Translation
Transformers
PyTorch
Oriya
English
IndicTrans
text2text-generation
indictrans2
odia
custom_code
Instructions to use anuj1541/indictrans2-ory-en-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anuj1541/indictrans2-ory-en-v1 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="anuj1541/indictrans2-ory-en-v1", trust_remote_code=True)# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("anuj1541/indictrans2-ory-en-v1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
IndicTrans2 Odia→English v1 (current best snapshot)
Frozen baseline before extended fine-tuning (10–15 epochs).
Load
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from IndicTransToolkit import IndicProcessor
# Tokenizer always from the official IndicTrans2 base (custom class).
# Weights from this repo.
BASE = "ai4bharat/indictrans2-indic-en-dist-200M"
REPO = "anuj1541/indictrans2-ory-en-v1"
tokenizer = AutoTokenizer.from_pretrained(BASE, trust_remote_code=True)
model = AutoModelForSeq2SeqLM.from_pretrained(REPO, trust_remote_code=True)
Eval (500 test samples)
| Metric | Score |
|---|---|
| BLEU ↑ | 62.23 |
| ChrF ↑ | 87.29 |
| METEOR ↑ | 51.28 |
| TER ↓ | 34.97 |
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Model tree for anuj1541/indictrans2-ory-en-v1
Base model
ai4bharat/indictrans2-indic-en-dist-200M