scenario-TCR-NER_data-univner_en

haryoaw/scenario-TCR-NER_data-univner_en

scenario-TCR-NER_data-univner_en is a machine learning model from haryoaw. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.

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

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At a glance

License: mit
Model type: xlm-roberta
Access: Public
Created: Sep 12, 2024
Updated: Sep 12, 2024
SHA: 278f005c

Base models

Model card

From the Hugging Face model README

scenario-TCR-NER_data-univner_en

This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2275
Precision: 0.7534
Recall: 0.6859
F1: 0.7181
Accuracy: 0.9722

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0841	1.28	500	0.1016	0.7187	0.6674	0.6921	0.9716
0.025	2.55	1000	0.1149	0.7384	0.6683	0.7016	0.9705
0.0161	3.83	1500	0.1161	0.7189	0.6953	0.7069	0.9726
0.0105	5.1	2000	0.1658	0.7504	0.6520	0.6978	0.9694
0.0059	6.38	2500	0.1509	0.7280	0.6902	0.7086	0.9726
0.0045	7.65	3000	0.1774	0.7381	0.6686	0.7016	0.9714
0.0033	8.93	3500	0.1494	0.7248	0.7279	0.7263	0.9746
0.0027	10.2	4000	0.1712	0.7501	0.6899	0.7188	0.9725
0.002	11.48	4500	0.2144	0.7732	0.6472	0.7046	0.9700
0.0016	12.76	5000	0.1804	0.7570	0.6871	0.7203	0.9732
0.0023	14.03	5500	0.1733	0.7599	0.7060	0.7319	0.9739
0.0013	15.31	6000	0.1777	0.7421	0.7145	0.7280	0.9735
0.0011	16.58	6500	0.2035	0.7550	0.6835	0.7175	0.9723
0.0012	17.86	7000	0.2195	0.7617	0.6605	0.7075	0.9714
0.0008	19.13	7500	0.2064	0.7358	0.6891	0.7117	0.9722
0.0005	20.41	8000	0.2327	0.7599	0.6780	0.7166	0.9719
0.0006	21.68	8500	0.2303	0.7646	0.6611	0.7091	0.9712
0.0005	22.96	9000	0.2317	0.7535	0.6721	0.7104	0.9715
0.0004	24.23	9500	0.2436	0.7574	0.6670	0.7093	0.9708
0.0003	25.51	10000	0.2548	0.7661	0.6583	0.7082	0.9707
0.0004	26.79	10500	0.2275	0.7534	0.6859	0.7181	0.9722

Framework versions

Transformers 4.33.3
Pytorch 2.1.1+cu121
Datasets 2.14.5
Tokenizers 0.13.3

Type: xlm-roberta
License: mit
Created: Sep 12, 2024
Updated: Sep 12, 2024