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xlm-roberta-base-NER-favsbot

Token Classification
Transformers
PyTorch
xlm-roberta
generated_from_trainer
Eval Results
Inference Endpoints
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metadata
license: mit
tags:
  - generated_from_trainer
datasets:
  - favsbot
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: xlm-roberta-base-NER-favsbot
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: favsbot
          type: favsbot
          config: default
          split: train
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.5555555555555556
          - name: Recall
            type: recall
            value: 0.4722222222222222
          - name: F1
            type: f1
            value: 0.5105105105105106
          - name: Accuracy
            type: accuracy
            value: 0.6900452488687783

xlm-roberta-base-NER-favsbot

This model is a fine-tuned version of xlm-roberta-base on the favsbot dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0572
  • Precision: 0.5556
  • Recall: 0.4722
  • F1: 0.5105
  • Accuracy: 0.6900

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: 1.5e-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
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 4 2.4303 0.1448 0.3556 0.2058 0.1855
No log 2.0 8 2.3220 0.1465 0.3556 0.2075 0.1991
No log 3.0 12 2.1842 0.2486 0.2389 0.2436 0.4593
No log 4.0 16 1.9552 0.4 0.0111 0.0216 0.4367
No log 5.0 20 1.6989 0.0 0.0 0.0 0.4321
No log 6.0 24 1.6532 0.5 0.0056 0.0110 0.4344
No log 7.0 28 1.5724 0.3649 0.15 0.2126 0.5045
No log 8.0 32 1.5164 0.3654 0.2111 0.2676 0.5271
No log 9.0 36 1.4448 0.4203 0.1611 0.2329 0.5090
No log 10.0 40 1.3922 0.4833 0.1611 0.2417 0.5158
No log 11.0 44 1.3409 0.5395 0.2278 0.3203 0.5498
No log 12.0 48 1.2831 0.5824 0.2944 0.3911 0.5950
No log 13.0 52 1.2269 0.5714 0.3556 0.4384 0.6335
No log 14.0 56 1.1766 0.5625 0.4 0.4675 0.6606
No log 15.0 60 1.1408 0.5540 0.4278 0.4828 0.6674
No log 16.0 64 1.1159 0.56 0.4667 0.5091 0.6810
No log 17.0 68 1.0908 0.5658 0.4778 0.5181 0.6855
No log 18.0 72 1.0722 0.5658 0.4778 0.5181 0.6923
No log 19.0 76 1.0615 0.5592 0.4722 0.5120 0.6900
No log 20.0 80 1.0572 0.5556 0.4722 0.5105 0.6900

Framework versions

  • Transformers 4.21.1
  • Pytorch 1.12.1
  • Datasets 2.4.0
  • Tokenizers 0.12.1