ParsBERT-nli-FarsTail-FarSick

This model is a fine-tuned version of HooshvareLab/bert-fa-zwnj-base on the FarsTail and FarSick datasets. It achieves the following results on the evaluation set:

• Loss: 0.8730
• Accuracy: 0.8055
• Precision (macro): 0.7900
• Precision (micro): 0.8055
• Recall (macro): 0.7926
• Recall (micro): 0.7926
• F1 (macro): 0.7909
• F1 (micro): 0.8055

How to use

import torch
import transformers

model_name_or_path = "parsi-ai-nlpclass/ParsBERT-nli-FarsTail-FarSick"
config = transformers.AutoConfig.from_pretrained(model_name_or_path)
tokenizer_pb = transformers.AutoTokenizer.from_pretrained(model_name_or_path)
model_pb = transformers.AutoModelForSequenceClassification.from_pretrained(model_name_or_path,
                                                                           num_labels=3)
premise = "سلام خوبی؟"
hypothesis = "آره خوبم"
print(model_pb(**tokenizer_pb(premise, hypothesis, return_tensors='pt')))

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: 5

Training results

Framework versions

• Transformers 4.37.2
• Pytorch 2.1.0+cu121
• Datasets 2.17.1
• Tokenizers 0.15.2