metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- finer-139
- nlpaueb/finer-139
metrics:
- precision
- recall
- f1
- accuracy
base_model: google/bert_uncased_L-2_H-128_A-2
model-index:
- name: bertiny-finetuned-finer
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: finer-139
type: finer-139
args: finer-139
metrics:
- type: precision
value: 0.5339285714285714
name: Precision
- type: recall
value: 0.036011080332409975
name: Recall
- type: f1
value: 0.06747151077513258
name: F1
- type: accuracy
value: 0.9847166143263048
name: Accuracy
bertiny-finetuned-finer
This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the finer-139 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0882
- Precision: 0.5339
- Recall: 0.0360
- F1: 0.0675
- Accuracy: 0.9847
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 |
|---|---|---|---|---|---|---|---|
| 0.0871 | 1.0 | 11255 | 0.0952 | 0.0 | 0.0 | 0.0 | 0.9843 |
| 0.0864 | 2.0 | 22510 | 0.0895 | 0.7640 | 0.0082 | 0.0162 | 0.9844 |
| 0.0929 | 3.0 | 33765 | 0.0882 | 0.5339 | 0.0360 | 0.0675 | 0.9847 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1