parsbert-persian-sentiment-3class_Fine-Tuned

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:

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:

• optimizer: Nadam
• learning_rate=1e-3
• training_precision: float32

Training results

• Accuracy: 0.8914
• Precision: 0.8922
• Recall: 0.8914
• F1 Score: 0.8911

Framework versions

• Transformers 4.57.6
• TensorFlow 2.19.0
• Datasets 4.0.0
• Tokenizers 0.22.2

Usage (TensorFlow)

#python from transformers import AutoTokenizer, TFAutoModelForSequenceClassification import tensorflow as tf

MODEL_ID = "Rasooli26/parsbert-persian-sentiment-3class_Fine-Tuned"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=False) model = TFAutoModelForSequenceClassification.from_pretrained(MODEL_ID)

def predict(text: str): inputs = tokenizer( text, return_tensors="tf", truncation=True, padding=True, max_length=128 ) probs = tf.nn.softmax(model(**inputs).logits, axis=-1) pred_id = int(tf.argmax(probs, axis=-1).numpy()[0]) return model.config.id2label[pred_id], probs.numpy()

label, probs = predict("این محصول بسیار عالی است") print(label, probs)

Optional: add PyTorch usage (only if you want)

Because your repo was originally TF-based, PyTorch users may need from_tf=True unless you also uploaded PyTorch weights:

## Usage (PyTorch)

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import torch.nn.functional as F

MODEL_ID = "Rasooli26/parsbert-persian-sentiment-3class_Fine-Tuned"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=False)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID, from_tf=True)
model.eval()

inputs = tokenizer("این محصول بسیار عالی است", return_tensors="pt", truncation=True, padding=True, max_length=128)

with torch.no_grad():
    probs = F.softmax(model(**inputs).logits, dim=-1)

pred_id = int(torch.argmax(probs, dim=-1).item())
print(model.config.id2label[pred_id], probs.tolist())