lilt-ruroberta

Tatiana239/lilt-ruroberta

lilt-ruroberta is a token classification model from Tatiana239. Use it when you need labels on individual words, such as names. It is set up for transformers.

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

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At a glance

Task: Token Classification
Library: transformers
Model type: lilt
Access: Public
Created: Jan 19, 2023
Updated: Jan 23, 2023
SHA: 7dbc54c4

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Model card

From the Hugging Face model README

lilt-ruroberta

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4919
Comment: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6}
Date: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
Labname: {'precision': 0.5833333333333334, 'recall': 0.6666666666666666, 'f1': 0.6222222222222222, 'number': 21}
Laboratory: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
Measure: {'precision': 0.5833333333333334, 'recall': 0.7777777777777778, 'f1': 0.6666666666666666, 'number': 9}
Ref Value: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}
Result: {'precision': 0.25, 'recall': 0.25, 'f1': 0.25, 'number': 12}
Overall Precision: 0.4528
Overall Recall: 0.4
Overall F1: 0.4248
Overall Accuracy: 0.8698

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: 5e-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
training_steps: 25
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Comment	Date	Labname	Laboratory	Measure	Ref Value	Result	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
2.4398	5.0	5	1.5928	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12}	0.0	0.0	0.0	0.5850
1.4788	10.0	10	1.1857	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12}	0.0	0.0	0.0	0.6512
0.9806	15.0	15	0.8188	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.21875, 'recall': 0.3333333333333333, 'f1': 0.2641509433962264, 'number': 21}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.5, 'recall': 0.1111111111111111, 'f1': 0.1818181818181818, 'number': 9}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12}	0.1667	0.1333	0.1481	0.7660
0.6358	20.0	20	0.5763	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.41935483870967744, 'recall': 0.6190476190476191, 'f1': 0.5, 'number': 21}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.7, 'recall': 0.7777777777777778, 'f1': 0.7368421052631577, 'number': 9}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}	{'precision': 0.42857142857142855, 'recall': 0.25, 'f1': 0.3157894736842105, 'number': 12}	0.4182	0.3833	0.4	0.8675
0.4712	25.0	25	0.4919	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.5833333333333334, 'recall': 0.6666666666666666, 'f1': 0.6222222222222222, 'number': 21}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.5833333333333334, 'recall': 0.7777777777777778, 'f1': 0.6666666666666666, 'number': 9}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}	{'precision': 0.25, 'recall': 0.25, 'f1': 0.25, 'number': 12}	0.4528	0.4	0.4248	0.8698

Framework versions

Transformers 4.25.1
Pytorch 1.12.1
Datasets 2.8.0
Tokenizers 0.13.2

Task: Token Classification
Library: transformers
Type: lilt
Created: Jan 19, 2023
Updated: Jan 23, 2023