Instructions to use Patcas/AssertDocWorks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Patcas/AssertDocWorks with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Patcas/AssertDocWorks") model = AutoModelForSeq2SeqLM.from_pretrained("Patcas/AssertDocWorks", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Patcas/AssertDocWorks: direct link, hf CLI and curl.
- Browser
- Download file 1.44 kB
-
https://huggingface.co/Patcas/AssertDocWorks/resolve/main/README.md
- Command line
-
hf download hf://Patcas/AssertDocWorks/README.md
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curl -L -o README.md https://huggingface.co/Patcas/AssertDocWorks/resolve/main/README.md
1.44 kB
metadata
base_model: Patcas/my_awesome-assert-new
tags:
- generated_from_trainer
model-index:
- name: AssertDocWorks
results: []
AssertDocWorks
This model is a fine-tuned version of Patcas/my_awesome-assert-new on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1414
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 230 | 1.3268 |
| No log | 2.0 | 460 | 1.1885 |
| 1.3504 | 3.0 | 690 | 1.1397 |
| 1.3504 | 4.0 | 920 | 1.1414 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1