metadata

license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
  - generated_from_trainer
datasets:
  - cnec
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: CNEC2_0_Supertypes_xlm-roberta-large
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: cnec
          type: cnec
          config: default
          split: validation
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.8074141048824593
          - name: Recall
            type: recall
            value: 0.861969111969112
          - name: F1
            type: f1
            value: 0.8338001867413634
          - name: Accuracy
            type: accuracy
            value: 0.9655222367086774

CNEC2_0_Supertypes_xlm-roberta-large

This model is a fine-tuned version of FacebookAI/xlm-roberta-large on the cnec dataset. It achieves the following results on the evaluation set:

Loss: 0.2044
Precision: 0.8074
Recall: 0.8620
F1: 0.8338
Accuracy: 0.9655

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: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
lr_scheduler_warmup_steps: 500
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.5564	1.11	500	0.1852	0.6302	0.7558	0.6873	0.9502
0.1786	2.22	1000	0.1552	0.6952	0.8069	0.7469	0.9568
0.1219	3.33	1500	0.1665	0.6860	0.8214	0.7476	0.9577
0.087	4.44	2000	0.1616	0.7572	0.8263	0.7902	0.9595
0.0689	5.56	2500	0.1679	0.7670	0.8243	0.7946	0.9616
0.0442	6.67	3000	0.1612	0.7346	0.8364	0.7822	0.9631
0.0353	7.78	3500	0.1864	0.8099	0.8576	0.8331	0.9653
0.0205	8.89	4000	0.1950	0.8026	0.8653	0.8328	0.9654
0.0133	10.0	4500	0.2044	0.8074	0.8620	0.8338	0.9655

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0