Instructions to use NiGuLa/dccuchile_bert-base-spanish-wwm-cased_ep10_lr1e-05_batchpergpu16_gpu1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NiGuLa/dccuchile_bert-base-spanish-wwm-cased_ep10_lr1e-05_batchpergpu16_gpu1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NiGuLa/dccuchile_bert-base-spanish-wwm-cased_ep10_lr1e-05_batchpergpu16_gpu1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NiGuLa/dccuchile_bert-base-spanish-wwm-cased_ep10_lr1e-05_batchpergpu16_gpu1") model = AutoModelForSequenceClassification.from_pretrained("NiGuLa/dccuchile_bert-base-spanish-wwm-cased_ep10_lr1e-05_batchpergpu16_gpu1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 091078cfb4d2ca3581ecc8d86efdfb8b1bd69cb435c457c2a02b6b7d2ef2f5e0
- Size of remote file:
- 5.71 kB
- SHA256:
- f4f636cd6bfd371e8d14ee4b4350e0d35ee7e1f14d861476208f478d60672ff2
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