metadata

base_model: haryoaw/scenario-TCR-NER_data-univner_en
library_name: transformers
license: mit
metrics:
  - precision
  - recall
  - f1
  - accuracy
tags:
  - generated_from_trainer
model-index:
  - name: scenario-non-kd-pre-ner-full-mdeberta_data-univner_en44
    results: []

scenario-non-kd-pre-ner-full-mdeberta_data-univner_en44

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

Loss: 0.1490
Precision: 0.8349
Recall: 0.8271
F1: 0.8310
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
0.0023	1.2755	500	0.1120	0.8158	0.8344	0.8250	0.9851
0.0019	2.5510	1000	0.1055	0.7790	0.8468	0.8115	0.9840
0.0019	3.8265	1500	0.1115	0.8134	0.8395	0.8263	0.9847
0.0013	5.1020	2000	0.1248	0.7938	0.8251	0.8091	0.9835
0.0016	6.3776	2500	0.1281	0.8302	0.8147	0.8224	0.9845
0.0012	7.6531	3000	0.1241	0.8179	0.8137	0.8158	0.9841
0.001	8.9286	3500	0.1191	0.8184	0.8302	0.8243	0.9842
0.0005	10.2041	4000	0.1335	0.7951	0.8395	0.8167	0.9837
0.001	11.4796	4500	0.1376	0.8125	0.8344	0.8233	0.9843
0.0005	12.7551	5000	0.1282	0.8244	0.8313	0.8278	0.9847
0.0007	14.0306	5500	0.1247	0.8294	0.8354	0.8324	0.9848
0.0003	15.3061	6000	0.1440	0.8143	0.8354	0.8247	0.9841
0.0009	16.5816	6500	0.1456	0.8384	0.8219	0.8301	0.9840
0.0003	17.8571	7000	0.1401	0.8154	0.8416	0.8283	0.9849
0.0002	19.1327	7500	0.1478	0.8306	0.8323	0.8314	0.9850
0.0003	20.4082	8000	0.1448	0.8306	0.8271	0.8288	0.9850
0.0003	21.6837	8500	0.1416	0.8240	0.8385	0.8312	0.9847
0.0002	22.9592	9000	0.1501	0.8159	0.8395	0.8276	0.9843
0.0001	24.2347	9500	0.1457	0.8243	0.8354	0.8298	0.9845
0.0001	25.5102	10000	0.1474	0.8258	0.8344	0.8301	0.9846
0.0001	26.7857	10500	0.1493	0.8244	0.8406	0.8324	0.9847
0.0001	28.0612	11000	0.1497	0.8418	0.8209	0.8312	0.9847
0.0001	29.3367	11500	0.1490	0.8349	0.8271	0.8310	0.9846

Framework versions

Transformers 4.44.2
Pytorch 2.1.1+cu121
Datasets 2.14.5
Tokenizers 0.19.1