Instructions to use JJ-Tae/Pretraining_MFM_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JJ-Tae/Pretraining_MFM_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JJ-Tae/Pretraining_MFM_v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("JJ-Tae/Pretraining_MFM_v1") model = AutoModelForMaskedLM.from_pretrained("JJ-Tae/Pretraining_MFM_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from JJ-Tae/Pretraining_MFM_v1: direct link, hf CLI and curl.
- Browser
- Download file 1.07 kB
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https://huggingface.co/JJ-Tae/Pretraining_MFM_v1/resolve/main/README.md
- Command line
-
hf download hf://JJ-Tae/Pretraining_MFM_v1/README.md
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curl -L -o README.md https://huggingface.co/JJ-Tae/Pretraining_MFM_v1/resolve/main/README.md
1.07 kB
metadata
license: mit
base_model: microsoft/deberta-base
tags:
- generated_from_trainer
model-index:
- name: Pretraining_MFM_v1
results: []
Pretraining_MFM_v1
This model is a fine-tuned version of microsoft/deberta-base on an unknown dataset.
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:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Training results
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2