Instructions to use Patcas/plbart-docs-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Patcas/plbart-docs-v3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Patcas/plbart-docs-v3") model = AutoModelForSeq2SeqLM.from_pretrained("Patcas/plbart-docs-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Patcas/plbart-docs-v3: direct link, hf CLI and curl.
- Browser
- Download file 1.72 kB
-
https://huggingface.co/Patcas/plbart-docs-v3/resolve/main/README.md
- Command line
-
hf download hf://Patcas/plbart-docs-v3/README.md
-
curl -L -o README.md https://huggingface.co/Patcas/plbart-docs-v3/resolve/main/README.md
1.72 kB
metadata
base_model: Patcas/plbart-works
tags:
- generated_from_trainer
model-index:
- name: plbart-docs-v3
results: []
plbart-docs-v3
This model is a fine-tuned version of Patcas/plbart-works on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9986
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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 230 | 0.9920 |
| No log | 2.0 | 460 | 0.9308 |
| 0.9908 | 3.0 | 690 | 0.9395 |
| 0.9908 | 4.0 | 920 | 0.9661 |
| 0.3269 | 5.0 | 1150 | 0.9877 |
| 0.3269 | 6.0 | 1380 | 0.9714 |
| 0.1753 | 7.0 | 1610 | 0.9781 |
| 0.1753 | 8.0 | 1840 | 0.9893 |
| 0.0913 | 9.0 | 2070 | 0.9951 |
| 0.0913 | 10.0 | 2300 | 0.9986 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1