Text Classification
Transformers
PyTorch
Safetensors
Spanish
roberta
biomedical
clinical
spanish
bsc-bio-ehr-es
Eval Results (legacy)
text-embeddings-inference
Instructions to use IIC/bsc-bio-ehr-es-caresC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/bsc-bio-ehr-es-caresC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IIC/bsc-bio-ehr-es-caresC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIC/bsc-bio-ehr-es-caresC") model = AutoModelForSequenceClassification.from_pretrained("IIC/bsc-bio-ehr-es-caresC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c7d75fe31956672f83bd8e716a1e24aa41800f51c000fda6cfcfdf147cc41572
- Size of remote file:
- 499 MB
- SHA256:
- 752cad3261ba512ce83a7d847ca10b8f822f2fbef31e44c05b2cb8bf1e2523b8
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