File size: 2,006 Bytes
b3d87ff |
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 |
---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-b8-10
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. -->
# mt5-small-finetuned-b8-10
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7340
- Rouge1: 0.0359
- Rouge2: 0.0077
- Rougel: 0.0357
- Rougelsum: 0.0357
- Gen Len: 10.8384
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 12.4116 | 1.0 | 1357 | 3.3739 | 0.0033 | 0.0002 | 0.0033 | 0.0033 | 16.3309 |
| 1.7237 | 2.0 | 2714 | 1.4076 | 0.0022 | 0.0 | 0.0021 | 0.0022 | 4.5805 |
| 1.4447 | 3.0 | 4071 | 1.2431 | 0.0031 | 0.0 | 0.0031 | 0.0031 | 4.0912 |
| 1.3493 | 4.0 | 5428 | 1.2140 | 0.0247 | 0.0026 | 0.0248 | 0.0247 | 7.3331 |
| 1.2809 | 5.0 | 6785 | 3.7340 | 0.0359 | 0.0077 | 0.0357 | 0.0357 | 10.8384 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|