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---
license: apache-2.0
tags:
- classification
- generated_from_trainer
datasets:
- poem_sentiment
metrics:
- accuracy
base_model: bert-base-uncased
model-index:
- name: clasificador-poem-sentiment
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: poem_sentiment
type: poem_sentiment
config: default
split: train
args: default
metrics:
- type: accuracy
value: 0.9038461538461539
name: Accuracy
---
<!-- 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. -->
# clasificador-poem-sentiment
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the poem_sentiment dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5088
- Accuracy: 0.9038
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 112 | 0.4324 | 0.8654 |
| No log | 2.0 | 224 | 0.4070 | 0.875 |
| No log | 3.0 | 336 | 0.5088 | 0.9038 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|