bert-finetuned-ner-CRF

Bramve/bert-finetuned-ner-CRF

bert-finetuned-ner-CRF is a token classification model from Bramve. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Jan 3, 2024
Updated: Jan 3, 2024
SHA: b47e8f04

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Base models

Model card

From the Hugging Face model README

bert-finetuned-ner-CRF

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

Loss: 0.2466
Entity Metrics: {'O': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0}, 'B-ADR': {'precision': 0.9518716577540107, 'recall': 0.994413407821229, 'f1': 0.9726775956284153}, 'I-ADR': {'precision': 0.9411764705882353, 'recall': 0.9943502824858758, 'f1': 0.9670329670329672}, 'B-Disease': {'precision': 0.6923076923076923, 'recall': 0.2903225806451613, 'f1': 0.4090909090909091}, 'I-Disease': {'precision': 0.5, 'recall': 0.4482758620689655, 'f1': 0.4727272727272727}, 'B-Drug': {'precision': 0.9836065573770492, 'recall': 0.975609756097561, 'f1': 0.9795918367346939}, 'I-Drug': {'precision': 0.991869918699187, 'recall': 0.991869918699187, 'f1': 0.991869918699187}, 'B-Finding': {'precision': 0.7777777777777778, 'recall': 0.19444444444444445, 'f1': 0.3111111111111111}, 'I-Finding': {'precision': 0.5555555555555556, 'recall': 0.4411764705882353, 'f1': 0.49180327868852464}, 'B-Symptom': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0}, 'I-Symptom': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0}}
Macro F1: 0.5996
Micro F1: 0.9171

Model description

More information needed

Intended uses & limitations

More information needed

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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Entity Metrics	Macro F1	Micro F1
No log	1.0	100	0.2871	{'O': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0}, 'B-ADR': {'precision': 0.9502762430939227, 'recall': 0.9608938547486033, 'f1': 0.9555555555555556}, 'I-ADR': {'precision': 0.9361702127659575, 'recall': 0.9943502824858758, 'f1': 0.9643835616438357}, 'B-Disease': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0}, 'I-Disease': {'precision': 0.5, 'recall': 0.034482758620689655, 'f1': 0.06451612903225806}, 'B-Drug': {'precision': 0.9516129032258065, 'recall': 0.959349593495935, 'f1': 0.9554655870445343}, 'I-Drug': {'precision': 0.9918032786885246, 'recall': 0.983739837398374, 'f1': 0.9877551020408164}, 'B-Finding': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0}, 'I-Finding': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0}, 'B-Symptom': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0}, 'I-Symptom': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0}}	0.4480	0.9078
No log	2.0	200	0.2496	{'O': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0}, 'B-ADR': {'precision': 0.9516129032258065, 'recall': 0.9888268156424581, 'f1': 0.9698630136986301}, 'I-ADR': {'precision': 0.9411764705882353, 'recall': 0.9943502824858758, 'f1': 0.9670329670329672}, 'B-Disease': {'precision': 0.5555555555555556, 'recall': 0.16129032258064516, 'f1': 0.25}, 'I-Disease': {'precision': 0.5454545454545454, 'recall': 0.41379310344827586, 'f1': 0.47058823529411764}, 'B-Drug': {'precision': 0.9834710743801653, 'recall': 0.967479674796748, 'f1': 0.9754098360655739}, 'I-Drug': {'precision': 0.991869918699187, 'recall': 0.991869918699187, 'f1': 0.991869918699187}, 'B-Finding': {'precision': 0.75, 'recall': 0.08333333333333333, 'f1': 0.15}, 'I-Finding': {'precision': 0.56, 'recall': 0.4117647058823529, 'f1': 0.4745762711864407}, 'B-Symptom': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0}, 'I-Symptom': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0}}	0.5681	0.9159
No log	3.0	300	0.2466	{'O': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0}, 'B-ADR': {'precision': 0.9518716577540107, 'recall': 0.994413407821229, 'f1': 0.9726775956284153}, 'I-ADR': {'precision': 0.9411764705882353, 'recall': 0.9943502824858758, 'f1': 0.9670329670329672}, 'B-Disease': {'precision': 0.6923076923076923, 'recall': 0.2903225806451613, 'f1': 0.4090909090909091}, 'I-Disease': {'precision': 0.5, 'recall': 0.4482758620689655, 'f1': 0.4727272727272727}, 'B-Drug': {'precision': 0.9836065573770492, 'recall': 0.975609756097561, 'f1': 0.9795918367346939}, 'I-Drug': {'precision': 0.991869918699187, 'recall': 0.991869918699187, 'f1': 0.991869918699187}, 'B-Finding': {'precision': 0.7777777777777778, 'recall': 0.19444444444444445, 'f1': 0.3111111111111111}, 'I-Finding': {'precision': 0.5555555555555556, 'recall': 0.4411764705882353, 'f1': 0.49180327868852464}, 'B-Symptom': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0}, 'I-Symptom': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0}}	0.5996	0.9171

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cpu
Datasets 2.16.0
Tokenizers 0.15.0

Task: Token Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Jan 3, 2024
Updated: Jan 3, 2024