Instructions to use fubuki119/opus-mt-en-hi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fubuki119/opus-mt-en-hi 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="fubuki119/opus-mt-en-hi")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("fubuki119/opus-mt-en-hi") model = AutoModelForSeq2SeqLM.from_pretrained("fubuki119/opus-mt-en-hi") - Notebooks
- Google Colab
- Kaggle
opus-mt-en-hi
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-hi on iitb-english-hindi dataset. It achieves the following results on the evaluation set:
- Loss: 3.5595
- Bleu: 11.9246
Model description
More information needed
Training and evaluation data
This model is trained on Helsinki-NLP/opus-mt-en-hi on iitb-english-hindi. Here's a tokenized and pre-processed version of this dataset -> iitb-english-hindi-tokenized
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for fubuki119/opus-mt-en-hi
Base model
Helsinki-NLP/opus-mt-en-hi