Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use elopezlopez/Bio_ClinicalBERT_fold_2_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_2_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_2_binary_v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("elopezlopez/Bio_ClinicalBERT_fold_2_binary_v1") model = AutoModelForSequenceClassification.from_pretrained("elopezlopez/Bio_ClinicalBERT_fold_2_binary_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from elopezlopez/Bio_ClinicalBERT_fold_2_binary_v1: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/elopezlopez/Bio_ClinicalBERT_fold_2_binary_v1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://elopezlopez/Bio_ClinicalBERT_fold_2_binary_v1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/elopezlopez/Bio_ClinicalBERT_fold_2_binary_v1/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- ecd195fb4b9b5e375d75e3da35a01bcf3c852987d50eb2077a31eaec7750714d
- Size of remote file:
- 433 MB
- SHA256:
- 2c6622f5c7e52dbead8a5aa37cfba1bae9b4419e0b3002c9c0c4fbcaa8ff1b3b
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