Instructions to use gnokit/flan-t5-dialogue-summary-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gnokit/flan-t5-dialogue-summary-finetune with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("gnokit/flan-t5-dialogue-summary-finetune") model = AutoModelForSeq2SeqLM.from_pretrained("gnokit/flan-t5-dialogue-summary-finetune", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from gnokit/flan-t5-dialogue-summary-finetune: direct link, hf CLI and curl.
- Browser
- Download file 1.34 kB
-
https://huggingface.co/gnokit/flan-t5-dialogue-summary-finetune/resolve/main/README.md
- Command line
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hf download hf://gnokit/flan-t5-dialogue-summary-finetune/README.md
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curl -L -o README.md https://huggingface.co/gnokit/flan-t5-dialogue-summary-finetune/resolve/main/README.md
1.34 kB
metadata
library_name: transformers
license: apache-2.0
base_model: gnokit/flan-t5-dialogue-summary-finetune
tags:
- generated_from_trainer
model-index:
- name: flan-t5-dialogue-summary-finetune
results: []
flan-t5-dialogue-summary-finetune
This model is a fine-tuned version of gnokit/flan-t5-dialogue-summary-finetune on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 29.8390
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Framework versions
- Transformers 4.49.0
- Pytorch 2.4.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0