Instructions to use daze-unlv/almanach-camembert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use daze-unlv/almanach-camembert-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultipleChoice tokenizer = AutoTokenizer.from_pretrained("daze-unlv/almanach-camembert-base") model = AutoModelForMultipleChoice.from_pretrained("daze-unlv/almanach-camembert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from daze-unlv/almanach-camembert-base: direct link, hf CLI and curl.
- Browser
- Download file 1.51 kB
-
https://huggingface.co/daze-unlv/almanach-camembert-base/resolve/main/README.md
- Command line
-
hf download hf://daze-unlv/almanach-camembert-base/README.md
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curl -L -o README.md https://huggingface.co/daze-unlv/almanach-camembert-base/resolve/main/README.md
1.51 kB
metadata
license: mit
base_model: almanach/camembert-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: almanach-camembert-base
results: []
almanach-camembert-base
This model is a fine-tuned version of almanach/camembert-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.3863
- Accuracy: 0.2618
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3866 | 1.0 | 2857 | 1.3863 | 0.2321 |
| 1.3863 | 2.0 | 5714 | 1.3863 | 0.2276 |
| 1.3865 | 3.0 | 8571 | 1.3863 | 0.2618 |
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0