Instructions to use IB13/sft_t5_base_processed_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IB13/sft_t5_base_processed_model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("IB13/sft_t5_base_processed_model") model = AutoModelForSeq2SeqLM.from_pretrained("IB13/sft_t5_base_processed_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from IB13/sft_t5_base_processed_model: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/IB13/sft_t5_base_processed_model/resolve/main/README.md
- Command line
-
hf download hf://IB13/sft_t5_base_processed_model/README.md
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curl -L -o README.md https://huggingface.co/IB13/sft_t5_base_processed_model/resolve/main/README.md
1.06 kB
metadata
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
model-index:
- name: sft_t5_base_processed_model
results: []
sft_t5_base_processed_model
This model is a fine-tuned version of google/flan-t5-base 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 4
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0