transformers pytorch bert text-classification generated_from_trainer dataset:silicone license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- silicone
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
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
---

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# bert-large-cased

This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/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
