Persian-Text-Sentiment-Bert-LORA

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Persian-Text-Sentiment-Bert-LORA

This model is a Adapter for HooshvareLab/bert-base-parsbert-uncased on SeyedAli/Persian-Text-Sentiment 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

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

Framework versions

• Transformers 4.35.1
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1