Instructions to use sshleifer/tiny-distilroberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/tiny-distilroberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sshleifer/tiny-distilroberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/tiny-distilroberta-base") model = AutoModelForMaskedLM.from_pretrained("sshleifer/tiny-distilroberta-base") - Inference
- Notebooks
- Google Colab
- Kaggle
model_max_length = max_position_embeddings - 2 for roberta (when padding_idx == 2)
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
CHANGED
|
@@ -1 +1 @@
|
|
| 1 |
-
{"model_max_length":
|
|
|
|
| 1 |
+
{"model_max_length": 510}
|