Instructions to use NiGuLa/dccuchile_bert-base-spanish-wwm-cased_ep10_lr1e-06_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-06_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-06_batchpergpu16_gpu1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NiGuLa/dccuchile_bert-base-spanish-wwm-cased_ep10_lr1e-06_batchpergpu16_gpu1") model = AutoModelForSequenceClassification.from_pretrained("NiGuLa/dccuchile_bert-base-spanish-wwm-cased_ep10_lr1e-06_batchpergpu16_gpu1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e8788c2fa9056b02fac8986e3a14d96424f5506fd3f56361b384002591e850c
- Size of remote file:
- 439 MB
- SHA256:
- eb023daa76f720116c1342b5c2265f06262e83f885dd7178d528dbf5bd7e0334
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