Text Classification
Transformers
PyTorch
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use HealthNLP/pubmedbert_dtr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HealthNLP/pubmedbert_dtr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HealthNLP/pubmedbert_dtr")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HealthNLP/pubmedbert_dtr") model = AutoModelForSequenceClassification.from_pretrained("HealthNLP/pubmedbert_dtr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from HealthNLP/pubmedbert_dtr: direct link, hf CLI and curl.
- Browser
- Download file 227 Bytes
-
https://huggingface.co/HealthNLP/pubmedbert_dtr/resolve/main/eval_results.json
- Command line
-
hf download hf://HealthNLP/pubmedbert_dtr/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/HealthNLP/pubmedbert_dtr/resolve/main/eval_results.json
227 Bytes
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.7168998718261719, | |
| "eval_loss": 1.326256513595581, | |
| "eval_runtime": 40.7056, | |
| "eval_samples": 1858, | |
| "eval_samples_per_second": 45.645, | |
| "eval_steps_per_second": 2.874 | |
| } |