lilT_fintuning

Oumay/lilT_fintuning

lilT_fintuning is a token classification model from Oumay. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.

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

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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: lilt
Access: Public
Created: Jan 8, 2024
Updated: Jan 8, 2024
SHA: 00ebac25

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Base models

SCUT-DLVCLab/lilt-roberta-en-base

Model card

From the Hugging Face model README

lilT_fintuning

This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.6381
Answer: {'precision': 0.8744075829383886, 'recall': 0.9033047735618115, 'f1': 0.8886213124623721, 'number': 817}
Header: {'precision': 0.6261682242990654, 'recall': 0.5630252100840336, 'f1': 0.5929203539823009, 'number': 119}
Question: {'precision': 0.8998194945848376, 'recall': 0.9257195914577531, 'f1': 0.9125858123569794, 'number': 1077}
Overall Precision: 0.8752
Overall Recall: 0.8952
Overall F1: 0.8851
Overall Accuracy: 0.8174

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: 2500
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.4236	10.53	200	0.9243	{'precision': 0.8401360544217688, 'recall': 0.9069767441860465, 'f1': 0.872277810476751, 'number': 817}	{'precision': 0.5333333333333333, 'recall': 0.40336134453781514, 'f1': 0.45933014354066987, 'number': 119}	{'precision': 0.8789571694599627, 'recall': 0.8765088207985144, 'f1': 0.8777312877731288, 'number': 1077}	0.8470	0.8609	0.8539	0.8079
0.0472	21.05	400	1.2753	{'precision': 0.8249721293199554, 'recall': 0.9057527539779682, 'f1': 0.8634772462077013, 'number': 817}	{'precision': 0.5, 'recall': 0.5798319327731093, 'f1': 0.5369649805447471, 'number': 119}	{'precision': 0.8778195488721805, 'recall': 0.8672237697307336, 'f1': 0.8724894908921065, 'number': 1077}	0.8304	0.8659	0.8478	0.7910
0.014	31.58	600	1.3381	{'precision': 0.8335233751425314, 'recall': 0.8947368421052632, 'f1': 0.8630460448642266, 'number': 817}	{'precision': 0.6292134831460674, 'recall': 0.47058823529411764, 'f1': 0.5384615384615384, 'number': 119}	{'precision': 0.8754416961130742, 'recall': 0.9201485608170845, 'f1': 0.8972385694884564, 'number': 1077}	0.8475	0.8833	0.8650	0.8046
0.0063	42.11	800	1.4519	{'precision': 0.8738095238095238, 'recall': 0.8984088127294981, 'f1': 0.8859384429692213, 'number': 817}	{'precision': 0.5833333333333334, 'recall': 0.6470588235294118, 'f1': 0.6135458167330677, 'number': 119}	{'precision': 0.9008341056533827, 'recall': 0.9025069637883009, 'f1': 0.901669758812616, 'number': 1077}	0.8693	0.8857	0.8775	0.8092
0.0036	52.63	1000	1.6211	{'precision': 0.8363228699551569, 'recall': 0.9130966952264382, 'f1': 0.8730251609128145, 'number': 817}	{'precision': 0.584070796460177, 'recall': 0.5546218487394958, 'f1': 0.5689655172413793, 'number': 119}	{'precision': 0.8984302862419206, 'recall': 0.903435468895079, 'f1': 0.900925925925926, 'number': 1077}	0.8549	0.8867	0.8705	0.8039
0.0029	63.16	1200	1.6274	{'precision': 0.871007371007371, 'recall': 0.8678090575275398, 'f1': 0.8694052728387494, 'number': 817}	{'precision': 0.5714285714285714, 'recall': 0.5042016806722689, 'f1': 0.5357142857142857, 'number': 119}	{'precision': 0.8844404003639672, 'recall': 0.9025069637883009, 'f1': 0.8933823529411765, 'number': 1077}	0.8627	0.8649	0.8638	0.8008
0.0018	73.68	1400	1.6562	{'precision': 0.8401360544217688, 'recall': 0.9069767441860465, 'f1': 0.872277810476751, 'number': 817}	{'precision': 0.6132075471698113, 'recall': 0.5462184873949579, 'f1': 0.5777777777777778, 'number': 119}	{'precision': 0.8892921960072595, 'recall': 0.9099350046425255, 'f1': 0.8994951812758146, 'number': 1077}	0.8545	0.8872	0.8706	0.8096
0.001	84.21	1600	1.6388	{'precision': 0.8534090909090909, 'recall': 0.9192166462668299, 'f1': 0.8850913376546846, 'number': 817}	{'precision': 0.63, 'recall': 0.5294117647058824, 'f1': 0.5753424657534247, 'number': 119}	{'precision': 0.9009174311926605, 'recall': 0.9117920148560817, 'f1': 0.9063221042916475, 'number': 1077}	0.8676	0.8922	0.8797	0.8103
0.0007	94.74	1800	1.6278	{'precision': 0.8545454545454545, 'recall': 0.9204406364749081, 'f1': 0.8862698880377136, 'number': 817}	{'precision': 0.6078431372549019, 'recall': 0.5210084033613446, 'f1': 0.5610859728506787, 'number': 119}	{'precision': 0.8909740840035746, 'recall': 0.9257195914577531, 'f1': 0.9080145719489982, 'number': 1077}	0.8620	0.8997	0.8804	0.8216
0.0002	105.26	2000	1.6381	{'precision': 0.8744075829383886, 'recall': 0.9033047735618115, 'f1': 0.8886213124623721, 'number': 817}	{'precision': 0.6261682242990654, 'recall': 0.5630252100840336, 'f1': 0.5929203539823009, 'number': 119}	{'precision': 0.8998194945848376, 'recall': 0.9257195914577531, 'f1': 0.9125858123569794, 'number': 1077}	0.8752	0.8952	0.8851	0.8174
0.0002	115.79	2200	1.6545	{'precision': 0.8757467144563919, 'recall': 0.8971848225214198, 'f1': 0.8863361547762998, 'number': 817}	{'precision': 0.625, 'recall': 0.5462184873949579, 'f1': 0.5829596412556054, 'number': 119}	{'precision': 0.8902765388046388, 'recall': 0.9266480965645311, 'f1': 0.908098271155596, 'number': 1077}	0.8710	0.8922	0.8815	0.8155
0.0002	126.32	2400	1.6477	{'precision': 0.8658823529411764, 'recall': 0.9008567931456548, 'f1': 0.8830233953209357, 'number': 817}	{'precision': 0.6116504854368932, 'recall': 0.5294117647058824, 'f1': 0.5675675675675675, 'number': 119}	{'precision': 0.8930817610062893, 'recall': 0.9229340761374187, 'f1': 0.9077625570776255, 'number': 1077}	0.8679	0.8907	0.8791	0.8167

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0

Task: Token Classification
Library: transformers
Type: lilt
License: mit
Created: Jan 8, 2024
Updated: Jan 8, 2024