Translation
Transformers
PyTorch
TensorBoard
Safetensors
English
marian
text2text-generation
Generated from Trainer
Instructions to use SRDdev/HingFlow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SRDdev/HingFlow 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="SRDdev/HingFlow")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("SRDdev/HingFlow") model = AutoModelForSeq2SeqLM.from_pretrained("SRDdev/HingFlow", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - bleu | |
| model-index: | |
| - name: HingFlow | |
| results: [] | |
| datasets: | |
| - cfilt/iitb-english-hindi | |
| language: | |
| - en | |
| library_name: transformers | |
| pipeline_tag: translation | |
| # HingFlow | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1887 | |
| - Bleu: 72.3468 | |
| - Gen Len: 5.9953 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 2e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 2 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | |
| | 0.1505 | 1.0 | 1000 | 0.2053 | 71.6108 | 5.8418 | | |
| | 0.1057 | 2.0 | 2000 | 0.1887 | 72.3468 | 5.9953 | | |
| ### Framework versions | |
| - Transformers 4.29.0 | |
| - Pytorch 2.0.0+cu118 | |
| - Datasets 2.12.0 | |
| - Tokenizers 0.13.3 |