COHeN

This model is a fine-tuned version of BERiT on the COHeN dataset. It achieves the following results on the evaluation set:

• Loss: 0.4418
• Accuracy: 0.8622

Model Description

COHeN (Classification of Old Hebrew via Neural Net) is a text classification model for Biblical Hebrew that assigns Hebrew texts to one of four chronological phases: Archaic Biblical Hebrew (ABH), Classical Biblical Hebrew (CBH), Transitional Biblical Hebrew (TBH), or Late Biblical Hebrew (LBH). It allows scholars to check their intuition regarding the dating of particular verses.

How to Use

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = 'gngpostalsrvc/COHeN'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

Training Procedure

COHeN was trained on the COHeN dataset for 20 epochs using a Tesla T4 GPU. Further training did not yield significant improvements in performance.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0027
• weight_decay: 0.0049
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Framework versions

• Transformers 4.24.7
• Pytorch 1.12.1+cu113
• Datasets 2.11.0
• Tokenizers 0.13.3