Instructions to use NazzX1/LED-note-modified with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NazzX1/LED-note-modified with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("NazzX1/LED-note-modified") model = AutoModelForSeq2SeqLM.from_pretrained("NazzX1/LED-note-modified", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from NazzX1/LED-note-modified: direct link, hf CLI and curl.
- Browser
- Download file 1.74 kB
-
https://huggingface.co/NazzX1/LED-note-modified/resolve/main/README.md
- Command line
-
hf download hf://NazzX1/LED-note-modified/README.md
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curl -L -o README.md https://huggingface.co/NazzX1/LED-note-modified/resolve/main/README.md
1.74 kB
metadata
library_name: transformers
base_model: MingZhong/DialogLED-base-16384
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: LED-note-modified
results: []
LED-note-modified
This model is a fine-tuned version of MingZhong/DialogLED-base-16384 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4517
- Rouge1: 0.1441
- Rouge2: 0.0896
- Rougel: 0.1067
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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- 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: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel |
|---|---|---|---|---|---|---|
| 0.4719 | 1.0 | 800 | 0.4643 | 0.1437 | 0.0886 | 0.1062 |
| 0.4267 | 2.0 | 1600 | 0.4517 | 0.1441 | 0.0896 | 0.1067 |
Framework versions
- Transformers 4.51.0
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1