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
File size: 590 Bytes
4b9ef9a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"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
}
|