metadata

license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
  - generated_from_trainer
datasets:
  - cnec
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: CNEC1_1_extended_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: 0.8595505617977528
          - name: Recall
            type: recall
            value: 0.8995189738107964
          - name: F1
            type: f1
            value: 0.8790806999216505
          - name: Accuracy
            type: accuracy
            value: 0.9695206428373511

CNEC1_1_extended_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: 0.2397
Precision: 0.8596
Recall: 0.8995
F1: 0.8791
Accuracy: 0.9695

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3533	1.72	500	0.1415	0.7483	0.8439	0.7933	0.9609
0.1509	3.44	1000	0.1352	0.8073	0.8685	0.8368	0.9664
0.1072	5.15	1500	0.1533	0.8151	0.8739	0.8434	0.9674
0.0778	6.87	2000	0.1740	0.8400	0.8781	0.8586	0.9668
0.059	8.59	2500	0.1676	0.8365	0.8942	0.8644	0.9699
0.0475	10.31	3000	0.1699	0.8295	0.8813	0.8546	0.9678
0.0381	12.03	3500	0.1876	0.8418	0.8985	0.8692	0.9686
0.0287	13.75	4000	0.2100	0.8446	0.8979	0.8705	0.9681
0.0238	15.46	4500	0.2007	0.8466	0.8995	0.8722	0.9702
0.0186	17.18	5000	0.2201	0.8568	0.8926	0.8743	0.9689
0.0161	18.9	5500	0.2200	0.8573	0.8990	0.8776	0.9700
0.014	20.62	6000	0.2326	0.8601	0.8974	0.8784	0.9697
0.0104	22.34	6500	0.2370	0.8639	0.8990	0.8811	0.9696
0.0099	24.05	7000	0.2397	0.8596	0.8995	0.8791	0.9695

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0