Instructions to use JAlexis/bertFast_01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JAlexis/bertFast_01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="JAlexis/bertFast_01")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("JAlexis/bertFast_01") model = AutoModelForQuestionAnswering.from_pretrained("JAlexis/bertFast_01", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 897 Bytes
1abf312 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | optim = AdamW(model.parameters(), lr=5e-5) #tasa de aprendizaje # Se inicializa el cargador de datos para los datos de entrenamiento train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True) for epoch in range(7): Epoch 0: 100%|ββββββββββ| 94/94 [01:18<00:00, 1.19it/s, loss=2.38] Epoch 1: 100%|ββββββββββ| 94/94 [01:18<00:00, 1.19it/s, loss=1.9] Epoch 2: 100%|ββββββββββ| 94/94 [01:18<00:00, 1.19it/s, loss=1.56] Epoch 3: 100%|ββββββββββ| 94/94 [01:18<00:00, 1.19it/s, loss=1.44] Epoch 4: 100%|ββββββββββ| 94/94 [01:18<00:00, 1.19it/s, loss=1.52] Epoch 5: 100%|ββββββββββ| 94/94 [01:18<00:00, 1.19it/s, loss=0.492] Epoch 6: 100%|ββββββββββ| 94/94 [01:18<00:00, 1.19it/s, loss=0.195] PrecisiΓ³n del modelo ajustado: 0.7771084337349398 |