Instructions to use dabyzz/clasificador-muchocine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dabyzz/clasificador-muchocine with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dabyzz/clasificador-muchocine")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dabyzz/clasificador-muchocine") model = AutoModelForSequenceClassification.from_pretrained("dabyzz/clasificador-muchocine") - Notebooks
- Google Colab
- Kaggle
clasificador-muchocine
This model is a fine-tuned version of mrm8488/electricidad-base-discriminator on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.4218
- Accuracy: 0.4413
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 | 388 | 1.3403 | 0.3794 |
| 1.4032 | 2.0 | 776 | 1.2914 | 0.4297 |
| 1.0124 | 3.0 | 1164 | 1.4218 | 0.4413 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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