metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - ncbi_disease
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: MLMA_lab9_task2
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: ncbi_disease
          type: ncbi_disease
          config: ncbi_disease
          split: validation
          args: ncbi_disease
        metrics:
          - name: Precision
            type: precision
            value: 0.015873015873015872
          - name: Recall
            type: recall
            value: 0.14866581956797967
          - name: F1
            type: f1
            value: 0.028683500858053445
          - name: Accuracy
            type: accuracy
            value: 0.6365342039100904

MLMA_lab9_task2

This model is a fine-tuned version of microsoft/biogpt on the ncbi_disease dataset. It achieves the following results on the evaluation set:

Loss: 1.2509
Precision: 0.0159
Recall: 0.1487
F1: 0.0287
Accuracy: 0.6365

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
1.153	1.0	680	1.0671	0.0122	0.1258	0.0223	0.5452
1.02	2.0	1360	1.0418	0.0098	0.0203	0.0132	0.6791
0.9552	3.0	2040	1.0269	0.0135	0.1677	0.0250	0.5282
0.926	4.0	2720	1.0390	0.0143	0.0940	0.0248	0.6686
0.9156	5.0	3400	1.0200	0.0135	0.2046	0.0253	0.4679
0.8791	6.0	4080	1.0543	0.0131	0.2745	0.0250	0.3149
0.8672	7.0	4760	1.0545	0.0141	0.2732	0.0267	0.3471
0.8627	8.0	5440	1.0734	0.0145	0.0826	0.0246	0.7220
0.8375	9.0	6120	1.1068	0.0156	0.1410	0.0281	0.6451
0.8235	10.0	6800	1.0796	0.0158	0.1537	0.0286	0.6210
0.8157	11.0	7480	1.1476	0.0143	0.1690	0.0263	0.5737
0.7957	12.0	8160	1.1369	0.0143	0.1525	0.0262	0.6155
0.7937	13.0	8840	1.2014	0.0151	0.1741	0.0278	0.5808
0.7765	14.0	9520	1.2249	0.0160	0.1449	0.0289	0.6443
0.7661	15.0	10200	1.2509	0.0159	0.1487	0.0287	0.6365

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3