Instructions to use PeterBanning71/bart-large-finetuned-eLife with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PeterBanning71/bart-large-finetuned-eLife 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="PeterBanning71/bart-large-finetuned-eLife")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("PeterBanning71/bart-large-finetuned-eLife") model = AutoModelForSeq2SeqLM.from_pretrained("PeterBanning71/bart-large-finetuned-eLife", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from PeterBanning71/bart-large-finetuned-eLife: direct link, hf CLI and curl.
- Browser
- Download file 1.75 kB
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https://huggingface.co/PeterBanning71/bart-large-finetuned-eLife/resolve/main/README.md
- Command line
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hf download hf://PeterBanning71/bart-large-finetuned-eLife/README.md
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curl -L -o README.md https://huggingface.co/PeterBanning71/bart-large-finetuned-eLife/resolve/main/README.md
1.75 kB
metadata
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-finetuned-eLife
results: []
bart-large-finetuned-eLife
This model is a fine-tuned version of facebook/bart-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.1974
- Rouge1: 19.8802
- Rouge2: 5.0391
- Rougel: 14.8302
- Rougelsum: 17.0969
- Gen Len: 20.0
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 2.6794 | 1.0 | 544 | 2.2901 | 19.6619 | 4.9774 | 14.5683 | 16.7854 | 20.0 |
| 2.3548 | 2.0 | 1088 | 2.2297 | 18.7282 | 4.8773 | 13.9942 | 16.2091 | 20.0 |
| 2.1888 | 3.0 | 1632 | 2.1974 | 19.8802 | 5.0391 | 14.8302 | 17.0969 | 20.0 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2