|
--- |
|
library_name: transformers |
|
license: apache-2.0 |
|
base_model: google/flan-t5-large |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
model-index: |
|
- name: flanT5_large_Fact_U_T1 |
|
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. --> |
|
|
|
# flanT5_large_Fact_U_T1 |
|
|
|
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co./google/flan-t5-large) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.1337 |
|
- Accuracy: 0.7718 |
|
- Precision: 0.8116 |
|
- Recall: 0.7308 |
|
- F1 score: 0.7690 |
|
|
|
## 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: 1 |
|
- eval_batch_size: 1 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Accuracy | F1 score | Precision | Recall | Validation Loss | |
|
|:-------------:|:------:|:-----:|:--------:|:--------:|:---------:|:------:|:---------------:| |
|
| 1.221 | 0.3923 | 2500 | 0.6682 | 0.6659 | 0.6990 | 0.6357 | 1.1740 | |
|
| 1.2301 | 0.7846 | 5000 | 0.6635 | 0.6977 | 0.6548 | 0.7466 | 1.5113 | |
|
| 1.0764 | 1.1768 | 7500 | 0.6894 | 0.6741 | 0.7418 | 0.6176 | 1.2812 | |
|
| 1.0245 | 1.5691 | 10000 | 0.7153 | 0.6676 | 0.8497 | 0.5498 | 1.2591 | |
|
| 0.986 | 1.9614 | 12500 | 0.7259 | 0.6830 | 0.8567 | 0.5679 | 1.2615 | |
|
| 0.8337 | 2.3537 | 15000 | 0.7271 | 0.6915 | 0.8387 | 0.5882 | 1.1350 | |
|
| 0.807 | 2.7460 | 17500 | 0.74 | 0.7034 | 0.8647 | 0.5928 | 1.0071 | |
|
| 0.7575 | 3.1382 | 20000 | 0.7353 | 0.6930 | 0.8729 | 0.5747 | 1.5670 | |
|
| 0.5663 | 3.5305 | 22500 | 0.7435 | 0.7341 | 0.7963 | 0.6810 | 1.0824 | |
|
| 0.6546 | 3.9228 | 25000 | 0.7424 | 0.7319 | 0.7973 | 0.6765 | 1.1824 | |
|
| 0.4215 | 4.3151 | 27500 | 0.7435 | 0.7465 | 0.7679 | 0.7262 | 1.7775 | |
|
| 0.4255 | 4.7074 | 30000 | 0.7635 | 0.7698 | 0.7796 | 0.7602 | 1.3931 | |
|
| 0.3478 | 5.0996 | 32500 | 0.7635 | 0.7581 | 0.8098 | 0.7127 | 1.6014 | |
|
| 0.2632 | 5.4919 | 35000 | 0.7447 | 0.7331 | 0.8032 | 0.6742 | 1.4911 | |
|
| 0.2555 | 5.8842 | 37500 | 0.7588 | 0.7453 | 0.8264 | 0.6787 | 1.7558 | |
|
| 0.2237 | 6.2765 | 40000 | 0.7588 | 0.7574 | 0.7940 | 0.7240 | 1.8132 | |
|
| 0.1014 | 6.6688 | 42500 | 0.7647 | 0.7596 | 0.8103 | 0.7149 | 1.8028 | |
|
| 0.15 | 7.0610 | 45000 | 0.7682 | 0.7733 | 0.7869 | 0.7602 | 1.7902 | |
|
| 0.076 | 7.4533 | 47500 | 0.7706 | 0.7559 | 0.8459 | 0.6833 | 2.1883 | |
|
| 0.1015 | 7.8456 | 50000 | 0.7694 | 0.7531 | 0.8494 | 0.6765 | 1.8640 | |
|
| 0.0876 | 8.2379 | 52500 | 0.78 | 0.7823 | 0.8058 | 0.7602 | 2.0889 | |
|
| 0.095 | 8.6302 | 55000 | 0.7859 | 0.7797 | 0.8385 | 0.7285 | 1.7835 | |
|
| 0.0873 | 9.0224 | 57500 | 0.7718 | 0.7651 | 0.8229 | 0.7149 | 1.8784 | |
|
| 0.0444 | 9.4147 | 60000 | 0.7706 | 0.7761 | 0.7879 | 0.7647 | 2.2505 | |
|
| 0.0486 | 9.8070 | 62500 | 2.1337 | 0.7718 | 0.8116 | 0.7308 | 0.7690 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.44.2 |
|
- Pytorch 2.3.0+cu121 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.19.1 |
|
|
