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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- rotten_tomatoes
metrics:
- accuracy
model-index:
- name: bert-finetuned-rottentomatoes
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: rotten_tomatoes
type: rotten_tomatoes
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.849906191369606
---
<!-- 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-finetuned-rottentomatoes
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the rotten_tomatoes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6639
- Accuracy: 0.8499
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.417 | 1.0 | 1067 | 0.4210 | 0.8349 |
| 0.2965 | 2.0 | 2134 | 0.4945 | 0.8471 |
| 0.1447 | 3.0 | 3201 | 0.6639 | 0.8499 |
### Framework versions
- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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