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 trainer_state.json from HealthNLP/pubmedbert_dtr: direct link, hf CLI and curl.
- Browser
- Download file 590 Bytes
-
https://huggingface.co/HealthNLP/pubmedbert_dtr/resolve/refs%2Fpr%2F1/trainer_state.json
- Command line
-
hf download hf://HealthNLP/pubmedbert_dtr@refs/pr/1/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/HealthNLP/pubmedbert_dtr/resolve/refs%2Fpr%2F1/trainer_state.json
590 Bytes
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 3.0, | |
| "global_step": 441, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 3.0, | |
| "step": 441, | |
| "total_flos": 360451299070800.0, | |
| "train_loss": 0.3852307585631909, | |
| "train_runtime": 523.2251, | |
| "train_samples_per_second": 13.405, | |
| "train_steps_per_second": 0.843 | |
| } | |
| ], | |
| "max_steps": 441, | |
| "num_train_epochs": 3, | |
| "total_flos": 360451299070800.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |