metadata

base_model: haryoaw/scenario-TCR-NER_data-univner_half
library_name: transformers
license: mit
metrics:
  - precision
  - recall
  - f1
  - accuracy
tags:
  - generated_from_trainer
model-index:
  - name: scenario-non-kd-po-ner-full-xlmr_data-univner_half44
    results: []

scenario-non-kd-po-ner-full-xlmr_data-univner_half44

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

Loss: 0.1451
Precision: 0.8545
Recall: 0.8580
F1: 0.8562
Accuracy: 0.9846

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: 44
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
0.0099	0.5828	500	0.0855	0.8429	0.8336	0.8382	0.9835
0.0104	1.1655	1000	0.0903	0.8392	0.8524	0.8458	0.9841
0.0085	1.7483	1500	0.0941	0.8319	0.8524	0.8420	0.9835
0.0076	2.3310	2000	0.1037	0.8424	0.8443	0.8433	0.9832
0.0071	2.9138	2500	0.0983	0.8252	0.8550	0.8399	0.9831
0.007	3.4965	3000	0.0964	0.8389	0.8547	0.8467	0.9834
0.0059	4.0793	3500	0.0988	0.8418	0.8510	0.8464	0.9839
0.0051	4.6620	4000	0.1094	0.8410	0.8472	0.8441	0.9833
0.0055	5.2448	4500	0.1039	0.8380	0.8521	0.8450	0.9834
0.0043	5.8275	5000	0.1120	0.8416	0.8362	0.8389	0.9827
0.0046	6.4103	5500	0.1148	0.8447	0.8450	0.8449	0.9833
0.004	6.9930	6000	0.1135	0.8323	0.8491	0.8406	0.9832
0.0036	7.5758	6500	0.1127	0.8556	0.8377	0.8465	0.9835
0.0031	8.1585	7000	0.1192	0.8365	0.8489	0.8427	0.9835
0.0029	8.7413	7500	0.1280	0.8403	0.8540	0.8471	0.9834
0.0035	9.3240	8000	0.1272	0.8445	0.8371	0.8408	0.9831
0.0031	9.9068	8500	0.1132	0.8556	0.8465	0.8510	0.9842
0.0021	10.4895	9000	0.1302	0.8383	0.8512	0.8447	0.9835
0.0023	11.0723	9500	0.1264	0.8347	0.8472	0.8409	0.9830
0.0024	11.6550	10000	0.1191	0.8446	0.8523	0.8484	0.9837
0.0021	12.2378	10500	0.1301	0.8419	0.8469	0.8444	0.9834
0.0027	12.8205	11000	0.1231	0.8437	0.8541	0.8489	0.9840
0.002	13.4033	11500	0.1329	0.8345	0.8482	0.8413	0.9828
0.0017	13.9860	12000	0.1320	0.8418	0.8575	0.8495	0.9837
0.0016	14.5688	12500	0.1298	0.8462	0.8439	0.8450	0.9836
0.0016	15.1515	13000	0.1368	0.8440	0.8274	0.8356	0.9831
0.0019	15.7343	13500	0.1281	0.8491	0.8453	0.8472	0.9837
0.001	16.3170	14000	0.1348	0.8469	0.8523	0.8496	0.9839
0.0014	16.8998	14500	0.1345	0.8405	0.8515	0.8460	0.9834
0.001	17.4825	15000	0.1449	0.8449	0.8357	0.8403	0.9833
0.0011	18.0653	15500	0.1414	0.8478	0.8541	0.8509	0.9840
0.0013	18.6480	16000	0.1414	0.8486	0.8448	0.8466	0.9837
0.001	19.2308	16500	0.1417	0.8522	0.8549	0.8535	0.9839
0.0011	19.8135	17000	0.1400	0.8430	0.8474	0.8452	0.9835
0.001	20.3963	17500	0.1369	0.8503	0.8498	0.8501	0.9836
0.0007	20.9790	18000	0.1349	0.8512	0.8551	0.8532	0.9843
0.001	21.5618	18500	0.1345	0.8532	0.8495	0.8514	0.9843
0.0009	22.1445	19000	0.1397	0.8585	0.8420	0.8502	0.9843
0.0004	22.7273	19500	0.1394	0.8522	0.8575	0.8548	0.9846
0.0006	23.3100	20000	0.1433	0.8470	0.8576	0.8522	0.9843
0.0005	23.8928	20500	0.1447	0.8526	0.8512	0.8519	0.9841
0.0004	24.4755	21000	0.1418	0.8492	0.8547	0.8519	0.9843
0.0004	25.0583	21500	0.1444	0.8546	0.8556	0.8551	0.9845
0.0003	25.6410	22000	0.1458	0.8527	0.8554	0.8541	0.9843
0.0004	26.2238	22500	0.1448	0.8541	0.8502	0.8521	0.9843
0.0003	26.8065	23000	0.1449	0.8474	0.8579	0.8526	0.9842
0.0004	27.3893	23500	0.1448	0.8553	0.8579	0.8566	0.9846
0.0004	27.9720	24000	0.1427	0.8589	0.8527	0.8558	0.9844
0.0002	28.5548	24500	0.1439	0.8541	0.8582	0.8561	0.9846
0.0003	29.1375	25000	0.1446	0.8564	0.8560	0.8562	0.9847
0.0002	29.7203	25500	0.1451	0.8545	0.8580	0.8562	0.9846

Framework versions

Transformers 4.44.2
Pytorch 2.1.1+cu121
Datasets 2.14.5
Tokenizers 0.19.1