File size: 2,023 Bytes
ff2b25b c9dc406 ff2b25b c9dc406 ff2b25b c9dc406 ff2b25b b293e76 ff2b25b d8fd43b ff2b25b b293e76 c9dc406 ff2b25b b293e76 d8fd43b ff2b25b d8fd43b |
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 |
---
base_model: google/pegasus-xsum
tags:
- generated_from_trainer
model-index:
- name: pegasus-legalease
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. -->
# pegasus-legalease
This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1372
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.09 | 250 | 4.9954 |
| 5.2592 | 0.18 | 500 | 4.3175 |
| 5.2592 | 0.27 | 750 | 1.3074 |
| 2.3819 | 0.35 | 1000 | 1.1987 |
| 2.3819 | 0.44 | 1250 | 1.1678 |
| 1.3113 | 0.53 | 1500 | 1.1491 |
| 1.3113 | 0.62 | 1750 | 1.1369 |
| 1.2158 | 0.71 | 2000 | 1.1273 |
| 1.2158 | 0.8 | 2250 | 1.1165 |
| 1.2119 | 0.89 | 2500 | 1.1137 |
| 1.2119 | 0.98 | 2750 | 1.1147 |
| 1.2307 | 1.06 | 3000 | 1.1210 |
| 1.2307 | 1.15 | 3250 | 1.1246 |
| 1.2107 | 1.24 | 3500 | 1.1269 |
| 1.2107 | 1.33 | 3750 | 1.1372 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|