Instructions to use silmi224/led with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use silmi224/led with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="silmi224/led")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("silmi224/led") model = AutoModelForSeq2SeqLM.from_pretrained("silmi224/led", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from silmi224/led: direct link, hf CLI and curl.
- Browser
- Download file 1.43 kB
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https://huggingface.co/silmi224/led/resolve/main/README.md
- Command line
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hf download hf://silmi224/led/README.md
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curl -L -o README.md https://huggingface.co/silmi224/led/resolve/main/README.md
1.43 kB
metadata
license: apache-2.0
base_model: allenai/led-base-16384
tags:
- summarization
- generated_from_trainer
model-index:
- name: led
results: []
led
This model is a fine-tuned version of allenai/led-base-16384 on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 2.5049
- eval_rouge1_precision: 0.2436
- eval_rouge1_recall: 0.3162
- eval_rouge1_fmeasure: 0.2711
- eval_runtime: 3740.5655
- eval_samples_per_second: 0.802
- eval_steps_per_second: 0.025
- epoch: 0.13
- step: 30
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: 5e-05
- 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: 1
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.15.1