sarch7040/Deshika
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How to use VIITPune/praTran with Transformers:
# Use a pipeline as a high-level helper
# Warning: Pipeline type "translation" is no longer supported in transformers v5.
# You must load the model directly (see below) or downgrade to v4.x with:
# 'pip install "transformers<5.0.0'
from transformers import pipeline
pipe = pipeline("translation", model="VIITPune/praTran") # Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("VIITPune/praTran")
model = AutoModelForSeq2SeqLM.from_pretrained("VIITPune/praTran")This model is a fine-tuned version of facebook/m2m100_418M on Deshika dataset which is a prallel corpus of Prakrit with their corresponding English Translations It achieves the following results on the evaluation set:
praTran is a finetuned version of facebook/m2m100_418M which was trained on a downstream task.
This model is intended to use for academic purposes.
The models translation is not that good at this current stage to the language being extremely low resource. Impro
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len |
|---|---|---|---|---|---|---|
| No log | 1.0 | 74 | 4.9214 | 2.8147 | 0.2385 | 31.7864 |
| No log | 2.0 | 148 | 3.1788 | 5.0267 | 0.3144 | 29.7695 |
| No log | 3.0 | 222 | 2.0374 | 6.4844 | 0.3399 | 30.2237 |
| No log | 4.0 | 296 | 1.4798 | 7.4708 | 0.3768 | 31.3932 |
| No log | 5.0 | 370 | 1.3269 | 8.4241 | 0.3851 | 30.7356 |
Base model
facebook/m2m100_418M