Instructions to use Matthijs0/DistilBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Matthijs0/DistilBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Matthijs0/DistilBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Matthijs0/DistilBERT") model = AutoModelForQuestionAnswering.from_pretrained("Matthijs0/DistilBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| # DistilBERT | |
| The DistilBERT model is a [BERT](https://huggingface.co/distilbert/distilbert-base-uncased) model fine-tuned on the | |
| [NewsQA](https://huggingface.co/datasets/lucadiliello/newsqa) dataset. | |
| ## Hyperparameters | |
| ``` | |
| batch_size = 16 | |
| n_epochs = 3 | |
| max_seq_len = 512 | |
| learning_rate = 2e-5 | |
| optimizer=AdamW | |
| lr_schedule = LinearWarmup | |
| weight_decay=0.01 | |
| embeds_dropout_prob = 0.1 | |
| ``` |