Create README.md

README.md

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+---
+license: mit
+---
+
+# COHeN
+
+This model is a fine-tuned version of [BERiT](https://huggingface.co/gngpostalsrvc/BERiT) on the [COHeN dataset](https://huggingface.co/datasets/gngpostalsrvc/COHeN). 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
+