README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - plo_dunfiltered_config
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: albert-large-v2-finetuned-ner_with_callbacks
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: plo_dunfiltered_config
          type: plo_dunfiltered_config
          args: PLODunfiltered
        metrics:
          - name: Precision
            type: precision
            value: 0.9655166719570215
          - name: Recall
            type: recall
            value: 0.9608483288141474
          - name: F1
            type: f1
            value: 0.9631768437660728
          - name: Accuracy
            type: accuracy
            value: 0.9589410429715819

albert-large-v2-finetuned-ner_with_callbacks

This model is a fine-tuned version of albert-large-v2 on the plo_dunfiltered_config dataset. It achieves the following results on the evaluation set:

• Loss: 0.1235
• Precision: 0.9655
• Recall: 0.9608
• F1: 0.9632
• Accuracy: 0.9589

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: 2e-05
• train_batch_size: 8
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1377	0.49	7000	0.1294	0.9563	0.9422	0.9492	0.9436
0.1244	0.98	14000	0.1165	0.9589	0.9504	0.9546	0.9499
0.107	1.48	21000	0.1140	0.9603	0.9509	0.9556	0.9511
0.1088	1.97	28000	0.1086	0.9613	0.9551	0.9582	0.9536
0.0918	2.46	35000	0.1059	0.9617	0.9582	0.9600	0.9556
0.0847	2.95	42000	0.1067	0.9620	0.9586	0.9603	0.9559
0.0734	3.44	49000	0.1188	0.9646	0.9588	0.9617	0.9574
0.0725	3.93	56000	0.1065	0.9660	0.9599	0.9630	0.9588
0.0547	4.43	63000	0.1273	0.9662	0.9602	0.9632	0.9590
0.0542	4.92	70000	0.1235	0.9655	0.9608	0.9632	0.9589
0.0374	5.41	77000	0.1401	0.9647	0.9613	0.9630	0.9586
0.0417	5.9	84000	0.1380	0.9641	0.9622	0.9632	0.9588

Framework versions

• Transformers 4.18.0
• Pytorch 1.10.1+cu111
• Datasets 2.1.0
• Tokenizers 0.12.1