Instructions to use bdpc/SciBERT_20K_steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bdpc/SciBERT_20K_steps with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bdpc/SciBERT_20K_steps")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bdpc/SciBERT_20K_steps") model = AutoModelForSequenceClassification.from_pretrained("bdpc/SciBERT_20K_steps") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 0.32, | |
| "train_loss": 0.03969836797714233, | |
| "train_runtime": 16800.0993, | |
| "train_samples_per_second": 38.095, | |
| "train_steps_per_second": 1.19 | |
| } |