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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_MT
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_MT
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: 1.9144
- Accuracy: 0.7959
- Precision: 0.8188
- Recall: 0.76
- F1 score: 0.7883
## 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: 5
### Training results
| Training Loss | Epoch | Step | Accuracy | F1 score | Precision | Recall | Validation Loss |
|:-------------:|:------:|:-----:|:--------:|:--------:|:---------:|:------:|:---------------:|
| 1.1594 | 0.2103 | 2500 | 0.7306 | 0.6914 | 0.8091 | 0.6035 | 1.1807 |
| 0.956 | 0.4205 | 5000 | 0.78 | 0.7556 | 0.85 | 0.68 | 1.0125 |
| 0.8973 | 0.6308 | 7500 | 1.2023 | 0.7529 | 0.8772 | 0.5882 | 0.7042 |
| 0.9154 | 0.8410 | 10000 | 1.0591 | 0.7771 | 0.8458 | 0.6776 | 0.7524 |
| 0.8148 | 1.0513 | 12500 | 1.1675 | 0.7753 | 0.8087 | 0.7212 | 0.7624 |
| 0.6499 | 1.2616 | 15000 | 0.9862 | 0.8076 | 0.8501 | 0.7471 | 0.7952 |
| 0.6059 | 1.4718 | 17500 | 1.0780 | 0.7659 | 0.7404 | 0.8188 | 0.7777 |
| 0.5391 | 1.6821 | 20000 | 1.2307 | 0.7694 | 0.7928 | 0.7294 | 0.7598 |
| 0.479 | 1.8923 | 22500 | 1.2428 | 0.7735 | 0.7675 | 0.7847 | 0.7760 |
| 0.3085 | 2.1026 | 25000 | 1.3597 | 0.7676 | 0.7571 | 0.7882 | 0.7723 |
| 0.226 | 2.3129 | 27500 | 1.6552 | 0.7776 | 0.7757 | 0.7812 | 0.7784 |
| 0.2293 | 2.5231 | 30000 | 1.4472 | 0.7847 | 0.7909 | 0.7741 | 0.7824 |
| 0.2201 | 2.7334 | 32500 | 1.3059 | 0.7982 | 0.7972 | 0.8 | 0.7986 |
| 0.2119 | 2.9437 | 35000 | 1.6964 | 0.7882 | 0.7981 | 0.7718 | 0.7847 |
| 0.087 | 3.1539 | 37500 | 1.9933 | 0.7818 | 0.7801 | 0.7847 | 0.7824 |
| 0.102 | 3.3642 | 40000 | 1.6337 | 0.7859 | 0.7866 | 0.7847 | 0.7856 |
| 0.0925 | 3.5744 | 42500 | 1.8106 | 0.7894 | 0.7808 | 0.8047 | 0.7926 |
| 0.1071 | 3.7847 | 45000 | 1.6925 | 0.7865 | 0.7691 | 0.8188 | 0.7932 |
| 0.077 | 3.9950 | 47500 | 1.8706 | 0.7929 | 0.8044 | 0.7741 | 0.7890 |
| 0.036 | 4.2052 | 50000 | 2.0159 | 0.7865 | 0.7822 | 0.7941 | 0.7881 |
| 0.0534 | 4.4155 | 52500 | 1.9290 | 0.7882 | 0.7862 | 0.7918 | 0.7890 |
| 0.0516 | 4.6257 | 55000 | 1.9351 | 0.7959 | 0.8180 | 0.7612 | 0.7885 |
| 0.0471 | 4.8360 | 57500 | 1.9144 | 0.7959 | 0.8188 | 0.76 | 0.7883 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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