us-lsi/muchocine
Updated • 210 • 4
How to use sauc-abadal-lloret/bert-base-uncased-es-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="sauc-abadal-lloret/bert-base-uncased-es-sentiment-analysis") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("sauc-abadal-lloret/bert-base-uncased-es-sentiment-analysis")
model = AutoModelForSequenceClassification.from_pretrained("sauc-abadal-lloret/bert-base-uncased-es-sentiment-analysis")This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on the muchocine dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.541 | 1.0 | 49 | 0.4618 | 0.7781 |
| 0.3157 | 2.0 | 98 | 0.4989 | 0.7742 |
| 0.1294 | 3.0 | 147 | 0.6931 | 0.8 |
| 0.0541 | 4.0 | 196 | 0.8284 | 0.7935 |
| 0.0254 | 5.0 | 245 | 0.9713 | 0.7923 |
Base model
dccuchile/bert-base-spanish-wwm-uncased