đ M2M100_Sanskrit_English Model
This model is fine - tuned from M2M100 for Sanskrit to English translation, trained on the Itihasa dataset with translations from Sanskrit epics.
đ Quick Start
Use the following code to start using the model:
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("Swamitucats/M2M100_Sanskrit_English")
tokenizer = AutoTokenizer.from_pretrained("Swamitucats/M2M100_Sanskrit_English")
sanskrit_text = "Your Sanskrit text here"
inputs = tokenizer(sanskrit_text, return_tensors="pt")
outputs = model.generate(**inputs)
english_translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(english_translation)
⨠Features
- Fine - tuned from M2M100 for Sanskrit to English translation.
- Trained on the Itihasa dataset containing translations from Sanskrit epics.
đĻ Installation
No specific installation steps are provided in the original document.
đģ Usage Examples
Basic Usage
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("Swamitucats/M2M100_Sanskrit_English")
tokenizer = AutoTokenizer.from_pretrained("Swamitucats/M2M100_Sanskrit_English")
sanskrit_text = "Your Sanskrit text here"
inputs = tokenizer(sanskrit_text, return_tensors="pt")
outputs = model.generate(**inputs)
english_translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(english_translation)
đ Documentation
Model Details
- Developed by: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): ['sa', 'en']
- License: mit
- Finetuned from model [optional]: facebook/m2m100_418M
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
đ License
The model is released under the MIT license.
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