Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use elopezlopez/Bio_ClinicalBERT_fold_10_binary_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use elopezlopez/Bio_ClinicalBERT_fold_10_binary_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="elopezlopez/Bio_ClinicalBERT_fold_10_binary_v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("elopezlopez/Bio_ClinicalBERT_fold_10_binary_v1") model = AutoModelForSequenceClassification.from_pretrained("elopezlopez/Bio_ClinicalBERT_fold_10_binary_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from elopezlopez/Bio_ClinicalBERT_fold_10_binary_v1: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/elopezlopez/Bio_ClinicalBERT_fold_10_binary_v1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://elopezlopez/Bio_ClinicalBERT_fold_10_binary_v1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/elopezlopez/Bio_ClinicalBERT_fold_10_binary_v1/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- f025db78e0f8c90ea8febc6e3e0be4ed68ee29da09673322cc88f9320583e8b5
- Size of remote file:
- 433 MB
- SHA256:
- d519eff2ef783b6c7f3a01861d4592ea7aec5951325b14f76256d1d9e9eb436f
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