metadata
tags:
- generated_from_trainer
datasets:
- silicone
metrics:
- accuracy
model-index:
- name: spanbert-base-cased
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: silicone
type: silicone
config: swda
split: test
args: swda
metrics:
- name: Accuracy
type: accuracy
value: 0.7114959469417833
spanbert-base-cased
This model is a fine-tuned version of SpanBERT/spanbert-base-cased on the silicone dataset. It achieves the following results on the evaluation set:
- Loss: 1.0346
- Accuracy: 0.7115
- Micro-precision: 0.7115
- Micro-recall: 0.7115
- Micro-f1: 0.7115
- Macro-precision: 0.2484
- Macro-recall: 0.2508
- Macro-f1: 0.2412
- Weighted-precision: 0.6569
- Weighted-recall: 0.7115
- Weighted-f1: 0.6741
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Micro-precision | Micro-recall | Micro-f1 | Macro-precision | Macro-recall | Macro-f1 | Weighted-precision | Weighted-recall | Weighted-f1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.043 | 1.0 | 2980 | 1.0346 | 0.7115 | 0.7115 | 0.7115 | 0.7115 | 0.2484 | 0.2508 | 0.2412 | 0.6569 | 0.7115 | 0.6741 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2