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---
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_Task2_semantic_pred
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_Task2_semantic_pred
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: 0.9725
- Accuracy: 0.7979
- Precision: 0.8195
- Recall: 0.7566
- F1 score: 0.7868
## 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 | Validation Loss | Accuracy | Precision | Recall | F1 score |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| 1.3836 | 0.4074 | 2500 | 1.7839 | 0.5072 | 0.0 | 0.0 | 0.0 |
| 1.3837 | 0.8147 | 5000 | 0.7528 | 0.5072 | 0.0 | 0.0 | 0.0 |
| 1.2525 | 1.2221 | 7500 | 0.7166 | 0.4928 | 0.4928 | 1.0 | 0.6603 |
| 1.2899 | 1.6295 | 10000 | 1.5233 | 0.6310 | 0.5765 | 0.9471 | 0.7167 |
| 1.4214 | 2.0368 | 12500 | 1.2841 | 0.6571 | 0.6021 | 0.8968 | 0.7205 |
| 1.4003 | 2.4442 | 15000 | 1.1805 | 0.6115 | 0.5615 | 0.9656 | 0.7101 |
| 1.3396 | 2.8516 | 17500 | 0.9273 | 0.6206 | 0.5683 | 0.9577 | 0.7133 |
| 1.3938 | 3.2589 | 20000 | 1.5375 | 0.6858 | 0.7322 | 0.5714 | 0.6419 |
| 1.4272 | 3.6663 | 22500 | 1.2804 | 0.7158 | 0.7548 | 0.6270 | 0.6850 |
| 1.2643 | 4.0737 | 25000 | 1.2878 | 0.7262 | 0.7692 | 0.6349 | 0.6957 |
| 1.3015 | 4.4810 | 27500 | 1.1752 | 0.7158 | 0.7532 | 0.6296 | 0.6859 |
| 1.3525 | 4.8884 | 30000 | 1.1726 | 0.7249 | 0.6946 | 0.7884 | 0.7385 |
| 1.2839 | 5.2957 | 32500 | 1.0721 | 0.7223 | 0.7586 | 0.6402 | 0.6944 |
| 1.2047 | 5.7031 | 35000 | 1.3045 | 0.7223 | 0.7089 | 0.7407 | 0.7245 |
| 1.226 | 6.1105 | 37500 | 1.1262 | 0.7366 | 0.7486 | 0.7011 | 0.7240 |
| 1.1001 | 6.5178 | 40000 | 1.2328 | 0.7445 | 0.7407 | 0.7407 | 0.7407 |
| 1.1083 | 6.9252 | 42500 | 1.1119 | 0.7353 | 0.7030 | 0.8016 | 0.7491 |
| 1.0281 | 7.3326 | 45000 | 1.1205 | 0.7471 | 0.7434 | 0.7434 | 0.7434 |
| 1.0396 | 7.7399 | 47500 | 1.1243 | 0.7640 | 0.7958 | 0.7011 | 0.7454 |
| 0.9913 | 8.1473 | 50000 | 0.9882 | 0.7823 | 0.8187 | 0.7169 | 0.7645 |
| 0.9364 | 8.5547 | 52500 | 1.1489 | 0.7705 | 0.7488 | 0.8042 | 0.7755 |
| 1.0081 | 8.9620 | 55000 | 0.9507 | 0.7810 | 0.7807 | 0.7725 | 0.7766 |
| 0.9026 | 9.3694 | 57500 | 1.0181 | 0.7888 | 0.7888 | 0.7804 | 0.7846 |
| 0.8961 | 9.7768 | 60000 | 0.9725 | 0.7979 | 0.8195 | 0.7566 | 0.7868 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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