Translation
Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use iRpro16/model_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iRpro16/model_trainer with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("translation", model="iRpro16/model_trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("iRpro16/model_trainer") model = AutoModelForSeq2SeqLM.from_pretrained("iRpro16/model_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from iRpro16/model_trainer: direct link, hf CLI and curl.
- Browser
- Download file 1.14 kB
-
https://huggingface.co/iRpro16/model_trainer/resolve/main/README.md
- Command line
-
hf download hf://iRpro16/model_trainer/README.md
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curl -L -o README.md https://huggingface.co/iRpro16/model_trainer/resolve/main/README.md
1.14 kB
metadata
license: apache-2.0
base_model: t5-small
tags:
- translation
- generated_from_trainer
model-index:
- name: model_trainer
results: []
model_trainer
This model is a fine-tuned version of t5-small 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1
- Datasets 2.20.0
- Tokenizers 0.19.1