metadata

license: apache-2.0
base_model: bert-large-cased
tags:
  - generated_from_trainer
datasets:
  - universal_dependencies
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-large-cased-upos
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: universal_dependencies
          type: universal_dependencies
          config: en_ewt
          split: validation
          args: en_ewt
        metrics:
          - name: Precision
            type: precision
            value: 0.8688031595250056
          - name: Recall
            type: recall
            value: 0.8557292884764056
          - name: F1
            type: f1
            value: 0.8617720995316154
          - name: Accuracy
            type: accuracy
            value: 0.8904395106479384

bert-large-cased-upos

This model is a fine-tuned version of bert-large-cased on the universal_dependencies dataset. It achieves the following results on the evaluation set:

Loss: 0.4114
Precision: 0.8688
Recall: 0.8557
F1: 0.8618
Accuracy: 0.8904

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: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	438	0.5774	0.8348	0.7595	0.7893	0.8099
No log	2.0	876	0.4787	0.8114	0.7967	0.8000	0.8385
No log	3.0	1314	0.4345	0.8227	0.8302	0.8213	0.8601
No log	4.0	1752	0.4140	0.8257	0.8430	0.8304	0.8727
No log	5.0	2190	0.4211	0.8405	0.8525	0.8441	0.8787
No log	6.0	2628	0.4114	0.8688	0.8557	0.8618	0.8904
No log	7.0	3066	0.4582	0.8454	0.8572	0.8503	0.8911
No log	8.0	3504	0.4771	0.8447	0.8588	0.8508	0.8894
No log	9.0	3942	0.4799	0.8545	0.8626	0.8577	0.8918
No log	10.0	4380	0.4919	0.8539	0.8642	0.8579	0.8937

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.21.0
Tokenizers 0.19.1