Instructions to use subhasisj/ar-TAPT-MLM-MiniLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subhasisj/ar-TAPT-MLM-MiniLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="subhasisj/ar-TAPT-MLM-MiniLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("subhasisj/ar-TAPT-MLM-MiniLM") model = AutoModelForMaskedLM.from_pretrained("subhasisj/ar-TAPT-MLM-MiniLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from subhasisj/ar-TAPT-MLM-MiniLM: direct link, hf CLI and curl.
- Browser
- Download file 1.08 kB
-
https://huggingface.co/subhasisj/ar-TAPT-MLM-MiniLM/resolve/main/README.md
- Command line
-
hf download hf://subhasisj/ar-TAPT-MLM-MiniLM/README.md
-
curl -L -o README.md https://huggingface.co/subhasisj/ar-TAPT-MLM-MiniLM/resolve/main/README.md
1.08 kB
metadata
tags:
- generated_from_trainer
model-index:
- name: ar-TAPT-MLM-MiniLM
results: []
ar-TAPT-MLM-MiniLM
This model is a fine-tuned version of subhasisj/MiniLMv2-qa-encoder 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: 1
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.0
- Tokenizers 0.12.1