metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xglue
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xglue
type: xglue
config: ner
split: validation.es
args: ner
metrics:
- name: Precision
type: precision
value: 0.6037969459347916
- name: Recall
type: recall
value: 0.6720257234726688
- name: F1
type: f1
value: 0.6360869565217391
- name: Accuracy
type: accuracy
value: 0.9488508424567125
bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the xglue dataset. It achieves the following results on the evaluation set:
- Loss: 0.2202
- Precision: 0.6038
- Recall: 0.6720
- F1: 0.6361
- Accuracy: 0.9489
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: 8
- eval_batch_size: 8
- 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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 191 | 0.2359 | 0.5659 | 0.6309 | 0.5967 | 0.9397 |
| No log | 2.0 | 382 | 0.2136 | 0.5754 | 0.6681 | 0.6183 | 0.9464 |
| 0.1605 | 3.0 | 573 | 0.2202 | 0.6038 | 0.6720 | 0.6361 | 0.9489 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2