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] |
When should you replace a co2 clynder in an inflatable pwd? | 0 | [
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] |
When should you replace the co2 pfd? | 0 | [
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When should you replace the co2 cylinder in a pdf? | 0 | [
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] |
When should you replace the CO2 cylinder in a inflatable PFD? | 0 | [
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When should you replace the co2 cilinder in an inflatable PFD? | 0 | [
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] |
When should you replace the co2 cylinder? | 0 | [
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] |
When should you replace the CO2 cylinder in an inflatable PFD? | 0 | [
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] |
When should you replace the CO2 container in an inflatable PFD? | 0 | [
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] |
When should you replace the co2 cylinder in an inflatable PFC? | 0 | [
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] |
When should you replace the CO2 cylinder in an inflateable PFD? | 0 | [
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Dataset Card for "wikianswers_embeddings_768"
Dataset Summary
nikhilchigali/wikianswers_embeddings_768 is a subset of the embedding-data/WikiAnswers (Link)
As opposed to the original dataset with 3,386,256 rows, this dataset contains only .13% of the total rows(sets). The sets of sentences have been unraveled into individual items with corresponding cluster IDs to identify sentences from the same set. Each Sentence has its associated cluster ID and embeddings of dimension 768.
Languages
English.
Dataset Structure
Each example in the dataset contains a sentence and its cluster id of other equivalent sentences. The sentences in the same cluster are paraphrases of each other. The embeddings for the dataset are created using the all-distilroberta-v1 model.
{"sentence": [sentence], "cluster": [cluster_id], "embedding_768": [embeddings]}
{"sentence": [sentence], "cluster": [cluster_id], "embedding_768": [embeddings]}
{"sentence": [sentence], "cluster": [cluster_id], "embedding_768": [embeddings]}
...
{"sentence": [sentence], "cluster": [cluster_id], "embedding_768": [embeddings]}
Usage Example
Install the 🤗 Datasets library with pip install datasets and load the dataset from the Hub with:
from datasets import load_dataset
dataset = load_dataset("nikhilchigali/wikianswers_embeddings_768")
The dataset is loaded as a DatasetDict and has the format for N examples:
DatasetDict({
train: Dataset({
features: ['sentence', "cluster", "embedding_768"],
num_rows: N
})
})
Review an example i with:
dataset["train"][i]
Source Data
embedding-data/WikiAnswers on HuggingFace (Link)
Note: This dataset is for the owner's personal use and claims no rights whatsoever.
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