transformers tensorboard safetensors bert text-classification generated_from_trainer dataset:glue base_model:google-bert/bert-large-uncased base_model:finetune:google-bert/bert-large-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: bert-large-uncased
datasets:
- glue
license: apache-2.0
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: trainer
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: glue
      type: glue
      config: cola
      split: validation
      args: cola
    metrics:
    - type: accuracy
      value: 0.8465963566634708
      name: Accuracy
    - type: f1
      value: 0.8064540073113251
      name: F1
    - type: precision
      value: 0.840606542828289
      name: Precision
    - type: recall
      value: 0.7876439727431708
      name: Recall
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# trainer

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4490
- Accuracy: 0.8466
- F1: 0.8065
- Precision: 0.8406
- Recall: 0.7876

## 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: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 267  | 0.3860          | 0.8370   | 0.7999 | 0.8184    | 0.7876 |
| 0.3455        | 2.0   | 534  | 0.4490          | 0.8466   | 0.8065 | 0.8406    | 0.7876 |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
