us-lsi/muchocine
Updated • 254 • 4
How to use EstherT/clasificador-muchocine with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="EstherT/clasificador-muchocine") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("EstherT/clasificador-muchocine")
model = AutoModelForSequenceClassification.from_pretrained("EstherT/clasificador-muchocine", device_map="auto")This model is a fine-tuned version of mrm8488/electricidad-base-discriminator on the 'muchocine' dataset. It achieves the following results on the evaluation set:
This model predicts a 1-5 star_rating for a movie based on a short review in Spanish.
The model uses the train split of the 'muchocine' dataset, containing 3,872 reviews.
The original dataset was randomized and subsequently split into a training set (80%) and a testing set (20%).
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 388 | 1.2937 | 0.4335 |
| 1.4261 | 2.0 | 776 | 1.2515 | 0.4839 |
| 1.0492 | 3.0 | 1164 | 1.3389 | 0.4671 |