Huggingface datasets map batched. , without loading the entire dataset into memory).

Huggingface datasets map batched map() to process big datasets, its speed degraded very fast and my disk was filled up, then the process crashed. map( group_texts, batched=True, num_proc=num_proc, ) This code comes from the processing of the run_mlm. Need for speed Hi! When it comes to tensors, PyArrow (the storage format we use) only understands 1D arrays, so we would have to store (potentially) a significant amount of metadata to be able to restore the types after map fully. However, in the mapped dataset, these tensors have turned to lists! import torch from datasets import load_dataset pr Hi, just started using the Huggingface library. fit(). You can also remove a column using :func:`Dataset. Notifications You must be signed in to change notification settings; Fork 2. map(preprocess2, batched=True, num_proc=8) ds = ds. py example script with my custom dataset, but I am getting out of memory error, even using the keep_in_memory=True parameter. Apply data augmentations to your dataset with set_transform(). map(lambda e: tokenizer(e[‘texts Important. huggingface. I tried a lot of parameters combinations but it always hangs. I use map like this:. This guide shows specific methods for processing text datasets. map(). I am running the script on a Slurm cluster with 128 CPUs, no GPU. column_names, batched = True, num_proc = 1, desc = "Selecting rows with Dataset. dataset. (for context: i am using a translation model to translate multiple SFT, DPO datasets to multiple other language from english) I’ve been using the . dataset = load_dataset(‘csv’, data_files=filepath) When we apply map functions on the datasets like below, the cache size keeps growing df= df. Fast tokenizers need a lot of texts to be able to leverage parallelism in Rust (a bit like a GPU needs a batch of examples to be more efficient). Caching policy All the methods in this chapter store the updated dataset in a cache file indexed by a hash of current state and all the argument used to call the method. map function, the "batch_size" is by default set as 1000. 16 Suppose I have a dataset with 100 rows and I have a func that could turn each row into 10 rows. Notifications You must be signed in to change notification settings; Oct 19, 2023, 2:26 PM Mario Šaško ***@***. The corresponding Similar to the Dataset. def preprocess_function(samples): speech_list = [speech_file_to_array_fn(path) for path in samples[input_column]] target_list = 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 result = dataset. I also think this would be better suited for the forum at https://discuss. The code is using only one gpu. Dataset built from list of texts. tf. nn. What works: Using DataLoader with You signed in with another tab or window. map(collate_fn, batched=True, batch_size=8, remove_columns=laion_ds. iter(batch_size=) but this cannot be used in combination with a torch DataLoader since it just returns an iterator. Therefore, when doing a Dataset. Dataset instance. I’ve tried different batch_size and still get the same errors. From the docs I see that mapping your input of n sample to an output of m samples should be possible. ; token_type_ids: indicates which sequence a token belongs to if there is more than one sequence. map (here), the example given in “Batch processing” → “Split long examples” says “Batch processing enables interesting applications such as splitting long sentences into shorter chunks and data augmentation” with the following code: def chunk_examples(examples): chunks = [] for sentence in examples["sentence1"]: chunks += Note. Commented May 19, tokenized_dataset = tokenized_dataset. Code: from transformers import AutoTokenizer from datasets import Dataset data = { "text":[ "This is a test" ] } dataset = Dataset. Dataset object can be iterated over to yield batches of data, and can be passed directly to methods like model. Batch mapping¶. class SQUAD(Dataset): def __init__(self): # Load our training dataset and tokenizer self. utils. config. 3. DataParallel(model). I found that no matter how much batch_size is set, the speed is the same. groupby this column). from_dict(data) model_name = 'roberta-large-mnli' tokenizer = I'm implementing a worker function whose runtime will depend on specific examples (e. In this example, batched_dataset is still an IterableDataset, but each item yielded is now a batch of 32 samples instead of a single sample. Often times you may want to modify the structure and content of your dataset before you use it to train a model. map(lambda x: tokenizer(x['text']), batched=True) But it doesn't work as it throws the error: KeyError: 'text' Can you please guide me on how to fix it? Steps to reproduce the bug `from datasets import load_dataset; dataset = load_dataset("amazon_reviews_multi")` Then this code: `from transformers import AutoTokenizer Batch mapping¶. Combining the utility of datasets. Here is my code: model_name_or_path = &quot;faceb My use case involved building multiple samples from a single sample. Once you have a preprocessing function, use the map() function to speed up processing by Hi, I have tested with simple custom text data. A simplified, (mostly) reproducible example (on a 16 GB RAM) is below. Need for speed It creates files under cache directory. In the dataset preprocessing step using . The fastest way to tokenize your entire dataset is to Typical EncoderDecoderModel that works on a Pre-coded Dataset The code snippet snippet as below is frequently used to train an EncoderDecoderModel from Huggingface’s transformer library from transformers import EncoderDecoderModel from transformers import PreTrainedTokenizerFast multibert = Important. Map. Is I have a datasets. This opens the door to many interesting applications such as tokenization, splitting long sentences into shorter chunks, and data augmentation >>> augmented_dataset = smaller_dataset. The map() function supports processing batches of examples at once which speeds up Important. forward(batch) return out dataset = I am running the run_mlm. Operate on batches by setting batched=True. map() method as done in the run_mlm. data. I am wondering how can I pass model and tokenizer to my processing function along with the batch when using the map method. The batched=True argument I am seeing different results when I do dataset. ***> wrote: Hi! You should use the batched map for the best performance (with num_proc=1) - the fast tokenizers can process a batch's samples in parallel. 🤗 Datasets provides the necessary tools to do this, but since each dataset is so different, the processing approach will vary individually. g. Thanks! huggingface / datasets Public. FYI, I am using multiprocessing by setting num_proc parameter of map(). From each row in the dataset, I’d like to have from 0 to infinite number of rows in the new dataset, each having a portion of the textual data. How cloud I do. On the other hand, a dataset that loaded from the disk (via memory mapping) uses the directory from which the def select_rows (examples): # `key` is a column name that exists in the original dataset # The following line simulates no matches found, so we return an empty batch result = {'key': []} return result filtered_dataset = dataset. You signed out in another tab or window. Create a function to preprocess the audio array with the feature extractor, and truncate and pad the sequences into tidy rectangular tensors. map, at some point it hangs and never finishes running. Output: Dataset({ features: ['filepath', 'class', 'fold'], num_rows: 6810 }) When I attempt to map using a preprocess function this works correctly: def preprocess So, the function 'preprocess_function' below is made for huggingface datasets. I am using dataset. Tensor objects out of our datasets, and how to stream data from Hugging Face Dataset objects to Keras methods like model. , for llama2-7b: # - Get tokenized train data set # Note: Setting `batched=True` in the `dataset. #SBATCH --ntasks=1 --cpus-per-task=128 --mem=50000M #SBATCH --time=200:00:00 Code - should be Saved searches Use saved searches to filter your results more quickly tokenized_datasets = final_dataset. I want to build embeddings using In the document of Dataset. I cannot even use for loop, values of the dictionary are not modified in a loop. map() to a function that returns a dict of torch tensors (like a tokenizer from the repo transformers). map with num_proc of 1 or none is fine but num_proc over 1 occurs PermissionError. Assume I have the following Dataset object to represent that: import Dataset. To sketch it I wanted to do something similar to def measure_sth(examples, model): batch = COLLATE_FUNCTION(examples) out = model. It seems to be working really well, and saves a huge amount of disk space compared to downloading a dataset like OSCAR locally. map] with batch mode is very powerful. map method: from datasets import Dataset from transformers import AutoModel, AutoTokenizer checkpoint = 'sentence-transformers/p Thank you for reply! @mariosasko I’m not for sure about cache_files, but dataset should be cached to disk I guess?Cause there is some tips like “found cached files from” before go map. I’m using wav2vec2 for emotion classification (following @m3hrdadfi’s notebook). I want to know if is it possible to execute the dataset. select(range(10)) or train_datasets = train_dataset. The function is applied on-the-fly on the examples when iterating over the dataset. , while most examples take 0. dataset = load_dataset("squad", split="train") self. def tokenize_function(example): Hi, I am preprocessing the Wikipedia dataset. A subsequent call to any of the methods detailed here (like datasets. I searched the internet but could not find any relevant answer. In your last step since you are adding the tokenized_texts it might be possible the vectors are getting concatenated instead of adding up and thus giving a 1999(excluding the cls token). , without loading the entire dataset into memory). map() function from datasets with batched=True, and batch_size specified. timeseries_dataset_from_array. map( preprocess_function, batched=True, I’m currently working with the Hugging Face datasets library and need to apply transformations to multiple datasets (such as ds_khan and ds_mathematica) using the . I also pass the batch size argument when calling the timeseries_dataset_from_array function, so my dataset is a BatchDataset. map(preprocess4, batched=True, num_proc=8) As mentioned above, It creates lot of cache files at each step. When I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect. Is there a way I could do it using the package? Currently I got a length mismatch issue when using map. map( tokenize_function, batched=True, num_proc=args. The ability to control the size of the generated dataset can be leveraged for many interesting use-cases. For a guide on how to process any type of dataset, take a look at the general process guide. map(batched=True)) preserve individual data samples? How do I access each individual sample after batch mapping? I have a 50K dataset Hello, I’m trying to batch a streaming dataset. This seems to be the approach that worked for me. 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools - huggingface/datasets TypeError when applying map after set_format (type='torch') Loading I want to call DatasetDict map function with parameters, and I dont know how to do it. map return a batch of examples (multiple rows) instead of an example (single row) while batched is set to False? I'm augmenting my dataset by splitting Instead of processing a single example at a time, you should use the batched map for the best performance (with num_proc=1) - the fast tokenizers can process a batch's I have a dataset: Dataset({ features: ['text', 'request_index'], num_rows: 1000 }) The dataset contains 1000 rows for N request_index. map ( select_rows, remove_columns = dataset. I ran this with num_proc=2, not sure if setting it to all cpu cores would make much of a I’m trying to pre-process my dataset for the Donut model and despite completeing the mapping it is running for about TensorFlow¶. map() with batch mode is very powerful. map(tokenize, batched=True) in notebook Is there an established method of adding type hinting to map/batched map functions? This is mainly for other human readers to understand what the input/output row/batch should look like, but would be a “nice to have” if it also allowed IDE type checking. Hi! With the batched flag in map, you control whether your map function will get a single example to process or a batch of samples, which size is determined by batch_size (1000 by default), in a single call. I’m thinking a method to datasets. for train_dataset. A reproducible kaggle kernel can be found here. isYufeng June 6, 2024, tokenized_data = dataset. I tried various combinations like converting model to model = torch. Need for speed Hi ! Yes you can remove the other columns with: laion_ds_batched = laion_ds. map to get the same result. Often times, it is faster to work with batches of The Dataset. I don’t think I changed any parameters to the map function. If you are using TensorFlow, you can use to_tf_dataset to wrap the dataset with a tf. map(function, batched=True) functionality. json", unk_token=“[UNK]”, pad_token=“[PAD]”, word_delimiter @fingerprint (inplace = True) def cast_ (self, features: Features): """ Cast the dataset to a new set of features. Looks like a multiprocessing issue. Batch mapping Combining the utility of Dataset. This document is a quick introduction to using datasets with TensorFlow, with a particular focus on how to get tf. `np. map() also supports working with batches of examples. This doesn't happen with datasets version 2. This style of batched fetching is only used by streaming datasets, right? I’d need to roll my own wrapper to do the same on-the-fly chunking on a local dataset loaded from disk? Yes indeed, though you can stream the data from your disk as well if you want. datasets version: 2. map(), etc) will thus reuse the cached file instead of recomputing the operation (even in another python When I set batched=False then the progress bar shows green color which indicates success, but if I set batched=True then the progress bar shows red color and does not reach 100%. This is what I have done so far: coco_train = load_dataset("facebook/pmd", use_auth_token=hf_token, name="coco", I’m exploring using streaming datasets with a function that preprocesses the text, tokenizes it into training samples, and then applies some noise to the input_ids (à la BART In the How-to map section, there are examples of using batch mapping to: Split long sentences into shorter chunks. 1 Like. ipynb at master · huggingface/notebooks · GitHub. 000 PIL-image as numpy array or tensorflow tensor and convert it to tensorflow-dataset. map must also convert the when the "batched" argument is set to true in dataset. When mapping is used on a dataset with more than one process, there is a weird behavior when trying to use filter, it's like only the samples from one worker are retrieved, one needs to specify the same num_proc in filter for it to work properly. . Sample code: datasets = load_dataset('csv', data_files={ 'train': tokenizer = Wav2Vec2CTCTokenizer(r"D:\Work\Speech to text\Dataset\tamil_voice\Processed csv\vocab. ***> wrote: Hi I don’t think this is a request for a dataset like you labeled it. I am trying to train a language model in tensorflow using the nice new TF notebooks notebooks/language_modeling_from_scratch-tf. sort(), datasets. py example script of transformers. Basically, I process documents through a model to extract the last_hidden_state, using the "map" method on a Dataset object, but would like to average the result over a categorical column at the end (i. from datasets import load_dataset Using Datasets with TensorFlow. e. 5k; Star 18. Dataset format. I have a multi-GPU system, and doing the above usually takes about ~10 minutes. The second call to map should reuse the cached processed dataset from mds1, but it instead it redoes the tokenization because of the behavior of dumps. I am particularly interested in interleaving these transformed datasets while keeping the data Hello all, I have a dataset object train_ds. py Steps to reproduce the bug block_size = data_args. It allows you to speed up processing, and freely control the size of the generated dataset. ', 'Amrozi accused his brother, whom he called " the witness ", of deliberately Batch mapping¶. Indeed, by default, datasets performs an expensive cast on the values returned by map to convert them to one of the types supported by PyArrow (the underlying storage format used by datasets). Here is my code: def _get_embeddings(texts): I’m getting this issue when I am trying to map-tokenize a large custom data set. My custom dataset is a set of CSV files, but for now, I’m only loading a single file (200 Mb) with 200 million rows. 0 OS: Ubuntu 20 LTS When I used HuggingFace dataset. It is helpful to understand how Huggingface datasets package advises using map() to process data in batches. The current implementation loads each element of a batch individually which can The tokenizer returns a dictionary with three items: input_ids: the numbers representing the tokens in the text. What I want is a mapped dataset that has 1000 rows. I am using this LED model here. How to optimize it in terms of runtime and disk space ? I’ve been discovering HuggingFace recently. I've loaded a dataset and am trying to apply a map() function to it. need a lot of texts to be able to leverage parallelism in Rust. map(preprocess_1, num_cores=8) df= df. 16%). Args: features (:class:`datasets. Since the used dataset Wikipedia is large, I hope the processing is one time and can be reused later. map(tokenize_func, batched=True) Related topics Topic Replies Typical EncoderDecoderModel that works on a Pre-coded Dataset The code snippet snippet as below is frequently used to train an EncoderDecoderModel from Huggingface’s transformer library from transformers import EncoderDecoderModel from transformers import PreTrainedTokenizerFast multibert = 1. csv", "test" Hi ! Currently a dataset that is in memory doesn't know doesn't know in which directory it has to read/write cache files. Is there any way I can do that with Datasets. map() is to speed up processing functions. map (augment_data, batched= True, remove_columns=dataset. tokenizedDataset = dataset. map(preprocess_function, batched=True) Dataset map and flatten - Datasets - Hugging Face Forums Loading I am creating a timeseries Dataset using tf. The dataset consists of a text file that has a whole document in each line, meaning that each line overpasses the normal 512 tokens limit of most tokenizers. map(zero_shot_classify_sequences, batched=True, batch_size=10), the output does not look like I’d expect. map(, batched=True, num_proc=4) vs dataset. from_pretrained(model_name) tokenized_datasets = dataset. Before running the script I have about 128 Gb free disk, when I run the script it creates a Note. map with the following arguments, tokenized_ds = dataset. This is my tokenizer method. I’d like to apply zero-shot classification on all these texts in a batched way using HuggingFace Datasets’ . This opens the door to many interesting applications such as tokenization, splitting long sentences into shorter chunks, and data augmentation I’m trying to tokenize a dataset and move all the torch tensors to gpu, but somehow this doesn’t work: import datasets cola = datasets. The fastest way to tokenize your entire dataset is to I am tokenizing my dataset with a customized tokenize_function to tokenize 2 different texts and then append them toghether, this is the code: # Load the datasets data_files = { "train": "train_pair. I ran this with num_proc=2, not sure if setting it to all cpu cores would make much of a Map ¶ Some of the more powerful applications of 🤗 Datasets come from using datasets. If batched is Hi, I’m having an issue of running out of memory when trying to use the map function on a Dataset. 0. By default, datasets return regular Python objects: integers, floats, strings, lists, etc. Features that generated a TypedDict object (with a row/batch version)? The tokenizer returns a dictionary with three items: input_ids: the numbers representing the tokens in the text. column_names) Hello, I tried to use one of my data collators inside a function passed to the datasets. 2. In the code below the data is filtered differently Background Huggingface datasets package advises using map() to process data in batches. block_size IGNORE_INDEX = This guide shows specific methods for processing image datasets. Learn how to: Use map() with image dataset. DEFAULT_MAX_BATCH_SIZE. how do I make 0 rows in Hi! Adding “batched reduce” has been attempted once in Add reduce function by AJDERS · Pull Request #5533 · huggingface/datasets · GitHub, but we decided not to merge it for the reasons mentioned in the PR. preprocessing_num_workers, I’m running datasets. This suggests workers are assigned a list of jobs at the beginning, leaving them idle when they’re I’m running datasets. object` was a deprecated alias for the builtin `object`. The map() function can apply transforms over an entire dataset. I think the problem is in the I/O operations done in the map function, but I don’t know what the I am using 31 workers (preprocessing_num_workers=31) and thus it creates 31 cache*. Code is modified from run_clm. It is advised to set batched to True whenever possible for better performance (e. Usually it hangs at the same %. A dataset in non streaming mode needs to have a fixed number of samples known in advance as well as a I posted an answer bellow with the specifics from the HuggingFace Datasets people :) – Daniel Díez. There, you can find a Colab that explains how to use Dataset. The default batch size is 1000, but you can adjust it with the batch_size argument. In the How-to map section, there are examples of using batch mapping to: Split long The ability to control the size of the generated dataset can be leveraged for many interesting use-cases. keras. The goal was to measure something on model outputs. , our fast tokenizers can process a batch in parallel). map` with `feature` but :func:`cast_` is in-place (doesn't copy the data to a new dataset) and is thus faster. You switched accounts on another tab or window. map(preprocess_function) Column 1 named input_ids expected length 599 but got length 1500 · Issue #1817 · huggingface/datasets · GitHub. Align dataset labels with label ids for NLI datasets. 8. Code; Issues 628; Pull requests 80; Discussions; Actions; Batched dataset map throws exception that cannot cast fixed length array to Sequence #6654. Dataset. You can specify whether the function should be batched or not with the ``batched`` parameter: - If batched is False, then the function takes 1 example in and should How to tokenize using map - Datasets - Hugging Face Forums Loading Hi! Thanks for reporting and providing a reproducible example. 500 images corentinm7/MyoQuant-SDH-Data · Datasets at I am using the run_mlm. Describe the bug. Stopping it and re-running doesn’t help (yet, cached files are loaded properly) I run dataset. Similar to the Dataset. from datasets import load_dataset, load_metric from transformers import AutoTokenizer raw_datasets = load_dataset(" Skip to main content ["input_ids"] return model_inputs tokenized_datasets = raw_datasets. The most important thing to remember is to call the audio array in the feature extractor since the array - the actual speech signal - is the model input. arrow files in my_path/train (there is only a train split). This cast is not needed on NumPy arrays as PyArrow supports them natively, so one way to make this I’d like to apply zero-shot classification on all these texts in a batched way using HuggingFace Datasets’ . map() function for a regular Dataset, 🤗 Datasets features IterableDataset. It’s extremely slow, with 12it/s, which totals 140h to process the dataset. I am using map on this batched Dataset (ds), 用UIE中的代码为例，当map中batched=True时(不执行print那行)，会报错"TypeError: list indices must be integers or slices, not str" 当batched=Fase时，执行print(train_ds[0])正常，执行print(train_ds[0: 5]) 则也会报错"TypeError: list indices must be integers or slices, not str" def map (self, function: Callable, batched: bool = False, batch_size: int = 1000): """ Return a dataset with the specified map function. I had used map() function to I am trying to transform my data to dataset format to use it with a bert tokenizer but I get this error : raise TypeError( TypeError: Provided `function` which is Describe the bug. Defaults to False (returns the whole datasetas once) batch_size (int, optional) — The size (number of rows) of the batches if batched is True. For my application, I need to continue to reference the original dataset's columns. Learn how to: Tokenize a dataset with map(). So, any pointer resolving it would be much appreciated. Clearly, during debugging I can see that the shapes are perfectly what I expect when they go through their transformations via map - however when I iterate over the dataset, then I’m getting un-batched arrays that are clearly 2D Yet, when I’m running the dataset. So in your case, this means that some workers finished processing their shards earlier than others. load(audio, sr=16000) This guide shows specific methods for processing image datasets. Using . Thanks very much. map(function, batched=True) However, when I do updated_dataset = dataset. In this example, batched_dataset is still an IterableDataset, but each item yielded is now a . I am preprocessing this data and experimenting with both datasets. On Tue, Nov 10, 2020 at 12:21 PM Thomas Wolf ***@***. map() for processing an IterableDataset. I apply the tokenizer to my custom dataset using the datasets. we try to keep the I have the following simple code copied from Huggingface examples: model_checkpoint = "distilgpt2" from transformers import AutoTokenizer tokenizer = AutoTokenizer. map(my_processing_func, model, tokenizer, batched=True) when I do this it Hi, I have csv files with about 1 million rows containing textual data. According to the docs, it returns a tf. I’m curious what the best way to encode these labels to integers would be. map() method in Hugging Face Transformers is typically used with the Datasets library, which is a separate library also developed by Hugging Face. with training_args. In their example code on pretraining masked language model, they use map() to tokenize all data at a stroke tokenized_datasets = raw_datasets. Does that mean my map function failed or something else? I am running it this problem while using the datasets library from huggingface. py provided in the transformers repository to pretrain bert. map. Motivation. Instead of transforming all the data at once. from_pretrained(model_checkpoint, use_fast=True) def Hi! I am currently using the datasets library for the Trainer function to fine-tune a pre-trained model. Apply data augmentations to a dataset with set_transform(). map(preprocess_function, num_proc=4, batched=True, remo Hello, I have a the following issue. I have function with the following API: def tokenize_function(tokenizer, examples): s1 = examples["premise"] s2 = examples The map() method from a dataset does not retain the tensor that is selected in the return_tensor argument. Combining the utility of Dataset. For a given text, I get the following: Hi, I’m trying to use map on a dataset of size about 100GB, it hangs every time. It already support an option to do batch iteration via . py example. Running it with one proc or with a smaller set it seems work. def my_processing_func(batch, model, tokenizer): –code– I am using map like this new_dataset = my_dataset. column_names, batch_size= 8) >>> augmented_dataset[: 9]["data"] ['Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence . -. Tokenizer Spend time even longer than training. Background Huggingface datasets package advises using map() to process data in batches. Share. Map The map() function can apply transforms over an entire dataset. Hi, could you add an implementation of a batched IterableDataset. Defaults to datasets. map() with num_proc=64, and after a while the cpu utilization falls far below 100% (3. Hi ! TL;DR: How to process (resize+rescale) a huggingface dataset of 16. Thanks! (also, gently pinging @lhoestq and @patrickvonplaten) Code Reference: # Loading the created dataset No, the batch size should not be the same as for the training. I have a large dataset. Need for speed Combining the utility of [Dataset. ; These values are actually the model inputs. I will have to watch the course these days. In the How-to Map section, there are examples of using batch mapping to: Split long Does batch mapping ( i. map(), etc) will thus reuse the cached file instead of recomputing the operation (even in another python Hi, I have audio dataset. map method is 1,000 which is more than enough for the use case. This means a tf. The tokenizer returns a dictionary with three items: input_ids: the numbers representing the tokens in the text. map(preprocess1, batched=True, num_proc=8) ds = ds. And Trainer’s I’m trying to pre-process my dataset for the Donut model and despite completeing the mapping it is running for about 100 mins -. map(, batched=True, num_proc=16) Here is the output: Map (num_proc=4 I apply Dataset. The primary purpose of datasets. Same is being done with Huggingface datasets as Feature request. In their example code on pretraining masked language model, they use map() to tokenize all data Can I make dataset. Thoughts? Thanks! dataset[‘test’]. Closed keesjandevries opened this issue Feb 9, 2024 · 2 comments Hi, I am new to the Huggingface community and currently facing difficulty in running an example evaluation script on multi-gpu. But once I use DeepSpeed (deepspeed --include localhost:0,1,2), the process takes I’m trying to pre-process my dataset for the Donut model and despite completeing the mapping it is running for about 100 mins -. The fingerprint is computed by hashing the code and the variables in your map function. numpy ops to manipulate those numpy arrays. main_process_first(desc="train dataset map pre-processing"): train_dataset = train_dataset. The fastest way to tokenize your entire dataset is to Describe the bug. map() operations as in below ds = ds. However, I find it always re-computing instead of load from the disk. map() 方法有一个 batched 参数，如果设置为 True, map 函数将会分批执行所需要进行的操作（批量大小是可配置的，但默认为 1,000）。例如，之前对所有 HTML 进行转义的 map 函数运行需要一些时间（您可以从进度条中读取所用时间）。 They use a load_dataset without importing the datasets module. For pandas, I am using number of cores as by batch count ( 1 million/num_cores is batch size) and process them in parallel. The default in the Dataset. I notice the description of the I am processing textual data. Just a view of what I need to do: # this is how my dataset looks like dataset = [(1, 2, 3), (5, 7 Hi ! Computing the fingerprint of the mapped dataset is necessary for the caching mechanism to work. ; attention_mask: indicates whether a token should be masked or not. For example, you may want to remove a column or cast it as a different type. cache/huggingface, but only reclaimed a small fraction of my disk space (3GB). As for why it’s faster, it’s all explained in the course. This dataset I tokenize using Dataset. map(preprocess_2, num_cores=8) Is there a way to disable caching on each map() function applied. The dataset is of version 1. Scenario: Interleaving two iterable datasets of unequal lengths (all_exhausted), followed by a batch mapping with batch size 2 to effectively merge the two datasets and get a sample from each dataset in a single batch, with drop_last_batch=True to skip the last batch in case it doesn't have two samples. Here is my code: model_name_or_path = "facebook/wav2vec2-base-100k-vox Describe the bug I'm using Huggingface Datasets library to load the dataset in google colab When I do, data = train_dataset. As I read here dataset splits into num_proc parts and each part processes separately: When num_proc > 1, map splits the dataset into num_proc shards, each of which is mapped to one of the num_proc workers. I know that the starting point of the training is to actually load the data using the datasets package. map(tokenize_function, tokenizer, batched=True) I’m getting error: TypeError: list indices must be integers or slices, not str How can I call map function in my example ? I have a large dataset that I want to use for eval/other tasks that requires a trained model to do inference on it. map(), it throws an error, and I’m not sure what is triggering it in the first place. 01s in worker, several examples may take 50s). The weirdest part is when inspecting the sizes of the tensors as shown below, both tokenized_captions["input_ids"] and image_features show Describe the bug When I was training model on Multiple GPUs by DDP, the dataset is tokenized multiple times after main process. Reload to refresh your session. from datasets import load_dataset Does your map function work for non-batched encoding? I always first focus on making non-batched approach working before optimizing further. map(preprocess3, batched=True, num_proc=8) ds = ds. This batching is done on-the-fly as you iterate over the You can set it manually if you google the max seq len for your model e. cuda() but still it is using only one 4. 13. SOLVED: Module 'numpy' has no attribute 'object'. map( process_data_to_model_inputs, batched=True, batch_size=b =====> Colab reproducer <====== I’m using set_format('numpy') for my dataset and using jax. The primary objective of batch mapping is to speed up processing. In the example code on pretraining masked language model, they use map() to tokenize all data at a stroke before the train loop. And reusing it should let us reuse the same map computation for the same dataset. Since a lot of the examples in OSCAR are much I am trying to run a notebook that uses the huggingface library dataset class. EDIT: Is there a way to make from a single row multiple rows, i. It stopped at about 25. I would like to understand what is the process to build a text dataset that tokenizes each line, having previously split the I’m exploring using streaming datasets with a function that preprocesses the text, tokenizes it into training samples, and then applies some noise to the input_ids (à la BART pretraining). Augment a dataset with additional tokens. map and pandas with multiprocessing. I’ve loaded a dataset and am trying to apply a map() function to it. map() on 160k items. PyTorch tensors or Python lists), which would make this process huggingface / datasets Public. Features`): New features to cast the dataset to. Hi, I have a similar issue as OP but the suggested solutions do not work for my case. It allows you to apply a processing function to each example in a dataset, independently or in batches. I’ve uploaded my first dataset, consisting of 16. encoded_context = self I am trying to run a notebook that uses the huggingface library dataset class. The name of the fields in the Dataset. map(lambda examples: tokeni I’m using a custom dataset from a CSV file where the labels are strings. from datasets import load_dataset datasets = load_dataset("squad") I'll suggest avoiding datasets as a variable and refactor the variable name to: squad_datasets = load_dataset("squad") We should be able to initialize a tokenizer. Dataset objects are natively understood by Keras. Dataset. map from strings to token sequence, you need to remove the original columns (as they are not 1:1). So just a single column called “text”. So you can disable this with set_caching_enabled(True), but every time you re-run your code it will recompute the map call. Have looked online and no trace of anyone having similar issues. map(f, input_columns="my_ Batch mapping. def prepare_dataset(batch): audio = batch["audio"] wav, sr = librosa. map() function, but in a way that mimics streaming (i. co. Let’s say I have a dataset of 1000 audio files of varying lengths from 5 seconds to 20 seconds, all sampled in 16 kHz. As outlined here, the following collate function drops 5 out of possible 6 elements in the batch (it is 6 because out of the eight, two are bad links in laion). how do I make multiple rows in the new dataset from a row in the old dataset? Is there a way to skip rows, i. So it takes time because it hashes your big dictionary. I also tried sharding it into smaller data sets, but that didn’t help. load_dataset(‘linxinyuan/cola’) cola_tokenized = cola. When using Huggingface Tokenizer with return_overflowing_tokens=True, the results can have multiple token sequence per input string. dataset = load_dataset("json", data_files=data_files) tokenizer = AutoTokenizer. means they can be passed directly to methods like model. map` function of Hugging Face's datasets library processes the data in batches rather than one item at a time, significantly speeding up the tokenization and preprocessing steps. Hello, I am trying to load a custom dataset that I will then use for language modeling. But, the for loop doesn’t hang it only has no effect. 1k saying that there is error with memory allocation. 6k. Also, a map transform can return different value types for the same column (e. Saved searches Use saved searches to filter your results more quickly Batch mapping Combining the utility of Dataset. However, I am not able to run this on multi-gpu. rrowInvalid: Column 1 named test_col expected length 100 but got length 1000 batched (bool) — Set to True to return a generator that yields the dataset as batches of batch_size rows. I tried to delete ~/. 5. map method, I apply a function that reads the audios from the disk, resamples them and applies Wav2Vec2FeatureExtractor, which normalizes the audio and converts it to torch tensor. I defined the function that I want to apply on batches as follows: def zero_shot_classify_sequences(examples, thr Batch mapping Combining the utility of datasets. Is there a workaround for this without having to @lhoestq If I am applying multiple . map() function during runtime. Environment info. frkmtn yfti wlxe kecxcu spjrpr cgzp qdant xfnnxi hsrve ijp