Instructions to use sshleifer/tiny-distilbert-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/tiny-distilbert-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sshleifer/tiny-distilbert-base-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sshleifer/tiny-distilbert-base-cased") model = AutoModelForTokenClassification.from_pretrained("sshleifer/tiny-distilbert-base-cased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from sshleifer/tiny-distilbert-base-cased: direct link, hf CLI and curl.
- Browser
- Download file 238 kB
-
https://huggingface.co/sshleifer/tiny-distilbert-base-cased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://sshleifer/tiny-distilbert-base-cased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/sshleifer/tiny-distilbert-base-cased/resolve/main/flax_model.msgpack
238 kB
- Xet hash:
- 047003aeef4367e8386e92057535ad30b23122b8da2184e849c15bb3682340ce
- Size of remote file:
- 238 kB
- SHA256:
- 75dba9140a4ce5c8c59fae94d627b90e76f18cb307230674b2bff3ff6a145bf9
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