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---
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
|