File size: 2,244 Bytes
9945406 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-xsum-finetuned-natural-questions
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-xsum-finetuned-natural-questions
This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2729
- Rouge1: 19.7211
- Rouge2: 17.4272
- Rougel: 19.0681
- Rougelsum: 19.3677
## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| No log | 0.99 | 34 | 0.2562 | 17.9806 | 15.2059 | 16.807 | 17.5533 |
| No log | 1.99 | 68 | 0.1845 | 14.6261 | 10.494 | 13.0132 | 13.8392 |
| No log | 2.98 | 102 | 0.2171 | 17.3737 | 14.7893 | 16.5485 | 16.8383 |
| No log | 4.0 | 137 | 0.3474 | 17.6187 | 14.727 | 16.5614 | 17.1476 |
| No log | 4.99 | 171 | 0.3462 | 17.7103 | 15.1403 | 16.9424 | 17.3123 |
| 0.1255 | 5.99 | 205 | 0.3355 | 19.2782 | 16.5525 | 18.4283 | 18.8422 |
| 0.1255 | 6.98 | 239 | 0.2281 | 19.8816 | 17.4387 | 19.238 | 19.552 |
| 0.1255 | 7.94 | 272 | 0.2729 | 19.7211 | 17.4272 | 19.0681 | 19.3677 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|