metadata

license: mit
base_model: gpt2-xl
tags:
  - generated_from_trainer
datasets:
  - tyzhu/lmind_nq_train600_eval300_v1_docidx
metrics:
  - accuracy
model-index:
  - name: lmind_nq_train600_eval300_v1_docidx_gpt2-xl
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: tyzhu/lmind_nq_train600_eval300_v1_docidx
          type: tyzhu/lmind_nq_train600_eval300_v1_docidx
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8462492584802891

lmind_nq_train600_eval300_v1_docidx_gpt2-xl

This model is a fine-tuned version of gpt2-xl on the tyzhu/lmind_nq_train600_eval300_v1_docidx dataset. It achieves the following results on the evaluation set:

Loss: 0.3697
Accuracy: 0.8462

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.4931	0.5	28	2.3904	0.5749
2.4699	1.0	56	2.1014	0.6050
1.9006	1.5	84	1.8079	0.6393
1.9317	2.0	112	1.5510	0.6722
1.3984	2.5	140	1.2850	0.7075
1.3662	3.0	168	1.0900	0.7374
0.9041	3.5	196	0.8909	0.7670
0.9056	4.0	224	0.7502	0.7903
0.6131	4.5	252	0.6304	0.8067
0.6101	5.0	280	0.5429	0.8215
0.3772	5.5	308	0.4872	0.8287
0.4265	6.0	336	0.4437	0.8357
0.2552	6.5	364	0.4226	0.8389
0.2875	7.0	392	0.4019	0.8418
0.1874	7.5	420	0.3965	0.8430
0.1958	8.0	448	0.3812	0.8441
0.1443	8.5	476	0.3852	0.8450
0.1535	9.0	504	0.3791	0.8456
0.1236	9.5	532	0.3849	0.8456
0.1221	10.0	560	0.3697	0.8462

Framework versions

Transformers 4.34.0
Pytorch 2.1.0+cu121
Datasets 2.14.5
Tokenizers 0.14.1