transformers pytorch bert text-classification classification generated_from_trainer dataset:poem_sentiment license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- poem_sentiment
license: apache-2.0
metrics:
- accuracy
tags:
- classification
- generated_from_trainer
model-index:
- name: clasificador-poem-sentiment
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: poem_sentiment
      type: poem_sentiment
      config: default
      split: test
      args: default
    metrics:
    - type: accuracy
      value: 0.8653846153846154
      name: Accuracy
---

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# 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: 0.6175
- Accuracy: 0.8654

## 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  | 0.4312          | 0.8558   |
| No log        | 2.0   | 224  | 0.5355          | 0.8846   |
| No log        | 3.0   | 336  | 0.6175          | 0.8654   |


### Framework versions

- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
