my-new-model-id

ShubhLM/my-new-model-id

my-new-model-id is a token classification model from ShubhLM. 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. --

Downloads · 30 days

3% of all-time downloads

All-time downloads

218

Public

Repo size

7.2 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin451 MB · 100%

At a glance

Task: Token Classification
Library: transformers
Model type: layoutlm
Access: Public
Created: Jun 27, 2023
Updated: Jun 27, 2023
SHA: f5ba65b7

Try a prompt

Datasets

funsd

Model card

From the Hugging Face model README

my-new-model-id

This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the funsd dataset. It achieves the following results on the evaluation set:

Loss: 0.6585
Answer: {'precision': 0.7224043715846995, 'recall': 0.8170580964153276, 'f1': 0.7668213457076567, 'number': 809}
Header: {'precision': 0.2777777777777778, 'recall': 0.33613445378151263, 'f1': 0.30418250950570347, 'number': 119}
Question: {'precision': 0.7818343722172751, 'recall': 0.8244131455399061, 'f1': 0.8025594149908593, 'number': 1065}
Overall Precision: 0.7236
Overall Recall: 0.7923
Overall F1: 0.7564
Overall Accuracy: 0.8164

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: 3e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.8057	1.0	10	1.5463	{'precision': 0.016229712858926344, 'recall': 0.016069221260815822, 'f1': 0.01614906832298137, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.16498993963782696, 'recall': 0.07699530516431925, 'f1': 0.10499359795134443, 'number': 1065}	0.0732	0.0477	0.0577	0.3858
1.4605	2.0	20	1.2487	{'precision': 0.24860335195530725, 'recall': 0.3300370828182942, 'f1': 0.2835900159320234, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.45215485756026297, 'recall': 0.5812206572769953, 'f1': 0.5086277732128184, 'number': 1065}	0.3627	0.4446	0.3995	0.5992
1.1219	3.0	30	0.9461	{'precision': 0.45073375262054505, 'recall': 0.5315203955500618, 'f1': 0.4878048780487805, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.5785597381342062, 'recall': 0.6638497652582159, 'f1': 0.6182772190642762, 'number': 1065}	0.5204	0.5705	0.5443	0.6840
0.847	4.0	40	0.7978	{'precision': 0.5685131195335277, 'recall': 0.723114956736712, 'f1': 0.6365614798694234, 'number': 809}	{'precision': 0.07142857142857142, 'recall': 0.025210084033613446, 'f1': 0.037267080745341616, 'number': 119}	{'precision': 0.6571180555555556, 'recall': 0.7107981220657277, 'f1': 0.6829048263419035, 'number': 1065}	0.6050	0.6749	0.6380	0.7407
0.6811	5.0	50	0.7134	{'precision': 0.6322444678609063, 'recall': 0.7416563658838071, 'f1': 0.6825938566552902, 'number': 809}	{'precision': 0.2328767123287671, 'recall': 0.14285714285714285, 'f1': 0.17708333333333334, 'number': 119}	{'precision': 0.7031924072476272, 'recall': 0.7652582159624414, 'f1': 0.7329136690647481, 'number': 1065}	0.6566	0.7185	0.6862	0.7763
0.5706	6.0	60	0.6581	{'precision': 0.663820704375667, 'recall': 0.7688504326328801, 'f1': 0.7124856815578464, 'number': 809}	{'precision': 0.23376623376623376, 'recall': 0.15126050420168066, 'f1': 0.1836734693877551, 'number': 119}	{'precision': 0.7145187601957586, 'recall': 0.8225352112676056, 'f1': 0.7647315582714972, 'number': 1065}	0.6768	0.7607	0.7163	0.7990
0.5016	7.0	70	0.6413	{'precision': 0.6694386694386695, 'recall': 0.796044499381953, 'f1': 0.7272727272727273, 'number': 809}	{'precision': 0.19626168224299065, 'recall': 0.17647058823529413, 'f1': 0.18584070796460178, 'number': 119}	{'precision': 0.7557446808510638, 'recall': 0.8338028169014085, 'f1': 0.7928571428571429, 'number': 1065}	0.6921	0.7792	0.7331	0.8033
0.4435	8.0	80	0.6286	{'precision': 0.6945031712473573, 'recall': 0.8121137206427689, 'f1': 0.7487179487179487, 'number': 809}	{'precision': 0.23931623931623933, 'recall': 0.23529411764705882, 'f1': 0.23728813559322035, 'number': 119}	{'precision': 0.7668122270742358, 'recall': 0.8244131455399061, 'f1': 0.7945701357466064, 'number': 1065}	0.7079	0.7842	0.7441	0.8108
0.4078	9.0	90	0.6405	{'precision': 0.6957470010905126, 'recall': 0.788627935723115, 'f1': 0.7392815758980301, 'number': 809}	{'precision': 0.2711864406779661, 'recall': 0.2689075630252101, 'f1': 0.270042194092827, 'number': 119}	{'precision': 0.7794508414526129, 'recall': 0.8262910798122066, 'f1': 0.8021877848678213, 'number': 1065}	0.7163	0.7777	0.7457	0.8137
0.3657	10.0	100	0.6364	{'precision': 0.7142857142857143, 'recall': 0.8096415327564895, 'f1': 0.7589803012746235, 'number': 809}	{'precision': 0.2807017543859649, 'recall': 0.2689075630252101, 'f1': 0.27467811158798283, 'number': 119}	{'precision': 0.7870452528837621, 'recall': 0.8328638497652582, 'f1': 0.8093065693430658, 'number': 1065}	0.7294	0.7898	0.7584	0.8098
0.335	11.0	110	0.6427	{'precision': 0.7027896995708155, 'recall': 0.8096415327564895, 'f1': 0.7524411257897761, 'number': 809}	{'precision': 0.26865671641791045, 'recall': 0.3025210084033613, 'f1': 0.2845849802371542, 'number': 119}	{'precision': 0.7813620071684588, 'recall': 0.8187793427230047, 'f1': 0.7996331957817515, 'number': 1065}	0.7163	0.7842	0.7487	0.8132
0.3103	12.0	120	0.6505	{'precision': 0.7311946902654868, 'recall': 0.8170580964153276, 'f1': 0.7717454757734967, 'number': 809}	{'precision': 0.2595419847328244, 'recall': 0.2857142857142857, 'f1': 0.27199999999999996, 'number': 119}	{'precision': 0.7859054415700267, 'recall': 0.8272300469483568, 'f1': 0.8060384263494967, 'number': 1065}	0.7310	0.7908	0.7597	0.8160
0.3007	13.0	130	0.6494	{'precision': 0.7219193020719739, 'recall': 0.8182941903584673, 'f1': 0.7670915411355737, 'number': 809}	{'precision': 0.27692307692307694, 'recall': 0.3025210084033613, 'f1': 0.2891566265060241, 'number': 119}	{'precision': 0.7930419268510259, 'recall': 0.8347417840375587, 'f1': 0.8133577310155535, 'number': 1065}	0.7320	0.7963	0.7628	0.8161
0.2831	14.0	140	0.6593	{'precision': 0.7202185792349727, 'recall': 0.8145859085290482, 'f1': 0.7645011600928074, 'number': 809}	{'precision': 0.273972602739726, 'recall': 0.33613445378151263, 'f1': 0.3018867924528302, 'number': 119}	{'precision': 0.7793468667255075, 'recall': 0.8291079812206573, 'f1': 0.8034576888080072, 'number': 1065}	0.7211	0.7938	0.7557	0.8144
0.2913	15.0	150	0.6585	{'precision': 0.7224043715846995, 'recall': 0.8170580964153276, 'f1': 0.7668213457076567, 'number': 809}	{'precision': 0.2777777777777778, 'recall': 0.33613445378151263, 'f1': 0.30418250950570347, 'number': 119}	{'precision': 0.7818343722172751, 'recall': 0.8244131455399061, 'f1': 0.8025594149908593, 'number': 1065}	0.7236	0.7923	0.7564	0.8164

Framework versions

Transformers 4.31.0.dev0
Pytorch 2.0.0+cpu
Datasets 2.1.0
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: layoutlm
Created: Jun 27, 2023
Updated: Jun 27, 2023