scibert_all_deep – AI Model by jialinselenasong | AlphaNeural AI

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jialinselenasong/
scibert_all_deep

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token-classification

generated_from_trainer

allenai/scibert_scivocab_cased

autotrain_compatible

endpoints_compatible

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scibert_all_deep

This model is a fine-tuned version of allenai/scibert_scivocab_cased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.8270
Precision: 0.6648
Recall: 0.7172
F1: 0.6900
Accuracy: 0.8207

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	363	0.5559	0.6191	0.6867	0.6511	0.8131
0.6741	2.0	726	0.5344	0.6271	0.7101	0.6660	0.8203
0.3917	3.0	1089	0.5548	0.6558	0.7064	0.6801	0.8205
0.3917	4.0	1452	0.5835	0.6717	0.7110	0.6908	0.8246
0.271	5.0	1815	0.6643	0.6524	0.7255	0.6870	0.8196
0.188	6.0	2178	0.7021	0.6724	0.7067	0.6892	0.8222
0.1437	7.0	2541	0.7594	0.6555	0.7180	0.6853	0.8191
0.1437	8.0	2904	0.7916	0.6664	0.7109	0.6879	0.8194
0.114	9.0	3267	0.8123	0.6582	0.7225	0.6888	0.8203
0.0943	10.0	3630	0.8270	0.6648	0.7172	0.6900	0.8207

Framework versions

Transformers 4.40.1
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1