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") - Notebooks
- Google Colab
- Kaggle
metadata
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
https://github.com/SRDdev/HingFlow
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