metadata
license: agpl-3.0
tags:
- generated_from_trainer
datasets:
- mim_gold_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: XLMR-ENIS-finetuned-conll_ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: mim_gold_ner
type: mim_gold_ner
args: mim-gold-ner
metrics:
- name: Precision
type: precision
value: 0.8754622097322882
- name: Recall
type: recall
value: 0.8425622775800712
- name: F1
type: f1
value: 0.8586972290729725
- name: Accuracy
type: accuracy
value: 0.9860744627305035
XLMR-ENIS-finetuned-conll_ner
This model is a fine-tuned version of vesteinn/XLMR-ENIS on the mim_gold_ner dataset. It achieves the following results on the evaluation set:
- Loss: 0.0713
- Precision: 0.8755
- Recall: 0.8426
- F1: 0.8587
- Accuracy: 0.9861
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0493 | 1.0 | 2904 | 0.0673 | 0.8588 | 0.8114 | 0.8344 | 0.9841 |
| 0.0277 | 2.0 | 5808 | 0.0620 | 0.8735 | 0.8275 | 0.8499 | 0.9855 |
| 0.0159 | 3.0 | 8712 | 0.0713 | 0.8755 | 0.8426 | 0.8587 | 0.9861 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3