scenario-kd-po-ner-full-xlmr_data-univner_full44

This model is a fine-tuned version of haryoaw/scenario-TCR-NER_data-univner_full on the None dataset. It achieves the following results on the evaluation set:

• Loss: 47.6447
• Precision: 0.8149
• Recall: 0.8310
• F1: 0.8229
• Accuracy: 0.9817

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: 8
• eval_batch_size: 32
• seed: 44
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• 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
102.0525	0.2911	500	80.2861	0.7542	0.7806	0.7672	0.9771
74.8813	0.5822	1000	72.2140	0.7820	0.8016	0.7917	0.9792
68.8504	0.8732	1500	68.2456	0.7966	0.8015	0.7991	0.9798
64.7421	1.1643	2000	64.8443	0.7960	0.8084	0.8021	0.9801
61.5075	1.4554	2500	62.6862	0.8082	0.8088	0.8085	0.9803
59.5531	1.7465	3000	60.6463	0.8092	0.8171	0.8131	0.9807
57.7737	2.0375	3500	59.1526	0.8051	0.8212	0.8131	0.9807
55.8137	2.3286	4000	57.6582	0.8162	0.8156	0.8159	0.9811
54.535	2.6197	4500	56.4505	0.8116	0.8277	0.8196	0.9816
53.4845	2.9108	5000	55.3659	0.8145	0.8256	0.8200	0.9812
52.2754	3.2019	5500	54.6164	0.8185	0.8100	0.8142	0.9810
51.3189	3.4929	6000	53.6241	0.8019	0.8319	0.8167	0.9811
50.5871	3.7840	6500	52.9170	0.8191	0.8325	0.8258	0.9818
49.8884	4.0751	7000	52.2826	0.8184	0.8279	0.8231	0.9815
49.1939	4.3662	7500	51.7106	0.8129	0.8368	0.8247	0.9817
48.7488	4.6573	8000	51.2393	0.8162	0.8352	0.8256	0.9820
48.2589	4.9483	8500	50.8117	0.8196	0.8286	0.8241	0.9816
47.5894	5.2394	9000	50.3237	0.8130	0.8300	0.8214	0.9814
47.2432	5.5305	9500	50.0318	0.8211	0.8279	0.8245	0.9817
47.0358	5.8216	10000	49.7203	0.8077	0.8364	0.8218	0.9815
46.6045	6.1126	10500	49.4091	0.8207	0.8273	0.8240	0.9819
46.3028	6.4037	11000	49.1730	0.8191	0.8303	0.8247	0.9821
46.0718	6.6948	11500	48.9163	0.8261	0.8357	0.8309	0.9823
45.7463	6.9859	12000	48.7349	0.8247	0.8329	0.8288	0.9823
45.5286	7.2770	12500	48.4513	0.8184	0.8321	0.8252	0.9820
45.3206	7.5680	13000	48.2289	0.8219	0.8355	0.8286	0.9820
45.2792	7.8591	13500	48.2565	0.8258	0.8299	0.8279	0.9821
45.0271	8.1502	14000	47.9724	0.8234	0.8377	0.8305	0.9821
44.8801	8.4413	14500	47.8890	0.8183	0.8362	0.8272	0.9822
44.8699	8.7324	15000	47.8381	0.8210	0.8322	0.8265	0.9821
44.72	9.0234	15500	47.7280	0.8169	0.8316	0.8242	0.9818
44.5876	9.3145	16000	47.7288	0.8213	0.8305	0.8259	0.9820
44.538	9.6056	16500	47.6315	0.8200	0.8345	0.8272	0.9821
44.5589	9.8967	17000	47.6447	0.8149	0.8310	0.8229	0.9817

Framework versions

• Transformers 4.44.2
• Pytorch 2.1.1+cu121
• Datasets 2.14.5
• Tokenizers 0.19.1