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---
license: apache-2.0
tags:
- classification
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
model-index:
- name: clasificador-glue
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: irony
split: test
args: irony
metrics:
- name: Accuracy
type: accuracy
value: 0.6836734693877551
---
<!-- 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-glue
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3017
- Accuracy: 0.6837
## 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 | 358 | 0.8826 | 0.6084 |
| 0.6268 | 2.0 | 716 | 0.6036 | 0.7079 |
| 0.3358 | 3.0 | 1074 | 1.3017 | 0.6837 |
### Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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