Instructions to use medspaner/roberta-es-clinical-trials-temporal-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use medspaner/roberta-es-clinical-trials-temporal-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="medspaner/roberta-es-clinical-trials-temporal-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("medspaner/roberta-es-clinical-trials-temporal-ner") model = AutoModelForTokenClassification.from_pretrained("medspaner/roberta-es-clinical-trials-temporal-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3365790f7b942c2eafa4f11defb505bd831931732492017d91be8b5ec23830c1
- Size of remote file:
- 3.06 kB
- SHA256:
- 3bf96d95d6baeed8e5b7b6582552eff2b42ba3b41df4e03f26b81e93ba0e39ca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.