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 train_results.json from HealthNLP/pubmedbert_conmod: direct link, hf CLI and curl.
- Browser
- Download file 195 Bytes
-
https://huggingface.co/HealthNLP/pubmedbert_conmod/resolve/main/train_results.json
- Command line
-
hf download hf://HealthNLP/pubmedbert_conmod/train_results.json
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curl -L -o train_results.json https://huggingface.co/HealthNLP/pubmedbert_conmod/resolve/main/train_results.json
195 Bytes
| { | |
| "epoch": 10.0, | |
| "train_loss": 0.21409231649862753, | |
| "train_runtime": 515.9059, | |
| "train_samples": 2338, | |
| "train_samples_per_second": 45.318, | |
| "train_steps_per_second": 0.717 | |
| } |