events

This is a text classification dataset. It is intended for machine learning research and experimentation.

This dataset is obtained via formatting another publicly available data to be compatible with our AutoIntent Library.

Usage

It is intended to be used with our AutoIntent Library:

from autointent import Dataset

events = Dataset.from_datasets("AutoIntent/events")

Source

This dataset is taken from knowledgator/events_classification_biotech and formatted with our AutoIntent Library:

from datasets import load_dataset
from autointent import Dataset

# load original data
events_dataset = load_dataset("knowledgator/events_classification_biotech", trust_remote_code=True)
intent_names = sorted(set(name for intents in events_dataset["train"]["all_labels"] for name in intents))
names_to_remove = [
    "partnerships & alliances",
    "patent publication",
    "subsidiary establishment",
    "department establishment",
]
for n in names_to_remove:
    intent_names.remove(n)
name_to_id = {name: i for i, name in enumerate(intent_names)}

# extract utetrances
def transform(example: dict):
    return {
        "utterance": example["content"],
        "label": [
            name_to_id[intent_name] for intent_name in example["all_labels"] if intent_name not in names_to_remove
        ],
    }

multilabel_events_dataset = events_dataset["train"].map(
    transform, remove_columns=events_dataset["train"].features.keys()
)

# filter bad samples
intents = [{"id": i, "name": name} for i, name in enumerate(intent_names)]
utterances = []
oos_utterances = []
for rec in multilabel_events_dataset.to_list():
    if len(rec["label"]) == 0:
        rec.pop("label")
        oos_utterances.append(rec["utterance"])
    if rec["utterance"] is None:
        continue
    utterances.append(rec)
events_converted = Dataset.from_dict({"intents": intents, "train": utterances})