metadata

tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: tabert-4k-naamapadam
    results: []

tabert-4k-naamapadam

This model is a fine-tuned version of livinNector/tabert-4k on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2805
Precision: 0.7758
Recall: 0.8034
F1: 0.7894
Accuracy: 0.9077

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: 64
eval_batch_size: 128
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.4467	0.05	400	0.3882	0.7144	0.6655	0.6891	0.8755
0.3775	0.1	800	0.3540	0.7122	0.7155	0.7138	0.8845
0.3571	0.15	1200	0.3432	0.7329	0.7266	0.7297	0.8872
0.3461	0.21	1600	0.3360	0.7252	0.7368	0.7309	0.8893
0.3456	0.26	2000	0.3359	0.7388	0.7470	0.7428	0.8896
0.3318	0.31	2400	0.3298	0.7460	0.7435	0.7447	0.8908
0.326	0.36	2800	0.3255	0.7490	0.7391	0.7440	0.8940
0.3264	0.41	3200	0.3243	0.7493	0.7605	0.7549	0.8953
0.3189	0.46	3600	0.3231	0.7305	0.7715	0.7504	0.8936
0.3119	0.51	4000	0.3125	0.7645	0.7525	0.7584	0.8985
0.3111	0.57	4400	0.3100	0.7479	0.7729	0.7602	0.8970
0.3088	0.62	4800	0.3148	0.7510	0.7749	0.7628	0.8966
0.3047	0.67	5200	0.3089	0.7581	0.7728	0.7654	0.8981
0.3054	0.72	5600	0.3073	0.7615	0.7709	0.7662	0.8990
0.3028	0.77	6000	0.3066	0.7466	0.7835	0.7646	0.8984
0.3007	0.82	6400	0.3035	0.7555	0.7791	0.7671	0.8995
0.2923	0.87	6800	0.3004	0.7647	0.7829	0.7737	0.9008
0.2927	0.93	7200	0.3050	0.7700	0.7646	0.7673	0.9002
0.2949	0.98	7600	0.2979	0.7686	0.7723	0.7704	0.9014
0.2758	1.03	8000	0.3013	0.7713	0.7783	0.7748	0.9030
0.2699	1.08	8400	0.3019	0.7503	0.7997	0.7742	0.9017
0.2688	1.13	8800	0.3002	0.7593	0.7940	0.7762	0.9017
0.2625	1.18	9200	0.2926	0.7590	0.7941	0.7762	0.9033
0.2671	1.23	9600	0.2922	0.7640	0.8019	0.7825	0.9043
0.267	1.29	10000	0.2895	0.7719	0.7877	0.7797	0.9044
0.2611	1.34	10400	0.2897	0.7704	0.7978	0.7839	0.9053
0.2666	1.39	10800	0.2896	0.7688	0.7887	0.7786	0.9042
0.2563	1.44	11200	0.2894	0.7672	0.7981	0.7823	0.9045
0.2598	1.49	11600	0.2841	0.7705	0.7960	0.7831	0.9058
0.2549	1.54	12000	0.2854	0.7695	0.7975	0.7832	0.9065
0.2558	1.59	12400	0.2873	0.7619	0.8108	0.7856	0.9045
0.2564	1.65	12800	0.2863	0.7757	0.7897	0.7826	0.9062
0.2618	1.7	13200	0.2860	0.7778	0.7899	0.7838	0.9066
0.2659	1.75	13600	0.2831	0.7748	0.8013	0.7879	0.9073
0.254	1.8	14000	0.2811	0.7761	0.7978	0.7868	0.9079
0.2628	1.85	14400	0.2807	0.7713	0.8028	0.7868	0.9069
0.2552	1.9	14800	0.2806	0.7756	0.7990	0.7872	0.9077
0.2568	1.95	15200	0.2805	0.7758	0.8034	0.7894	0.9077

Framework versions

Transformers 4.29.2
Pytorch 2.0.0
Datasets 2.12.0
Tokenizers 0.13.3