Instructions to use hf-tiny-model-private/tiny-random-MPNetForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-MPNetForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-tiny-model-private/tiny-random-MPNetForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-MPNetForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-MPNetForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51a1b19c8b8491c4ed82174401c413eec5c6efd76a54cae95f4529484d079a29
- Size of remote file:
- 957 kB
- SHA256:
- 8c626364e8e62070f7ee65ad2ca960840f8103a8fe0f392671d83cbf145af69b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.