# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Jacobo/axiothea")
model = AutoModelForMaskedLM.from_pretrained("Jacobo/axiothea")Quick Links
axiothea
This is an experimental roberta model trained with an ancient Greek corpus of about 900 MB, which was scrapped from the web and post-processed. Duplicate texts and editorial punctuation were removed. The training dataset will be soon available in the Huggingface datasets hub. Training a model of ancient Greek is challenging given that it is a low resource language from which 50% of the register has only survived in fragmentary texts. The model is provided by the Diogenet project at the University of California, San Diego.
It achieves the following results on the evaluation set:
- Loss: 3.3351
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:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.7013 | 1.0 | 341422 | 4.8813 |
| 4.2866 | 2.0 | 682844 | 4.4422 |
| 4.0496 | 3.0 | 1024266 | 4.2132 |
| 3.8503 | 4.0 | 1365688 | 4.0246 |
| 3.6917 | 5.0 | 1707110 | 3.8756 |
| 3.4917 | 6.0 | 2048532 | 3.7381 |
| 3.3907 | 7.0 | 2389954 | 3.6107 |
| 3.2876 | 8.0 | 2731376 | 3.5044 |
| 3.1994 | 9.0 | 3072798 | 3.3980 |
| 3.0806 | 10.0 | 3414220 | 3.3095 |
Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.14.0
- Tokenizers 0.10.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Jacobo/axiothea")