Instructions to use meandmichael8011/test-repo1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use meandmichael8011/test-repo1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="meandmichael8011/test-repo1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("meandmichael8011/test-repo1") model = AutoModelForMaskedLM.from_pretrained("meandmichael8011/test-repo1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from meandmichael8011/test-repo1: direct link, hf CLI and curl.
- Browser
- Download file 980 Bytes
-
https://huggingface.co/meandmichael8011/test-repo1/resolve/main/README.md
- Command line
-
hf download hf://meandmichael8011/test-repo1/README.md
-
curl -L -o README.md https://huggingface.co/meandmichael8011/test-repo1/resolve/main/README.md
980 Bytes
metadata
library_name: transformers
license: mit
base_model: camembert-base
tags:
- generated_from_keras_callback
model-index:
- name: test-repo1
results: []
test-repo1
This model is a fine-tuned version of camembert-base 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 4.48.2
- TensorFlow 2.18.0
- Datasets 3.2.0
- Tokenizers 0.21.0