Transformers
PyTorch
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use Alqayed2024/finetuning-code-summarization-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Alqayed2024/finetuning-code-summarization-3000-samples with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Alqayed2024/finetuning-code-summarization-3000-samples") model = AutoModelForSeq2SeqLM.from_pretrained("Alqayed2024/finetuning-code-summarization-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,233 Bytes
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license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
model-index:
- name: finetuning-code-summarization-3000-samples
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-code-summarization-3000-samples
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0421
- eval_runtime: 8.9366
- eval_samples_per_second: 33.57
- eval_steps_per_second: 16.785
- step: 0
## 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: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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