Let’s provide a more detailed explanation of each step in the code: # Step 1: Import Required Libraries from datasets import load_dataset, concatenate_datasets In this step, we import the necessary libraries for the program. We need the “load_dataset” function to load the IMDb movie revie...
If the dataset does not need splits, i.e., no training and validation split, more like a table. How can I let the load_dataset function return a Dataset object directly rather than return a DatasetDict object with only one key-value pair...
Also, we would use the Alpaca sample dataset fromHuggingFace, which required datasets package to acquire. pip install datasets Then, use the following code to acquire the data we need. from datasets import load_dataset # Load the dataset dataset = load_dataset("tatsu-lab/alpaca") train = dat...
dataset = datasets.load_dataset("ami-iit/dataset_name", split="train", streaming=True, use_auth_token=True) ``` It is important to log in to the Hugging Face Hub before loading the dataset, use `huggingface-cli login` to log in. The `use_auth_token=True` argument is necessary to ...
from datasets import load_dataset import pandas as pd # https://huggingface.co/datasets/MongoDB/embedded_movies # Make sure you have an Hugging Face token(HF_TOKEN) in your development environemnt dataset = load_dataset("MongoDB/airbnb_embeddings") # Convert the dataset to a pandas ...
n_gpus: 2 # small hack to make sure we see all our samples This part of the config basically does the following things it uses theldm.data.simple.hf_datasetfunction to create a dataset for training from the namelambdalabs/pokemon-blip-cpationsthis is on the Huggingface Hub but ...
from datasets import load_dataset dataset = load_dataset("superb", "asr") dataset[0]*** OUTPUT *** {'chapter_id': 1240, 'file': 'path/to/file.flac', 'audio': { 'array': array([0., 0.003, -0.0002,..., dtype=float32), ...
We first load our data into aTorchTabularTextDataset, which works with PyTorch’s data loaders that include the text inputs for HuggingFace Transformers and our specified categorical feature columns and numerical feature columns. For this, we also need to load our HuggingFace tokenizer. ...
You can evaluate the system separately on each of these question sets to get a more granular understanding of the strengths and weaknesses of your system. In addition to curating a dataset of questions, you may also want to write out ground truth answers to the questions. While these are ...
model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14") processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) ...