README.md

metadata

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

Persian-Text-Sentiment-Bert-LORA

This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on an unknown dataset. 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_max_length=100, add_special_tokens = True)
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