Instructions to use HealthNLP/pubmedbert_conmod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HealthNLP/pubmedbert_conmod with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HealthNLP/pubmedbert_conmod")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HealthNLP/pubmedbert_conmod") model = AutoModelForSequenceClassification.from_pretrained("HealthNLP/pubmedbert_conmod", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download all_results.json from HealthNLP/pubmedbert_conmod: direct link, hf CLI and curl.
- Browser
- Download file 465 Bytes
-
https://huggingface.co/HealthNLP/pubmedbert_conmod/resolve/main/all_results.json
- Command line
-
hf download hf://HealthNLP/pubmedbert_conmod/all_results.json
-
curl -L -o all_results.json https://huggingface.co/HealthNLP/pubmedbert_conmod/resolve/main/all_results.json
465 Bytes
| { | |
| "epoch": 10.0, | |
| "eval_accuracy": 0.81431645154953, | |
| "eval_f1": "[0.92816572 0.56028369 0.1 0.2633452 ]", | |
| "eval_loss": 0.6886363625526428, | |
| "eval_runtime": 7.7747, | |
| "eval_samples": 1858, | |
| "eval_samples_per_second": 238.981, | |
| "eval_steps_per_second": 3.859, | |
| "train_loss": 0.21409231649862753, | |
| "train_runtime": 515.9059, | |
| "train_samples": 2338, | |
| "train_samples_per_second": 45.318, | |
| "train_steps_per_second": 0.717 | |
| } |