Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use yeye776/t5-multi-context with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yeye776/t5-multi-context with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("yeye776/t5-multi-context") model = AutoModelForSeq2SeqLM.from_pretrained("yeye776/t5-multi-context", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from yeye776/t5-multi-context: direct link, hf CLI and curl.
- Browser
- Download file 1.21 kB
-
https://huggingface.co/yeye776/t5-multi-context/resolve/main/README.md
- Command line
-
hf download hf://yeye776/t5-multi-context/README.md
-
curl -L -o README.md https://huggingface.co/yeye776/t5-multi-context/resolve/main/README.md
1.21 kB
metadata
license: cc-by-4.0
base_model: yeye776/t5-large-finetuned-multi_v3
tags:
- generated_from_trainer
model-index:
- name: t5-multi-context
results: []
t5-multi-context
This model is a fine-tuned version of yeye776/t5-large-finetuned-multi_v3 on the None 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: 0.0007
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 15
Training results
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1