transformers tensorboard safetensors bert text-classification generated_from_trainer dataset:RobZamp/sick base_model:google-bert/bert-base-cased base_model:finetune:google-bert/bert-base-cased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: bert-base-cased
datasets:
- RobZamp/sick
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-fp-sick
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: sick
      type: RobZamp/sick
      config: default
      split: validation
      args: default
    metrics:
    - type: accuracy
      value: 0.8545454545454545
      name: Accuracy
---

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# bert-base-cased-fp-sick

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the sick dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3973
- Accuracy: 0.8545

## 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: 64
- eval_batch_size: 32
- seed: 34
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 70   | 0.6825          | 0.6727   |
| No log        | 2.0   | 140  | 0.4141          | 0.8505   |
| No log        | 3.0   | 210  | 0.3973          | 0.8545   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
