SciBERT_100K_steps

bdpc/SciBERT_100K_steps

SciBERT_100K_steps is a text classification model from bdpc. Use it when you need a label for a piece of text. It is set up for transformers.

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

Downloads · 30 days

30% of all-time downloads

All-time downloads

Public

Repo size

880 MB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin440 MB · 100%

At a glance

Task: Text Classification
Library: transformers
Model type: bert
Access: Public
Created: Feb 5, 2024
Updated: Feb 6, 2024
SHA: 22a727be

Try a prompt

Base models

allenai/scibert_scivocab_uncased

Model card

From the Hugging Face model README

SciBERT_100K_steps

This model is a fine-tuned version of allenai/scibert_scivocab_uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0144
Accuracy: 0.9947
Precision: 0.7850
Recall: 0.6355
F1: 0.7024
Hamming: 0.0053

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
training_steps: 100000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Hamming
0.1681	0.08	5000	0.0487	0.9902	0.0	0.0	0.0	0.0098
0.032	0.16	10000	0.0223	0.9930	0.8068	0.3728	0.5100	0.0070
0.0201	0.24	15000	0.0186	0.9937	0.7815	0.4970	0.6076	0.0063
0.018	0.32	20000	0.0172	0.9941	0.7763	0.5550	0.6472	0.0059
0.017	0.4	25000	0.0166	0.9942	0.7864	0.5624	0.6558	0.0058
0.0166	0.47	30000	0.0163	0.9943	0.7707	0.5880	0.6671	0.0057
0.0163	0.55	35000	0.0160	0.9943	0.7802	0.5809	0.6659	0.0057
0.0159	0.63	40000	0.0158	0.9944	0.7719	0.6012	0.6759	0.0056
0.0157	0.71	45000	0.0155	0.9945	0.7750	0.6104	0.6829	0.0055
0.0154	0.79	50000	0.0153	0.9945	0.7734	0.6202	0.6884	0.0055
0.0153	0.87	55000	0.0151	0.9945	0.7823	0.6072	0.6837	0.0055
0.0152	0.95	60000	0.0151	0.9945	0.7813	0.6124	0.6866	0.0055
0.0148	1.03	65000	0.0149	0.9946	0.7843	0.6208	0.6930	0.0054
0.0143	1.11	70000	0.0148	0.9946	0.7802	0.6231	0.6929	0.0054
0.0142	1.19	75000	0.0148	0.9946	0.7714	0.6377	0.6982	0.0054
0.0141	1.27	80000	0.0146	0.9947	0.7837	0.6281	0.6973	0.0053
0.0141	1.34	85000	0.0146	0.9947	0.7836	0.6374	0.7030	0.0053
0.014	1.42	90000	0.0145	0.9947	0.7859	0.6326	0.7010	0.0053
0.0139	1.5	95000	0.0145	0.9947	0.7875	0.6317	0.7010	0.0053
0.0139	1.58	100000	0.0144	0.9947	0.7850	0.6355	0.7024	0.0053

Framework versions

Transformers 4.35.0.dev0
Pytorch 2.0.1+cu118
Datasets 2.7.1
Tokenizers 0.14.1

Task: Text Classification
Library: transformers
Type: bert
Created: Feb 5, 2024
Updated: Feb 6, 2024