Instructions to use Amna100/DebertaMLMnc2c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Amna100/DebertaMLMnc2c with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Amna100/DebertaMLMnc2c")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Amna100/DebertaMLMnc2c") model = AutoModelForMaskedLM.from_pretrained("Amna100/DebertaMLMnc2c", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Amna100/DebertaMLMnc2c: direct link, hf CLI and curl.
- Browser
- Download file 1.43 kB
-
https://huggingface.co/Amna100/DebertaMLMnc2c/resolve/main/README.md
- Command line
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hf download hf://Amna100/DebertaMLMnc2c/README.md
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curl -L -o README.md https://huggingface.co/Amna100/DebertaMLMnc2c/resolve/main/README.md
1.43 kB
metadata
license: mit
base_model: microsoft/deberta-base
tags:
- generated_from_trainer
model-index:
- name: DebertaMLMnc2c
results: []
DebertaMLMnc2c
This model is a fine-tuned version of microsoft/deberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0107
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.7415 | 1.0 | 1840 | 1.9151 |
| 2.417 | 2.0 | 3680 | 1.4787 |
| 2.1174 | 3.0 | 5520 | 1.1759 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0