may be string contained.\r\nsuch as\r\n```\r\na = [1,2,3,4,'<unk>']\r\ntorch.tensor(a)\r\n```", "I didn't know tokenizers could return strings in the token ids. Which tokenizer are you using to get this @Doragd ?", "> I didn't know tokenizers could return strings in the token ids. Which tokenizer are you using to get this @Doragd ?\r\n\r\ni'm sorry that i met this issue in another place (not in huggingface repo). ", "@akhilkapil do you have strings in your dataset ? When you set the dataset format to \"pytorch\" you should exclude columns with strings as pytorch can't make tensors out of strings", "Closing due to inactivity." ]

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[ "Indeed. Solution 1 is the simplest.\r\n\r\nThis is actually a recurring problem.\r\nI think we should scan all the datasets with regexpr to fix the use of `open()` without encodings.\r\nAnd probably add a test in the CI to forbid using this in the future.", "I'm happy to tackle the broader problem - will open a PR when it's ready!", "That would be awesome!", "I've created a simple function that seems to do the trick:\r\n\r\n```python\r\ndef apply_encoding_on_file_open(filepath: str):\r\n \"\"\"Apply UTF-8 encoding for all instances where a non-binary file is opened.\"\"\"\r\n \r\n with open(filepath, 'r', encoding='utf-8') as input_file:\r\n regexp = re.compile(r\"\"\"\r\n (?!.*\\b(?:encoding|rb|wb|wb+|ab|ab+)\\b)\r\n (open)\r\n \\((.*)\\)\r\n \"\"\")\r\n input_text = input_file.read()\r\n match = regexp.search(input_text)\r\n \r\n if match:\r\n print('Found match!', match.group())\r\n # append utf-8 encoding to matching groups in-place\r\n output = regexp.sub(lambda m: m.group()[:-1]+', encoding=\"utf-8\")', input_text)\r\n with open(filepath, 'w', encoding='utf-8') as output_file:\r\n output_file.write(output)\r\n else:\r\n print(\"No match found!\")\r\n```\r\n\r\nThe regexp does a negative lookahead to avoid matching on cases where the encoding is already specified or when binary files are involved.\r\n\r\nFrom an implementation perspective:\r\n\r\n* Would it make sense to include this function in `nlp-cli` so that we can run something like\r\n```\r\nnlp-cli fix_encoding path/to/folder\r\n```\r\nand the command recursively fixes all files in the target?\r\n* What is the desired behaviour in the CI test? Here we could either have a simple script that we run as a `job` in the CI and raises an error if a missing encoding is detected. Alternatively we could incorporate this behaviour into the CLI and run that in the CI.\r\n\r\nPlease let me know what you prefer among the alternatives.\r\n", "I realised I was overthinking the problem, so decided to just run the regexp over the codebase and make the PR. In other words, we can ignore my comments about using the CLI 😸 " ]

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[ "Thanks!" ]

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[ "The cast function is now called inside `features.encode_example`.\r\nI also added `encode_batch` that was missing.\r\n\r\nMoreover I used the cast function in `Dataset.map` to support torch/tensorflow tensors or numpy arrays inputs.\r\n\r\nThere are tests for tensors inputs in metrics and in .map", "I think we can merge" ]

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[ "One note on features inference:\r\n\r\nif an arrow type is `struct of items` where each item is a `list`, then we return a `dict` in which each item is a `Sequence`.\r\nIt means that we don't use the Sequence <-> dict swap when we infer features.\r\n\r\nIt's fine because the swap is generally used in dataset scripts, in which features are defined (inferred features are discarded)", "If it's fine for you @thomwolf we can merge this one :) ", "Yes this is fine I think!" ]

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[ "I think the problem is related to `wiki_dpr` dataset which is making the circle CI failed as you can see:\r\n```\r\nFAILED tests/test_dataset_common.py::AWSDatasetTest::test_load_dataset_wiki_dpr\r\nFAILED tests/test_hf_gcp.py::TestDatasetOnHfGcp::test_script_synced_with_s3_wiki_dpr/dummy_psgs_w100_no_embeddings\r\nFAILED tests/test_hf_gcp.py::TestDatasetOnHfGcp::test_script_synced_with_s3_wiki_dpr/dummy_psgs_w100_with_nq_embeddings\r\nFAILED tests/test_hf_gcp.py::TestDatasetOnHfGcp::test_script_synced_with_s3_wiki_dpr/psgs_w100_no_embeddings\r\nFAILED tests/test_hf_gcp.py::TestDatasetOnHfGcp::test_script_synced_with_s3_wiki_dpr/psgs_w100_with_nq_embeddings\r\n\r\n```\r\nI'm facing the same issues with my last commits, I tried to rebase from master but it still not working. Maybe @lhoestq can help with.", "Hello, I am confused about the next steps I need to do. Did the forced merge solve the issue ?", "Hello :)\r\nI think you can just rebase from master and it should solve the CI error" ]

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[ "Thanks @TevenLeScao for finding this issue", "Thanks @lhoestq for catching this ❤️" ]

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[ "Sorry one nit: Could we use named arguments for the call to BLEURT?\r\n\r\ni.e. \r\n scores = self.scorer.score(references=references, candidates=predictions)\r\n\r\n(i.e. so it is less bug prone)\r\n", "Following up on Ankur's comment---we are going to drop support for\npositional (not named) arguments in the future releases because it seems to\ncause bugs and confusion. I hope it doesn't create too much of a mess.\n\nLe jeu. 30 juil. 2020 à 10:44, ankparikh <[email protected]> a\nécrit :\n\n> Sorry one nit: Could we use named arguments for the call to BLEURT?\n>\n> i.e.\n> scores = self.scorer.score(references=references, candidates=predictions)\n>\n> (i.e. so it is less bug prone)\n>\n> —\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/nlp/pull/455#issuecomment-666414514>, or\n> unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ABTMRNGAN2PMECS5K4DIHJDR6GBMLANCNFSM4PL323FA>\n> .\n>\n", "> Following up on Ankur's comment---we are going to drop support for positional (not named) arguments in the future releases because it seems to cause bugs and confusion. I hope it doesn't create too much of a mess. Le jeu. 30 juil. 2020 à 10:44, ankparikh <[email protected]> a écrit :\r\n> […](#)\r\n> Sorry one nit: Could we use named arguments for the call to BLEURT? i.e. scores = self.scorer.score(references=references, candidates=predictions) (i.e. so it is less bug prone) — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <[#455 (comment)](https://github.com/huggingface/nlp/pull/455#issuecomment-666414514)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ABTMRNGAN2PMECS5K4DIHJDR6GBMLANCNFSM4PL323FA> .\r\n\r\nChanged @ankparikh @tsellam, thanks for taking a look!", "We should avoid positional arguments in metrics on our side as well. It's a dangerous source of errors indeed." ]

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