metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- silicone
metrics:
- accuracy
base_model: bert-large-cased
model-index:
- name: bert-large-cased
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: silicone
type: silicone
config: swda
split: test
args: swda
metrics:
- type: accuracy
value: 0.7280766396462786
name: Accuracy
bert-large-cased
This model is a fine-tuned version of bert-large-cased on the silicone dataset. It achieves the following results on the evaluation set:
- Loss: 0.8807
- Accuracy: 0.7281
- Micro-precision: 0.7281
- Micro-recall: 0.7281
- Micro-f1: 0.7281
- Macro-precision: 0.4591
- Macro-recall: 0.3825
- Macro-f1: 0.3855
- Weighted-precision: 0.6943
- Weighted-recall: 0.7281
- Weighted-f1: 0.6977
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.8835 | 1.0 | 2980 | 0.8807 | 0.7281 | 0.7281 | 0.7281 | 0.7281 | 0.4591 | 0.3825 | 0.3855 | 0.6943 | 0.7281 | 0.6977 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2