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---
library_name: transformers
base_model: t-tech/T-lite-it-1.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: T-lite-it-1.0-pseudo-base
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. -->
# t-lite_part1-2_lr1e4_wsd_bs128
This model is a fine-tuned version of [t-tech/T-lite-it-1.0](https://huggingface.co/t-tech/T-lite-it-1.0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3980
- Accuracy: 0.6669
## 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
- seed: 42
- distributed_type: multi-GPU
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: warmup_stable_decay
- lr_scheduler_warmup_steps: 100
- num_epochs: 0.5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log | 0.0001 | 1 | 1.4751 | 0.6606 |
| 1.5071 | 0.0354 | 500 | 1.4113 | 0.6647 |
| 1.5003 | 0.0709 | 1000 | 1.4080 | 0.6649 |
| 1.4959 | 0.1063 | 1500 | 1.4063 | 0.6654 |
| 1.5019 | 0.1418 | 2000 | 1.4054 | 0.6655 |
| 1.4891 | 0.1772 | 2500 | 1.4047 | 0.6656 |
| 1.4916 | 0.2126 | 3000 | 1.4040 | 0.6657 |
| 1.496 | 0.2481 | 3500 | 1.4034 | 0.6657 |
| 1.495 | 0.2835 | 4000 | 1.4032 | 0.6657 |
| 1.4934 | 0.3189 | 4500 | 1.4030 | 0.6658 |
| 1.4849 | 0.3544 | 5000 | 1.4029 | 0.6660 |
| 1.4833 | 0.3898 | 5500 | 1.4024 | 0.6661 |
| 1.4909 | 0.4253 | 6000 | 1.4023 | 0.6661 |
| 1.4923 | 0.4607 | 6500 | 1.4000 | 0.6665 |
| 1.4965 | 0.4961 | 7000 | 1.3979 | 0.6669 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.3.0a0+6ddf5cf85e.nv24.04
- Datasets 2.18.0
- Tokenizers 0.20.3
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