Views
No views yet
MarianMTModel. It’s ideal for general-purpose translation, educational use, and light regulatory or formal communication tasks between English and French.Helsinki-NLP/opus-mt-en-fr| Attribute | Value |
|---|---|
| Base Model | Helsinki-NLP/opus-mt-en-fr |
| Dataset | Parallel English-French corpus |
| Task Type | Translation |
| Max Token Length | 128 |
| Epochs | 3 |
| Batch Size | 16 |
| Optimizer | AdamW |
| Loss Function | CrossEntropyLoss |
| Framework | PyTorch + Transformers |
| Hardware | CUDA-enabled GPU |
| Metric | Score |
|---|---|
| BLEU Score | 27.82 |
1from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
2import torch
3
4model_name = "AventIQ-AI/MarianMT-Text-Translation-AI-Model-en-fr"
5tokenizer = AutoTokenizer.from_pretrained(model_name)
6model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
7model.eval()
8
9def translate(text):
10 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
11 finetuned_model.to(device)
12 inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
13 outputs = finetuned_model.generate(**inputs)
14 return tokenizer.decode(outputs[0], skip_special_tokens=True)
15
16# Example
17print(translate("Hello, how are you?"))finetuned-model/
├── config.json ✅ Model architecture & config
├── pytorch_model.bin ✅ Model weights
├── tokenizer_config.json ✅ Tokenizer settings
├── tokenizer.json ✅ Tokenizer vocabulary (JSON format)
├── source.spm ✅ SentencePiece model for source language
├── target.spm ✅ SentencePiece model for target language
├── special_tokens_map.json ✅ Special tokens mapping
├── generation_config.json ✅ (Optional) Generation defaults
├── README.md ✅ Model card