Modelos-acredita-2026
Collection
Estos modelos son afinamientos y modelos desde cero con GPT. • 8 items • Updated
How to use raulgdp/bert-beto-uncased-2026 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="raulgdp/bert-beto-uncased-2026") # Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("raulgdp/bert-beto-uncased-2026")
model = AutoModelForTokenClassification.from_pretrained("raulgdp/bert-beto-uncased-2026", device_map="auto")This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on the None 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1083 | 1.0 | 521 | 0.1194 | 0.7776 | 0.7681 | 0.7728 | 0.9637 |
| 0.0688 | 2.0 | 1042 | 0.1125 | 0.7995 | 0.8257 | 0.8124 | 0.9684 |
| 0.0416 | 3.0 | 1563 | 0.1172 | 0.8121 | 0.8376 | 0.8246 | 0.9692 |
| 0.0288 | 4.0 | 2084 | 0.1345 | 0.8043 | 0.8271 | 0.8155 | 0.9681 |
| 0.02 | 5.0 | 2605 | 0.1493 | 0.8146 | 0.8334 | 0.8239 | 0.9696 |
Base model
dccuchile/bert-base-spanish-wwm-uncased