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---
library_name: transformers
license: apache-2.0
base_model: HooshvareLab/bert-fa-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: Bert-Sentiment-Fa
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. -->
# Bert-Sentiment-Fa
This model is a fine-tuned version of [HooshvareLab/bert-fa-base-uncased](https://huggingface.co/HooshvareLab/bert-fa-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0154
- Accuracy: 0.8333
- F1: 0.8456
## 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: 5e-06
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- 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 | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 68 | 0.5588 | 0.8333 | 0.8419 |
| No log | 2.0 | 136 | 0.5678 | 0.8417 | 0.8557 |
| No log | 3.0 | 204 | 0.6083 | 0.8208 | 0.8311 |
| No log | 4.0 | 272 | 0.6749 | 0.8167 | 0.8285 |
| No log | 5.0 | 340 | 0.7690 | 0.8292 | 0.8424 |
| No log | 6.0 | 408 | 0.8706 | 0.8208 | 0.8328 |
| No log | 7.0 | 476 | 0.8554 | 0.8292 | 0.8424 |
| 0.0725 | 8.0 | 544 | 0.8950 | 0.825 | 0.8390 |
| 0.0725 | 9.0 | 612 | 0.9200 | 0.825 | 0.8390 |
| 0.0725 | 10.0 | 680 | 0.9511 | 0.8292 | 0.8455 |
| 0.0725 | 11.0 | 748 | 0.9698 | 0.8375 | 0.8490 |
| 0.0725 | 12.0 | 816 | 0.9829 | 0.8333 | 0.8456 |
| 0.0725 | 13.0 | 884 | 1.0022 | 0.8333 | 0.8456 |
| 0.0725 | 14.0 | 952 | 1.0130 | 0.8333 | 0.8456 |
| 0.0167 | 15.0 | 1020 | 1.0154 | 0.8333 | 0.8456 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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