---
license: apache-2.0
base_model: bert-large-cased
tags:
- generated_from_trainer
datasets:
- RobZamp/sick
metrics:
- accuracy
model-index:
- name: bert-large-cased-fp-sick
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: sick
      type: RobZamp/sick
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8626262626262626
---

<!-- 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. -->

# bert-large-cased-fp-sick

This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the sick dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3773
- Accuracy: 0.8626

## 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: 59
- 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   | 0.5329          | 0.8      |
| No log        | 2.0   | 140  | 0.3852          | 0.8667   |
| No log        | 3.0   | 210  | 0.3773          | 0.8626   |


### Framework versions

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