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---
tags:
- generated_from_trainer
datasets:
- dataset
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: dccuchile-distilbert-base-spanish-uncased-finetuned-with-spanish-tweets-clf-cleaned-ds
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: dataset
type: dataset
config: 60-20-20
split: dev
args: 60-20-20
metrics:
- name: Accuracy
type: accuracy
value: 0.650310988251555
- name: F1
type: f1
value: 0.6518765643027159
- name: Precision
type: precision
value: 0.6625453481119005
- name: Recall
type: recall
value: 0.6498098682990169
---
<!-- 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. -->
# dccuchile-distilbert-base-spanish-uncased-finetuned-with-spanish-tweets-clf-cleaned-ds
This model is a fine-tuned version of [dccuchile/distilbert-base-spanish-uncased](https://huggingface.co/dccuchile/distilbert-base-spanish-uncased) on the dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6605
- Accuracy: 0.6503
- F1: 0.6519
- Precision: 0.6625
- Recall: 0.6498
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.8558 | 1.0 | 543 | 0.7705 | 0.6628 | 0.6408 | 0.6585 | 0.6404 |
| 0.5485 | 2.0 | 1086 | 0.8657 | 0.6593 | 0.6436 | 0.6578 | 0.6388 |
| 0.3071 | 3.0 | 1629 | 1.3021 | 0.6586 | 0.6556 | 0.6551 | 0.6581 |
| 0.1581 | 4.0 | 2172 | 1.6605 | 0.6503 | 0.6519 | 0.6625 | 0.6498 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
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