Instructions to use vumichien/tiny-albert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vumichien/tiny-albert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vumichien/tiny-albert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("vumichien/tiny-albert") model = AutoModelForTokenClassification.from_pretrained("vumichien/tiny-albert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from vumichien/tiny-albert: direct link, hf CLI and curl.
- Browser
- Download file 931 Bytes
-
https://huggingface.co/vumichien/tiny-albert/resolve/main/README.md
- Command line
-
hf download hf://vumichien/tiny-albert/README.md
-
curl -L -o README.md https://huggingface.co/vumichien/tiny-albert/resolve/main/README.md
931 Bytes
metadata
tags:
- generated_from_keras_callback
model-index:
- name: tiny-albert
results: []
tiny-albert
This model is a fine-tuned version of hf-internal-testing/tiny-albert on an unknown dataset. It achieves the following results on the evaluation set:
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
Training results
Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Tokenizers 0.12.1