metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - accuracy
model-index:
  - name: distilbert-legal-chunk
    results: []

distilbert-legal-chunk

This model is a fine-tuned version of distilbert-base-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0699
Precision: 0.8994
Recall: 0.8721
Macro F1: 0.8855
Micro F1: 0.8855
Accuracy: 0.9789
Marker F1: 0.9804
Marker Precision: 0.9687
Marker Recall: 0.9925
Reference F1: 0.9791
Reference Precision: 0.9804
Reference Recall: 0.9778
Term F1: 0.8670
Term Precision: 0.8844
Term Recall: 0.8502

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	Macro F1	Micro F1	Accuracy	Marker F1	Marker Precision	Marker Recall	Reference F1	Reference Precision	Reference Recall	Term F1	Term Precision	Term Recall
0.0857	1.0	3125	0.0966	0.8374	0.7889	0.8124	0.8124	0.9676	0.6143	0.5874	0.6437	0.9628	0.9423	0.9842	0.8291	0.8656	0.7955
0.058	2.0	6250	0.0606	0.8869	0.9146	0.9006	0.9006	0.9814	0.9405	0.9126	0.9702	0.9689	0.9511	0.9873	0.8923	0.8805	0.9045
0.0415	3.0	9375	0.0642	0.9077	0.9131	0.9104	0.9104	0.9823	0.9524	0.9262	0.9801	0.9742	0.9614	0.9873	0.9021	0.9026	0.9016
0.0283	4.0	12500	0.0646	0.9066	0.9089	0.9077	0.9077	0.9819	0.9564	0.9326	0.9815	0.9712	0.9555	0.9873	0.8986	0.9008	0.8965

Framework versions

Transformers 4.21.3
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1