metadata

language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - surrey-nlp/PLOD-unfiltered
metrics:
  - precision
  - recall
  - f1
  - accuracy
widget:
  - text: >-
      Light dissolved inorganic carbon (DIC) resulting from the oxidation of
      hydrocarbons.
  - text: >-
      RAFs are plotted for a selection of neurons in the dorsal zone (DZ) of
      auditory cortex in Figure 1.
  - text: >-
      Images were acquired using a GE 3.0T MRI scanner with an upgrade for
      echo-planar imaging (EPI).
base_model: albert-large-v2
model-index:
  - name: albert-large-v2-finetuned-ner_with_callbacks
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: surrey-nlp/PLOD-unfiltered
          type: token-classification
          args: PLODunfiltered
        metrics:
          - type: precision
            value: 0.9655166719570215
            name: Precision
          - type: recall
            value: 0.9608483288141474
            name: Recall
          - type: f1
            value: 0.9631768437660728
            name: F1
          - type: accuracy
            value: 0.9589410429715819
            name: Accuracy

albert-large-v2-finetuned-ner_with_callbacks

This model is a fine-tuned version of albert-large-v2 on the PLOD-unfiltered 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