transformers tensorboard safetensors bert text-classification generated_from_trainer dataset:bigbench base_model:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: bert-base-uncased
datasets:
- bigbench
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bigbench_entailedpolarity-bert-base-uncased
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: bigbench
      type: bigbench
      config: entailed_polarity
      split: train
      args: entailed_polarity
    metrics:
    - type: accuracy
      value: 0.9583333333333334
      name: Accuracy
---

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# bigbench_entailedpolarity-bert-base-uncased

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the bigbench dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3706
- Accuracy: 0.9583

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 24   | 0.5613          | 0.7917   |
| No log        | 2.0   | 48   | 0.5337          | 0.7917   |
| No log        | 3.0   | 72   | 0.3178          | 0.875    |
| No log        | 4.0   | 96   | 0.1924          | 0.9167   |
| No log        | 5.0   | 120  | 0.2007          | 0.9583   |
| No log        | 6.0   | 144  | 0.2766          | 0.9583   |
| No log        | 7.0   | 168  | 0.3031          | 0.9583   |
| No log        | 8.0   | 192  | 0.3291          | 0.9583   |
| No log        | 9.0   | 216  | 0.3381          | 0.9583   |
| No log        | 10.0  | 240  | 0.3454          | 0.9583   |
| No log        | 11.0  | 264  | 0.3508          | 0.9583   |
| No log        | 12.0  | 288  | 0.3554          | 0.9583   |
| No log        | 13.0  | 312  | 0.3592          | 0.9583   |
| No log        | 14.0  | 336  | 0.3624          | 0.9583   |
| No log        | 15.0  | 360  | 0.3650          | 0.9583   |
| No log        | 16.0  | 384  | 0.3671          | 0.9583   |
| No log        | 17.0  | 408  | 0.3686          | 0.9583   |
| No log        | 18.0  | 432  | 0.3696          | 0.9583   |
| No log        | 19.0  | 456  | 0.3704          | 0.9583   |
| No log        | 20.0  | 480  | 0.3706          | 0.9583   |


### Framework versions

- Transformers 4.35.2
- Pytorch 1.10.1+cu102
- Datasets 2.15.0
- Tokenizers 0.15.0
