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---
base_model: HooshvareLab/bert-base-parsbert-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: Persian-Text-Sentiment-Bert-LORA
results: []
license: mit
language:
- fa
library_name: peft
pipeline_tag: text-classification
datasets:
- SeyedAli/Persian-Text-Sentiment
---
<!-- 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. -->
# Persian-Text-Sentiment-Bert-LORA
This model is a Adapter for [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on an unknown dataset in Persian Sentment Analysis Task.
It achieves the following results on the evaluation set:
- Loss: 0.3427
- Precision: 0.8579
- Recall: 0.8543
- F1-score: 0.8540
- Accuracy: 0.8543
## Model description
More information needed
## Intended uses & limitations
This is how to use this model in an example
```python
from peft import PeftModel
from transformers import pipeline
modelname="SeyedAli/Persian-Text-Sentiment-Bert-LORA"
tokenizer=AutoTokenizer.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
model=AutoModelForSequenceClassification.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
model = PeftModel.from_pretrained(model, modelname)
pipe = pipeline("text-classification", model=model,tokenizer=tokenizer)
pipe('خیلی کتاب خوبی بود')
```
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 | Precision | Recall | F1-score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:|
| 0.3939 | 1.0 | 3491 | 0.3835 | 0.8457 | 0.8404 | 0.8398 | 0.8404 |
| 0.3722 | 2.0 | 6982 | 0.3677 | 0.8513 | 0.8457 | 0.8451 | 0.8457 |
| 0.3553 | 3.0 | 10473 | 0.3576 | 0.8539 | 0.8495 | 0.8491 | 0.8495 |
| 0.3618 | 4.0 | 13964 | 0.3525 | 0.8546 | 0.8513 | 0.8509 | 0.8513 |
| 0.3534 | 5.0 | 17455 | 0.3485 | 0.8557 | 0.8521 | 0.8517 | 0.8521 |
| 0.3423 | 6.0 | 20946 | 0.3470 | 0.8562 | 0.8530 | 0.8526 | 0.8530 |
| 0.3455 | 7.0 | 24437 | 0.3453 | 0.8573 | 0.8535 | 0.8531 | 0.8535 |
| 0.347 | 8.0 | 27928 | 0.3428 | 0.8575 | 0.8539 | 0.8535 | 0.8539 |
| 0.344 | 9.0 | 31419 | 0.3429 | 0.8578 | 0.8546 | 0.8542 | 0.8546 |
| 0.335 | 10.0 | 34910 | 0.3427 | 0.8579 | 0.8543 | 0.8540 | 0.8543 |
### Framework versions
- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1 |