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-kd-scr-ner-full_data-univner_full55
    results: []

scenario-kd-scr-ner-full_data-univner_full55

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: nan
Precision: 0.0
Recall: 0.0
F1: 0.0
Accuracy: 0.9405

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: 55
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	Accuracy
5.3686	1.2755	500	nan	0.9405
0.0	2.5510	1000	nan	0.9405
0.0	3.8265	1500	nan	0.9405
0.0	5.1020	2000	nan	0.9405
0.0	6.3776	2500	nan	0.9405
0.0	7.6531	3000	nan	0.9405
0.0	8.9286	3500	nan	0.9405
0.0	10.2041	4000	nan	0.9405
0.0	11.4796	4500	nan	0.9405
0.0	12.7551	5000	nan	0.9405
0.0	14.0306	5500	nan	0.9405
0.0	15.3061	6000	nan	0.9405
0.0	16.5816	6500	nan	0.9405
0.0	17.8571	7000	nan	0.9405
0.0	19.1327	7500	nan	0.9405
0.0	20.4082	8000	nan	0.9405
0.0	21.6837	8500	nan	0.9405
0.0	22.9592	9000	nan	0.9405
0.0	24.2347	9500	nan	0.9405
0.0	25.5102	10000	nan	0.9405
0.0	26.7857	10500	nan	0.9405

Framework versions

Transformers 4.44.2
Pytorch 2.1.1+cu121
Datasets 2.14.5
Tokenizers 0.19.1