distilbert-finetuned-ner-for-articles

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

distilbert-finetuned-ner-for-articles

This model is a fine-tuned version of distilbert-base-cased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.4393
• Precision: 0.6848
• Recall: 0.6391
• F1: 0.6611
• Accuracy: 0.8736

Model description

Distilbert finetuned for detecting crime, accidents, and natural disaster occurrences.

Tags (IOBES/BIOES tagging format):

• O: not an entity
• S-CRIME
• S-CRIMINAL
• S-VICTIM
• S-SUSPECT
• S-TIMEDATE: date with month, day, year, either one, two, or all of them together
• S-TIMEWORD: words signifying time (last, weekend, earlier, week, today, etc.)
• S-TIMEDAY: days of the week
• S-TIMEDAYPART: morning, afternoon, evening, night
• S-TIMENUM: 4:31, 6:30, etc.
• S-TIMEMISC: New Year, Christmas, etc.
• S-LOC: location word (mentioned alone)
• B-LOC: beginning (part of a series of location names mentioned)
• I-LOC: inside
• E-LOC: end (the last location word specified)
• S-LOCWORD: junction, island, street, etc.
• S-LOCDIR: north, south, etc.
• S-ACCIDENT
• S-NATDISAS: type of natural disaster
• S-OTHEROCC: other occurrences (not really labeled much in the dataset)

Dataset used is of size 502, manually annotated the dataset from the paper "MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification" using Doccano (a free NER annotation tool).

Intended uses & limitations

• Needs a bigger dataset.
• More training is highly recommended.

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Framework versions

• Transformers 4.40.1
• Pytorch 2.2.1+cu121
• Datasets 2.19.0
• Tokenizers 0.19.1