Instructions to use theojolliffe/model-2-bart-reverse-raw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use theojolliffe/model-2-bart-reverse-raw with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("theojolliffe/model-2-bart-reverse-raw") model = AutoModelForSeq2SeqLM.from_pretrained("theojolliffe/model-2-bart-reverse-raw", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from theojolliffe/model-2-bart-reverse-raw: direct link, hf CLI and curl.
- Browser
- Download file 1.59 kB
-
https://huggingface.co/theojolliffe/model-2-bart-reverse-raw/resolve/main/README.md
- Command line
-
hf download hf://theojolliffe/model-2-bart-reverse-raw/README.md
-
curl -L -o README.md https://huggingface.co/theojolliffe/model-2-bart-reverse-raw/resolve/main/README.md
1.59 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: model-2-bart-reverse-raw
results: []
model-2-bart-reverse-raw
This model is a fine-tuned version of eugenesiow/bart-paraphrase on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1556
- Rouge1: 63.5215
- Rouge2: 58.8297
- Rougel: 60.5701
- Rougelsum: 63.2683
- Gen Len: 19.4672
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: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 0.1276 | 1.0 | 12767 | 0.1556 | 63.5215 | 58.8297 | 60.5701 | 63.2683 | 19.4672 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1