transformers safetensors bert fill-mask generated_from_trainer dataset:datasets/all_binary_and_xe_ey_fae_counterfactual base_model:bert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- datasets/all_binary_and_xe_ey_fae_counterfactual
metrics:
- accuracy
model-index:
- name: testing
  results:
  - task:
      name: Masked Language Modeling
      type: fill-mask
    dataset:
      name: datasets/all_binary_and_xe_ey_fae_counterfactual
      type: datasets/all_binary_and_xe_ey_fae_counterfactual
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6740440005371309
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# testing

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the datasets/all_binary_and_xe_ey_fae_counterfactual dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7135
- Accuracy: 0.6740

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 100
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.068         | 0.0   | 5    | 1.7758          | 0.6650   |
| 1.9159        | 0.0   | 10   | 1.7192          | 0.6736   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
