Text Classification
Transformers
PyTorch
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use HealthNLP/pubmedbert_tlink with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HealthNLP/pubmedbert_tlink with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HealthNLP/pubmedbert_tlink")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HealthNLP/pubmedbert_tlink") model = AutoModelForSequenceClassification.from_pretrained("HealthNLP/pubmedbert_tlink", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from HealthNLP/pubmedbert_tlink: direct link, hf CLI and curl.
- Browser
- Download file 228 Bytes
-
https://huggingface.co/HealthNLP/pubmedbert_tlink/resolve/main/eval_results.json
- Command line
-
hf download hf://HealthNLP/pubmedbert_tlink/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/HealthNLP/pubmedbert_tlink/resolve/main/eval_results.json
228 Bytes
| { | |
| "epoch": 10.0, | |
| "eval_accuracy": 0.8376963138580322, | |
| "eval_loss": 1.120042324066162, | |
| "eval_runtime": 0.7684, | |
| "eval_samples": 1146, | |
| "eval_samples_per_second": 1491.385, | |
| "eval_steps_per_second": 93.7 | |
| } |