Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Size:
1K - 10K
License:
File size: 1,647 Bytes
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---
license: mit
task_categories:
- token-classification
task_ids:
- named-entity-recognition
dataset_info:
features:
- name: tokens
list: string
- name: tags
list: string
splits:
- name: train
num_bytes: 1761536
num_examples: 3361
- name: validation
num_bytes: 900871
num_examples: 1819
download_size: 472719
dataset_size: 2662407
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
---
# Proposed Active Learning Data split
## Overview
This dataset release represents a proposed and experimental data split(processed in BIO format) designed specifically to support and validate planned active learning (AL) cycles for biomedical Named Entity Recognition (NER).
The current version is not a final benchmark split. Instead, it serves as an initial, controlled setup for testing active learning strategies, model uncertainty sampling,
and iterative annotation workflows prior to large-scale development.
Both splits (train and validation) have been carefully curated to ensure coverage of all three target entity types:
- **CellLine**
- **CellType**
- **Tissue**
For details on how the splits were created, please refer to raw data and documentation available [here](https://huggingface.co/datasets/OTAR3088/AL_Test_data)
This BIO format has been generated using a Biomedical transformer-based tokenizer for consistency with downstream model training.
---
## Intended Use
### Primary Use
- Supervised NER training for biomedical NLP tasks
### Not Intended For
- Clinical or patient-level decision making
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