Instructions to use Patcas/plbart-docs-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Patcas/plbart-docs-v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Patcas/plbart-docs-v1") model = AutoModelForSeq2SeqLM.from_pretrained("Patcas/plbart-docs-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Patcas/plbart-docs-v1: direct link, hf CLI and curl.
- Browser
- Download file 1.72 kB
-
https://huggingface.co/Patcas/plbart-docs-v1/resolve/main/README.md
- Command line
-
hf download hf://Patcas/plbart-docs-v1/README.md
-
curl -L -o README.md https://huggingface.co/Patcas/plbart-docs-v1/resolve/main/README.md
1.72 kB
metadata
base_model: Patcas/plbart-works
tags:
- generated_from_trainer
model-index:
- name: plbart-docs-v1
results: []
plbart-docs-v1
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: 0.9167
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 | 0.9578 |
| No log | 2.0 | 460 | 0.8802 |
| 0.9795 | 3.0 | 690 | 0.8768 |
| 0.9795 | 4.0 | 920 | 0.8875 |
| 0.335 | 5.0 | 1150 | 0.8897 |
| 0.335 | 6.0 | 1380 | 0.9047 |
| 0.1643 | 7.0 | 1610 | 0.8998 |
| 0.1643 | 8.0 | 1840 | 0.9090 |
| 0.0945 | 9.0 | 2070 | 0.9151 |
| 0.0945 | 10.0 | 2300 | 0.9167 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1