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:
- d96910b2b7708f60591457ad1094680baeee47951a2f2bcdef0fb25f27daa959
- Size of remote file:
- 439 MB
- SHA256:
- 324c6f77afeee708321271ae555fc1bea253c29aaeada30f07dc928d0b0c3f8c
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