|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: t5_recommendation_jobs3 |
|
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. --> |
|
|
|
# t5_recommendation_jobs3 |
|
|
|
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.7154 |
|
- Rouge1: 53.3020 |
|
- Rouge2: 31.8649 |
|
- Rougel: 52.6180 |
|
- Rougelsum: 52.6507 |
|
- Gen Len: 4.2934 |
|
|
|
## 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: 0.0001 |
|
- 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: 15 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
|
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
|
| No log | 0.99 | 93 | 0.7607 | 47.7139 | 25.7252 | 47.3810 | 47.4008 | 3.9914 | |
|
| No log | 1.99 | 187 | 0.7516 | 49.1554 | 27.2693 | 48.5465 | 48.5321 | 4.2381 | |
|
| No log | 3.0 | 281 | 0.7454 | 49.6795 | 27.8710 | 49.2537 | 49.2633 | 4.1665 | |
|
| No log | 4.0 | 375 | 0.7407 | 49.8898 | 27.7613 | 49.4210 | 49.4315 | 4.1331 | |
|
| No log | 4.99 | 468 | 0.7360 | 51.3330 | 29.6585 | 50.9846 | 51.0159 | 4.0724 | |
|
| 0.6327 | 5.99 | 562 | 0.7222 | 50.9951 | 29.7573 | 50.6261 | 50.6555 | 4.1354 | |
|
| 0.6327 | 7.0 | 656 | 0.7175 | 51.8101 | 30.5342 | 51.3743 | 51.3883 | 4.0903 | |
|
| 0.6327 | 8.0 | 750 | 0.7122 | 51.9497 | 30.8316 | 51.4403 | 51.4551 | 4.2553 | |
|
| 0.6327 | 8.99 | 843 | 0.7144 | 52.3842 | 30.7131 | 51.8160 | 51.8629 | 4.1883 | |
|
| 0.6327 | 9.99 | 937 | 0.7134 | 52.4103 | 31.1474 | 51.8047 | 51.8294 | 4.2903 | |
|
| 0.5576 | 11.0 | 1031 | 0.7125 | 52.8364 | 31.2692 | 52.1248 | 52.1554 | 4.3261 | |
|
| 0.5576 | 12.0 | 1125 | 0.7093 | 52.7446 | 30.9128 | 52.0864 | 52.1538 | 4.4202 | |
|
| 0.5576 | 12.99 | 1218 | 0.7104 | 52.9125 | 31.4285 | 52.2397 | 52.2962 | 4.2918 | |
|
| 0.5576 | 13.99 | 1312 | 0.7127 | 53.4228 | 32.2228 | 52.6175 | 52.6691 | 4.2265 | |
|
| 0.5576 | 14.88 | 1395 | 0.7154 | 53.3020 | 31.8649 | 52.6180 | 52.6507 | 4.2934 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.27.0 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.8.0 |
|
- Tokenizers 0.13.3 |
|
|
