Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use Patcas/codet5-base-step-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Patcas/codet5-base-step-1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Patcas/codet5-base-step-1") model = AutoModelForSeq2SeqLM.from_pretrained("Patcas/codet5-base-step-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Patcas/codet5-base-step-1: direct link, hf CLI and curl.
- Browser
- Download file 1.45 kB
-
https://huggingface.co/Patcas/codet5-base-step-1/resolve/main/README.md
- Command line
-
hf download hf://Patcas/codet5-base-step-1/README.md
-
curl -L -o README.md https://huggingface.co/Patcas/codet5-base-step-1/resolve/main/README.md
1.45 kB
metadata
license: apache-2.0
base_model: Salesforce/codet5-base
tags:
- generated_from_trainer
model-index:
- name: codet5-base-step-1
results: []
codet5-base-step-1
This model is a fine-tuned version of Salesforce/codet5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.3552
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 | 1.7018 |
| No log | 2.0 | 460 | 1.4720 |
| 2.1684 | 3.0 | 690 | 1.3744 |
| 2.1684 | 4.0 | 920 | 1.3552 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1