File size: 1,734 Bytes
e7e940c e0eea3a e7e940c f9ed31a e7e940c e0eea3a e7e940c e0eea3a e7e940c |
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 |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: summarise_v6
results: []
---
# summarise_v6
This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0497
- Rouge2 Precision: 0.3109
- Rouge2 Recall: 0.406
- Rouge2 Fmeasure: 0.3375
## Model description
More information needed
## Intended uses & limitations
max_input_length = 3072
max_output_length = 1000
led.config.max_length = 1000
led.config.min_length = 100
## 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: 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 | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 1.7163 | 0.22 | 10 | 1.2307 | 0.1428 | 0.5118 | 0.2089 |
| 1.632 | 0.44 | 20 | 1.1337 | 0.36 | 0.3393 | 0.3181 |
| 1.0916 | 0.67 | 30 | 1.0738 | 0.2693 | 0.3487 | 0.2731 |
| 1.573 | 0.89 | 40 | 1.0497 | 0.3109 | 0.406 | 0.3375 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 1.2.1
- Tokenizers 0.12.1
|