Instructions to use dzinampini/code-to-json-documentor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dzinampini/code-to-json-documentor with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dzinampini/code-to-json-documentor") model = AutoModelForSeq2SeqLM.from_pretrained("dzinampini/code-to-json-documentor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from dzinampini/code-to-json-documentor: direct link, hf CLI and curl.
- Browser
- Download file 1.58 kB
-
https://huggingface.co/dzinampini/code-to-json-documentor/resolve/main/README.md
- Command line
-
hf download hf://dzinampini/code-to-json-documentor/README.md
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curl -L -o README.md https://huggingface.co/dzinampini/code-to-json-documentor/resolve/main/README.md
1.58 kB
metadata
library_name: transformers
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
model-index:
- name: code-to-json-documentor
results: []
code-to-json-documentor
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.0414
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 1 | 3.0574 |
| No log | 2.0 | 2 | 3.0554 |
| No log | 3.0 | 3 | 3.0528 |
| No log | 4.0 | 4 | 3.0471 |
| No log | 5.0 | 5 | 3.0414 |
Framework versions
- Transformers 4.53.1
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.2