File size: 1,596 Bytes
eb4eafb 1696782 eb4eafb 1696782 eb4eafb 51ab8ee eb4eafb 51ab8ee 1696782 eb4eafb 1696782 eb4eafb 702e05e eb4eafb 702e05e 51ab8ee eb4eafb 3dd66ce eb4eafb 702e05e 3dd66ce |
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 |
---
base_model: kaizerBox/retnet-summarization_small
tags:
- generated_from_trainer
datasets:
- xsum
model-index:
- name: retnet-summarization_small
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. -->
# retnet-summarization_small
This model is a fine-tuned version of [kaizerBox/retnet-summarization_small](https://huggingface.co/kaizerBox/retnet-summarization_small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1299
## 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: 0.001
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.3711 | 1.0 | 4610 | 4.1533 |
| 4.3448 | 2.0 | 9220 | 4.1370 |
| 4.3247 | 3.0 | 13830 | 4.1299 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|