Instructions to use Patcas/v2-plbart-works with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Patcas/v2-plbart-works with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Patcas/v2-plbart-works") model = AutoModelForSeq2SeqLM.from_pretrained("Patcas/v2-plbart-works", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Patcas/v2-plbart-works: direct link, hf CLI and curl.
- Browser
- Download file 1.23 kB
-
https://huggingface.co/Patcas/v2-plbart-works/resolve/main/README.md
- Command line
-
hf download hf://Patcas/v2-plbart-works/README.md
-
curl -L -o README.md https://huggingface.co/Patcas/v2-plbart-works/resolve/main/README.md
1.23 kB
metadata
base_model: Patcas/plbart-works
tags:
- generated_from_trainer
model-index:
- name: v2-plbart-works
results: []
v2-plbart-works
This model is a fine-tuned version of Patcas/plbart-works 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Gen Len |
|---|---|---|---|---|
| No log | 1.0 | 63 | 1.8034 | 1.0 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0