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--- |
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base_model: HooshvareLab/bert-base-parsbert-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- accuracy |
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model-index: |
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- name: Persian-Text-Sentiment-Bert-LORA |
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results: [] |
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license: mit |
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language: |
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- fa |
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library_name: peft |
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pipeline_tag: text-classification |
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datasets: |
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- SeyedAli/Persian-Text-Sentiment |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Persian-Text-Sentiment-Bert-LORA |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3427 |
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- Precision: 0.8579 |
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- Recall: 0.8543 |
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- F1-score: 0.8540 |
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- Accuracy: 0.8543 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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This is how to use this model in an example |
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```python |
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from peft import PeftModel |
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from transformers import pipeline |
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modelname="SeyedAli/Persian-Text-Sentiment-Bert-LORA" |
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tokenizer=AutoTokenizer.from_pretrained("HooshvareLab/bert-base-parsbert-uncased") |
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model=AutoModelForSequenceClassification.from_pretrained("HooshvareLab/bert-base-parsbert-uncased") |
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model = PeftModel.from_pretrained(model, modelname) |
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pipe = pipeline("text-classification", model=model,tokenizer=tokenizer) |
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pipe('خیلی کتاب خوبی بود') |
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``` |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1-score | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:| |
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| 0.3939 | 1.0 | 3491 | 0.3835 | 0.8457 | 0.8404 | 0.8398 | 0.8404 | |
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| 0.3722 | 2.0 | 6982 | 0.3677 | 0.8513 | 0.8457 | 0.8451 | 0.8457 | |
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| 0.3553 | 3.0 | 10473 | 0.3576 | 0.8539 | 0.8495 | 0.8491 | 0.8495 | |
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| 0.3618 | 4.0 | 13964 | 0.3525 | 0.8546 | 0.8513 | 0.8509 | 0.8513 | |
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| 0.3534 | 5.0 | 17455 | 0.3485 | 0.8557 | 0.8521 | 0.8517 | 0.8521 | |
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| 0.3423 | 6.0 | 20946 | 0.3470 | 0.8562 | 0.8530 | 0.8526 | 0.8530 | |
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| 0.3455 | 7.0 | 24437 | 0.3453 | 0.8573 | 0.8535 | 0.8531 | 0.8535 | |
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| 0.347 | 8.0 | 27928 | 0.3428 | 0.8575 | 0.8539 | 0.8535 | 0.8539 | |
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| 0.344 | 9.0 | 31419 | 0.3429 | 0.8578 | 0.8546 | 0.8542 | 0.8546 | |
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| 0.335 | 10.0 | 34910 | 0.3427 | 0.8579 | 0.8543 | 0.8540 | 0.8543 | |
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### Framework versions |
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- Transformers 4.35.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
